From 59db48dbebad49e4cf769d900ff4c1e35e00f66c Mon Sep 17 00:00:00 2001 From: Peter Nagy Date: Thu, 7 Aug 2025 12:58:34 +0100 Subject: [PATCH 01/10] Remove Streaming Writer from docs --- docs/apis/portal-api/http-requests.md | 6 +- docs/apis/portal-api/setup.md | 1 - docs/apis/query-api/http-requests.md | 6 +- docs/apis/query-api/setup.md | 1 - docs/apis/streaming-reader-api/setup.md | 19 -- .../streaming-writer-api/create-stream.md | 140 ------------ .../streaming-writer-api/http-requests.md | 89 -------- .../images/apis-tokens.png | Bin 115343 -> 0 bytes docs/apis/streaming-writer-api/overview.md | 14 -- docs/apis/streaming-writer-api/send-data.md | 155 -------------- docs/apis/streaming-writer-api/send-event.md | 128 ----------- docs/apis/streaming-writer-api/setup.md | 200 ------------------ .../streaming-writer-api/stream-metadata.md | 116 ---------- docs/blog/posts/stream-processing-glossary.md | 2 +- docs/develop/apis-overview.md | 12 +- docs/develop/integrate-data/overview.md | 7 +- docs/develop/integrate-data/streaming-apis.md | 183 ---------------- .../databases/influxdb/telegraf.md | 1 - docs/kb/glossary.md | 4 - docs/manage/troubleshooting.md | 6 +- docs/tutorials/clickstream/get-project.md | 2 +- .../tutorials/clickstream/webshop-frontend.md | 4 +- docs/tutorials/computer-vision/web-ui.md | 2 +- .../predictive-maintenance/get-project.md | 2 +- .../sentiment-analysis/ui-service.md | 13 +- mkdocs.yml | 10 - 26 files changed, 18 insertions(+), 1105 deletions(-) delete mode 100644 docs/apis/streaming-writer-api/create-stream.md delete mode 100644 docs/apis/streaming-writer-api/http-requests.md delete mode 100644 docs/apis/streaming-writer-api/images/apis-tokens.png delete mode 100644 docs/apis/streaming-writer-api/overview.md delete mode 100644 docs/apis/streaming-writer-api/send-data.md delete mode 100644 docs/apis/streaming-writer-api/send-event.md delete mode 100644 docs/apis/streaming-writer-api/setup.md delete mode 100644 docs/apis/streaming-writer-api/stream-metadata.md delete mode 100644 docs/develop/integrate-data/streaming-apis.md diff --git a/docs/apis/portal-api/http-requests.md b/docs/apis/portal-api/http-requests.md index 74076742..9881c58b 100644 --- a/docs/apis/portal-api/http-requests.md +++ b/docs/apis/portal-api/http-requests.md @@ -32,13 +32,13 @@ The API endpoint URL has the following format: https://-.platform.quix.io/ ``` -So, for example, the endpoint URL for the streaming writer might resemble the following: +So, for example, the endpoint URL for the query API might resemble the following: ``` -https://writer-acme-weather.platform.quix.io/ +https://telemetry-query-acme-weather.platform.quix.io/ ``` -The API is `writer`, and the environment ID is `acme-weather`. +The API is `telemetry-query`, and the environment ID is `acme-weather`. ## HTTP method diff --git a/docs/apis/portal-api/setup.md b/docs/apis/portal-api/setup.md index 807e1f11..33b6e915 100644 --- a/docs/apis/portal-api/setup.md +++ b/docs/apis/portal-api/setup.md @@ -101,7 +101,6 @@ The URLs for the API references are specific to your environment, so you can eas | API | API reference URL (Swagger documentation)| |---|---| -| Streaming Writer | https://writer-``.platform.quix.io/swagger | | Streaming Reader | No HTTP/REST interface - SignalR (WebSockets or Long Polling)| | Portal | https://portal-api.platform.quix.io/swagger | | Query | https://telemetry-query-``.platform.quix.io/swagger | diff --git a/docs/apis/query-api/http-requests.md b/docs/apis/query-api/http-requests.md index d7df620e..9aa1bf16 100644 --- a/docs/apis/query-api/http-requests.md +++ b/docs/apis/query-api/http-requests.md @@ -30,13 +30,13 @@ The API endpoint URL has the following format: https://-.platform.quix.io/ ``` -So, for example, the endpoint URL for the streaming writer might resemble the following: +So, for example, the endpoint URL for the query API might resemble the following: ``` -https://writer-acme-weather.platform.quix.io/ +https://telemetry-query-acme-weather.platform.quix.io/ ``` -The API is `writer`, and the environment ID is `acme-weather`. +The API is `telemetry-query`, and the environment ID is `acme-weather`. ## HTTP method diff --git a/docs/apis/query-api/setup.md b/docs/apis/query-api/setup.md index 0004ef36..0bdd18d8 100644 --- a/docs/apis/query-api/setup.md +++ b/docs/apis/query-api/setup.md @@ -96,7 +96,6 @@ The URLs for the API references are specific to your environment, so you can eas | API | API reference URL (Swagger documentation)| |---|---| -| Streaming Writer | https://writer-``.platform.quix.io/swagger | | Streaming Reader | No HTTP/REST interface - SignalR (WebSockets or Long Polling)| | Portal | https://portal-api.platform.quix.io/swagger | | Query | https://telemetry-query-``.platform.quix.io/swagger | diff --git a/docs/apis/streaming-reader-api/setup.md b/docs/apis/streaming-reader-api/setup.md index 067eba4e..9bcc6925 100644 --- a/docs/apis/streaming-reader-api/setup.md +++ b/docs/apis/streaming-reader-api/setup.md @@ -101,7 +101,6 @@ The URLs for the API references are specific to your environment, so you can eas | API | API reference URL (Swagger documentation)| |---|---| -| Streaming Writer | https://writer-``.platform.quix.io/swagger | | Streaming Reader | No HTTP/REST interface - SignalR (WebSockets or Long Polling)| | Portal | https://portal-api.platform.quix.io/swagger | | Query | https://telemetry-query-``.platform.quix.io/swagger | @@ -155,24 +154,6 @@ You should replace the text `YOUR_PAT` with your PAT. You should also replace `YOUR_ENVIRONMENT_ID` with your environment ID. -=== "Writer API" - - ```javascript - var signalR = require("@microsoft/signalr"); - const token = "YOUR_PAT" - const environmentId = "YOUR_ENVIRONMENT_ID" - - const options = { - accessTokenFactory: () => token - }; - - const connection = new signalR.HubConnectionBuilder() - .withUrl("https://writer-" + environmentId + ".platform.quix.io/hub", options) - .build(); - - connection.start().then(() => console.log("SignalR connected.")); - ``` - === "Reader API" ```javascript diff --git a/docs/apis/streaming-writer-api/create-stream.md b/docs/apis/streaming-writer-api/create-stream.md deleted file mode 100644 index a3f42445..00000000 --- a/docs/apis/streaming-writer-api/create-stream.md +++ /dev/null @@ -1,140 +0,0 @@ -# Create a new Stream - -Data is typicaly published to a stream within a topic. As a stream is confined to a topic partition, order of delivery is guaranteed for a stream. - -!!! tip - - This method is optional. You can also create a stream implicitly by sending data to a stream that doesn’t already exist. If a stream does not exist it is created for you. - -## Using the `/streams` endpoint - -To create a new stream, send a `POST` request to: - - /topics/${topicName}/streams - -You should replace `${topicName}` in the endpoint URL with the name of the topic you wish to create the stream in. For example, if your topic is named "cars", your endpoint URL will be `/topics/cars/streams`. - -### Example request - -You can create a new Stream with an absolute minimum of effort by passing an empty JSON object in the payload: - -=== "Curl" - - ``` shell - curl "https://${domain}.platform.quix.io/topics/${topicName}/streams" \ - -X POST \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d '{}' - ``` - -=== "Node.js" - - ``` javascript - const https = require('https'); - - const data = "{}"; - - const options = { - hostname: domain + '.platform.quix.io', - path: '/topics/' + topicName + '/streams', - method: 'POST', - headers: { - 'Authorization': 'Bearer ' + token, - 'Content-Type': 'application/json' - } - }; - - const req = https.request(options, res => { - res.on('data', d => { - let streamId = JSON.parse(d).streamId; - console.log(streamId); - }); - }); - - req.write(data); - req.end(); - ``` - -For most real-world cases, you’ll also want to provide some or all of the following: - -* `name` - the human friendly name of the stream -* `location` - the location of the stream's persisted data in the data catalogue (retrieved using Query API) -* `metadata` - metadata for the stream (retrieved using Query API, or Data Explorer) -* `parents` - the list of stream ids of the parent streams -* `timeOfRecording` - the datetime of the stream recording - -For example, here’s a more useful payload: - -```json -{ - "name": "cardata", - "location": "simulations/trials", - "metadata": { - "rain": "light" - } -} -``` - -### Example response - -The JSON returned is an object with a single property, `streamId`. This contains the unique identifier of your newly created stream, and resembles the following: - -```json -{ - "streamId": "66fb0a2f-eb70-494e-9df7-c06d275aeb7c" -} -``` - -!!! tip - - If you’re following these guides in order, you’ll want to take note of that stream id. For curl examples, it’s convenient to keep it in an environment variable, for example: - - ``` bash - $ streamId=66fb0a2f-eb70-494e-9df7-c06d275aeb7c - ``` - -## Using SignalR - -```javascript -var signalR = require("@microsoft/signalr"); -const token = "YOUR_TOKEN" -const environmentId = "YOUR_ENVIRONMENT_ID" -const topic = "YOUR_TOPIC_NAME" - -const options = { - accessTokenFactory: () => token -}; - -const connection = new signalR.HubConnectionBuilder() - .withUrl("https://writer-" + environmentId + ".platform.quix.io/hub", options) - .build(); - -// Establish connection -connection.start().then(async () => { - console.log("Connected to Quix."); - - // Note, SignalR uses the same models as the HTTP endpoints, so if in doubt, check HTTP endpoint samples or Swagger for model. - let streamDetails = { - "name": "cardata", - "location": "simulations/trials", - "metadata": { - "rain": "light" - } - } - - // Send create details - console.log("Creating stream"); - let createdDetails = await connection.invoke("CreateStream", topic, streamDetails); - let streamId = createdDetails.streamId - console.log("Created stream " + streamId); -}); -``` - -!!! tip - - Also available as JsFiddle at [https://jsfiddle.net/QuixAI/cLno68fs/](https://jsfiddle.net/QuixAI/cLno68fs/){target=_blank} - -## 🏃‍♀️ Next step - -[Stream metadata :material-arrow-right-circle:{ align=right }](stream-metadata.md) diff --git a/docs/apis/streaming-writer-api/http-requests.md b/docs/apis/streaming-writer-api/http-requests.md deleted file mode 100644 index 19268d54..00000000 --- a/docs/apis/streaming-writer-api/http-requests.md +++ /dev/null @@ -1,89 +0,0 @@ -# HTTP requests - -How you send HTTP requests REST APIs varies depending on the client or language you’re using. - -## Sign requests using your PAT - -Make sure you accompany each request to the API with an `Authorization` header using your Personal Access Token (PAT) as a bearer token, as follows: - -``` http -Authorization: bearer -``` - -Replace `` with your Personal Access Token (PAT). - -For example, if you’re using Curl on the command line, you can set the header using the `-H` flag: - -``` shell -curl -H "Authorization: bearer " ... -``` - -If you fail to send a valid Authorization header, the API will respond with a `401 UNAUTHORIZED` status code. - -## Endpoint URLs - -APIs are available on a per-environment basis (with the exception of the Portal API), so the endpoint URL is based on your environment ID. - -Read the documentation on how to obtain your environment ID. - -The API endpoint URL has the following format: - -``` -https://-.platform.quix.io/ -``` - -So, for example, the endpoint URL for the streaming writer might resemble the following: - -``` -https://writer-acme-weather.platform.quix.io/ -``` - -The API is `writer`, and the environment ID is `acme-weather`. - -## HTTP method - -Endpoints in the API use HTTP methods such as `POST` and `PUT` methods. Ensure your HTTP client sends the correct request method. - -Using `curl`, you can specify the request method with the `-X ` flag, for example: - -```bash -curl -X PUT ... -``` - -## Payload - -For most methods, you’ll need to send a JSON object containing supported parameters. You’ll also need to set the appropriate content type for the payload you’re sending: - -```bash -curl -H "Content-Type: application/json" ... -``` - -!!! warning - - You **must** specify the content type of your payload. Failing to include this header will result in a `415 UNSUPPORTED MEDIA TYPE` status code. - -You can send data using the `curl` flag `-d`. This should be followed by either a string of JSON data, or a string starting with the *@* symbol, followed by a filename containing the JSON data. - -```bash -curl -d '{"key": "value"}' ... -curl -d "@data.json" ... -``` - -!!! tip - - By default, `-d` will send a `POST` request, so `-X POST` becomes unnecessary. - -## Complete Curl example - -You should structure most of your requests to the API around this pattern: - -```bash -curl -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d "@data.json" \ - https://${api-name}-${environment-id}.platform.quix.io/ -``` - -## 🏃‍♀️ Next step - -[Create a stream :material-arrow-right-circle:{ align=right }](create-stream.md) diff --git a/docs/apis/streaming-writer-api/images/apis-tokens.png b/docs/apis/streaming-writer-api/images/apis-tokens.png deleted file mode 100644 index 57aa355ab324a5c8ccfdc8a5a6e4de9636999078..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 115343 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".platform.quix.io/hub", options) - .build(); - -// Establish connection -connection.start().then(async () => { - console.log("Connected to Quix."); - - // Note, SignalR uses the same models as the HTTP endpoints, so if in doubt, check HTTP endpoint samples or Swagger for model. - let parameterData = { - "epoch": Date.now() * 1000000, // set now as time starting point, in nanoseconds - "timestamps": [ - 0, - 5000000000, // 5 seconds from now (see epoch) - 8000000000 - ], - "numericValues": { - "NumericParameter1": [ - 13.37, - 42, - 24.72 - ] - }, - "stringValues": { - "StringParameter1": [ - "Hello", - "World", - "!" - ] - }, - "tagValues": { - "Tag1": [ - "A", - "B", - null - ] - } - } - - // Send stream update details - console.log("Sending parameter data"); - await connection.invoke("SendParameterData", topic, streamId, parameterData); - console.log("Sent parameter data"); -}); -``` - -!!! tip - - Also available as JsFiddle at [https://jsfiddle.net/QuixAI/a41b8x0t/](https://jsfiddle.net/QuixAI a41b8x0t/){target=_blank} - - -## 🏃‍♀️ Next step - -[Send event data :material-arrow-right-circle:{ align=right }](send-event.md) diff --git a/docs/apis/streaming-writer-api/send-event.md b/docs/apis/streaming-writer-api/send-event.md deleted file mode 100644 index a7a5ca37..00000000 --- a/docs/apis/streaming-writer-api/send-event.md +++ /dev/null @@ -1,128 +0,0 @@ -# Send an Event - -You can add Events to your stream data to record discrete actions for future reference. - -## Sending event data - -To send event data to a stream, use the `POST` method with this endpoint: - -``` -/topics/${topicName}/streams/${streamId}/events/data -``` - -You should replace `${topicName}` with the name of the topic your stream belongs to, and `${streamId}` with the id of the stream you wish to send data to. For example: - -``` -/topics/cars/streams/66fb0a2f-eb70-494e-9df7-c06d275aeb7c/events/data -``` - -!!! tip - - You can create a new stream by supplying a `${streamId}` that doesn’t already exist. This avoids the need to call the [create stream endpoint](create-stream.md) separately. - -Your payload should be an array of events. Each event is an object containing the following properties: - -* `id` - a unique identifier for the event -* `timestamp` - the nanosecond-precise timestamp at which the event occurred -* `tags` - a object containing key-value string pairs representing tag values -* `value` - a string value associated with the event - -### Example request - -This example call adds a single event to a stream. The event has an example value and demonstrates use of a tag to include additional information: - -=== "curl" - - ```shell - curl -i "https://${domain}.platform.quix.io/topics/${topicName}/streams/${streamId}/events/data" \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d '[{ - "id": "Alert", - "timestamp": 1618133869000000000, - "tags": { - "capacity": "over" - }, - "value": "Help" - }]' - ``` - -=== "Node.js" - - ```javascript - const https = require('https'); - - const data = JSON.stringify({ - "id": "Alert", - "timestamp": 1618133869000000000, - "tags": { - "capacity": "over" - }, - "value": "Help" - }); - - const options = { - hostname: domain + '.platform.quix.io', - path: '/topics/' + topicName + '/streams/' + streamId + '/events/data', - method: 'POST', - headers: { - 'Authorization': 'Bearer ' + token, - 'Content-Type': 'application/json' - } - }; - - const req = https.request(options); - - req.write(data); - req.end(); - ``` - -### Response - -No payload is returned from this call. A 200 HTTP response code indicates success. If the call fails, you see either a 4xx or 5xx response code, indicating what went wrong. - -## Using SignalR - -The following code shows how to send event data using SignalR (WebSockets): - -```javascript -var signalR = require("@microsoft/signalr"); -const token = "YOUR_TOKEN" -const environmentId = "YOUR_ENVIRONMENT_ID" -const topic = "YOUR_TOPIC_NAME" -const streamId = "ID_OF_STREAM_TO_WRITE_TO" - -const options = { - accessTokenFactory: () => token -}; - -const connection = new signalR.HubConnectionBuilder() - .withUrl("https://writer-" + environmentId + ".platform.quix.io/hub", options) - .build(); - -// Establish connection -connection.start().then(async () => { - console.log("Connected to Quix."); - - // Note, SignalR uses the same models as the HTTP endpoints, so if in doubt, check HTTP endpoint samples or Swagger for model. - let eventData = [ - { - "timestamp": Date.now() * 1000000, // set now in nanoseconds, - "tags": { - "capacity": "over" - }, - "id": "Alert", - "value": "Successful sample run" - } - ] - - // Send stream update details - console.log("Sending event data"); - await connection.invoke("SendEventData", topic, streamId, eventData); - console.log("Sent event data"); -}); -``` - -!!! tip - - Also available as JsFiddle at [https://jsfiddle.net/QuixAI/h4fztrns/](https://jsfiddle.net/QuixAI/h4fztrns/){target=_blank} \ No newline at end of file diff --git a/docs/apis/streaming-writer-api/setup.md b/docs/apis/streaming-writer-api/setup.md deleted file mode 100644 index 5a1d8e5a..00000000 --- a/docs/apis/streaming-writer-api/setup.md +++ /dev/null @@ -1,200 +0,0 @@ -# Setup - -To use the APIs you'll typically need the following: - -1. A Quix Cloud account. - -??? info "Start for free" - [Book a session](https://quix.io/book-a-demo) with us to start for free. - - We will create a time-limited free account for you, and our experts will help you get started with your specific use case. - -2. Obtain a Personal Access Token - you need this to authenticate requests. - -3. Get your environment ID - you need this for request URLs. - -4. View the API reference documentation. - -5. If you intend to use WebSockets, and the API supports it, you'll need to install SignalR. - -Each of these is described in the following sections. - -## Project settings - -Useful information can also be found in the settings panels for your environment: - -1. Click `Settings` in the bottom left corner of the UI. - -2. From `Project settings` select the environment you are working with. - -3. Click `APIs and tokens`. - -You are now presented with a very useful panel. From here you have useful links to items such as API documentation (rendered with Swagger), as well as your Personal Access Tokens (PATs): - -![APIs and tokens](./images/apis-tokens.png){width=80%} - -## Personal access token (PAT) - -Personal Access Tokens, or PATs, are bearer tokens that can be used to authenticate the various Quix APIs, such as the Portal API. - -To obtain a PAT, log in to Quix, and click on your profile icon in the top right corner, then click `Personal Access Tokens`. You can then generate a PAT with a lifetime suitable for your use case. - -Alternatively, log in to Quix, and click `Settings` in the main left-hand navigation. Then, for a specific environment, click `APIs and tokens`. - -In the `APIs and tokens` dialog, you can click `Personal Access Tokens` to generate PATs, or a Streaming Token (SDK Token) for use with the Quix Streams client library. - -## Get environment ID - -When using APIs you’ll need to obtain an ID based on a specific environment. For example, endpoints for the Query API use a domain with the following pattern: - - https://telemetry-query-${environment-id}.platform.quix.io/ - -The environment ID is a combination of your organization and environment names, converted to URL friendly values. - -### Obtain your environment ID from the URL - -1. Go to the [Portal home](https://portal.cloud.quix.io/){target=_blank}. - -2. Locate the environment you’re interested in and open it. - -3. At this point, take note of the URL. It will be in the form: - - https://portal.cloud.quix.io/home?workspace={environment-id} - -### Obtain environment ID from settings - -You can also get the environment ID from Settings. Click `Settings` in the bottom-left corner of the portal, select the environment of interest. You can then copy the environment ID (previously known as the workspace ID) from the settings panel, as shown in the following screenshot: - -![Get environment ID from settings](../../images/get-environment-id/get-environment-id-settings.png){width=80%} - -### Example environment ID - -Given the following URL for the environment: - -``` -https://portal.cloud.quix.io/pipeline?workspace=joeengland-apitests-testing -``` - -The environment ID is: - -``` -joeengland-apitests-testing -``` - -The components of the environment ID are as follows: - -| joeengland | The name of the Quix account, known as the organization, in this case the personal account of Joe England | -| apitests | The name of the project | -| testing | The name of the environment | - -Copy the value for `environment-id` and use it wherever you need an environment ID. - -!!! note - - The `workspace=` parameter in the URL `https://portal.cloud.quix.io/home?workspace={environment-id}` is there for legacy reasons, and does in fact indicate an environment. - -## API reference documentation - -An OAS3 API reference guides are available for HTTP/REST APIs. These are rendered using [Swagger](https://swagger.io/){target=_blank}. - -The URLs for the API references are specific to your environment, so you can easily test out API calls on your environment using the API reference. You'll need to get you environment ID. The exception to this is the Portal API, which is account-wide. - -| API | API reference URL (Swagger documentation)| -|---|---| -| Streaming Writer | https://writer-``.platform.quix.io/swagger | -| Streaming Reader | No HTTP/REST interface - SignalR (WebSockets or Long Polling)| -| Portal | https://portal-api.platform.quix.io/swagger | -| Query | https://telemetry-query-``.platform.quix.io/swagger | - -Replace `` with your environment ID. - -!!! tip - - Once you access the API reference, you can select the version of the API you require from the `Select a definition` dropdown list. - -## Set up SignalR - -[Microsoft SignalR](https://learn.microsoft.com/en-us/aspnet/signalr/overview/getting-started/introduction-to-signalr){target=_blank} enables real-time bi-directional communication between Quix and a client. The underlying transport used is either WebSockets or Long POlling, depending on the client's capabilities. - -You'll use SignalR where you need high speed, real-time, communication between Quix and the client, as typically the request-response model of the HTTP/REST approach does not scale to this use case. - -This page explains how to set up Microsoft SignalR. - -### Installation - -You can install SignalR using `npm` (for Node.js applications) or using a CDN (for web browser applications): - -=== "npm" - - If you are using a package manager such as [npm](https://www.npmjs.com/){target=_blank}, you can install SignalR using: - - ``` shell - npm install @microsoft/signalr - ``` - -=== "CDN" - - You can also install SignalR using a CDN. For example, in HTML: - - ``` html - - - - Hello SignalR - - - ... - - ``` - -### Testing the connection - -Once you’ve installed the SignalR library, you can test it with the following code snippet. This opens a connection to the hub running on your custom subdomain, and checks authentication. - -You should replace the text `YOUR_PAT` with your PAT. - -You should also replace `YOUR_ENVIRONMENT_ID` with your environment ID. - -=== "Writer API" - - ```javascript - var signalR = require("@microsoft/signalr"); - const token = "YOUR_PAT" - const environmentId = "YOUR_ENVIRONMENT_ID" - - const options = { - accessTokenFactory: () => token - }; - - const connection = new signalR.HubConnectionBuilder() - .withUrl("https://writer-" + environmentId + ".platform.quix.io/hub", options) - .build(); - - connection.start().then(() => console.log("SignalR connected.")); - ``` - -=== "Reader API" - - ```javascript - var signalR = require("@microsoft/signalr"); - - const options = { - accessTokenFactory: () => 'YOUR_PAT' - }; - - const connection = new signalR.HubConnectionBuilder() - .withUrl("https://reader-YOUR_ENVIRONMNENT_ID.platform.quix.io/hub", options) - .build(); - - connection.start().then(() => console.log("SignalR connected.")); - ``` - -If the connection is successful, you see the console log "SignalR connected". - -!!! tip - - Also available as JsFiddle at [https://jsfiddle.net/QuixAI/L9ha4p5j/](https://jsfiddle.net/QuixAI/L9ha4p5j/){target=_blank} - -## 🏃‍♀️ Next step - -[Learn about HTTP requests :material-arrow-right-circle:{ align=right }](http-requests.md) diff --git a/docs/apis/streaming-writer-api/stream-metadata.md b/docs/apis/streaming-writer-api/stream-metadata.md deleted file mode 100644 index e826d8ac..00000000 --- a/docs/apis/streaming-writer-api/stream-metadata.md +++ /dev/null @@ -1,116 +0,0 @@ -# Add Stream metadata - -You can add arbitrary string metadata to any stream. You can also create a new stream by sending metadata using a stream ID that does not already exist. This is mainly used when retrieving persisted data using the Query API, but is also displayed in Data Explorer. This helps you identify data of interest. - -## How to add metadata to a stream - -Send a `PUT` request to the following endpoint to update a stream with the given properties: - - /topics/${topicName}/streams/${streamId} - -You should replace `${topicName}` with the name of the topic your stream belongs to, and `${streamId}` with the id of the stream you wish to update. For example: - - /topics/cars/streams/66fb0a2f-eb70-494e-9df7-c06d275aeb7c - -!!! tip - - You can create a new stream by supplying a `${streamId}` that doesn’t already exist. It will be initialized with the data you provide in the payload, and the ID you use in the endpoint. This avoids the need to call the [create stream endpoint](create-stream.md) separately. - -Your request should contain a payload consisting of JSON data containing the desired metadata. - -### Example request - -Below is an example payload demonstrating how to set a single item of metadata. Note that the `metadata` property references an object which contains key/value string-based metadata. - - - curl - - ```shell - curl "https://${domain}.platform.quix.io/topics/${topicName}/streams/${streamId}" \ - -X PUT \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d '{"metadata":{"fruit":"apple"}}' - ``` - - - Node.js - - ```javascript - const https = require('https'); - - const data = JSON.stringify({ metadata: { fruit: "apple" }}); - - const options = { - hostname: domain + '.platform.quix.io', - path: '/topics/' + topicName + '/streams/' + streamId, - method: 'PUT', - headers: { - 'Authorization': 'Bearer ' + token, - 'Content-Type': 'application/json' - } - }; - - const req = https.request(options); - - req.write(data); - req.end(); - ``` - -Since this is a PUT request, it will replace all the stream data with the payload contents. To maintain existing data, you should include it in the payload alongside your metadata, for example: - -```json -{ - "name": "Example stream", - "location": "/sub/dir", - "metadata": { - "fruit": "apple" - } -} -``` - -### Response - -No payload is returned from this call. A 200 HTTP response code indicates success. If the call fails, you should see either a 4xx or 5xx response code indicating what went wrong. For example, you’ll see a 405 code if you forget to specify the correct `PUT` method. - -## Using SignalR - -```javascript -var signalR = require("@microsoft/signalr"); -const token = "YOUR_TOKEN" -const environmentId = "YOUR_ENVIRONMENT_ID" -const topic = "YOUR_TOPIC_NAME" -const streamId = "ID_OF_STREAM_TO_UPDATE" - -const options = { - accessTokenFactory: () => token -}; - -const connection = new signalR.HubConnectionBuilder() - .withUrl("https://writer-" + environmentId + ".platform.quix.io/hub", options) - .build(); - -// Establish connection -connection.start().then(async () => { - console.log("Connected to Quix."); - - // Note, SignalR uses the same models as the HTTP endpoints, so if in doubt, check HTTP endpoint samples or Swagger for model. - let streamDetails = { - "name": "Example stream", - "location": "/sub/dir", - "metadata": { - "fruit": "apple" - } - } - - // Send stream update details - console.log("Updating stream"); - await connection.invoke("UpdateStream", topic, streamDetails); - console.log("Updated stream"); -}); -``` -!!! tip - - Also available as JsFiddle at [https://jsfiddle.net/QuixAI/ruywnz28/](https://jsfiddle.net/QuixAI/ruywnz28/){target=_blank} - -## 🏃‍♀️ Next step - -[Send parameter data :material-arrow-right-circle:{ align=right }](send-data.md) diff --git a/docs/blog/posts/stream-processing-glossary.md b/docs/blog/posts/stream-processing-glossary.md index 2263eeeb..09734b4b 100644 --- a/docs/blog/posts/stream-processing-glossary.md +++ b/docs/blog/posts/stream-processing-glossary.md @@ -497,7 +497,7 @@ clarifies and emphasizes that the data involved is produced in real time. In Quix, a stream represents a session of one data set that has a beginning and an (optional) ending, such as the journey of a single car during a race or one run-through of a game. Read more about Quix streams in our -[documentation](https://quix.io/docs/apis/streaming-writer-api/intro.html). +[documentation](https://quix.io/docs/quix-streams/introduction.html). ### Streaming analytics diff --git a/docs/develop/apis-overview.md b/docs/develop/apis-overview.md index 13fa8a6e..b386a0c6 100644 --- a/docs/develop/apis-overview.md +++ b/docs/develop/apis-overview.md @@ -2,7 +2,6 @@ Quix provides several APIs. These are: -* Streaming Writer API * Streaming Reader API * Portal API * Query API @@ -13,7 +12,7 @@ While [Quix Streams](https://quix.io/docs/quix-streams/introduction.html) is the * There are resource constraints which mean you can't run Quix Streams * The nature of the client (for example, a web browser) -In these situations Streaming Reader and Writer APIs can provide an alternative solution - for example, they can easily be accommodated in a modern web browser, or using most modern programming languages with an HTTP or SignalR client. +In these situations the Streaming Reader API can provide an alternative solution - for example, it can easily be accommodated in a modern web browser, or using most modern programming languages with a SignalR client. Portal API is useful for automating processes normally carried out manually in Quix. @@ -27,19 +26,10 @@ Use cases for these APIs are shown in the following table: | API | Interface | Purpose | Typical use case | Docs link | |---|---|---|---|----| -| Streaming Writer API | HTTP, SignalR (WebSockets) | Publish data to a Quix topic | External service, command line client | [Streaming Writer API](../apis/streaming-writer-api/overview.md) | | Streaming Reader API | SignalR (WebSockets and Long Polling)| Subscribe to a Quix topic | Web browser client, dashboard, command line client | [Streaming Reader API](../apis/streaming-reader-api/overview.md) | | Portal API | HTTP (REST)| Automate Quix | Creating and monitoring deployments | [Portal API](../apis/portal-api/overview.md) | | Query API | HTTP (REST) | Retrieve persisted data | Evaluate service is processing data correctly | [Query API](../apis/query-api/overview.md) | -## Streaming Writer API - -The [Streaming Writer API](../apis/streaming-writer-api/overview.md) enables you to stream data into a Quix topic. - -Streaming Writer provides both an HTTP interface and a [SignalR](https://learn.microsoft.com/en-us/aspnet/signalr/overview/getting-started/introduction-to-signalr){target=_blank} interface, with SignalR supporting WebSockets, for real-time data transfer. - -* [Read more about Streaming Writer API](../apis/streaming-writer-api/overview.md) - ## Streaming Reader API The [Streaming Reader](../apis/streaming-reader-api/overview.md) API enables you to stream data out of a Quix topic. diff --git a/docs/develop/integrate-data/overview.md b/docs/develop/integrate-data/overview.md index 1304f5d7..a82c1a1c 100644 --- a/docs/develop/integrate-data/overview.md +++ b/docs/develop/integrate-data/overview.md @@ -14,8 +14,7 @@ To ingest data: 3. Polling 4. Inbound webhooks 5. External source -6. Streaming APIs -7. Using Quix Streams +6. Using Quix Streams 8. Using a web app To publish data: @@ -31,9 +30,7 @@ If you want some example code you can use as a starting point for connecting you Low-frequency data from REST APIs can be polled from Quix using a library such as `requests`. -Quix also provides the [Streaming Writer API](../../apis/streaming-writer-api/overview.md) and the [Streaming Reader API](../../apis/streaming-reader-api/overview.md). - -When using the Streaming Writer API you can use the HTTP interface if a continous connection is not required. Faster data from web servers, browser clients, and IoT devices can interface using the SignalR interface, which can use WebSockets or Long Polling (depending on client support), where a continuous connection is required. +Quix also provides the [Streaming Reader API](../../apis/streaming-reader-api/overview.md) for reading data from Quix topics. The Streaming Reader API uses the SignalR interface, which can use WebSockets or Long Polling (depending on client support), providing a continuous connection for real-time data consumption. ## Integrations diff --git a/docs/develop/integrate-data/streaming-apis.md b/docs/develop/integrate-data/streaming-apis.md deleted file mode 100644 index 04f3a496..00000000 --- a/docs/develop/integrate-data/streaming-apis.md +++ /dev/null @@ -1,183 +0,0 @@ -# Using the Quix Streaming APIs - -Quix provides two Streaming APIs to enable a client (JavaScript or Node.js code) to read from and write to a Quix topic: - -1. [Streaming Writer API](../../apis/streaming-writer-api/overview.md) -2. [Streaming Reader API](../../apis/streaming-reader-api/overview.md) - -The Writer API is used to write data into Quix, that is, it is used by publishers. The Reader API is used to read data from Quix, and is therefore used by consumers. These are used typically by external services such as web browser client code, or perhaps IoT devices. - -The Streaming Writer API offers both HTTP interface and a SignalR interface. The Streaming Reader API only offers a SignalR interface. - -!!! note - - SignalR uses Long Polling or WebSockets for the transport, depending on client support. Most clients support WebSockets. - -The WebSockets interface provides a continuous end-to-end connection suitable for higher speed, real-time data transfer. This is a higher performance alternative to the request-response mode of operation of the HTTP interface. The Writer and Reader APIs both use the [Microsoft SignalR](https://learn.microsoft.com/en-us/aspnet/core/signalr/javascript-client?view=aspnetcore-5.0&tabs=visual-studio) technology to implement the WebSockets interface. - -Some example code that shows how to connect to Quix and write data into a Quix stream using the WebSockets interface is shown here: - -``` html - - - - Hello WebSockets - - - - -
- - - -
-
Timestamp: .
-
X Mouse position: .
-
Y Mouse position: .
- -
- - - - -``` - -This simple example just sends mouse cursor position to a Quix stream when you draw on a canvas. - -Code that could read mouse cursor position from a Quix stream is as follows: - -``` html - - - - -

Quix JavaScript Hello WebSockets

- - - -
- - - - - -``` - -This code uses the Streaming Reader API to read data from a Quix stream. diff --git a/docs/integrations/databases/influxdb/telegraf.md b/docs/integrations/databases/influxdb/telegraf.md index fef3559f..4177b038 100644 --- a/docs/integrations/databases/influxdb/telegraf.md +++ b/docs/integrations/databases/influxdb/telegraf.md @@ -225,7 +225,6 @@ Details about these Telegraf plugins can be found in the [Telegraf plugin direct ## Next steps * Learn how to build a complete [Python stream processing pipeline](../../../quix-cloud/quixtour/overview.md) -* Read about [Streaming Writer](../../../apis/streaming-writer-api/overview.md) * Try one of our [tutorials](../../../tutorials/overview.md) * Obtain an [InfluxDB account](https://www.influxdata.com/products/influxdb-cloud/serverless/){target=_blank} * Read the comprehensive [Telegraf documentation](https://docs.influxdata.com/telegraf/v1/){target=_blank} diff --git a/docs/kb/glossary.md b/docs/kb/glossary.md index 4c9164f5..416ea324 100644 --- a/docs/kb/glossary.md +++ b/docs/kb/glossary.md @@ -202,10 +202,6 @@ Read more about [streams](https://quix.io/docs/quix-streams/introduction.html). A [WebSockets API](../apis/streaming-reader-api/overview.md) used to stream any data directly from a topic to an external application. Most commonly used to read the results of a model or service to a real-time web application. Your application **reads** data from Quix. -## Streaming Writer API - -An [HTTP API](../apis/streaming-writer-api/overview.md) used to send telemetry data from any source to a topic in Quix. It should be used when it is not possible to use [Quix Streams](https://quix.io/docs/quix-streams/introduction.html). Your application **writes** data into Quix. - ## Template project A public project in GitHub that you can fork and import into Quix. See the [templates page](https://quix.io/templates){target=_blank}. diff --git a/docs/manage/troubleshooting.md b/docs/manage/troubleshooting.md index 40955b4d..81e8744e 100644 --- a/docs/manage/troubleshooting.md +++ b/docs/manage/troubleshooting.md @@ -119,12 +119,8 @@ The APIs that require a valid bearer token are: 1. Portal API - https://portal-api.platform.quix.io/swagger/index.html - -2. Streaming Writer API - - - https://writer-[YOUR_ORGANIZATION_ID]-[YOUR_ENVIRONMENT_ID].platform.quix.io/index.html -3. Telemetry Query API +2. Telemetry Query API - https://telemetry-query-[YOUR_ORGANIZATION_ID]-[YOUR_ENVIRONMENT_ID].platform.quix.io/swagger/index.html diff --git a/docs/tutorials/clickstream/get-project.md b/docs/tutorials/clickstream/get-project.md index b519a527..81fdb304 100644 --- a/docs/tutorials/clickstream/get-project.md +++ b/docs/tutorials/clickstream/get-project.md @@ -22,7 +22,7 @@ You'll need to add the following secrets: | `redis_port` | Data ingestion, Real-time dashboard, Data aggregation, Data enrichment, Event detection | Port for the Redis Cloud database | | `redis_username` | Data ingestion, Real-time dashboard, Data aggregation, Data enrichment, Event detection | Username for the Redis Cloud database - typically `default`| | `redis_password` | Data ingestion, Real-time dashboard, Data aggregation, Data enrichment, Event detection | Password for the Redis Cloud database - found from Security section | -| `bearer_token` | Webshop frontend | A [PAT](../../develop/authentication/personal-access-token.md) that the web app uses to authenticate the Streaming Reader and Streaming Writer APIs | +| `bearer_token` | Webshop frontend | A [PAT](../../develop/authentication/personal-access-token.md) that the web app uses to authenticate the Streaming Reader API | You need to [create secrets](../../deploy/secrets-management.md) for these and then assign them to the corresponding [environment variables](../../deploy/environment-variables.md). diff --git a/docs/tutorials/clickstream/webshop-frontend.md b/docs/tutorials/clickstream/webshop-frontend.md index 45e48dc8..81efae68 100644 --- a/docs/tutorials/clickstream/webshop-frontend.md +++ b/docs/tutorials/clickstream/webshop-frontend.md @@ -21,9 +21,9 @@ This service subscribes to the following topics: It also publishes data back to the `click-data` topic - this is data about a user's real-time interaction with the webshop frontend (selection of products), it is published to this topic so that the data can be enriched and passed to the data aggregator and event detection service. The event detection service then publishes a suitable offer to the `special-offers` topic. As the webshop frontend subscribes to `special-offers`, it can present the correct offer popup when it receives the offer data. -## Streaming Reader and Writer APIs +## Streaming Reader API -Note because this service is a web app (running in your browser) it communicates with Quix topics using the [Streaming Reader API](../../apis/streaming-reader-api/overview.md) and the [Streaming Writer API](../../apis/streaming-writer-api/overview.md). Streaming Writer API enables a web app to publish data to a topic, and the Streaming reader API enables the web app to subscribe to a topic. +Note because this service is a web app (running in your browser) it communicates with Quix topics using the [Streaming Reader API](../../apis/streaming-reader-api/overview.md) to subscribe to topics. These APIs need to be authenticated, and so for this service you need to configure a [PAT](../../develop/authentication/personal-access-token.md), referred to as a bearer token in this service. diff --git a/docs/tutorials/computer-vision/web-ui.md b/docs/tutorials/computer-vision/web-ui.md index 722d87dc..3ad966d4 100644 --- a/docs/tutorials/computer-vision/web-ui.md +++ b/docs/tutorials/computer-vision/web-ui.md @@ -66,7 +66,7 @@ So, simplifying, after connection to the Quix topic, on a `ParameterDataReceived !!! note - A web client can also write data into a Quix topic using the [Quix Streaming Writer API](../../apis/streaming-writer-api/overview.md), but in this app you only consume (read) data. + In this app you only consume (read) data from Quix topics. The data read from the topic is as follows: diff --git a/docs/tutorials/predictive-maintenance/get-project.md b/docs/tutorials/predictive-maintenance/get-project.md index ba572835..4ee07892 100644 --- a/docs/tutorials/predictive-maintenance/get-project.md +++ b/docs/tutorials/predictive-maintenance/get-project.md @@ -21,7 +21,7 @@ You'll need to configure the following credentials for each Quix service that ne | `INFLUXDB_MEASUREMENT_NAME` | The InfluxDB measurement to read data from. If not specified, the name of the output topic will be used. | | `TIMESTAMP_COLUMN` | This is the field in your data that represents the timestamp in nanoseconds. If you leave this blank, the message timestamp received from the broker is used. Case sensitive. Optional. | | `CONSUMER_GROUP_NAME` | The name of the consumer group to use when consuming from Kafka. | - + The above is a list of environment variables that you are going to configure. diff --git a/docs/tutorials/sentiment-analysis/ui-service.md b/docs/tutorials/sentiment-analysis/ui-service.md index b9268afd..b8fb7511 100644 --- a/docs/tutorials/sentiment-analysis/ui-service.md +++ b/docs/tutorials/sentiment-analysis/ui-service.md @@ -15,7 +15,6 @@ This provides the UI for you to interact with this app. The key ideas on this page: * How a web client can read data from a Quix topic using Quix Streaming Reader API -* How a web client can write data to a Quix topic using Quix Streaming Writer API * WebSockets as a way of streaming data into a web client * Microsoft SignalR is the WebSockets technology used in the Streaming Reader API * Access an external web application from the pipeline view @@ -28,12 +27,7 @@ The key thing this service does is provide a UI that implements the chat interfa The most important thing to understand is how this service reads and writes data to and from the Quix pipeline. -This is done through use of two APIs: - -* [Quix Streaming Reader API](../../apis/streaming-reader-api/overview.md) -* [Quix Streaming Writer API](../../apis/streaming-writer-api/overview.md) - -The Streaming Writer is used to write both published and draft messages to the sentiment analysis service. Note the UI provides sentiment analysis of messages as they are being typed, that is, in the draft state, as they have not yet been sent to the chat room. +This is done through use of the [Quix Streaming Reader API](../../apis/streaming-reader-api/overview.md). The Streaming Reader is used to read the sentiment from the sentiment analysis service for both the sent messages and draft messages. @@ -44,7 +38,7 @@ The four topics involved are: * `chat-with-sentiment` - topic with sentiment for sent messages * `drafts_sentiment` - topic with sentiment for draft messages -So, the web UI uses the Writer API to write to both `chat-messages` and `drafts` and the Reader API to read from both `chat-with-sentiment` and `drafts_sentiment`. +So, the web UI uses the Reader API to read from both `chat-with-sentiment` and `drafts_sentiment`. The Streaming Reader API has both an HTTP and WebSockets interface you can use to interface with a Quix topic. This web client uses the WebSockets interface. This enables data to be streamed from the Quix topic into the web client with good performance. This is a more efficient method than using the request-response method of HTTP. @@ -65,14 +59,11 @@ readerHubConnection.on("ParameterDataReceived", (payload: ParameterData) => { }); ``` -The Streaming Writer API is [used in a similar way](../../apis/streaming-writer-api/overview.md). - ## See also For more information refer to: * [Quix Streaming Reader API](../../apis/streaming-reader-api/overview.md) - read about the API used by clients external to Quix to read data from a Quix topic. -* [Quix Streaming Writer API](../../apis/streaming-writer-api/overview.md) - read about the API used by clients external to Quix to write data to a Quix topic. ## 🏃‍♀️ Next step diff --git a/mkdocs.yml b/mkdocs.yml index 6ade1b70..259533fe 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -255,7 +255,6 @@ nav: - 'Polling a REST API': 'develop/integrate-data/polling.md' - 'Handling inbound webhooks': 'develop/integrate-data/inbound-webhooks.md' - 'Using an external source': 'develop/integrate-data/external-source.md' - - 'Using the Quix Streaming APIs': 'develop/integrate-data/streaming-apis.md' - 'Using Quix Streams': 'develop/integrate-data/quix-streams.md' - 'Posting data to a Quix web app': 'develop/integrate-data/web-app.md' - 'Handling compressed data': 'develop/integrate-data/compressed-data.md' @@ -271,14 +270,6 @@ nav: - 'Generating events': 'develop/process/timeseries-events.md' - 'Using Quix Streaming APIs': - 'Overview': 'develop/apis-overview.md' - - 'Streaming Writer API': - - 'Overview': 'apis/streaming-writer-api/overview.md' - - 'Setup': 'apis/streaming-writer-api/setup.md' - - 'HTTP requests': 'apis/streaming-writer-api/http-requests.md' - - 'Create a new Stream': 'apis/streaming-writer-api/create-stream.md' - - 'Add Stream metadata': 'apis/streaming-writer-api/stream-metadata.md' - - 'Send Parameter data': 'apis/streaming-writer-api/send-data.md' - - 'Send an Event': 'apis/streaming-writer-api/send-event.md' - 'Streaming Reader API': - 'Overview': 'apis/streaming-reader-api/overview.md' - 'Setup': 'apis/streaming-reader-api/setup.md' @@ -772,7 +763,6 @@ plugins: 'platform/tutorials/sentiment-analysis/summary.md': 'tutorials/sentiment-analysis/summary.md' 'platform/tutorials/slack-alerting/slack-alerting.md': 'tutorials/slack-alerting/slack-alerting.md' 'platform/tutorials/train-and-deploy-ml/index.md': 'tutorials/train-and-deploy-ml/overview.md' - 'apis/streaming-writer-api/index.md': 'apis/streaming-writer-api/overview.md' 'apis/streaming-reader-api/index.md': 'apis/streaming-reader-api/overview.md' 'apis/portal-api/index.md': 'apis/portal-api/overview.md' 'apis/query-api/index.md': 'apis/query-api/overview.md' From a7474a4eb2ff8f56d73f86ec0b05948c456aac10 Mon Sep 17 00:00:00 2001 From: Peter Nagy Date: Thu, 7 Aug 2025 16:24:10 +0100 Subject: [PATCH 02/10] Remove telemetry query api docs --- docs/apis/portal-api/http-requests.md | 6 +- docs/apis/portal-api/setup.md | 5 +- docs/apis/query-api/aggregate-tags.md | 79 ------------ docs/apis/query-api/aggregate-time.md | 42 ------- docs/apis/query-api/filter-tags.md | 82 ------------- docs/apis/query-api/http-requests.md | 83 ------------- docs/apis/query-api/images/apis-tokens.png | Bin 115343 -> 0 bytes docs/apis/query-api/overview.md | 30 ----- docs/apis/query-api/raw-data.md | 82 ------------- docs/apis/query-api/setup.md | 108 ---------------- docs/apis/query-api/streams-filtered.md | 115 ------------------ docs/apis/query-api/streams-models.md | 114 ----------------- docs/apis/query-api/streams-paged.md | 68 ----------- docs/apis/streaming-reader-api/setup.md | 5 +- docs/develop/apis-overview.md | 14 --- docs/develop/integrate-data/jupyter-nb.md | 2 +- docs/kb/glossary.md | 8 -- docs/kb/what-is-quix.md | 1 - docs/manage/overview.md | 14 +-- docs/manage/testing-data-store.md | 27 ---- docs/manage/troubleshooting.md | 10 +- .../train-and-deploy-ml/create-data.md | 2 +- .../train-and-deploy-ml/import-data.md | 2 +- mkdocs.yml | 13 -- 24 files changed, 13 insertions(+), 899 deletions(-) delete mode 100644 docs/apis/query-api/aggregate-tags.md delete mode 100644 docs/apis/query-api/aggregate-time.md delete mode 100644 docs/apis/query-api/filter-tags.md delete mode 100644 docs/apis/query-api/http-requests.md delete mode 100644 docs/apis/query-api/images/apis-tokens.png delete mode 100644 docs/apis/query-api/overview.md delete mode 100644 docs/apis/query-api/raw-data.md delete mode 100644 docs/apis/query-api/setup.md delete mode 100644 docs/apis/query-api/streams-filtered.md delete mode 100644 docs/apis/query-api/streams-models.md delete mode 100644 docs/apis/query-api/streams-paged.md delete mode 100644 docs/manage/testing-data-store.md diff --git a/docs/apis/portal-api/http-requests.md b/docs/apis/portal-api/http-requests.md index 9881c58b..8f0eea95 100644 --- a/docs/apis/portal-api/http-requests.md +++ b/docs/apis/portal-api/http-requests.md @@ -32,13 +32,13 @@ The API endpoint URL has the following format: https://-.platform.quix.io/ ``` -So, for example, the endpoint URL for the query API might resemble the following: +So, for example, the endpoint URL for the portal API might resemble the following: ``` -https://telemetry-query-acme-weather.platform.quix.io/ +https://reader-demo-project-env.platform.quix.io/ ``` -The API is `telemetry-query`, and the environment ID is `acme-weather`. +Note that the Portal API is account-wide and doesn't require an environment ID in the URL. ## HTTP method diff --git a/docs/apis/portal-api/setup.md b/docs/apis/portal-api/setup.md index 33b6e915..a0e64cf2 100644 --- a/docs/apis/portal-api/setup.md +++ b/docs/apis/portal-api/setup.md @@ -45,9 +45,9 @@ In the `APIs and tokens` dialog, you can click `Personal Access Tokens` to gener ## Get environment ID -When using APIs you’ll need to obtain an ID based on a specific environment. For example, endpoints for the [Query API](../../apis/query-api/overview.md) use a domain with the following pattern: +When using APIs you'll need to obtain an ID based on a specific environment. For example, endpoints for environment-specific APIs use a domain with the following pattern: - https://telemetry-query-${environment-id}.platform.quix.io/ + https://-${environment-id}.platform.quix.io/ The environment ID is a combination of your organization and environment names, converted to URL friendly values. @@ -103,7 +103,6 @@ The URLs for the API references are specific to your environment, so you can eas |---|---| | Streaming Reader | No HTTP/REST interface - SignalR (WebSockets or Long Polling)| | Portal | https://portal-api.platform.quix.io/swagger | -| Query | https://telemetry-query-``.platform.quix.io/swagger | Replace `` with your environment ID. diff --git a/docs/apis/query-api/aggregate-tags.md b/docs/apis/query-api/aggregate-tags.md deleted file mode 100644 index a43d4c18..00000000 --- a/docs/apis/query-api/aggregate-tags.md +++ /dev/null @@ -1,79 +0,0 @@ -# Aggregate data by tags - -If you need to compare data across different values for a given tag, you’ll want to group results by that tag. You can do so using the `/parameters/data` endpoint. - -## Using the groupBy property - -You can supply a list of Tags in the `groupBy` array to aggregate results by. For example, you could group a set of Speed readings by the LapNumber they occurred on using something like: - -```json -{ - "from": 1612191286000000000, - "to": 1612191386000000000, - "numericParameters": [{ - "parameterName": "Speed" - }], - "groupBy": [ "LapNumber" ] -} -``` - -With these settings alone, we’ll get the `LapNumber` tag included in our results, alongside the existing timestamps and requested parameters, for example: - -```json -{ - "timestamps": [ - 1612191286000000000, - 1612191287000000000, - ... - ], - "numericValues": { - "Speed": [ - 307.8333333333333, - 313.8421052631579, - ... - ] - }, - "tagValues": { - "LapNumber": [ - "3.0", - "4.0", - ... - ] - } -} -``` - -## Using aggregationType - -For meaningful aggregations, you should specify a type of aggregation function for each parameter. When specifying the parameters to receive, include the `aggregationType` in each parameter object like so: - -```json -"numericParameters": [{ - "parameterName": "Speed", - "aggregationType": "mean" -}] -``` - -Standard aggregation functions are provided including `max`, `count`, and `spread`. When you group by a tag and specify how to aggregate parameter values, the result will represent that aggregation. For example, the following results demonstrate the average speed that was recorded against each lap: - -```json -{ - "timestamps": [ - 1612191286000000000, - 1612191286000000000 - ], - "numericValues": { - "mean(Speed)": [ - 213.36765142704252, - 173.77710595934278 - ] - }, - "stringValues": {}, - "tagValues": { - "LapNumber": [ - "3.0", - "4.0" - ] - } -} -``` diff --git a/docs/apis/query-api/aggregate-time.md b/docs/apis/query-api/aggregate-time.md deleted file mode 100644 index 8c03c38c..00000000 --- a/docs/apis/query-api/aggregate-time.md +++ /dev/null @@ -1,42 +0,0 @@ -# Aggregate data by time - -You can downsample and upsample persisted data using the `/parameters/data` endpoint. - -## Aggregating and interpolating - -The JSON payload can include a `groupByTime` property, an object with the following members: - -* `timeBucketDuration` - The duration, in nanoseconds, for one aggregated value. -* `interpolationType` - Specify how additional values should be generated when interpolating. - -For example, imagine you have a set of speed data, with values recorded at 1-second intervals. You can group such data into 2-second intervals, aggregated by mean average, with the following: - -```json -{ - "groupByTime": { - "timeBucketDuration": 2000000000, - }, - "numericParameters": [{ - "parameterName": "Speed", - "aggregationType": "Mean" - }] -} -``` - -You can specify an `interpolationType` to define how any missing values are generated. `Linear` will provide a value in linear proportion, while `Previous` will repeat the value before the one that was missing: - -```json -{ - "from": 1612191286000000000, - "to": 1612191295000000000, - "numericParameters": [{ - "parameterName": "Speed", - "aggregationType": "First" - }], - "groupByTime": { - "timeBucketDuration": 2000000000, - "interpolationType": "None" - }, - "streamIds": [ "302b1de3-2338-43cb-8148-3f0d6e8c0b8a" ] -} -``` diff --git a/docs/apis/query-api/filter-tags.md b/docs/apis/query-api/filter-tags.md deleted file mode 100644 index cf9b54de..00000000 --- a/docs/apis/query-api/filter-tags.md +++ /dev/null @@ -1,82 +0,0 @@ -# Tag filtering - -If you supply Tags with your parameter data, they will act as indexes, so they can be used to efficiently filter data. - -## Using tag filters - -When calling the `/parameters/data` endpoint, you can include a `tagFilters` property in your payload. This property references an array of objects, each with the following structure: - -* tag - The name of the tag to filter by -* operator - A comparison operator -* value - The value to compare against - -For example, to fetch only the data recorded on the second lap, you can filter on a `LapNumber` tag as follows: - -```json -{ - "tagFilters": [{ - "tag": "LapNumber", - "operator": "Equal", - "value": "2.0" - }] -} -``` - -Note that the value can also be an array, in which case data that matches the chosen operator for any value is returned: - -```json -{ - "tagFilters": [{ - "tag": "LapNumber", - "operator": "Equal", - "value": [ "2.0", "4.0" ] - }] -} -``` - -But also note that multiple filters for the same tag apply in combination, so: - -```json -{ - "tagFilters": [{ - "tag": "LapNumber", - "operator": "Equal", - "value": "2.0" - },{ - "tag": "LapNumber", - "operator": "Equal", - "value": "4.0" - }] -} -``` - -Is incorrect because a LapNumber cannot be both "2.0" and "4.0". - -### Supported operators - -Each object in the `tagFilters` array can support the following `operator` values: - -* Equal -* NotEqual -* Like -* NotLike - -`Equal` and `NotEqual` test for true/false exact string matches. - -`Like` and `NotLike` perform a regular expression match, so you can search by pattern. For example, to get the Speed parameter values tagged with a LapNumber which is either 2 or 4, you can use the expression `^\[24\]\\.` to match values 2.0 and 4.0: - -```bash -curl "https://telemetry-query-testing-quickstart.platform.quix.io/parameters/data" \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d '{ - "tagFilters": [{ - "tag": "LapNumber", - "operator": "Like", - "value": "^[24]\\." - }], - "numericParameters": [{"parameterName": "Speed"}], - "from": 1612191182000000000, - "to": 1612191189000000000 - }' -``` diff --git a/docs/apis/query-api/http-requests.md b/docs/apis/query-api/http-requests.md deleted file mode 100644 index 9aa1bf16..00000000 --- a/docs/apis/query-api/http-requests.md +++ /dev/null @@ -1,83 +0,0 @@ -# HTTP requests - -How you send HTTP requests REST APIs varies depending on the client or language you’re using. - -## Sign requests using your PAT - -Make sure you accompany each request to the API with an `Authorization` header using your Personal Access Token (PAT) as a bearer token, as follows: - -``` http -Authorization: bearer -``` - -Replace `` with your Personal Access Token (PAT). - -For example, if you’re using Curl on the command line, you can set the header using the `-H` flag: - -``` shell -curl -H "Authorization: bearer " ... -``` - -If you fail to send a valid Authorization header, the API will respond with a `401 UNAUTHORIZED` status code. - -## Endpoint URLs - -APIs are available on a per-environment basis (with the exception of the Portal API), so the endpoint URL is based on your environment ID. - -The API endpoint URL has the following format: - -``` -https://-.platform.quix.io/ -``` - -So, for example, the endpoint URL for the query API might resemble the following: - -``` -https://telemetry-query-acme-weather.platform.quix.io/ -``` - -The API is `telemetry-query`, and the environment ID is `acme-weather`. - -## HTTP method - -Endpoints in the API use HTTP methods such as `POST` and `PUT` methods. Ensure your HTTP client sends the correct request method. - -Using `curl`, you can specify the request method with the `-X ` flag, for example: - -```bash -curl -X PUT ... -``` - -## Payload - -For most methods, you’ll need to send a JSON object containing supported parameters. You’ll also need to set the appropriate content type for the payload you’re sending: - -```bash -curl -H "Content-Type: application/json" ... -``` - -!!! warning - - You **must** specify the content type of your payload. Failing to include this header will result in a `415 UNSUPPORTED MEDIA TYPE` status code. - -You can send data using the `curl` flag `-d`. This should be followed by either a string of JSON data, or a string starting with the *@* symbol, followed by a filename containing the JSON data. - -```bash -curl -d '{"key": "value"}' ... -curl -d "@data.json" ... -``` - -!!! tip - - By default, `-d` will send a `POST` request, so `-X POST` becomes unnecessary. - -## Complete Curl example - -You should structure most of your requests to the API around this pattern: - -```bash -curl -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d "@data.json" \ - https://${api-name}-${environment-id}.platform.quix.io/ -``` diff --git a/docs/apis/query-api/images/apis-tokens.png b/docs/apis/query-api/images/apis-tokens.png deleted file mode 100644 index 57aa355ab324a5c8ccfdc8a5a6e4de9636999078..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 115343 zcmdRWg`y2sm^q-96OML+5~qG}0j@sUXrIDWM?U-3Ul3CEX_{ zlxv1ol9DiaNl6-*qn(A-Qv?bMM@QKC*)HHTheH&|I>+--Qp 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zH+%<|HAZmvCI0*`l~<1K2UY9rP|w*3+IQD^k0vBls$c*Hc1z8Yzwc?UmFAnJD_0ag zmPa#Z-WVJD&Uwe|2OT%roqVLAuUF9l`+>uOG=IF~sf%Kz`_&)_9S1SgoUf_7&-yd0 zJaAIM&E?&~xC@ zK}@|@l1%RkgMfc>a{U_6`E~E4>hsX|Ak9fVOYg}HJn2ylZ^LHz=2fgVd9|XOs+Gp! zZ|(Hc5ua;xXX}w~yYd-ig9=MZ@PbVBV7J0FXJD*@p!oQBGl0E8rd9rbrYOcnClSDS zFZ1n03`" \ - -H "Content-Type: application/json" \ - -d '{"numericParameters":[{"parameterName":"Speed"}]}' -``` - -If you just had a single parameter value in the stored data, the response from the above call might look something like this: - -```json -{ - "timestamps": [ - 1612191100000000000 - ], - "numericValues": { - "Speed": [ - 104.22222222222224 - ] - }, - "stringValues": {}, - "tagValues": {}, -} -``` - -### Restricting by Stream or time - -In reality, you’ll have far more data in the stored data, so you’ll want to filter it. Three remaining properties of the request object allow you to do so: - - - `streamIds` - - - `from` - - - `to` - -Each stream you create has a unique ID. You can view the ID of a persisted via the Data section of the UI. Supply a list of stream IDs to restrict fetched data to just those streams: - -```json -{ - "streamIds": [ - "302b1de3-2338-43cb-8148-3f0d6e8c0b8a", - "9feb07ac-b0b2-4591-bc7f-8f0c1295ed7c" - ] -} -``` - -You can also restrict data to a certain time span using the `from` and `to` properties. These each expect a timestamp in nanoseconds, for example: - -```json -{ - "from": 1612191286000000000, - "to": 1612191386000000000 -} -``` - -These timestamps cover a range of 100 seconds. diff --git a/docs/apis/query-api/setup.md b/docs/apis/query-api/setup.md deleted file mode 100644 index 0bdd18d8..00000000 --- a/docs/apis/query-api/setup.md +++ /dev/null @@ -1,108 +0,0 @@ -# Setup - -To use the APIs you'll typically need the following: - -1. [Sign up to Quix](https://portal.cloud.quix.io/self-sign-up). - -2. Obtain a Personal Access Token - you need this to authenticate requests. - -3. Get your environment ID - you need this for request URLs. - -4. View the API reference documentation. - -5. If you intend to use WebSockets, and the API supports it, you'll need to install SignalR. - -Each of these is described in the following sections. - -## Project settings - -Useful information can also be found in the settings panels for your environment: - -1. Click `Settings` in the bottom left corner of the UI. - -2. From `Project settings` select the environment you are working with. - -3. Click `APIs and tokens`. - -You are now presented with a very useful panel. From here you have useful links to items such as API documentation (rendered with Swagger), as well as your Personal Access Tokens (PATs): - -![APIs and tokens](./images/apis-tokens.png){width=80%} - -## Personal access token (PAT) - -Personal Access Tokens, or PATs, are bearer tokens that can be used to authenticate the various Quix APIs, such as the Portal API. - -To obtain a PAT, log in to Quix, and click on your profile icon in the top right corner, then click `Personal Access Tokens`. You can then generate a PAT with a lifetime suitable for your use case. - -Alternatively, log in to Quix, and click `Settings` in the main left-hand navigation. Then, for a specific environment, click `APIs and tokens`. - -In the `APIs and tokens` dialog, you can click `Personal Access Tokens` to generate PATs, or a Streaming Token (SDK Token) for use with the Quix Streams client library. - -## Get environment ID - -When using APIs you’ll need to obtain an ID based on a specific environment. For example, endpoints for the Query API use a domain with the following pattern: - - https://telemetry-query-${environment-id}.platform.quix.io/ - -The environment ID is a combination of your organization and environment names, converted to URL friendly values. - -### Obtain your environment ID from the URL - -1. Go to the [Portal home](https://portal.cloud.quix.io/){target=_blank}. - -2. Locate the environment you’re interested in and open it. - -3. At this point, take note of the URL. It will be in the form: - - https://portal.cloud.quix.io/home?workspace={environment-id} - -### Obtain environment ID from settings - -You can also get the environment ID from Settings. Click `Settings` in the bottom-left corner of the portal, select the environment of interest. You can then copy the environment ID (previously known as the workspace ID) from the settings panel, as shown in the following screenshot: - -![Get environment ID from settings](../../images/get-environment-id/get-environment-id-settings.png){width=80%} - -### Example environment ID - -Given the following URL for the environment: - -``` -https://portal.cloud.quix.io/pipeline?workspace=joeengland-apitests-testing -``` - -The environment ID is: - -``` -joeengland-apitests-testing -``` - -The components of the environment ID are as follows: - -| joeengland | The name of the Quix account, known as the organization, in this case the personal account of Joe England | -| apitests | The name of the project | -| testing | The name of the environment | - -Copy the value for `environment-id` and use it wherever you need an environment ID. - -!!! note - - The `workspace=` parameter in the URL `https://portal.cloud.quix.io/home?workspace={environment-id}` is there for legacy reasons, and does in fact indicate an environment. - -## API reference documentation - -An OAS3 API reference guides are available for HTTP/REST APIs. These are rendered using [Swagger](https://swagger.io/){target=_blank}. - -The URLs for the API references are specific to your environment, so you can easily test out API calls on your environment using the API reference. You'll need to get your environment ID. The exception to this is the Portal API, which is account-wide. - -| API | API reference URL (Swagger documentation)| -|---|---| -| Streaming Reader | No HTTP/REST interface - SignalR (WebSockets or Long Polling)| -| Portal | https://portal-api.platform.quix.io/swagger | -| Query | https://telemetry-query-``.platform.quix.io/swagger | - -Replace `` with your environment ID. - -!!! tip - - Once you access the API reference, you can select the version of the API you require from the `Select a definition` dropdown list. - diff --git a/docs/apis/query-api/streams-filtered.md b/docs/apis/query-api/streams-filtered.md deleted file mode 100644 index f250dcbb..00000000 --- a/docs/apis/query-api/streams-filtered.md +++ /dev/null @@ -1,115 +0,0 @@ -# Filtered streams - -To fetch specific streams, you can include various filters with your request to the `/streams` endpoint. - -## Fetch a single stream via ID - -The most basic filter matches against a stream’s ID: - -```bash -curl "https://${domain}.platform.quix.io/streams" \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d '{"streamIds": ["302b1de3-2338-43cb-8148-3f0d6e8c0b8a"]}' -``` - -Note that you can supply multiple IDs in the `streamIds` array to match multiple streams. - -## Filtering streams on basic properties - -The **location** of a stream defines its position in a hierarchy. A stream location looks just like a filesystem path. You can filter streams based on the start of this path, so you can easily find streams contained within any point in the hierarchy. For example, this query will find streams with a location of `/one` but it will also find streams with a `/one/two` location: - -```bash -curl "https://${domain}.platform.quix.io/streams" \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d '{"location": "/one"}' -``` - -!!! warning - - Since this is just a basic prefix match, filtering on a location named `/one` will also bring back matches for the location `/one111` as well as the location `/one/111`. If you want to strictly filter on an exact *directory* (and below), make sure to include a trailing slash, e.g. `/one/`. - -!!! note - - Filtering on topic uses a case insensitive *Equals* match. Filtering on a topic named "MyTopic" will match "mytopic" but will not match "MyTopic123" - -You can filter streams based on their use of a given **parameter** with the `parameterIds` property. For example, to find all streams that contain at least one single occurence of `Gear` data: - -```bash -curl "https://${domain}.platform.quix.io/streams" \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d '{"parameterIds": [ "Gear"] }' -``` - -You can filter based on the presence or absence of a certain stream **status**, for example, if the stream is `Open` or was `Interrupted`. The `includeStatuses` and `excludeStatuses` properties each take an array of values to act on. So to get all streams that aren’t Interrupted or Closed, use this query: - -```bash -curl "https://${domain}.platform.quix.io/streams" \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d '{"excludeStatuses": [ "Interrupted", "Closed" ]}' -``` - -## Filtering streams on metadata - -You can associate metadata with your streams. This can be used, for example, to store the circuit a car has travelled around, or the player of a particular run of a game. - -To filter on metadata, include the `metadata` property in the JSON object in your request body. This property’s value is an array of objects, each of which has two properties, `key` and `value`: - - - `key` - The exact, case-sensitive key of the metadata you’re interested in. - - - `value` - The exact, case-sensitive value of the metadata to match on - -If you have a metadata entry keyed as "circuit", you can match against it for an example value with this payload: - -```json -"metadata": [{ - "key": "circuit", - "value": "Sakhir Short" -}] -``` - -As before, the response is an array of Stream objects: - -```json -[{ - "streamId":"e6545c18-d20d-47bd-8997-f3f825c1a45c", - "name":"cardata", - "topic":"cardata", - "createdAt":"2021-03-31T13:04:43.368Z", - "lastUpdate":"2021-03-31T13:04:44.53Z", - "dataStart":1612191099000000000, - "dataEnd":1612191371000000000, - "status":"Closed", - "metadata":{ - "circuit":"Sakhir Short", - "player":"Swal", - "game":"Codemasters F1 2019" - }, - "parents":[], - "location":"/static data/" -}] -``` - -## Ordering results - -Calls to the `/streams` endpoint can include an `ordering` property in the payload. This references an array of properties to sort on, each one an object with the following properties: - - - `by` - A string representing the property to order by. - - - `direction` - A string, either "Asc" or "Desc", to define the sort direction. - -For example, to sort all streams in ascending order by topic: - -```bash -curl "https://${domain}.platform.quix.io/streams" \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d '{"ordering": [{ "by": "topic", "direction": "asc" }]}' -``` diff --git a/docs/apis/query-api/streams-models.md b/docs/apis/query-api/streams-models.md deleted file mode 100644 index 79429a91..00000000 --- a/docs/apis/query-api/streams-models.md +++ /dev/null @@ -1,114 +0,0 @@ -# Streams with models - -One stream can derive from another, for example, acting as a model in a pipeline. This relationship can be inspected using the `/streams/models` endpoint. - -## Fetching model data - -The hierarchy is represented as a parent/child structure where a stream can have an optional parent and any number of children. - -The `/streams/models` endpoint will return data in the same structure as [the `/streams` endpoint](streams-paged.md), with an additional property for each stream: `children`. This is an array of stream objects which may have their own children. - -The payload requirements are the same as those for `/streams`. You can fetch model information across all streams with an empty payload: - -```shell -curl "https://${domain}.platform.quix.io/streams/models" \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d "{}" -``` - -Here’s an example result for a stream with two children: - -```json -[{ - "children": [{ - "children": [], - "streamId": "79bbed17-5c71-4b0e-99f6-3596577b46d8", - "name": "new-child", - "topic": "cars", - "createdAt": "2021-04-08T15:27:09.19Z", - "lastUpdate": "2021-04-13T10:21:52.572Z", - "status": "Open", - "metadata": {}, - "parents": [ - "66fb0a2f-eb70-494e-9df7-c06d275aeb7c" - ], - "location": "/" - },{ - "children": [], - "streamId": "f003c1dd-9abe-49dd-afd2-f194d3d96035", - "name": "example1", - "topic": "cars", - "createdAt": "2021-04-12T11:50:38.504Z", - "lastUpdate": "2021-04-12T12:00:40.482Z", - "status": "Interrupted", - "metadata": { - "rain": "light" - }, - "parents": [ - "66fb0a2f-eb70-494e-9df7-c06d275aeb7c" - ], - "location": "/examples/first/" - }], - "streamId": "66fb0a2f-eb70-494e-9df7-c06d275aeb7c", - "topic": "cars", - "createdAt": "2021-04-08T14:12:29.807Z", - "lastUpdate": "2021-04-12T13:45:08.377Z", - "timeOfRecording": "2021-04-12T00:00:00Z", - "dataStart": 0, - "dataEnd": 1618233869000000000, - "status": "Interrupted", - "metadata": {}, - "parents": [], - "location": "/" -}] -``` - -And here’s an example with a child and a grandchild: - -```json -[{ - "children": [{ - "children": [{ - "children": [], - "streamId": "79bbed17-5c71-4b0e-99f6-3596577b46d8", - "name": "new-child", - "topic": "cars", - "createdAt": "2021-04-08T15:27:09.19Z", - "lastUpdate": "2021-04-13T10:30:11.495Z", - "status": "Open", - "metadata": {}, - "parents": [ - "f003c1dd-9abe-49dd-afd2-f194d3d96035" - ], - "location": "/" - } - ], - "streamId": "f003c1dd-9abe-49dd-afd2-f194d3d96035", - "name": "example1", - "topic": "cars", - "createdAt": "2021-04-12T11:50:38.504Z", - "lastUpdate": "2021-04-12T12:00:40.482Z", - "status": "Interrupted", - "metadata": { - "rain": "light" - }, - "parents": [ - "66fb0a2f-eb70-494e-9df7-c06d275aeb7c" - ], - "location": "/examples/first/" - } - ], - "streamId": "66fb0a2f-eb70-494e-9df7-c06d275aeb7c", - "topic": "cars", - "createdAt": "2021-04-08T14:12:29.807Z", - "lastUpdate": "2021-04-12T13:45:08.377Z", - "timeOfRecording": "2021-04-12T00:00:00Z", - "dataStart": 0, - "dataEnd": 1618233869000000000, - "status": "Interrupted", - "metadata": {}, - "parents": [], - "location": "/" -}] -``` diff --git a/docs/apis/query-api/streams-paged.md b/docs/apis/query-api/streams-paged.md deleted file mode 100644 index 95fd5c5f..00000000 --- a/docs/apis/query-api/streams-paged.md +++ /dev/null @@ -1,68 +0,0 @@ -# Paged streams - -You can fetch all streams within a environment, across topics and locations, with a single call. If you’re working with a large number of streams, you can use pagination parameters to group the results into smaller pages. - -## Fetching all streams - -The `/streams` endpoint provides read access to all streams within the environment. Sending an empty JSON object in your request body will return all streams. - -!!! warning - - Even if you’re not supplying any parameters, you must still send a valid empty object as JSON data in the body of your request. - -### Example request - -```shell -curl "https://${domain}.platform.quix.io/streams" \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d "{}" -``` - -### Example response - -The JSON returned consists of an array of Stream objects: - -```json -[{ - "streamId":"e6545c18-d20d-47bd-8997-f3f825c1a45c", - "name":"cardata", - "topic":"cardata", - "createdAt":"2021-03-31T13:04:43.368Z", - "lastUpdate":"2021-03-31T13:04:44.53Z", - "dataStart":1612191099000000000, - "dataEnd":1612191371000000000, - "status":"Closed", - "metadata":{}, - "parents":[], - "location":"/static data/" -}] -``` - -## Fetching streams page by page - -To reduce the size of the response, you should page these results with the `paging` property. Include this in the JSON object you send in the body of your request. The value of this property is an object with two members, `index` and `length`: - - - `index` - The index of the page you want returned. - - - `length` - The number of items (i.e. streams) per page. - -For example, to group all streams in pages of 10 and receive the 2nd page, use this value: - -```json -"paging": { - "index": 1, - "length": 10 -} -``` - -### Example request - -```shell -curl "https://${domain}.platform.quix.io/streams" \ - -H "Authorization: bearer ${token}" \ - -H "Content-Type: application/json" \ - -d '{"paging":{"index": 1,"length": 10}}' -``` diff --git a/docs/apis/streaming-reader-api/setup.md b/docs/apis/streaming-reader-api/setup.md index 9bcc6925..dbb5818e 100644 --- a/docs/apis/streaming-reader-api/setup.md +++ b/docs/apis/streaming-reader-api/setup.md @@ -45,9 +45,9 @@ In the `APIs and tokens` dialog, you can click `Personal Access Tokens` to gener ## Get environment ID -When using APIs you’ll need to obtain an ID based on a specific environment. For example, endpoints for the Query API use a domain with the following pattern: +When using APIs you'll need to obtain an ID based on a specific environment. For example, endpoints for environment-specific APIs use a domain with the following pattern: - https://telemetry-query-${environment-id}.platform.quix.io/ + https://-${environment-id}.platform.quix.io/ The environment ID is a combination of your organization and environment names, converted to URL friendly values. @@ -103,7 +103,6 @@ The URLs for the API references are specific to your environment, so you can eas |---|---| | Streaming Reader | No HTTP/REST interface - SignalR (WebSockets or Long Polling)| | Portal | https://portal-api.platform.quix.io/swagger | -| Query | https://telemetry-query-``.platform.quix.io/swagger | Replace `` with your environment ID. diff --git a/docs/develop/apis-overview.md b/docs/develop/apis-overview.md index b386a0c6..8af0839c 100644 --- a/docs/develop/apis-overview.md +++ b/docs/develop/apis-overview.md @@ -4,7 +4,6 @@ Quix provides several APIs. These are: * Streaming Reader API * Portal API -* Query API While [Quix Streams](https://quix.io/docs/quix-streams/introduction.html) is the main client library for Quix, there are certain use cases where you need an alternative: @@ -16,8 +15,6 @@ In these situations the Streaming Reader API can provide an alternative solution Portal API is useful for automating processes normally carried out manually in Quix. -Query API is useful for testing and examining data persisted into the Quix internal database. - Each of these APIs is described in more detail in the following sections. ## Comparing the APIs @@ -28,7 +25,6 @@ Use cases for these APIs are shown in the following table: |---|---|---|---|----| | Streaming Reader API | SignalR (WebSockets and Long Polling)| Subscribe to a Quix topic | Web browser client, dashboard, command line client | [Streaming Reader API](../apis/streaming-reader-api/overview.md) | | Portal API | HTTP (REST)| Automate Quix | Creating and monitoring deployments | [Portal API](../apis/portal-api/overview.md) | -| Query API | HTTP (REST) | Retrieve persisted data | Evaluate service is processing data correctly | [Query API](../apis/query-api/overview.md) | ## Streaming Reader API @@ -43,13 +39,3 @@ Streaming Reader uses Microsoft's [SignalR](https://learn.microsoft.com/en-us/as The [Portal API](../apis/portal-api/overview.md) gives access to Quix, enabling you to automate your project deployment, management and monitoring. For example, you could build command line tools in any language with an HTTP interface available, to create, deploy, and monitor services. [Read more about Portal API](../apis/portal-api/overview.md) - -## Query API - -The [Query API](../apis/query-api/overview.md) enables you to fetch persisted data from Quix. You can use it for exploring Quix, testing, prototyping applications, or working with persisted data in any language with HTTP capabilities. - -!!! note - - Query API was previously known as Data Catalogue API. You may still see occasional references to the Data Catalogue API in the UI and docs. - -[Read more about Query API](../apis/query-api/overview.md) diff --git a/docs/develop/integrate-data/jupyter-nb.md b/docs/develop/integrate-data/jupyter-nb.md index 032fe4bd..5a3d123e 100644 --- a/docs/develop/integrate-data/jupyter-nb.md +++ b/docs/develop/integrate-data/jupyter-nb.md @@ -8,7 +8,7 @@ In this documentation, you learn how to use Jupyter Notebook to analyze data per ## Why this is important -Although Quix is a real-time platform, to build real-time in-memory models and data processing pipelines, you need to understand data first. To help with that, Quix offers the option to persist data in topics. This data can be accessed using the [Query API](../../apis/query-api/overview.md). This helps make data discovery and analysis easier. +Although Quix is a real-time platform, to build real-time in-memory models and data processing pipelines, you need to understand data first. To help with that, Quix offers the option to persist data in topics. This helps make data discovery and analysis easier. ## Prerequisites diff --git a/docs/kb/glossary.md b/docs/kb/glossary.md index 416ea324..8e98b83e 100644 --- a/docs/kb/glossary.md +++ b/docs/kb/glossary.md @@ -159,14 +159,6 @@ A project is an entity that corresponds to a Git repository. That Git repository A project contains one or more [environments](#environment), so typically you create an environment as part of the project creation workflow, and then create additional environments as required. -## Query API - -!!! danger "Legacy feature" - - This feature is not available to new users. However, legacy users may still have access to this functionality. - -The [Query API](../apis/query-api/overview.md) is used to query persisted data. Most commonly used for dashboards, analytics and training ML models. Also useful to call historical data when running an ML model, or to call historical data from an external application. This API is primarily iused for testing and debugging purposes. - ## Quix UI Quix provides an online Integrated Development Environment (IDE) for Python projects. When you open any project, you will see the **Run** button, and a console during runtime, in addition to the IntelliSense. It also provides general UI for deploying your code, and managing your projects and environments. diff --git a/docs/kb/what-is-quix.md b/docs/kb/what-is-quix.md index 06266364..5bcdb46f 100644 --- a/docs/kb/what-is-quix.md +++ b/docs/kb/what-is-quix.md @@ -146,7 +146,6 @@ Quix provides numerous standard [connectors](../connectors/index.md) for both so Quix provides several APIs to help you work with streaming data. These include: * [**Streaming Reader API**](../apis/streaming-reader-api/overview.md): enables you to push live data from a Quix topic to your application, ensuring low latency by avoiding any disk operations. -* [**Query API**](../apis/query-api/overview.md): enables you to query persisted data streams. This is provided primarily for testing purposes. **Note:** available to legacy customers only. * [**Portal API**](../apis/portal-api/overview.md): enables you to automate Quix tasks such as creating environments, topics, and deployments. ### Quix Streams diff --git a/docs/manage/overview.md b/docs/manage/overview.md index 92fd3575..238b5e8e 100644 --- a/docs/manage/overview.md +++ b/docs/manage/overview.md @@ -63,9 +63,7 @@ Quix also has an additional capability called persistence. Enabling persistence ![Enable persistence](../images/manage/topic-persistence.png) -Enabling this feature persists topic data into a database for later retrieval using the [Query API](../apis/query-api/overview.md), or for replay using the replay service. - -The Query API is useful for testing and reviewing historical data. +Enabling this feature persists topic data into a database for later retrieval or for replay using the replay service. The replay service is used to play back persisted data into a topic. @@ -79,16 +77,6 @@ The replay service enables you to play persisted data back into a topic. You can read more about the [replay service](./replay.md) in the docs. -## Query API - -!!! danger "Legacy feature" - - This feature is not available to new users. However, legacy users may still have access to this functionality. - -The Query API enables you to programmatically retrieve persisted data from the database. - -You can read more about the [Query API](../apis/query-api/overview.md) in the docs. - ## Portal API The Portal API enables you to programmatically control Quix, and most objects in it. Using the Portal API you can manage entities including: diff --git a/docs/manage/testing-data-store.md b/docs/manage/testing-data-store.md deleted file mode 100644 index 11bfdfff..00000000 --- a/docs/manage/testing-data-store.md +++ /dev/null @@ -1,27 +0,0 @@ -# Data store for testing - -!!! danger "Legacy feature" - - This feature is not available to new users. However, legacy users may still have access to this functionality. - -Quix provides a data store for testing and debugging purposes. - -While [topics](../kb/glossary.md#topic) do provide a configurable retention time, persisting data into a database provides advantages - for example, you can perform powerful queries to retrieve historical data. This data can be retrieved and displayed using the Data Explorer, or retrieved using the [Query API](../apis/query-api/overview.md). - -Quix provides a very simple way to persist data in a topic. Simply locate the topic in your topic list, and click the `Persistence` button. - -!!! important - - You don't have to use the Quix data store. Quix provides numerous [connectors](../connectors/index.md) for common database technologies, so you can always store your data in the database of your choice. - -## Replay service - -!!! danger "Legacy feature" - - This feature is not available to new users. However, legacy users may still have access to this functionality. - -When data has been persisted, you have the option to not only query and display it, but replay it into your pipeline. This can be very useful for testing and debugging pipelines using historical data. - -See how to [use the Quix replay service](../manage/replay.md). - -See also an in-depth blog post on [stream reprocessing](https://quix.io/blog/intro-stream-reprocessing-python/){target=_blank}. diff --git a/docs/manage/troubleshooting.md b/docs/manage/troubleshooting.md index 81e8744e..ccfdea84 100644 --- a/docs/manage/troubleshooting.md +++ b/docs/manage/troubleshooting.md @@ -114,15 +114,9 @@ Example of the error received when trying to connect to the Streaming Reader API signalrcore.hub.errors.UnAuthorizedHubError -The APIs that require a valid bearer token are: +Quix provided APIs require a valid bearer token, such as the Portal API: -1. Portal API - - - https://portal-api.platform.quix.io/swagger/index.html - -2. Telemetry Query API - - - https://telemetry-query-[YOUR_ORGANIZATION_ID]-[YOUR_ENVIRONMENT_ID].platform.quix.io/swagger/index.html +- https://portal-api.platform.quix.io/swagger/index.html ## Service keeps failing and restarting diff --git a/docs/tutorials/train-and-deploy-ml/create-data.md b/docs/tutorials/train-and-deploy-ml/create-data.md index 48902290..c6e25ad8 100644 --- a/docs/tutorials/train-and-deploy-ml/create-data.md +++ b/docs/tutorials/train-and-deploy-ml/create-data.md @@ -46,6 +46,6 @@ Now generate the actual data for use later in the tutorial by completing the fol Once this service is deployed it will run as a [job](../../kb/glossary.md#job) and generate real-time data to the `f1-data`, and this will be persisted. -This data is retrieved later in this tutorial using Python code that uses the [Query API](../../apis/query-api/overview.md), generated for you by Quix. +This data is retrieved later in this tutorial using Python code generated for you by Quix. [Import data into Jupyter Notebook :material-arrow-right-circle:{ align=right }](./import-data.md) diff --git a/docs/tutorials/train-and-deploy-ml/import-data.md b/docs/tutorials/train-and-deploy-ml/import-data.md index a0f14b4a..ff911f35 100644 --- a/docs/tutorials/train-and-deploy-ml/import-data.md +++ b/docs/tutorials/train-and-deploy-ml/import-data.md @@ -4,7 +4,7 @@ This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. -From a Jupyter Notebook, you retrieve the data that was generated in Quix in the [previous part](./create-data.md), and which was persisted into the [Quix data store](../../apis/query-api/overview.md). +From a Jupyter Notebook, you retrieve the data that was generated in Quix in the [previous part](./create-data.md), and which was persisted into the Quix data store. ## Run Jupyter Notebook diff --git a/mkdocs.yml b/mkdocs.yml index 259533fe..a70a8fc4 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -292,19 +292,7 @@ nav: - 'Security': 'manage/security.md' - 'MLOps': 'manage/MLOps.md' - 'Using replay service': 'manage/replay.md' - - 'Testing using Quix data store': 'manage/testing-data-store.md' - 'Testing multiple environments': 'manage/testing-environments.md' - - 'Query API': - - 'Overview': 'apis/query-api/overview.md' - - 'Setup': 'apis/query-api/setup.md' - - 'HTTP requests': 'apis/query-api/http-requests.md' - - 'Paged streams': 'apis/query-api/streams-paged.md' - - 'Filtered streams': 'apis/query-api/streams-filtered.md' - - 'Streams with models': 'apis/query-api/streams-models.md' - - 'Raw data': 'apis/query-api/raw-data.md' - - 'Aggregate data by time': 'apis/query-api/aggregate-time.md' - - 'Aggregate data by tags': 'apis/query-api/aggregate-tags.md' - - 'Tag filtering': 'apis/query-api/filter-tags.md' - 'Portal API': - 'Overview': 'apis/portal-api/overview.md' - 'Setup': 'apis/portal-api/setup.md' @@ -765,7 +753,6 @@ plugins: 'platform/tutorials/train-and-deploy-ml/index.md': 'tutorials/train-and-deploy-ml/overview.md' 'apis/streaming-reader-api/index.md': 'apis/streaming-reader-api/overview.md' 'apis/portal-api/index.md': 'apis/portal-api/overview.md' - 'apis/query-api/index.md': 'apis/query-api/overview.md' 'platform/connectors/index.md': 'connectors/index.md' 'connectors/index.md': 'quix-connectors/templates/index.md' 'connectors/confluentkafkasource-source.md': 'quix-connectors/templates/confluentkafkasource-source.md' From e1c61a6db3d14673568e603609e01fcc2b1034dd Mon Sep 17 00:00:00 2001 From: Peter Nagy Date: Thu, 7 Aug 2025 16:34:46 +0100 Subject: [PATCH 03/10] Remove Topic persistence and replay service docs --- docs/deploy/deploy-external-image.md | 2 +- docs/develop/integrate-data/jupyter-nb.md | 4 +- docs/images/manage/topic-persistence.png | Bin 48949 -> 0 bytes docs/images/replay/replay-add-new.png | Bin 36921 -> 0 bytes .../replay/replay-add-persist-topic.png | Bin 33487 -> 0 bytes docs/images/replay/replay-configuration.png | Bin 43360 -> 0 bytes docs/images/replay/replay-play-data.png | Bin 9806 -> 0 bytes docs/images/replay/replay-select-stream.png | Bin 37546 -> 0 bytes .../databases/influxdb/replacing-kapacitor.md | 4 +- docs/manage/overview.md | 28 ---------- docs/manage/replay.md | 49 ------------------ docs/tutorials/data-science/3-data.md | 19 +------ .../train-and-deploy-ml/conclusion.md | 2 +- .../train-and-deploy-ml/create-data.md | 5 +- .../images/enable-topic-persistence.png | Bin 22298 -> 0 bytes mkdocs.yml | 2 - 16 files changed, 9 insertions(+), 106 deletions(-) delete mode 100644 docs/images/manage/topic-persistence.png delete mode 100644 docs/images/replay/replay-add-new.png delete mode 100644 docs/images/replay/replay-add-persist-topic.png delete mode 100644 docs/images/replay/replay-configuration.png delete mode 100644 docs/images/replay/replay-play-data.png delete mode 100644 docs/images/replay/replay-select-stream.png delete mode 100644 docs/manage/replay.md delete mode 100644 docs/tutorials/train-and-deploy-ml/images/enable-topic-persistence.png diff --git a/docs/deploy/deploy-external-image.md b/docs/deploy/deploy-external-image.md index 8adebdb9..569d6982 100644 --- a/docs/deploy/deploy-external-image.md +++ b/docs/deploy/deploy-external-image.md @@ -5,7 +5,7 @@ This section of the documentation provides guidance on how to ***deploy an exter Deploying an external image directly allows you to bypass application and version configurations, offering flexibility for non-standardized services or third-party containers. The deployment process is straightforward and integrated into the UI. -To summarize, your workflow for deploying an external image is: +To summarize, the steps for deploying an external image are: 1. **Prepare the external image**: Ensure it's built and pushed to a container registry accessible by the platform. diff --git a/docs/develop/integrate-data/jupyter-nb.md b/docs/develop/integrate-data/jupyter-nb.md index 5a3d123e..e004a3de 100644 --- a/docs/develop/integrate-data/jupyter-nb.md +++ b/docs/develop/integrate-data/jupyter-nb.md @@ -1,6 +1,6 @@ # Exporting Quix data to Jupyter Notebook -In this documentation, you learn how to use Jupyter Notebook to analyze data persisted in Quix. +In this documentation, you learn how to use Jupyter Notebook to analyze data from Quix. !!! danger "Legacy features" @@ -8,7 +8,7 @@ In this documentation, you learn how to use Jupyter Notebook to analyze data per ## Why this is important -Although Quix is a real-time platform, to build real-time in-memory models and data processing pipelines, you need to understand data first. To help with that, Quix offers the option to persist data in topics. This helps make data discovery and analysis easier. +Although Quix is a real-time platform, to build real-time in-memory models and data processing pipelines, you need to understand data first. 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za9-3%yPLgv5Bt#x_QRKNj;E{04SLB*+eIoFbE#JGN -See the project running in Quix - -See the deployed project - -
- - -## Technologies used - -Some of the technologies used by this template project are listed here. - -**Infrastructure:** - -* [Quix](https://quix.io/){target=_blank} -* [Docker](https://www.docker.com/){target=_blank} -* [Kubernetes](https://kubernetes.io/){target=_blank} - -**Backend:** - -* [Redpanda Cloud](https://redpanda.com/redpanda-cloud){target=_blank} -* [Quix Streams](https://github.com/quixio/quix-streams){target=_blank} -* [Redis Cloud](https://redis.com/redis-enterprise-cloud/overview/){target=_blank} -* [RocksDB](https://rocksdb.org/){target=_blank} - -**Frontend:** - -* [Streamlit](https://streamlit.io/){target=_blank} - -## GitHub repository - -The complete code for this project can be found in the [Quix GitHub repository](https://github.com/quixio/template-clickstream){target="_blank"}. - -## Getting help - -If you need any assistance while following the tutorial, we're here to help in the [Quix Community](https://quix.io/slack-invite){target="_blank"}. - -## Prerequisites - -You'll need a Quix Cloud account. - -??? info "Start for free" - [Book a session](https://quix.io/book-a-demo) with us to start for free. - - We will create a time-limited free account for you, and our experts will help you get started with your specific use case. - -## Redis Cloud - -If you want to run the project in your own Quix account, so you can customize it, then you'll need a free [Redis Cloud](https://redis.com/redis-enterprise-cloud/overview/){target=_blank} account. You'll need the following credentials: - -* Hostname (can be found in the `General` section for the database) -* Port (can be found in the `General` section for the database) -* Username (can be found in the `Security` section for the database) -* Password (can be found in the `Security` section for the database) - -### Git provider - -You also need to have a Git account. This could be GitHub, Bitbucket, GitLab, or any other Git provider you are familar with, and that supports SSH keys. The simplest option is to create a free [GitHub account](){target=_blank}. - -!!! tip - - While this tutorial uses an external Git account, Quix can also provide a Quix-hosted Git solution using Gitea for your own projects. You can watch a video on [how to create a project using Quix-hosted Git](https://www.loom.com/share/b4488be244834333aec56e1a35faf4db?sid=a9aa124a-a2b0-45f1-a756-11b4395d0efc){target=_blank}. - -??? "Transcript" - - 0:01 Hi there, welcome to this video on creating a quick project. I should point out before I get into this video that I'm using the beta development environment at Quix and so what you see may be slightly different when you're testing Quicks. - - 0:21 Okay, so having said that I'll get straight into creating a new project. You can see this button over here. I'm going to click that to create a new project. - - 0:33 The new workflow in Quix starts with creating a project. Everything is done inside a project. Generally speaking, I'm simplifying it quite a lot. - - 0:44 A project corresponds to a Git repository. So everything that you create, including your pipeline configuration and so on, will be stored in a Git repository. - - 0:59 So I'll give my project a name and I'll call it video project. And the first thing I need to do is specify where this Git repository is going to be. - - 1:12 Now, the simplest option is to get quick to do all the work for you and just create the Git repository. - - 1:21 And the second thing I need to do is a product called gitty to host the Git repository. So we can do that for you. - - 1:27 It all happens in the background. And as you'll see in later videos, you have complete control about what happens in that repository. - - 1:37 You can review pull requests and do, merges and all of those normal things that you would do in a Git workflow. - - 1:47 The other option is to use an external provider. So for example, you could use GitLab, GitHub, Bitbucket and so on. - - 1:57 You can use any provider that supports the use of an SS. H key. For the purposes of this video, I'm going to keep it simple for now and just use the Quix manage Git. - - 2:11 So I'm now ready to create the project. Now every project will have at least one environment and usually several environments. - - 2:28 So what is an environment? An environment roughly corresponds to a branch in your Git repository, but there's a specific for an environment. - - 2:41 As well as you'll see as we go through this dialogue. But the first thing we need to do is give our environment a name. - - 2:52 So I'm going to name it. Based on the typical development workflow, so usually we'd have production. Branch or environment and you might have staging and development. - - 3:09 So I'm going to create the production environment and as I was saying earlier, that's going to roughly correspond to a branch. - - 3:17 So in this next section, we're going to specify the, ,branch that this environment corresponds to and I want production to correspond to the main branch. - - 3:30 And so there's nothing really I need to do here. I could create a new branch if I wanted to, ,but I want to use the default branch which is main. - - 3:41 The other, thing that I can do here is protect this branch and what that means is that prevents developers from merging or committing content, making changes directly to the main branch. - - 3:58 In order to change the main branch you'll have to raise, a pull request, a git pull request and that would have to be reviewed and approved and then merged in the usual way. - - 4:13 So we definitely want that because for production we don't want changes being made directly. So I'm protecting that and you'll, We'll see later how we go about taking changes that we've made and say the develop branch or the dev branch and merge those into the main branch. - - 4:32 I'll cover that in another video. So for now let's just click continue. Now for each environment that you create you can specify how you want to host Kafka. - - 4:45 Now as you probably know Quix uses Kafka as its broker. And you have several options here. You if you want the simplest option and most convenient option is to just let Quix do all the hard work for you. - - 5:01 We will create them. Kafka broker and you don't need to do any configuration. We just do it all for you. - - 5:08 We make sure that scales nicely as well. We use Kubernetes and you know as I say it's the least. It's it's the quickest option especially. - - 5:22 If you're testing things out, but there are other options as you can see here. You can use your own self hosted Kafka or you can connect to a Confluent Cloud and we'll cover those options in later videos. - - 5:39 So for now, I'm just going to go with the simplest option and click. Continue. The other thing that you'll need to do here is specify the storage option for the environment. - - 5:51 So the key thing to point out at this point is we were talking about environments as corresponding to a branch in get. - - 6:02 That is true, but it's also these other things like the Kafka options that you select and also the storage options. - - 6:11 Now the storage option that you're selecting here is for any data that you might possess. So in Quix it's possible to persist the data that's published to topics. - - 6:27 You can store that using our something called the data catalog and if you store, if you persist your topics and store the messages that are in the topics. - - 6:43 In our storage facilities, then there's a small charge associated with that. However, you don't have to persist topics. You can use external database solutions for storage of your data. - - 7:01 So you can use. Some of our standard connectors or even write your own connector to connect to more or less any database technology that you want. - - 7:11 The other thing that's stored here is metadata associated with messages. So for now, I'm just going to choose the standard option and create. - - 7:23 The environment. Okay, that's it. We'll wait for the project and the environment to be created. And then we'll have a look at what's in there in subsequent videos. - - 7:42 Okay, thanks for watching. And see you in the next video. - -## The pipeline - -There are several *main* stages in the [pipeline](https://portal.cloud.quix.io/pipeline?workspace=demo-clickstreamanalysis-prod&token=pat-b88b3caf912641a1b0fa8b47b262868b){target=_blank}: - -1. *Clickstream producer* - loads clickstream data from a CSV file. This represents user interactions with a shopping website over a period of 15 days. This runs as a service and the CSV file is automatically read repeatedly. -2. *Data aggregation* - this service reads enriched data, performs various aggregations, and writes the results to Redis Cloud. These aggregations are then consumed by a Streamlit dashboard for visualization and analysis. -3. *Data enrichment* - this service enriches the click data with additional data read from Redis Cloud. This data includes the product category, and the visitor's gender, birthday and age. -4. *Data ingestion* - loads details of products in the web store and users from JSON files, and writes to Redis Cloud. -5. *Event detection* - a simple state machine that triggers a product offer event when conditions have been met. The offer is tailored to the demographic of the user. -6. *Webshop frontend* - this implements the online store. -7. *Real-time dashboard* - A Streamlit dashboard service displaying real-time data about the clickstream. It reads its data from Redis Cloud. - -More details are provided on all these services later in the tutorial. - -## Topics - -The following Kafka topics are present in the project: - -| Topic | Description | Producer service | Consumer service(s) -|---|---|---|---| -| `click-data` | The raw clickstream data loaded fro a CSV file | Clickstream generator | Data enrichment, Personalization demo | -| `enriched-click-data` | Adds product catgory and user data to clickstream data | Data enrichment | Data aggregation, Behavior detection | -| `special-offers` | Offers generated by Behavior detection - these are customized to the user demographic | Behavior detection | Personalization demo | - -## The parts of the tutorial - -This tutorial is divided up into several parts, to make it a more manageable learning experience. The parts are summarized here: - -1. [Get the project](get-project.md) - you get the project up and running in your Quix account. -2. [Clickstream producer](clickstream-producer.md) - take a look at the clickstream producer service. -3. [Data ingestion](./data-ingestion.md) - data ingestion job. Loads products and users from JSON files into Redis Cloud. -4. [Data enrichment](./data-enrichment.md) - this service enriches the clickstream data with product category, and additional user information. -5. [Data aggregation](./data-aggregation.md) - performs various aggregations on the data and adds them to Redis Cloud. RocksDB is used to hold state. -6. [Event detection](./event-detection.md) - implements a simple state machine that detects when conditions for an offer have been met. -7. [Webshop frontend](./webshop-frontend.md) - the UI for the online shop. -8. [Real-time dashboard](./realtime-dashboard.md) - a very useful real-time dashboard implementation using Streamlit. -9. [Lab: change offer](./change-offer.md) - in this part you customize the event detection service. -10. [Summary](summary.md). In this concluding part you are presented with a summary of the work you have completed, and also some next steps for more advanced learning about Quix. - -## 🏃‍♀️ Next step - -[Part 1 - Get the project :material-arrow-right-circle:{ align=right }](get-project.md) diff --git a/docs/tutorials/clickstream/realtime-dashboard.md b/docs/tutorials/clickstream/realtime-dashboard.md deleted file mode 100644 index ae1a7984..00000000 --- a/docs/tutorials/clickstream/realtime-dashboard.md +++ /dev/null @@ -1,21 +0,0 @@ -# Real-time dashboard - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -This service implements a real-time dashboard using [Streamlit](https://streamlit.io/){target=_blank}. - -![REal-time dashboard](./images/real-time-dashboard-pipeline-segment.png) - -It reads data from Redis Cloud and displays it in real time. The data displayed is primarily generated by the data aggregation service. - -![Streamlit dashboard](./images/streamlit-dashboard.png) - -The code in this service is entirely in Python. - -Once the charts are laid out, they are updated every one second in real time by reading the data from Redis Cloud, and then using it to populate the charts. - -## 🏃‍♀️ Next step - -[Part 9 - Lab :material-arrow-right-circle:{ align=right }](./change-offer.md) diff --git a/docs/tutorials/clickstream/summary.md b/docs/tutorials/clickstream/summary.md deleted file mode 100644 index 9b30b93e..00000000 --- a/docs/tutorials/clickstream/summary.md +++ /dev/null @@ -1,29 +0,0 @@ -# Summary - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this tutorial you have learned that it is possible to quickly fork a complete Quix project and get it up and running in your Quix account very quickly. This enables you to start your own project by using one of our templates. You can then modify our code, keeping your project in a public or private Git repository as required. Alternatively, you can build your own custom pipeline from a mix of our code samples, and your own custom sources, transforms, and destinations. - -In addition, you have seen how in Quix you can: - -* See the power of navigating your pipeline visually -* View the logs of a service as an aid to debugging -* View live data in the application view, or using the Quix Data Explorer -* Learned how you can host your project in a Git repository -* Learn how to examine the raw message data in the messages view -* Examine and edit the code of a service - -## Next Steps - -Here are some suggested next steps to continue on your Quix learning journey: - -* Build something with our [Code Samples](../../develop/code-samples.md). -* Try the [Chat sentiment analysis tutorial](../sentiment-analysis/overview.md). -* Try the [Computer vision tutorial](../computer-vision/overview.md). -* If you decide to build your own connectors and apps, you can contribute something to the Code Samples. Visit the [GitHub Code Samples repository](https://github.com/quixio/quix-samples){target=_blank}. Fork our Code Samples repo and submit your code, updates, and ideas. - -## Get help - -If you have any further questions, we're here to help in the [Quix Community](https://quix.io/slack-invite){target="_blank"}. diff --git a/docs/tutorials/clickstream/webshop-frontend.md b/docs/tutorials/clickstream/webshop-frontend.md deleted file mode 100644 index 81efae68..00000000 --- a/docs/tutorials/clickstream/webshop-frontend.md +++ /dev/null @@ -1,32 +0,0 @@ -# Webshop frontend - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -This service implements the webshop frontend. - -![Webshop frontend](./images/webshop-frontend-pipeline-segment.png) - -You can select age and gender, and browse various products in the online shop. The UI is shown here: - -![Webshop frontend](./images/webshop-frontend.png) - -## Topics - -This service subscribes to the following topics: - -* `click-data` - the raw clickstream data -* `special-offers` - the special offer to be applied (used to display tailored offer to user) - -It also publishes data back to the `click-data` topic - this is data about a user's real-time interaction with the webshop frontend (selection of products), it is published to this topic so that the data can be enriched and passed to the data aggregator and event detection service. The event detection service then publishes a suitable offer to the `special-offers` topic. As the webshop frontend subscribes to `special-offers`, it can present the correct offer popup when it receives the offer data. - -## Streaming Reader API - -Note because this service is a web app (running in your browser) it communicates with Quix topics using the [Streaming Reader API](../../apis/streaming-reader-api/overview.md) to subscribe to topics. - -These APIs need to be authenticated, and so for this service you need to configure a [PAT](../../develop/authentication/personal-access-token.md), referred to as a bearer token in this service. - -## 🏃‍♀️ Next step - -[Part 8 - Real-time dashboard :material-arrow-right-circle:{ align=right }](./realtime-dashboard.md) diff --git a/docs/tutorials/computer-vision/add-service.md b/docs/tutorials/computer-vision/add-service.md deleted file mode 100644 index 007f292c..00000000 --- a/docs/tutorials/computer-vision/add-service.md +++ /dev/null @@ -1,228 +0,0 @@ -# 👩‍🔬 Lab - Add a new service - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this lab you use everything you've learned so far, to add a new service to the pipeline. Specifically, you add a service to publish the number of cars captured by the TfL cams to a new topic. You will then observe the number of cars change in real time using the waveform view of the Quix Data Explorer. This service could be useful if you want to easily store the number of cars, or perhaps create an alarm if the number of cars rises above a certain threshold. This service is a simple example of filtering - where you filter out data you are not interested in for subsequent processing. - -You develop this service on a feature branch, and then you create a PR to merge your new feature into the tutorial branch. This is a common pattern for development - you can test your new service on the feature branch, and then test again, before final integration into the production `main` branch. - -## Create an environment - -To create a new environment (and branch): - -1. Click `+ New environment` to create a new environment (note, your screen will look slightly different to the one shown here): - - ![New environment](./images/new-environment.png) - -2. Create a new environment called `Cars Only`. - -3. Create a new branch called `cars-only`. To do this, from the branch dropdown click `+ New branch` which displays the New branch dialog. - - !!! important - - Make sure you branch from the `tutorial` branch, not `main`, as you are going to merge your changes back to the `tutorial` branch. - -4. Complete creation of the environment using the default options. - -5. On the projects screen, click your newly created environment, `Cars Only`. - -## Sync the environment - -You now see that the Quix environment is out of sync with the Git repository. You need to synchronize the Quix view of the environment, with that stored in the repository. - -To synchronize Quix with the repository: - -1. Click `Sync environment`: - - ![Sync environment](./images/sync-environment.png) - - The sync environment dialog is displayed, showing you the changes that are to be made to the `quix.yaml` file, which is the configuration file that defines the pipeline. - -2. Click `Sync environment`, and then `Go to pipeline`. - - In the pipeline view, you see the services building. Ensure all services are "Running" before continuing. - -## Add a transform - -You now add a transform to the output of the stream merge service. This is a convenient point, as the multiple streams are now merged to one stream (all cameras are merged into one stream), and this will make viewing the number of cars easier in the waveform view of the data explorer, as there is only one stream to examine. - -To create the transform: - -1. Click the small `+` on the output of the stream merge service, and then select `Transformation` from the dropdown list. - -2. Click `Preview code` for the `Starter transformation` in the Code Samples view. - -3. Click `Edit code`, and enter an application name of `Cars Only` and leave the path as the default, then click `Save`. - -4. Replace the complete `main.py` code with the following: - - ``` python - import quixstreams as qx - import os - import pandas as pd - import datetime - - client = qx.QuixStreamingClient() - - topic_consumer = client.get_topic_consumer(os.environ["input"], consumer_group = "empty-transformation") - topic_producer = client.get_topic_producer(os.environ["output"]) - - def on_dataframe_received_handler(stream_consumer: qx.StreamConsumer, df: pd.DataFrame): - d = df.to_dict() - if 'car' in d: - # Create a clean data frame - data = qx.TimeseriesData() - data.add_timestamp(datetime.datetime.utcnow()) \ - .add_value("Cars", d['car'][0]) - - stream_producer = topic_producer.get_or_create_stream(stream_id = stream_consumer.stream_id) - stream_producer.timeseries.buffer.publish(data) - - def on_stream_received_handler(stream_consumer: qx.StreamConsumer): - stream_consumer.timeseries.on_dataframe_received = on_dataframe_received_handler - - # subscribe to new streams being received - topic_consumer.on_stream_received = on_stream_received_handler - - print("Listening to streams. Press CTRL-C to exit.") - - # Handle termination signals and provide a graceful exit - qx.App.run() - ``` - - ??? example "Understand the code" - - The code is a little different to the starter transform. The handler for event data has been removed, along with its registration code, as you are only interested in time series data in this transform. This time series data is received in a pandas dataframe format. For ease of manipulation this is converted to a Python dictionary, so the car data can be simply extracted. - - If you want to check the format of the message processed here, you can use the message view for the stream merge service output, or the Data Explorer message view, to examine it in great detail. You will see something similar to the following: - - ``` json - { - "Epoch": 0, - "Timestamps": [ - 1694788651367069200 - ], - "NumericValues": { - "truck": [ - 1 - ], - "car": [ - 3 - ], - "lat": [ - 51.55164 - ], - "lon": [ - -0.01853 - ], - "delta": [ - -0.43226194381713867 - ] - }, - "StringValues": { - "image": [ - "iVBOR/snip/QmCC" - ] - }, - "TagValues": { - "parent_streamId": [ - "JamCams_00002.00820" - ] - } - } - ``` - - A new pandas dataframe is then created, as the data published to the output topic is only going to consist of a timestamp and the number of cars on it. This is an example of simple filtering. - - Once prepared, the dataframe is then published to the output topic. - -5. Edit environment variables, so that the input topic is `image-processed-merged` and the output topic is a new topic called `cars-only`, as shown in the following screenshot: - - ![Edit environment variables](./images/edit-env-variables.png) - - !!! tip - - These environment variables are used by the code. For example, the input topic is read by the code with the Python code `os.environ["input"]`. - -6. Click the tag icon (see screenshot), and give the code a tag such as `cars-only-v1`: - - ![Tag icon](./images/tag.png) - -7. Click the `Deploy` button and select the version tag `cars-only-v1` from the `Version tag` dropdown, and leaving all other values at their defaults, click `Deploy`. - -## View the data in real time - -You now use the Quix Data Explorer to view the cars data in real time. - -1. In the left-hand navigation, click `Data explorer`. - - !!! tip - - While this is the most direct way to access the Data Explorer, it's not the only way. You learn about other methods in other tutorials. You can, for example, click on the topic you want to view in the pipeline view, and then select `View live data` - that takes you into the Data Explorer. - -2. Click `Live data` and make sure the `cars-only` topic is selected. - -3. Check the `image-feed` stream checkbox, and also the `Cars` parameter data checkbox. - -4. Make sure Waveform view is selected. - - !!! tip - - If no data is visible, stop and start the TFL Camera Feed service, as it may be sleeping. - - You see the waveform showing the number of cars detected: - - ![Cars waveform](./images/cars-waveform.png) - -## Merge the feature - -Once you are sure that the changes on your feature branch are tested, you can then merge your changes onto the tutorial branch. Here your changes undergo further tests before finally being merged into production. - -To merge your feature branch, `cars-only` into `tutorial`: - -1. Select `Merge request` from the menu as shown: - - ![Merge request menu](./images/merge-request-menu.png) - -2. In the `Merge request` dialog, set the `cars-only` branch to merge into the `tutorial` branch. - -You are going to create a pull request, rather than perform a direct merge. This enables you to have the PR reviewed in GitHub (or other Git provider). You are also going to do a squash and merge, as much of the feature branch history is not required. - -To create the pull request: - -1. Click `Create pull request`. You are taken to your Git provider, in this case GitHub. - -2. Click the `Pull request` button. - -3. Add your description, and then click `Create pull request`. - -4. Get your PR reviewed and approved. Then squash and merge the commits: - - ![Squash and merge](./images/squash-and-merge.png) - - You can replace the prefilled description by something more succinct. Then click `Confirm squash and merge`. - - !!! tip - - You can just merge, you don't have to squash and merge. You would then retain the complete commit history for your service while it was being developed. Squash and merge is used in this case by way of example, as the commit messages generated while the service was being developed were deemed to be not useful in this case. - -## Resync the environment - -You have now merged your new feature into the `tutorial` branch in the Git repository. Your Quix view in the Tutorial environment is now out of sync with the Git repository. If you click on your Tutorial environment in Quix, you'll see it is now a commit (the merge commit) behind. - -You now need to make sure your Tutorial environment in Quix is synchronized with the Git repository. To do this: - -1. Click on `Sync environment`. The `Sync environment` dialog is displayed. - -2. Review the changes and click `Sync environment`. - -3. Click `Go to pipeline`. - -Your new service will build and start in the Tutorial environment, where you can now carry out further testing. When you are satisfied this feature can be released to production, then you would repeat the previous process to merge your changes to Production `main`. - -## 🏃‍♀️ Next step - -[Part 8 - Summary :material-arrow-right-circle:{ align=right }](summary.md) - diff --git a/docs/tutorials/computer-vision/get-project.md b/docs/tutorials/computer-vision/get-project.md deleted file mode 100644 index 77fa3b93..00000000 --- a/docs/tutorials/computer-vision/get-project.md +++ /dev/null @@ -1,158 +0,0 @@ -# Get the project - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -While you can see the [deployed project running in Quix](https://portal.cloud.quix.io/pipeline?workspace=demo-computervisiondemo-prod&token=eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6Ik1qVTBRVE01TmtJNVJqSTNOVEpFUlVSRFF6WXdRVFF4TjBSRk56SkNNekpFUWpBNFFqazBSUSJ9.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.Ndm0K2iNHPxDq1ohF-yb-6LzIqx_UY8Ptcq0kAwSNye12S3deX_eDkC4XqZqW2NoSLd3GsmWV9PZGetGGp2IlqshQFZtUMp6WP6hq917ZC1i8JFx93PAbY7NT_88nFDovVlaRcoTpWvI-03KbryLkAoB28c6qb3EFwjCWFBuy_yA4yjQ8uF0-AZ0R9Qi4IBaekXWqcgO0a91gVRg0oA_hnzJFoR-EnZ2G1ZSxtuVgnyyPuQTMUvzJuUT_IJTLzEB_kejX0pcXRZBIwHP8MWLB4mE5DtIdz4jm8WIA4eZJZ7ZCG4dk-adQwZ2BdkNknV5eEwRgRJL4ybaplkaDlR-dg){target=_blank}, it can be useful to learn how to get a project up and running in Quix. - -Once you have the project running in your Quix account, you can modify the project as required, and save your changes to your copy of the project. - -## Clone the project - -See the [clone a project documentation](../../create/clone-project.md) for further information on how to do this. - -## Add secrets - -As the project uses Quix API credentials, you'll now need to configure your credentials for the services that require API keys. The main ones are: - -* TfL camera feed - TfL API key -* Web UI - a bearer token ([PAT](../../apis/streaming-reader-api/setup.md#personal-access-token-pat)) - -You need to [create secrets](../../deploy/secrets-management.md) for these and then assign them to the appropriate [environment variables](../../deploy/environment-variables.md). - -### TfL camera feed - -To get this service to run, you'll need to configure it with your TfL API key. - -Create a new secret that contains your TfL API key. Now link that secret to the `tfl_api_key` environment variable in the TfL camera feed service. - -[Read more about environment variables and secret management](../../deploy/environment-variables.md). - -Watch the video: - -

- -??? "Transcript" - - 0:01 Hello again welcome back to this video of the computer vision tutorial. Now in the previous video we got the pipeline up and running, you synchronized Thank you. - - 0:16 Bye. To our environment. Everything was building quite happily. However, as we look around the pipeline, we can see that there is an issue, which is there's a runtime error occurring. - - 0:32 On the TFL camera feed service. And you can see that we've already had a number of restarts. So let's have a look, see what's going on here. - - 0:44 Well, the, the problem is apparent because we're we haven't configured our TFL API key. So that's what we need to do here to get this running. - - 0:58 Now in the prerequisites for the tutorial, I gave you a list of resources or the site to go to and you can sign up for the API key. - - 1:08 You can see there's a runtime error here. You haven't configured your TFL API key. So that's what we're going to do now. - - 1:15 So if you sign up to the site, you, you copy your API key See you next week. Then it needs to go in here. - - 1:22 But because we want to keep that information secret, it needs to be configured as a secret. So there's various ways you can do that. - - 1:31 But let's see the simplest way while we're on this screen. So I just got to variable. These are the environment variables that we have. - - 1:40 We'll go up here and click edit the variables. And you can see straight away that we have a missing secret associated with this variable here. - - 1:53 We need to create that. So if I click on here, it takes me to the secrets management dialogue. I can create a new key. - - 2:03 In this case, I'm going to call it TFL API key. And then I'll paste it in and I'm going to recycle this API key anyway. - - 2:16 So there's, with the secret keys, you can have a default value and then you can have a value specific. For a certain environment, we're in the tutorial environment here. - - 2:30 But in this case, I'm just going to put them as both as the same value. You could just put a default value that would be fine and save that. - - 2:41 Close the secrets management dialogue and you can see this drop down here, we now have the secret that I just created, which we can associate now. - - 2:57 With that variable and then I can redeploy the service with the new API key. So let me do that. So we can see the data has been loaded now. - - 3:22 Let's make sure we've got all the other configuration that we need done. But what I'll do, is I'll stop the video here now that we've configured this and show you the next piece of configuration in the next video. - -### Web UI service - -Note if you just want to try out the UI without performing the following steps, you can do that in the [demo](https://app-demo-computervisiondemo-prod.deployments.quix.io/){target=_blank}. - -You'll need a [PAT](../../apis/streaming-reader-api/setup.md#personal-access-token-pat) for the UI as it uses the Streaming ReaderAPI which needs to be authenticated. - -Once you have your PAT copied to the clipboard, create a new secret to contain it. Then link that secret to `bearerToken` environment variable for the UI. - -[Read more about environment variables and secret management](../../deploy/environment-variables.md). - -Watch the video: - -
- -??? "Transcript" - - 0:01 Hi, welcome back to another video. In the last video you saw that we were getting a runtime error on the TFL camera feed service so we needed to configure the TFL API key secret for that and you saw how to do that. - - 0:18 There is also another service that needs a bit of configuration and that's the project frontend so in this video I'm going to talk a bit about that. - - 0:29 I, because it's exactly the same technology. Because you saw in the previous video I've not gone through the whole process of creating a secret again but I did want to talk about this bearer token that you need for the project frontend. - - 0:46 So what is this bearer token? Well I've configured it to this secret PAT. What's a PAT? Well, PAT is a personal access token. - - 0:59 Why do we need the bearer token? Why do we need this personal access token? Well the bearer token is used by the front end which is web client to authenticate something called the streaming reader API. - - 1:15 The streaming reader API allows web client to read information from a quick topic and obviously in this case the front end needs to, needs to do that for this use case. - - 1:30 But that streaming reader API needs to be authenticated and you authenticate using a personal access token or PAT. And so once we have that PAT we can configure it in here in this variable called bearer token. - - 1:48 So where do we get the PAT from? There's several ways of doing it. Which are covered in documentation. However the simplest way is just to click on your profile pick and then click personal access tokens. - - 2:05 And in here you can click generate token. Give your token a name send an expiration date and then create the token. - - 2:25 The token's created, you can copy it to clipboard, and then once it's in the clipboard, you can configure the secret. - - 2:35 In this case, my secret is simply called PAT. So it contains that token copied for you. The clipboard or a token from a clipboard. - - 2:47 So that's, that should be all you need to do there. We can just quickly check this is all working. Let's go back. - - 2:58 To here, start the service up. I tend not to leave the camera feed running. So we can see some data coming through. - - 3:14 Now the green lines. Let's have a look in the project front end. Okay, so we can see here the real time. - - 3:30 Pictures, the, the frames that have been grabbed and coming in. So that looks, that looks good. That means the streaming reader has been authenticated and the web client is able to read the information from the topic. - - 3:46 Now we're getting this dialogue here because we've not configured a Google Maps API key, I don't have one, if you have one you can configure your project with a Google Maps API key. - - 4:02 But we have all the key information being displayed so, and you can see this is all working nicely, so that looks good. - - 4:16 Okay let's just quickly zoom in. Zoom in and out. Just make sure that's working okay. That looks fine. There we go. - - 4:29 So, yep, everything should be configured. So, just going back and recapping on that. You had to configure a couple of services. - - 4:38 You had to configure the TfL camera feed. You had to configure the TfL API key. That's your TfL API key that you get from that website. - - 4:50 In addition to that you had to configure what's called the bearer token, the bearer token variable here and to do that you generate a PAT, a PAT, a personal access token and configure it here. - - 5:07 Now just in case you've forgotten how to configure secrets, as I said before there are a couple of ways we can go in here and we can use this to access the secrets management facility. - - 5:27 But there are other ways if you, if you go into your environment configuration, for example, you can see there is the ability to visit the same. - - 5:39 Dialogue here. So if I wanted to create a new secret here, new secret, and I can put in my value here, default value, and then value. - - 5:55 Specific to the environment as well, and save my changes. So now if you're configuring a secret, like we were doing back in the pipeline, so if I go here, go here. - - 6:11 Here you can see the new secret in the dropdown. So I'll just escape out of that. Okay, so hopefully that should see you good for the configuration of the project. - - 6:27 And as always, thanks for watching and see you in the next video. - -#### Google Maps API key - -When testing the UI you see "For development purposes only" displayed on the map. Optionally, if you have a Google Maps API key, you can avoid this. - -To add your own Google Maps API key you need to edit `src/app/app.module.ts` and modify the `apiKey` field in `AgmCoreModule.forRoot` to include your Google Maps API key: - -``` typescript -AgmCoreModule.forRoot({apiKey: ''}), -``` - -### Other services - -Other optional services may require similar configuration, for example, the Quix Amazon S3 connector service requires your S3 credentials if you want to use it. - -## 🏃‍♀️ Next step - -[Part 2 - TfL camera feed :material-arrow-right-circle:{ align=right }](tfl-camera-feed.md) diff --git a/docs/tutorials/computer-vision/images/cars-waveform.png b/docs/tutorials/computer-vision/images/cars-waveform.png deleted file mode 100644 index 0cfcada3e3a1d783ac0e7a63be24de6469a34fad..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 319015 zcmeFZWmucrwlIpd)PMrTwGiA2w73;_iWF$EHn>Z0rxZ$XcPlN0QY^Sj38Z*|7S})s 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a/docs/tutorials/computer-vision/object-detection.md b/docs/tutorials/computer-vision/object-detection.md deleted file mode 100644 index f7f5f112..00000000 --- a/docs/tutorials/computer-vision/object-detection.md +++ /dev/null @@ -1,117 +0,0 @@ -# Object detection - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -This service takes frames from the frame grabber and detects objects in each frame. This service uses the [YOLOv8 object detection library](https://github.com/ultralytics/ultralytics){target=_blank}. - -![Object detection](./images/object-detection-pipeline-segment.png) - -## 💡 Key ideas - -The key ideas on this page: - -* Using the YOLOv8 library to detect objects in a frame -* Intro to Data frame handler: `on_dataframe_received_handler` -* How to view logs -* How to view the code of a Quix Application -* Using pipeline view to examine topics - -## What it does - -The key thing this service does is detect objects in frames passed to it. You will remember from the previous part of this tutorial, the frame grabber, that the frame grabber service outputs time series data, rather than event data. A different handler is invoked for time series data: - -``` python -def on_dataframe_received_handler(stream_consumer: qx.StreamConsumer, df: pd.DataFrame): -``` - -This callback receives the time series data in pandas dataframe format. Each dataframe received in the stream causes this handler to be invoked. - -Objects are detected in the frame by the YOLOv8 code. Data is published to the output stream. The messages on the output topic have the following format: - -``` json -{ - "Epoch": 0, - "Timestamps": [ - 1694003142728625200 - ], - "NumericValues": { - "car": [ - 5 - ], - "truck": [ - 2 - ], - "person": [ - 2 - ], - "traffic light": [ - 1 - ], - "lat": [ - 51.4739 - ], - "lon": [ - -0.09045 - ], - "delta": [ - -2.597770929336548 - ] - }, - "StringValues": {}, - "TagValues": {} -} -``` - -The key data here is the count of each vehicle type in the frame. Further, an annotated image (detected objects are marked with a green rectangle) is also included as binary data. The annotated image is used by the UI to display detected objects, as shown in the following screenshot: - -![Detected object](./images/detected-objects.png) - -## 👩‍🔬 Lab - Examine the logs - -In this section, you learn how to examine the logs for the service. The logs are a very useful resource when debugging a service - you can see trace messages output from the service, and any errors that are generated. - -To view the logs for a service: - -1. In the pipeline view, click on the object detection service tile. - -2. Click on the Logs tab, if not selected by default. - -3. You can now see the log messages being produced by the service: - - ![Object detection logs](./images/object-detection-logs.png) - - !!! tip - - There is a pause button to allow you to pause the logs (see the screenshot). There is also a button you can use to download the logs for the service. - -There also some tasks for you to carry out in the following sections. - -## 👩‍🔬 Lab - Examine the application code - -You now learn how to examine the code for the service. You may want to fix bugs in it, or otherwise improve the code for a service. Once the code is edited, you can use the `Redeploy` button the redeploy the service, even if it is already running. - -1. Click the panel indicated in the screenshot: - - ![object detection code](./images/object-detection-code.png){width=60%} - - This takes you to the code view. You can view or edit the complete code for this service here. - -2. You could for example, make some changes, and then redeploy the service using the `Redeploy` button, or simply test your changes using the `Run` button. - -3. In the code view, click the `History` tab to see the complete revision history for changes to the code. - -### Task - Check the output message format for this service - -Using what you have learned in previous parts of this tutorial, check the format of the messages published by this service. - -## See also - -For more information refer to: - -* [Quix Streams](https://quix.io/docs/quix-streams/introduction.html) - More about streams, publishing, consuming, events and much more. - -## 🏃‍♀️ Next step - -[Part 5 - Web UI :material-arrow-right-circle:{ align=right }](web-ui.md) diff --git a/docs/tutorials/computer-vision/other-services.md b/docs/tutorials/computer-vision/other-services.md deleted file mode 100644 index 32a8f7a0..00000000 --- a/docs/tutorials/computer-vision/other-services.md +++ /dev/null @@ -1,248 +0,0 @@ -# Other services - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -There are some additional services in the pipeline that provide useful functionality. These range from S3 storage of data to calculation of the maximum vehicles per day in a specific location. - -Briefly, these services are: - -* *Stream merge* - merges all the traffic cam streams into a single stream to make things easier to process in the UI. - -* *Cam vehicles* - calculates the total vehicles, where vehicle is defined as one of: car, 'bus', 'truck', 'motorbike'. This number is fed into the *Max vehicle window* service. - -* *Max Vehicle Window* - calculates the maximum vehicles over a time window of one day. This service sends messages to the Data API service. - -* *Data buffer* - this provides a one second data buffer. This helps reduce load on the Data API service. - -* *Data API* - this REST API service provides the following endpoints: - - * `max_vehicles` - last known maximum vehicle count for each camera. 24 hour rolling window. - * `detected_objects` - output from the computer vision service for all cameras. excludes images - * `vehicles` - the last known vehicle count for each camera - * `image` - the last image from the specified camera - - This API is called by the UI to obtain useful data. - -* *S3* - stores objects in Amazon Web Services (AWS) S3. This service enables you to persist any data or results you might like to keep more permanently. - -!!! tip - - If you ever need to obtain the stream ID, and it is not in the messsages available to the service, it is available through the stream object by using the `stream_id` property, for example, `stream_id = stream_consumer.stream_id`. - -## Stream merge - -This service prepares data for ease of processing by the UI. Merges all streams onto a single stream. The input stream is `image-processed`, the output stream is `image-processed-merged`. Note the code also decodes the image and then does a Base64 encode prior to passing to the output topic. The UI uses the Quix Streaming Reader to read the messages from `image-processed-merged`, including the Base64 encoded image data. - -The key code: - -``` python - # Callback triggered for each new parameter data. - def on_dataframe_handler(self, stream_consumer: qx.StreamConsumer, df: pd.DataFrame): - - df["TAG__parent_streamId"] = self.consumer_stream.stream_id - df['image'] = df["image"].apply(lambda x: str(base64.b64encode(x).decode('utf-8'))) - - self.producer_topic.get_or_create_stream("image-feed") \ - .timeseries.buffer.publish(df) -``` - -## Cam vehicles - -This service simply adds together objects of the following types: car, bus, truck, motorbike to obtain a total number of vehicles. It classes these objects as vehicles. The message output to the next stage in the pipeline, max vehicles, is as follows: - -``` json -{ - "Epoch": 0, - "Timestamps": [ - 1694077540745375700 - ], - "NumericValues": { - "truck": [ - 1 - ], - "car": [ - 2 - ], - "lat": [ - 51.4075 - ], - "lon": [ - -0.19236 - ], - "delta": [ - -2.177236557006836 - ], - "vehicles": [ - 3 - ] - }, - "StringValues": {}, - "BinaryValues": { - "image": [ - "(Binary of 157.97 KB)" - ] - }, - "TagValues": {} -} -``` - -In this example there are 2 cars, and 1 truck giving a `vehicles` count of 3. - -The main code is: - -``` python -def on_dataframe_received_handler(stream_consumer: qx.StreamConsumer, df: pd.DataFrame): - # List of vehicle columns - vehicle_columns = ['car', 'bus', 'truck', 'motorbike'] - - # Calculate the total vehicle count based on existing columns - total_vehicle_count = df.apply(lambda row: sum(row.get(column, 0) for column in vehicle_columns), axis=1) - - # Store vehicle count in the data frame - df["vehicles"] = total_vehicle_count - stream_producer = topic_producer.get_or_create_stream(stream_id = stream_consumer.stream_id) - # Publish data frame to the producer stream - stream_producer.timeseries.buffer.publish(df) -``` - -You can find out more about pandas DataFrames in the [pandas documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html){target=_blank}. - -## Max Vehicle Window - -The max vehicles service takes the total vehicle count and finds the maximum value over a one day window. This value is made available to the Data API service. The message passed to the Data API has the following format: - -``` json -{ - "Epoch": 0, - "Timestamps": [ - 1694088514402644000 - ], - "NumericValues": { - "max_vehicles": [ - 8 - ] - }, - "StringValues": {}, - "TagValues": { - "window_start": [ - "2023-09-06 12:08:12.394372" - ], - "window_end": [ - "2023-09-07 12:08:12.394372" - ], - "window": [ - "1d 0h 0m" - ], - "cam": [ - "JamCams_00001.08959" - ] - } -} -``` - -You can see the exact time window is recorded, along with the maximum vehicle count during that time window. This provides a crude measure of the capacity of the road. This capacity can then be used by the UI to calculate a percentage of capacity. For example, if there are 8 cars on a road, and the maximum seen is 10, then the road is considered to be at 80% capacity, and this is displayed on the UI, as shown in the following screenshot: - -![Road capacity](./images/road-capacity.png) - -This service uses state, as you need to save the maximum count reached during the time window. - -## Data buffer - -This service provides a one second data buffer. This reduces load on the Data API service. There are three input topics to the service, `max-vehicles`, `processed-images`, and `vehicle-counts`: and one output topic, `buffered-data`. - -## Data API - -The data service offloads calculations that could be done in the web client, and instead provides key data only when the UI needs it. The UI can request this data when it needs it through the REST API of the Data API service. - -The Data API provides the following endpoints: - -* max_vehicles -* detected_objects -* vehicles -* image - -These are used by the UI to obtain and then display the data on the web interface. - -### Max Vehicles - -Returns the maximum number of "vehicles" seen on a camera, where vehicles is one of cars, buses, trucks, or motorbikes. - -For a `GET` on the endpoint `/max_vehicles`, the response is a dictionary item per camera: - -* Key=camera name -* Value=max vehicle count - -Example response JSON: - -``` json -{ - "JamCams_00001.01251":2.0, - "JamCams_00001.01252":1.0 -} -``` - -This service is implemented as a simple [Flask web app](https://flask.palletsprojects.com/en/2.3.x/quickstart/){target=_blank} hosted in Quix. - -### Detected Objects - -Returns a dictionary of all the data for a given camera (except for the images as these are quite large to store, even temporarily). - -For a `GET` on the endpoint `/detected_objects`, the response is an array of: - -* Key=camera name -* Value=dictionary of the data - -Where the data dictionary is: - -* object counts (car: 3, bus: 11 etc) -* lat -* lon -* timestamp - -Using this you can plot/display every camera and its count as soon as you get this data. - -Example response JSON: - -``` json -[ - "JamCams_00001.01419": { - "car":{"0":3.0}, - "delta":{"0":-1.0003459453582764}, - "image":{"0":""},"lat":{"0":51.5596}, - "lon":{"0":-0.07424},"person":{"0":3.0}, - "timestamp":{"0":1692471825406959867}, - "traffic light":{"0":1.0}, - "truck":{"0":1.0} - }, - ... -] -``` - -### Vehicles - -The last known vehicle count for each camera. - -See the [README](https://github.com/quixio/computer-vision-demo/tree/main/Data%20API){target=_blank} for more information. - -### Image - -The last image from the specified camera. - -See the [README](https://github.com/quixio/computer-vision-demo/tree/main/Data%20API){target=_blank} for more information. - -## S3 - -This is the standard Quix code sample [AWS S3 destination connector](https://quix.io/docs/library_readmes/connectors/s3-destination.html). It takes messages on the input topic and writes them to S3. There is an optional batching facility whereby you can batch messages and then write them to S3 in a batch - this can be more efficient for higher frequency data. You can control batching based on time interval or message count. - -## See also - -For more information refer to: - -* [Connectors](../../connectors/index.md) - connectors, both source and destination. -* [Quix Streams](https://quix.io/docs/quix-streams/introduction.html) - the client library. - -## 🏃‍♀️ Next step - -[Part 7 - Add a new service :material-arrow-right-circle:{ align=right }](add-service.md) diff --git a/docs/tutorials/computer-vision/overview.md b/docs/tutorials/computer-vision/overview.md deleted file mode 100644 index a9894a12..00000000 --- a/docs/tutorials/computer-vision/overview.md +++ /dev/null @@ -1,233 +0,0 @@ -# Computer vision - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this tutorial you learn about a real-time computer vision application, using a [Quix template project](https://github.com/quixio/computer-vision-demo){target=_blank}. - -![Computer vision pipeline](../../images/project-templates/computer-vision-pipeline.png) - -The project uses the Transport for London (TfL) traffic cameras, known as Jam Cams, as the video input. The [YOLO v8](https://docs.ultralytics.com/) machine learning model is used to identify various objects such as types of vehicles. Additional services count the vehicles and finally the data is displayed on a map which is part of the web UI that has been created for this project. - -You'll fork the complete project from GitHub, and then create a Quix project from the forked repo, so you have a copy of the full application code running in your Quix account. You then examine the data flow through the project's pipeline, using tools provided by Quix. - -

      - -## Technologies used - -Some of the technologies used by this template project are listed here. - -**Infrastructure:** - -* [Quix](https://quix.io/){target=_blank} -* [Docker](https://www.docker.com/){target=_blank} -* [Kubernetes](https://kubernetes.io/){target=_blank} - -**Backend:** - -* [Confluent Cloud](https://www.confluent.io/lp/confluent-cloud/){target=_blank} -* [Quix Streams](https://github.com/quixio/quix-streams){target=_blank} -* [Flask](https://flask.palletsprojects.com/en/2.3.x/#){target=_blank} -* [pandas](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html){target=_blank} - -**Video capture:** - -* [TfL API](https://api-portal.tfl.gov.uk){target=_blank} -* [OpenCV](https://opencv.org/){target=_blank} - -**Object detection:** - -* [YOLOv8](https://github.com/ultralytics/ultralytics){target=_blank} - -**Frontend:** - -* [Angular](https://angular.io/){target=_blank} -* [Typescript](https://www.typescriptlang.org/){target=_blank} -* [Microsoft SignalR](https://learn.microsoft.com/en-us/aspnet/signalr/){target=_blank} -* [Google Maps](https://developers.google.com/maps){target=_blank} - -## GitHub repository - -The complete code for this project can be found in the [Quix GitHub repository](https://github.com/quixio/computer-vision-demo){target="_blank"}. - -## Getting help - -If you need any assistance while following the tutorial, we're here to help in the [Quix Community](https://quix.io/slack-invite){target="_blank"}. - -## Prerequisites - -You'll need a Quix Cloud account. - -??? info "Start for free" - [Book a session](https://quix.io/book-a-demo) with us to start for free. - - We will create a time-limited free account for you, and our experts will help you get started with your specific use case. - -### TfL account and API key - -You'll also need a [free TfL account](https://api-portal.tfl.gov.uk){target=_blank}. - -Follow these steps to locate your TfL API key: - - 1. Register for a [free TfL account](https://api-portal.tfl.gov.uk){target=_blank}. - - 2. Login and click the `Products` menu item. - - 3. You should have one product to choose from: `500 Requests per min.` - - 4. Click `500 Requests per min.` - - 5. Enter a name for your subscription into the box, for example "QuixFeed", and click `Register`. - - 6. You can now find your API Keys in the profile page. - -Later, you'll need to configure the TfL service with your own TfL API key. - -### Git provider - -You also need to have a Git account. This could be GitHub, Bitbucket, GitLab, or any other Git provider you are familar with, and that supports SSH keys. The simplest option is to create a free [GitHub account](){target=_blank}. - -!!! tip - - While this tutorial uses an external Git account, Quix can also provide a Quix-hosted Git solution using Gitea for your own projects. You can watch a video on [how to create a project using Quix-hosted Git](https://www.loom.com/share/b4488be244834333aec56e1a35faf4db?sid=a9aa124a-a2b0-45f1-a756-11b4395d0efc){target=_blank}. - - ??? "Transcript" - - 0:01 Hi there, welcome to this video on creating a quick project. I should point out before I get into this video that I'm using the beta development environment at Quix and so what you see may be slightly different when you're testing Quicks. - - 0:21 Okay, so having said that I'll get straight into creating a new project. You can see this button over here. I'm going to click that to create a new project. - - 0:33 The new workflow in Quix starts with creating a project. Everything is done inside a project. Generally speaking, I'm simplifying it quite a lot. - - 0:44 A project corresponds to a Git repository. So everything that you create, including your pipeline configuration and so on, will be stored in a Git repository. - - 0:59 So I'll give my project a name and I'll call it video project. And the first thing I need to do is specify where this Git repository is going to be. - - 1:12 Now, the simplest option is to get quick to do all the work for you and just create the Git repository. - - 1:21 And the second thing I need to do is a product called gitty to host the Git repository. So we can do that for you. - - 1:27 It all happens in the background. And as you'll see in later videos, you have complete control about what happens in that repository. - - 1:37 You can review pull requests and do, merges and all of those normal things that you would do in a Git workflow. - - 1:47 The other option is to use an external provider. So for example, you could use GitLab, GitHub, Bitbucket and so on. - - 1:57 You can use any provider that supports the use of an SS. H key. For the purposes of this video, I'm going to keep it simple for now and just use the Quix manage Git. - - 2:11 So I'm now ready to create the project. Now every project will have at least one environment and usually several environments. - - 2:28 So what is an environment? An environment roughly corresponds to a branch in your Git repository, but there's a specific for an environment. - - 2:41 As well as you'll see as we go through this dialogue. But the first thing we need to do is give our environment a name. - - 2:52 So I'm going to name it. Based on the typical development workflow, so usually we'd have production. Branch or environment and you might have staging and development. - - 3:09 So I'm going to create the production environment and as I was saying earlier, that's going to roughly correspond to a branch. - - 3:17 So in this next section, we're going to specify the, ,branch that this environment corresponds to and I want production to correspond to the main branch. - - 3:30 And so there's nothing really I need to do here. I could create a new branch if I wanted to, ,but I want to use the default branch which is main. - - 3:41 The other, thing that I can do here is protect this branch and what that means is that prevents developers from merging or committing content, making changes directly to the main branch. - - 3:58 In order to change the main branch you'll have to raise, a pull request, a git pull request and that would have to be reviewed and approved and then merged in the usual way. - - 4:13 So we definitely want that because for production we don't want changes being made directly. So I'm protecting that and you'll, We'll see later how we go about taking changes that we've made and say the develop branch or the dev branch and merge those into the main branch. - - 4:32 I'll cover that in another video. So for now let's just click continue. Now for each environment that you create you can specify how you want to host Kafka. - - 4:45 Now as you probably know Quix uses Kafka as its broker. And you have several options here. You if you want the simplest option and most convenient option is to just let Quix do all the hard work for you. - - 5:01 We will create them. Kafka broker and you don't need to do any configuration. We just do it all for you. - - 5:08 We make sure that scales nicely as well. We use Kubernetes and you know as I say it's the least. It's it's the quickest option especially. - - 5:22 If you're testing things out, but there are other options as you can see here. You can use your own self hosted Kafka or you can connect to a Confluent Cloud and we'll cover those options in later videos. - - 5:39 So for now, I'm just going to go with the simplest option and click. Continue. The other thing that you'll need to do here is specify the storage option for the environment. - - 5:51 So the key thing to point out at this point is we were talking about environments as corresponding to a branch in get. - - 6:02 That is true, but it's also these other things like the Kafka options that you select and also the storage options. - - 6:11 Now the storage option that you're selecting here is for any data that you might possess. So in Quix it's possible to persist the data that's published to topics. - - 6:27 You can store that using our something called the data catalog and if you store, if you persist your topics and store the messages that are in the topics. - - 6:43 In our storage facilities, then there's a small charge associated with that. However, you don't have to persist topics. You can use external database solutions for storage of your data. - - 7:01 So you can use. Some of our standard connectors or even write your own connector to connect to more or less any database technology that you want. - - 7:11 The other thing that's stored here is metadata associated with messages. So for now, I'm just going to choose the standard option and create. - - 7:23 The environment. Okay, that's it. We'll wait for the project and the environment to be created. And then we'll have a look at what's in there in subsequent videos. - - 7:42 Okay, thanks for watching. And see you in the next video. - -If you want to use the Quix AWS S3 service (optional), you'll need to provide your credentials for accessing AWS S3. - -## The pipeline - -There are several *main* stages in the [pipeline](https://portal.cloud.quix.io/pipeline?workspace=demo-computervisiondemo-prod&token=eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6Ik1qVTBRVE01TmtJNVJqSTNOVEpFUlVSRFF6WXdRVFF4TjBSRk56SkNNekpFUWpBNFFqazBSUSJ9.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.Ndm0K2iNHPxDq1ohF-yb-6LzIqx_UY8Ptcq0kAwSNye12S3deX_eDkC4XqZqW2NoSLd3GsmWV9PZGetGGp2IlqshQFZtUMp6WP6hq917ZC1i8JFx93PAbY7NT_88nFDovVlaRcoTpWvI-03KbryLkAoB28c6qb3EFwjCWFBuy_yA4yjQ8uF0-AZ0R9Qi4IBaekXWqcgO0a91gVRg0oA_hnzJFoR-EnZ2G1ZSxtuVgnyyPuQTMUvzJuUT_IJTLzEB_kejX0pcXRZBIwHP8MWLB4mE5DtIdz4jm8WIA4eZJZ7ZCG4dk-adQwZ2BdkNknV5eEwRgRJL4ybaplkaDlR-dg){target=_blank}: - -1. *TfL camera feed* - TfL Camera feed or "Jam Cams". This service retrieves the raw data from the TfL API endpoint. A list of all JamCams is retrieved, along with the camera data. The camera data contains a link to a video clip from the camera. These video clips are hosted by TfL in MP4 format on AWS S3. A stream is created for each camera, and the camera data published to this stream. Using multiple streams in this way enables a solution capable of horizontal scaling, through additional topic partitions and, optionally, replicated services in a consumer group. Once the camera list has been scanned, the service sleeps for a configurable amount of time, and then repeats the previous code. This reduces the load, and also means the API limit of 500 requests per minute is not exceeded. Messages are passed to the frame grabber. - -2. *TfL traffic camera frame grabber* - this service grabs frames from a TfL video file (MP4 format) at the rate specified. By default the grabber extracts one frame every 100 frames, which is typically one per five seconds of video. Messages are passed to the object detection service. - -3. *Object detection* - this service uses the YOLOv8 computer vision algorthm to detect objects within a given frame. - -4. *Stream merge* - merges the separate data streams (one for each camera) back into one, prior to sending to the UI. - -5. *Web UI* - a UI that displays: frames with the objects that have been identified, and a map with a count of objects at each camera's location. The web UI is a web client app that uses the [Quix Streaming Reader API](../../apis/streaming-reader-api/overview.md), to read data from a Quix topic. - -There are also some additional services in the pipeline: - -1. *Cam vehicles* - calculates the total vehicles, where vehicle is defined as one of: car, 'bus', 'truck', 'motorbike'. This number is published to its utput topic. The *Max vehicle window* service subscribes to this topic. - -2. *Max vehicle window* - calculates the total vehicles over a time window of one day. This service publishes messages its output topic. - -3. *Data buffer* - this provides a one second data buffer. This helps reduce load on the Data API service. - -4. *Data API* - this REST API service provides the following endpoints: - - * `max_vehicles` - last known maximum vehicle count for each camera. 24 hour rolling window. - * `detected_objects` - output from the computer vision service for all cameras. excludes images - * `vehicles` - the last known vehicle count for each camera - * `image` - the last image from the specified camera - - This API is called by the UI to obtain useful data. - -5. *S3* - stores objects in Amazon Web Services (AWS) S3. This service enables you to persist any data or results you might like to keep more permanently. - -More details are provided on all these services later in the tutorial. - -## The parts of the tutorial - -This tutorial is divided up into several parts, to make it a more manageable learning experience. The parts are summarized here: - -1. [Get the project](get-project.md) - you get the project up and running in your Quix account. - -2. [TfL camera feed](tfl-camera-feed.md) service. You examine the code and then see how to view the message data format used in the service, in real time. - -3. [Frame grabber](tfl-frame-grabber.md) service. You examine the code and then see how to view the message data format used in the service, in real time. - -4. [Object detection](object-detection.md) service. This is the YOLO v8 logic that identifies and annotates the objects identified in the frame. You examine the code and then see how to view the message data format used in the service, in real time. - -5. [Web UI](web-ui.md) service. This is a web client app that uses the Quix Streaming Reader API to read data from a Quix topic (the output of the stream merge service). There are various UI components that are beyond the scope of this tutorial. - -6. [Other services](other-services.md). The other services are fairly simple so are collected together for discussion. You can optionally investigate the message data format and code. - -7. [Add new service](add-service.md). You add a new service to a feature branch, test it, and then merge to the tutorial branch. - -8. [Summary](summary.md). In this concluding part you are presented with a summary of the work you have completed, and also some next steps for more advanced learning about Quix. - -## 🏃‍♀️ Next step - -[Part 1 - Get the project :material-arrow-right-circle:{ align=right }](get-project.md) diff --git a/docs/tutorials/computer-vision/summary.md b/docs/tutorials/computer-vision/summary.md deleted file mode 100644 index 04103ed4..00000000 --- a/docs/tutorials/computer-vision/summary.md +++ /dev/null @@ -1,30 +0,0 @@ -# Summary - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this tutorial you have learned that it is possible to quickly fork a complete Quix project and get it up and running in your Quix account very quickly. This enables you to start your own project by using one of our templates. You can then modify our code, keeping your project in a public or private Git repository as required. Alternatively, you can build your own custom pipeline from a mix of our code samples, and your own custom sources, transforms, and destinations. - -In addition, you have seen how in Quix you can: - -* See the power of navigating your pipeline visually -* View the logs of a service as an aid to debugging -* View live data in the application view, or using the Quix Data Explorer -* Learned how you can host your project in a Git repository -* Learn how to examine the raw message data in the messages view -* Examine and edit the code of a service - -## Next Steps - -Here are some suggested next steps to continue on your Quix learning journey: - -* Build something with our [code samples](../../develop/code-samples.md). You could take the camera feed, frame grabber, and object detection code samples, and add your own service to these, for example add a custom UI, or a simple service that just logs vehicles for a road that you have a specific interest in (maybe you live there). -* Try the [Chat sentiment analysis tutorial](../sentiment-analysis/overview.md). -* If you decide to build your own connectors and apps, you can contribute something to the Code Samples. Visit the [GitHub Code Samples repository](https://github.com/quixio/quix-samples){target=_blank}. Fork our Code Samples repo and submit your code, updates, and ideas. - -What will you build? Let us know! We’d love to feature your application or use case in our [newsletter](https://www.quix.io/community/). - -## Get help - -If you have any further questions, we're here to help in the [Quix Community](https://quix.io/slack-invite){target="_blank"}. diff --git a/docs/tutorials/computer-vision/tfl-camera-feed.md b/docs/tutorials/computer-vision/tfl-camera-feed.md deleted file mode 100644 index d9c15b47..00000000 --- a/docs/tutorials/computer-vision/tfl-camera-feed.md +++ /dev/null @@ -1,178 +0,0 @@ -# TfL camera feed - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this part of the tutorial you take a look at the TfL camera feed service. The main function of this service is to retrieve camera data from the TfL API and pass it to the frame grabber service. - -![TfL camera feed](./images/tfl-camera-feed-pipeline-segment.png) - -!!! tip - - In the pipeline view, you can always determine a topic name by hovering over the connecting line that represents that topic. You can also click the connecting line, to see its name, and optionally to jump to the Data Explorer to view live data for the topic. - -## 💡 Key ideas - -The key ideas on this page: - -* Reading external REST API -* Publishing event data with Quix Streams -* Multiple streams within a topic -* Passing video file URLs through the pipeline -* Explore raw message format using Quix - -## What it does - -The key thing this service does is retrieve the camera feeds from the TfL API endpoint, using your TfL API key. This is done using the `requests` library and a simple REST `GET`: - -``` python -cameras = requests.get( - "https://api.tfl.gov.uk/Place/Type/JamCam/?app_id=QuixFeed&app_key={}".format(api_key)) -``` - -With this data the code loops, writing the data for each camera to its own stream in the output topic, `tfl-cameras`. The code also adds a timestamp and the camera data, as a value called `camera`: - -``` python -producer_topic.get_or_create_stream(camera_id).events.add_timestamp_nanoseconds(time.time_ns()) \ - .add_value("camera", json.dumps(camera)) \ - .publish() -``` - -Note the stream name is derived from the camera ID, which has the format `JamCams_00001.01606`. - -!!! tip - - It is a common pattern to publish data to its own stream when it is from a different device or source. For example, if you had multiple IoT devices each with its own ID these would publish to their own stream. As streams are mapped to partitions by Quix Streams, the messages are guaranteed to be delivered in order. Publishing to multiple streams enables you to horizontally scale too. If you increased the number of paritions in the topic, the streams would be spread across all available partitions, enabling increased throughput and fault tolerance. Further, multiple consumer replicas could be used, and stream data would be processed by all available replicas in the consumer group. In the TfL camera feed service, data for each camera is published to its own stream for these reasons, the stream name being based on the camera ID. If you ever need to obtain the stream ID, and it is not in the messsages available to that service, it is available through the stream object by using the `stream_id` property, for example, `stream_id = stream_consumer.stream_id`. - -The code then sleeps for a configurable amount of time. This prevents exceeding the 500 API requests from being exceeded. - -The `publish` method from the previous code publishes data to the output topic in the following format: - -``` json -[ - { - "Timestamp": 1693925495304353500, - "Tags": {}, - "Id": "camera", - "Value": "" - } -] -``` - -The field `` has a format as shown in the following example: - -``` json -{ - "$type": "Tfl.Api.Presentation.Entities.Place, Tfl.Api.Presentation.Entities", - "id": "JamCams_00001.03766", - "url": "/Place/JamCams_00001.03766", - "commonName": "A20 Sidcup Bypass/Perry St", - "placeType": "JamCam", - "additionalProperties": [ - { - "$type": "Tfl.Api.Presentation.Entities.AdditionalProperties, Tfl.Api.Presentation.Entities", - "category": "payload", - "key": "available", - "sourceSystemKey": "JamCams", - "value": "false", - "modified": "2023-08-31T15:46:06.093Z" - }, - { - "$type": "Tfl.Api.Presentation.Entities.AdditionalProperties, Tfl.Api.Presentation.Entities", - "category": "payload", - "key": "imageUrl", - "sourceSystemKey": "JamCams", - "value": "https://s3-eu-west-1.amazonaws.com/jamcams.tfl.gov.uk/00001.03766.jpg", - "modified": "2023-08-31T15:46:06.093Z" - }, - { - "$type": "Tfl.Api.Presentation.Entities.AdditionalProperties, Tfl.Api.Presentation.Entities", - "category": "payload", - "key": "videoUrl", - "sourceSystemKey": "JamCams", - "value": "https://s3-eu-west-1.amazonaws.com/jamcams.tfl.gov.uk/00001.03766.mp4", - "modified": "2023-08-31T15:46:06.093Z" - }, - { - "$type": "Tfl.Api.Presentation.Entities.AdditionalProperties, Tfl.Api.Presentation.Entities", - "category": "cameraView", - "key": "view", - "sourceSystemKey": "JamCams", - "value": "West - A222 Perry St Twds Chislehurst", - "modified": "2023-08-31T15:46:06.093Z" - }, - { - "$type": "Tfl.Api.Presentation.Entities.AdditionalProperties, Tfl.Api.Presentation.Entities", - "category": "Description", - "key": "LastUpdated", - "sourceSystemKey": "JamCams", - "value": "Aug 31 2023 3:46PM", - "modified": "2023-08-31T15:46:06.093Z" - } - ], - "children": [], - "childrenUrls": [], - "lat": 51.4183, - "lon": 0.09822 -} -``` - -There is much useful data here, including a link to the camera's video stream, which is in a MP4 video file stored on AWS S3, for example `https://s3-eu-west-1.amazonaws.com/jamcams.tfl.gov.uk/00001.03766.mp4`. - -!!! tip - - It is more efficient to pass a link to a video through the pipeline than the video itself, as the video file size can be relatively large. - -This message is passed on to the next service in the pipeline, the frame grabber. - -## 👩‍🔬 Lab - Examine the data - -In this section, you learn how to use Quix to examine the message data format. There are various ways of doing this, and several ways are shown in later parts of this tutorial. Having clarity on the message format enables better undertanding of the data flow in the pipeline. - -To see the message format on the output topic of the service: - -1. In the pipeline view click on the TFL camera feed service tile. - -2. Click the `Messages` tab and then click on a message. You will see something similar to the following screenshot: - - ![Message view](./images/tfl-camera-feed-message-view.png) - - You might see messages that have the format: - - ``` json - { - "Name": null, - "Location": null, - "Metadata": {}, - "Parents": [], - "TimeOfRecording": null - } - ``` - - These are stream metadata messages and are not used in this tutorial. - -3. You can now see the message format in the right-hand pane: - - ``` json - [ - { - "Timestamp": 1693925495304353500, - "Tags": {}, - "Id": "camera", - "Value": "" - } - ] - ``` - - This is the data published to the output topic, and passed on to the frame grabber. - -## See also - -For more information refer to: - -* [Quix Streams](https://quix.io/docs/quix-streams/introduction.html) - More about streams, publishing, consuming, events and much more. - -## 🏃‍♀️ Next step - -[Part 3 - Frame grabber :material-arrow-right-circle:{ align=right }](tfl-frame-grabber.md) diff --git a/docs/tutorials/computer-vision/tfl-frame-grabber.md b/docs/tutorials/computer-vision/tfl-frame-grabber.md deleted file mode 100644 index 6d29ef9f..00000000 --- a/docs/tutorials/computer-vision/tfl-frame-grabber.md +++ /dev/null @@ -1,136 +0,0 @@ -# TfL frame grabber - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this part of the tutorial you learn about the TfL frame grabber service. The main job of the frame grabber is to grab frames from the TfL video feed file, and then pass this on to the object detection service. - -![TfL frame grabber](./images/tfl-frame-grabber-pipeline-segment.png) - -## 💡 Key ideas - -The key ideas on this page: - -* Using computer vision library to extract frames from a video file -* Publishing time series data -* Publishing binary data -* Using the Quix Data Explorer to examine raw message format - -## What it does - -The key thing this service does is extract frames from the TfL video file. By default the frame grabber grabs one frame in every 100 frames, which is typically one per five seconds of video. This is done using the [OpenCV](https://opencv.org/){target=_blank} Python library. - -The frame grabber needs to obtain the video URL, as that is where it's going to grab frames from. Much of the other information can be ignored, so this is filtered by the following code: - -``` python -camera_video_feed = list(filter(lambda x: x["key"] == "videoUrl", camera["additionalProperties"]))[0] -``` - -This create the `camera_video_feed`, which consists of the following data: - -``` json -{ - "$type": "Tfl.Api.Presentation.Entities.AdditionalProperties, Tfl.Api.Presentation.Entities", - "category": "payload", - "key": "videoUrl", - "sourceSystemKey": "JamCams", - "value": "https://s3-eu-west-1.amazonaws.com/jamcams.tfl.gov.uk/00001.03766.mp4", - "modified": "2023-08-31T15:46:06.093Z" -}, -``` - -The code then publishes the frames as binary data: - -``` python -self.stream_producer.timeseries.buffer.add_timestamp_nanoseconds(time.time_ns()) \ - .add_value("image", bytearray(frame_bytes)) \ - .add_value("lon", lon) \ - .add_value("lat", lat) \ - .publish() -``` - -Notice the data is now sent as time series data, rather than event data, with addition of a timestamp. - -Geolocation information from the camera data is also added to the message. The message then has the format: - -``` json -{ - "Epoch": 0, - "Timestamps": [ - 1693998068342837200 - ], - "NumericValues": { - "lon": [ - 0.22112 - ], - "lat": [ - 51.50047 - ] - }, - "StringValues": {}, - "BinaryValues": { - "image": [ - "(Binary of 31.67 KB)" - ] - }, - "TagValues": {} -} -``` - -This can be used by later stages of the pipeline to locate the capacity information, and frame thumbnail, on the map. - -## 👩‍🔬 Lab - Examine the data - -In this section, you learn how to use the Quix Data Explorer to examine data output from this service. The Data Explorer enables you to view data in real time. This is very useful when debugging a pipeline, or ensuring the data you are receiving is what you expect. - -To examine the data published by the service: - -1. In the pipeline view, click on the arrow (representing the output topic) on the right side of the frame grabber service tile, and select `Explore live data`. This opens a new tab and displays the Data Explorer. - -2. In the Data Explorer, ensure that live data is selected (it should be selected by default), and then click on messages to see all raw messages. - -3. Click on a message to see its data structure in JSON format. - - !!! tip - - There are actually two types of message here: stream metadata messages and actually data messages. The messages you're interested in have `timestamp` in them. You can ignore the metadata messages in this tutorial, as they are not used. - -4. Examine the data format that is being sent to the next stage of the pipeline, the object detection service. It should be similar to the following: - -``` json -{ - "Epoch": 0, - "Timestamps": [ - 1693998068342837200 - ], - "NumericValues": { - "lon": [ - 0.22112 - ], - "lat": [ - 51.50047 - ] - }, - "StringValues": {}, - "BinaryValues": { - "image": [ - "(Binary of 31.67 KB)" - ] - }, - "TagValues": {} -} -``` - -Here you see the timestamp, geolocation information, and the binary data of the frame sent. - -## See also - -For more information refer to: - -* [Quix Streams](https://quix.io/docs/quix-streams/introduction.html) - More about streams, publishing, consuming, event data, time series data, and much more. -* [OpenCV](https://opencv.org/){target=_blank} - More on how to use the OpenCV library. - -## 🏃‍♀️ Next step - -[Part 4 - Object detection :material-arrow-right-circle:{ align=right }](object-detection.md) diff --git a/docs/tutorials/computer-vision/web-ui.md b/docs/tutorials/computer-vision/web-ui.md deleted file mode 100644 index 3ad966d4..00000000 --- a/docs/tutorials/computer-vision/web-ui.md +++ /dev/null @@ -1,140 +0,0 @@ -# Web UI - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this part of the tutorial you learn about the web UI service. - -![Web UI pipeline](./images/web-ui-pipeline-segment.png) - -This provides the rather fancy interface for you to interact with this project. - -The following screenshot shows vehicle density at various points in London: - -![image processing web UI](./images/web-ui.png) - -## 💡 Key ideas - -The key ideas on this page: - -* How a web client can read data from a Quix topic using Quix Streaming Reader API -* WebSockets as a way of streaming data into a web client -* Microsoft SignalR is the WebSockets technology used in the Streaming Reader API -* Access an external web application from the pipeline view - -## What it does - -The key thing this service does is provide a UI that enables you to see vehicle data in real time, displayed on a Google map. - -The UI is an Angular web client written using Typescript. The most important thing to understand is how this service obtains data from the Quix pipeline. This is done through use of the [Quix Streaming Reader API](../../apis/streaming-reader-api/overview.md). - -The Streaming Reader API has both an HTTP and WebSockets interface you can use to interface with a Quix topic. This web client uses the WebSockets interface. This enables data to be streamed from the Quix topic into the web client with good performance. This is a more efficient method than using the request-response method of HTTP. - -The WebSockets interface uses Microsoft SignalR technology. You can read more about that in the [Quix SignalR documentation](../../apis/streaming-reader-api/setup.md#set-up-signalr) for the Reader API. - -In essence the code to read a topic needs to: - -1. Connect to Quix SignalR hub. -2. The web UI reads parameter data rather than event data, as that is the format used for inbound data in this case. -3. Handle "parameter data received" events using a callback. - -The web UI code to do this is similar to the following: - -``` javascript -ngAfterViewInit(): void { -this.getInitialData(); - -this.quixService.initCompleted$.subscribe((topicName) => { - this._topicName = topicName; - - this.quixService.ConnectToQuix().then(connection => { - this.connection = connection; - this.connection.on('ParameterDataReceived', (data: ParameterData) => { - this._parameterDataReceived$.next(data); - }); - this.subscribeToData(); - - this.connection.onreconnected((connectionId?: string) => { - if (connectionId) this.subscribeToData(); - }); - }); -}); -``` - -So, simplifying, after connection to the Quix topic, on a `ParameterDataReceived` event, the corresponding callback (event) handler is invoked. There are other events that can be subscribed to. You can read more about events and subscription in the [subscription and event documentation](../../apis/streaming-reader-api/subscriptions.md). - -!!! note - - In this app you only consume (read) data from Quix topics. - -The data read from the topic is as follows: - -``` json -{ - "Epoch": 0, - "Timestamps": [ - 1693573934793346000 - ], - "NumericValues": { - "car": [ - 7 - ], - "traffic light": [ - 1 - ], - "person": [ - 3 - ], - "lat": [ - 51.5107 - ], - "lon": [ - -0.11512 - ], - "delta": [ - -4.353343725204468 - ] - }, - "StringValues": { - "image": [ - "iVBO…to/v37HG18UyZ1Qz/fby/+yXUGc5UVWZfIHnX0iqM6aEAAAAASUVORK5CYII=" - ] - }, - "TagValues": { - "parent_streamId": [ - "JamCams_00001.02500" - ] - } -} -``` - -The interesting thing here is that the detected object image to be displayed by the UI is passed to it in Base64 encoded format, as the HTTP interface of the streaming reader API is being used. - -## Understand the code - -You learned how to explore the code for a service in previous parts of this tutorial. The web UI is a fairly standard web client using Angular. - -For more details on using the Quix Streaming Reader API see the [API documentation](../../apis/streaming-reader-api/overview.md). - -## 👩‍🔬 Lab - Explore the UI - -If you have not done so, explore the web UI. - -1. In your pipeline view, click on the `external link` icon: - - ![External link](./images/external-link.png) - -2. Now interact with the web UI. - -Have fun! - -## See also - -For more information refer to: - -* [Quix Streaming Reader API](../../apis/streaming-reader-api/overview.md) - read about the API used by clients external to Quix to read data from a Quix topic. - -## 🏃‍♀️ Next step - -[Part 6 - Other services :material-arrow-right-circle:{ align=right }](other-services.md) diff --git a/docs/tutorials/currency-alerting/currency-alerting.md b/docs/tutorials/currency-alerting/currency-alerting.md deleted file mode 100644 index 087d2fbe..00000000 --- a/docs/tutorials/currency-alerting/currency-alerting.md +++ /dev/null @@ -1,218 +0,0 @@ -# Currency alerting - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this tutorial you will learn how to build a real-time streaming pipeline that sends push notifications to your phone when the Bitcoin price reaches a certain threshold. - -## Learning objectives - -In this tutorial you will learn: - -* How to use an existing sample to interface Quix to a real-time event stream. In this tutorial this is a stream of trading data from CoinAPI. -* How to create a transformation. -* How to use an existing sample to interface Quix to a mobile device. - -## Getting help - -If you need any help, please sign up to the [Quix Community](https://quix.io/slack-invite){target=_blank}. - -## Prerequisites - -To complete this tutorial you will need the following accounts: - -* [Quix Cloud account](https://quix.io){target=_blank} - This enables you to create the real-time stream processing pipeline. - -??? info "Start for free" - [Book a session](https://quix.io/book-a-demo) with us to start for free. - - We will create a time-limited free account for you, and our experts will help you get started with your specific use case. - -* [CoinAPI](https://coinapi.io){target=_blank} - You can sign up for a free API key [here](https://www.coinapi.io/Pricing){target=_blank}. On the free tier, click the `GET A FREE API KEY` button, enter the requested information, and you will receive an email containing your API key. This enables you to access a stream of constantly updated BTC/USD exchange rate data. -* [Pushover](https://pushover.net){target=_blank} - You can sign up for a free account [here](https://pushover.net/signup){target=_blank}. Enter your details, and click `Signup`. You will receive a welcome email, and you can log in to Pushover to retrieve your user key and generate an API token. This enables you to send notifications to your phone. Install the Pushover mobile app from the [Apple App store](https://apps.apple.com/us/app/pushover-notifications/id506088175){target=_blank} or [Google Play](https://play.google.com/store/apps/details?id=net.superblock.pushover&hl=en){target=_blank}. - -## The pipeline you will create - -The objective of this tutorial is to create a pipeline that resembles the following example: - -![Alt text](./images/currency-pipeline.png) - -The colors describe the role of the microservice that is being deployed. The possible roles are as follows: - -
      Source — enables streaming of data into Quix from any external source, such as an API or WebSocket.
      -
      Transformation — implements the processing of data, for example, cleaning data or implementing a Machine Learning (ML) model.
      -
      Destination — enables streaming of processed data to an external destination, such as a database or dashboard.
      - -## Setting up the CoinAPI source - -In this section you will learn how to set up the source sample and deploy it in your pipeline as a microservice. - -This sample, when deployed as a microservice in the Quix pipeline, connects a live stream of updates for the currency pair: `BTC/USD`. This real-time exchange rate data is streamed in from the [CoinAPI](https://www.coinapi.io/){target=_blank} through its [WebSocket](https://en.wikipedia.org/wiki/WebSocket){target=_blank} interface. The free [sandbox version](https://docs.coinapi.io/#endpoints-2){target=_blank} is used for the purposes of this tutorial. - -To summarize this functionality: - -* The microservice streams the exchange rate data to a topic called `currency-exchange-rates`. -* Downstream microservices in the pipeline can then read fom the `currency-exchange-rates` topic and process it in different ways. In this tutorial, you will check the current Bitcoin price against a threshold. - -To set up the CoinAPI source, follow these steps: - -1. In [Quix](https://portal.cloud.quix.io/){target=_blank}, click the `Code Samples` icon in the main left-hand navigation. - -2. In the search box on the Code Samples page, enter "CoinAPI - Exchange Rate Feed". - - You will see the Coin API sample appear in the search results: ![CoinAPI sample](./images/coinapi.png "CoinAPI sample") - -3. Click the `Preview code` button, and on the page that appears, click the `Edit code` button. When you choose to edit a sample, Quix prompts you to create a copy of it as an application, as sample are read-only. - - Optionally, you could have clicked the `Deploy` button, which would have deployed the microservice directly. However, in this tutorial, you are given the opportunity to first look at the code, and modify it if necessary. - -4. In the `Setup` form, configure the following environment variables: - - | Field | Value | - | --- | --- | - | `Name` | Enter an application name or keep the default suggestion. | - | `output` | Select the output topic. In this case, select `currency-exchange-rates` from the list. | - | `coin_api_key` | The API key that you use to access CoinAPI. | - | `asset_id_base` | The short code for the _base_ currency that you want to track, for example BTC. | - | `asset_id_quote` | The short code for the _target_ currency in which prices will be quoted, for example, USD. | - -5. Click `Save as Application`. You now have a copy of the CoinAPI sample in your environment. - -6. Click the `Deploy` button. The sample is deployed as a service and automatically started. - - Once the sample has been deployed, you’ll be redirected to the portal home page, where you can see the service in the pipeline context, as was illustrated previously. - -7. Click the CoinAPI service card to inspect the logs: - - ![CoinAPI Step](./images/pipeline-coinstep.png) - -A successful deployment will resemble the following example: - -![CoinAPI Step](./images/success-coinapi.png) - -If there is an issue with the service, you can also inspect the `build logs` in the `Lineage` panel to check for any traces of a syntax error or other build issues. - -## Setting up the Threshold Alert transformation - -In this section you will learn how to implement a microservice that will compare data in the BTC/USD exchange rate stream against a specific price threshold. - -This microservice contains a simple algorithm that checks to see if a value has crossed a threshold in either direction. - -The data stream is transformed from a series of real-time price points into a series of price alerts. - -To summarize this functionality: - -* When the threshold criteria are met, the microservice writes an alert message to a topic called `currency-rate-alerts`. -* Downstream services can then read fom this topic and send alerts and notifications whenever they detect a new message. - -To set up the Threshold Alert sample, follow these steps: - -1. Click on the `Code Samples` icon in the left-hand navigation. - -2. In the search box on the Code Samples page, enter "Threshold Alerts". - - You will see the `Threshold Alert` sample appear in the search results: - - ![Threshold Alert](./images/threshold-alerts.png "Threshold Alert") - -3. Click the `Preview code` button, and on the page that appears, click the `Edit code` button. - -4. In the `Setup application` form, set the following environment variables: - - | Field | Value | - | --- | --- | - | `Name` | As usual, enter an application name or keep the default suggestion. | - | `input` | Select the input topic. In this case, select `currency-exchange-rates` from the list. | - | `output` | Select the output topic. In this case, select `currency-rate-alerts` from the list. | - | `parameterName` | Set this to `PRICE`. | - | `thresholdValue` | The price in USD that you'd like to get alerted about. For example, on the day that this tutorial was written, BTC was hovering around $16,300 so we entered `16300`. This increases the likelihood that some alerts are generated soon after deploying (otherwise it's hard to tell if it's working). | - | `msecs_before_recheck` | Enter the minimum delay in milliseconds between alerts. The default is 300 milliseconds (5 minutes), as this prevents numerous alerts when the price hovers around the threshold. | - -5. Click `Save as Application`. - - You now have a copy of the Threshold Alert sample in your environment. - -6. Click the `Deploy` button. - - The sample is deployed as a service and automatically started. Once the microservice has been deployed, you'll be redirected to the pipeline view. - -7. Click the Threshold Alert service card to inspect the logs. - -A successful deployment will resemble the following screenshot: - -![CoinAPI Step](./images/success-threshold.png) - -In the `Lineage` panel, you will notice that the two services are connected by a line, which indicates that they're both using the same topic, `currency-exchange-rates`. The CoinAPI service is _writing_ to `currency-exchange-rates`, and the Threshold Alert service is _reading_ from it. - -If there is an issue with the service, you can inspect the `build logs` in the `Lineage` panel, to check for any traces of a syntax error or other build issues. - -## Setting up the Pushover destination - -In this section you create a destination microservice that sends a push notification when a threshold alert condition occurs. - -This microservice reads from the `currency-rate-alerts` topic and whenever a new message arrives, it sends a push notification to the Pushover app on your mobile phone. - -It also reads the contents of the message and enriches the notification with details on how the threshold was crossed, that is, whether the price is moving up or down. - -To set up the push notification microservice, follow these steps: - -1. Click on the `Code Samples` icon in the left-hand navigation. - -2. In the search box on the Code Samples page, enter "Pushover". - - You will see the `Pushover Output` sample appear in the search results: - - ![Pushover Notifications](./images/library-pushover.png "Pushover Notifications") - -3. Click the `Preview code` button, and on the page that appears, click the `Edit code` button. - -4. On the `Setup application` page, complete the following fields: - - | Field | Value | - | --- | --- | - | `Name` | Enter an application name or keep the default suggestion. | - | `Input` | Select the input topic. In this case, select `currency-rate-alerts` from the list.Every message will be read from this topic, and turned into a push notification. | - | `base_url` | Leave the default value, `https://api.pushover.net/1/messages.json?`. If you decide to use another push notification app, your can always update this value. | - | `api_token` | Enter the API token that you generated for this application in your Pushover dashboard. For example: `azovmnbxxdxkj7j4g4wxxxdwf12xx4`. | - | `user_key` | Enter the user key that you received when you signed up with Pushover. For example: `u721txxxgmvuy5dxaxxxpzx5xxxx9e` | - -5. Click the `Save as Application`. You now have a copy of the Pushover notification sample in your environment. - -6. Click the `Deploy` button. - -You will now start receiving Pushover notifications on your phone, as shown here: - -![Pushover Notification Example](./images/pushover_notification.png){width=60%} - -Depending on your threshold value and the price fluctuations, it might take a few minutes for you to get a notification. While you are waiting to receive a notification, you can inspect the logs, as shown previously. - -![Pushover Logs](./images/success-pushover.png) - -* Don't worry if the logs only show "_Listening to Stream_" initially — remember that the Threshold service only writes a message to the `currency-rate-alerts` topic when the threshold has been crossed. -* This means that the `currency-rate-alerts` stream might be empty for a short while. -* Depending on your threshold, it might take a couple of minutes for messages to start arriving. - -You've now completed the tutorial. - -## Summary - -Here's what you accomplished in this tutorial: - -✅ You created a real-time web app, hosted on the Quix serverless compute environment. - -✅ You deployed three real-time serverless microservices: the CoinAPI source to read data from another platform, the Threshold Alert transformation to make decisions based on that data, and the Pushover destination to send push notifications to your phone. - -✅ You gained some experience in navigating Quix, and learned how to deploy microservices without needing to write any code. - -## Next steps - -To learn more, try one of these tutorials: - -* [Build a live video processing pipeline using the Transport for London (TfL) traffic cameras and the YOLO ML model for object detection](../computer-vision/overview.md) -* [Perform chat sentiment analysis](../sentiment-analysis/overview.md) -* [Gather and processes data from an RSS feed and get an alert when specific criteria are met](../rss-tutorial/rss-processing-pipeline.md) - -!!! tip "Getting Help" - - If you need any help, please sign up to the [Quix Community](https://quix.io/slack-invite){target=_blank}. \ No newline at end of file diff --git a/docs/tutorials/currency-alerting/images/CoinAPIer.png b/docs/tutorials/currency-alerting/images/CoinAPIer.png deleted file mode 100644 index 4f70d93c8088fc46f76fd74edd70f32839f80ccb..0000000000000000000000000000000000000000 GIT binary patch literal 0 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zh=8-*O|cJj?T5JXD`TlOK01PBp#EiT#Ay|6|0r zP;3Mtun(bcr%x#AEfa!fqLG`TM7-SIa>4>Q7?u)e>9C4Tv$l1FNL95R_whO+>MR0B z>9D<;-_=?m@LqO^QD8ZoOlml0sH0Y%m5A!i+nj1rv@4fg7s34cxO%J>528D9(Fjdr z)#dnKM_8igS!`SK_EzhClH`K7TrF?qQyCdfyWkD)_#z3t1xMMU67)Og7W84s`?m-G z{k(hKz@{gw=VoGEEY$_8;j0JSmroSTAnMbM9rF)i$`N=89yHxv#G=qK(8T+gaQb`gvtTi7%0ofid;SJvO<-gYCF>ieC4%evzssk`YxfW>DIktiutTriIS7UmIS8NJT`P3WcB3a~+@vT&@)N6jIQMGq~ z3us7@QJM~?VOzKb6d^4rS49Z#O^1v--hpufXQ>Vi6m84@h$FY zQ}99b1_yMeKRgOVtLuP)*utKz42b?fqen&cf<@$ - - === "Pre-filled" - - If you see a prepopulated query builder: - - ![Populated query builder](./images/query-a.png){width=250px} - - Follow these steps: - - 1. Select the `+` under `SELECT (Parameters & Events)`. - - 2. Select `total_num_bikes_available` from the list. - - 3. Again select the `+` under `SELECT (Parameters & Events)`. - - 4. Select `num_docks_available` from the list. - - - === "Empty" - - If you see an empty query builder: - - ![Un-populated query builder](./images/query-b.png){width=250px} - - Follow these steps: - - 1. Click `Add Query`. - - 2. Select `bikes-topic` under `From topic`. - - 3. Select the `New York Total Bikes Real Time` stream. - - 4. Click `Next`. - - 5. Select both parameters, that is both `num_docks_available` and `total_num_bikes_available`. - - 6. Click `Done`. - - - - Whichever options you used, you should be looking at a visualization of the two selected parameters: - - ![Data explorer](./images/data-explorer.png){width=600px} - - Note that your data won't look the same as ours, so don't be concerned if they aren't identical. - -8. Switch off `aggregation` to see all of the data. - -9. Select the `Code` tab to view the code to access this data set from outside of Quix. - - ![Data explorer settings](./images/data-explorer-settings.png) - - !!! hint - - You can copy and paste this code into a [`Jupyter Notebook`](https://jupyter.org/){target=_blank} or [`Google Colab Notebook`](https://colab.research.google.com/){target=_blank} and run it to get your data there. - - ![Collab Notebook](./images/results.png){width=450px} - -## Train the model - -At this point, you are collecting historical data and you know how to query it for use outside Quix to train your ML models. - -???- example "Need help training a model?" - - Follow our "How to train an ML model" tutorial [here](../train-and-deploy-ml/overview.md) - - You are walked through the process of getting the code to access the data (as described above), running the code in a Jupyter notebook, training the model and uploading your pickle file to Quix. - -It would take several weeks to accumulate enough historical data to train a model, so you will continue the tutorial with some pre-trained models already built by the Quix team. This was done using the very same data flow you've just built. You can find the Jupyter notebook code used to train the model in the [Quix GitHub repo](https://github.com/quixio/NY-bikes-tutorial/blob/1stversion/notebooks-and-sample-data/04%20-%20Train%20ML%20models.ipynb){target=_blank}. - -[Part 5 - Run the model :material-arrow-right-circle:{ align=right }](5-run.md) diff --git a/docs/tutorials/data-science/5-run.md b/docs/tutorials/data-science/5-run.md deleted file mode 100644 index b5bd90f6..00000000 --- a/docs/tutorials/data-science/5-run.md +++ /dev/null @@ -1,99 +0,0 @@ -# 5. Run the model - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -Quix has has already trained model artifacts and these have been included as pickle files in the prediction code application. This application is included in the open source Code Samples. You will use the Code Sample to run the model. - -## Prediction service code - -Get the code for the prediction service: - -1. Click on `Code Samples` in the left-hand navigation. - -2. Search for `New York` and click the `New York Bikes - Prediction` tile. - -3. Click `Edit code`. - -4. Leave the `Name` as it is. - -5. Ensure the `bike_input` is set to `bikes-topic`. - -6. Ensure the `weather_input` is set to `weather-topic`. - -7. Ensure the `output` is set to `NY-bikes-prediction`. - -8. Click `Save as Application`. - - This will save the code for this service to your environment. - -!!! note "Free Models" - - Look in the `MLModels` folder for the Quix pretrained ML models. You can upload your own and compare them to ours. Let us know how they compare. - -## Run in the dev environment - -You can now run the prediction model from this 'dev' environment to make sure it's working before deploying it to an always ready, production environment. - -1. Click `Run` in the top right-hand corner. - -2. Observe the `Console` tab at the bottom of the screen. - - - Any packages that are needed will be installed. - - - Any topics that didn't previously exist will be created. - - - Then the code will run. - - - You will see a line similar to this in the console output. - - ```shell - Current n bikes: 23742 Forecast 1h: 23663 Forecast 1 day: 22831 - ``` - - !!! note "Note about data" - - For a new prediction to be generated, the service has to receive data from both bikes (updated often) and weather feeds (only updated every 30 mins). - - When you test the model, you may want to force the weather service to produce some new data (to avoid waiting for 30 mins) by restarting the service: stop it and then re-deploy it. By doing this it will start generating predictions sooner. - -## Deploy the service - -Having verified that the code runs, you can now deploy it to the Quix serverless environment. Once deployed, it will run continuously, gathering data from the sources and producing predictions. - -1. Click `Running` to stop the code running. - -2. Click `Deploy` in the top right-hand corner near `Run`. - -3. On the `Deployment settings`, increase the memory to at least 1.5GB. - -4. Click `Deploy`. - - You will be redirected to the pipeline page and the code will be built, deployed and started. - -## See the model output - -Once the prediction service has started you can once more restart the `VisualCrossing Weather` service and view the data. - -You should be familiar with some of the following steps: - -1. Restart the `VisualCrossing Weather` service. - -2. Click `Persisted streams` in the left-hand menu. - -3. Click the toggle switch next to the `ny-bikes-prediction` topic to persist the data (wait for this to complete). - -4. Mouse over the `stream name` of one of the rows in the table. - -5. Click the `Visualize stream` button. - -6. Select both of the parameters (`timestamp_ny_prediction` and `forecast_1d`). - -7. You can select the `Waveform` tab to see a graphical representation of the forecast or select the `Table` tab to see the raw data. - -## Summary - -Congratulations, you have completed all the steps of this tutorial. The following page summarizes your learning and provides some suggestions for next steps to try. - -[Conclusion and next steps :material-arrow-right-circle:{ align=right }](6-conclusion.md) diff --git a/docs/tutorials/data-science/6-conclusion.md b/docs/tutorials/data-science/6-conclusion.md deleted file mode 100644 index 364d0cc7..00000000 --- a/docs/tutorials/data-science/6-conclusion.md +++ /dev/null @@ -1,29 +0,0 @@ -# 6. Conclusion - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this tutorial you have learned how to ingest data, using prebuilt code samples for real-time processing, how to view the data in different ways, and how to query the data to enable model training. - -## Code Samples used - -Here is a list of the open source Quix Code Samples used in this tutorial, with links to their code in GitHub: - -* [New York Bikes](https://github.com/quixio/quix-samples/tree/main/python/sources/NY-Citibikes){target=_blank} -* [VisualCrossing Weather](https://github.com/quixio/quix-samples/tree/main/python/sources/visualcrossing-weather){target=_blank} -* [New York Bikes - Prediction](https://github.com/quixio/quix-samples/tree/main/python/transformations/NY-Bikes-Predictions){target=_blank} - -## Next steps - -Here are some suggested next steps to continue on your Quix learning journey: - -* Try the [Event detection tutorial](../event-detection/overview.md). - -* If you decide to build your own connectors and apps, you can contribute something to the Code Samples. Visit the [GitHub Code Samples repository](https://github.com/quixio/quix-samples){target=_blank}. Fork our Code Samples repo and submit your code, updates, and ideas. - -What will you build? Let us know! Quix would like to feature your application or use case in our [newsletter](https://www.quix.io/community/){target=_blank}. - -## Getting help - -If you need any assistance, we're here to help in the [Quix Community](https://quix.io/slack-invite){target=_blank}. diff --git a/docs/tutorials/data-science/images/data-explorer-settings.png b/docs/tutorials/data-science/images/data-explorer-settings.png deleted file mode 100644 index 7ba39490320cf960f01f8c3269c86e91b8525750..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 672914 zcmb@t1z1&E*EWoav~-7bcc*}KN=kRfCN^Cn(k0RjQUcN?4I(8i-QC>{-{d^;p7VU? zeXi?|eIdBlnrrqP;~w|8SFobI1QI+RJPZsBl9c2dCGZCy1_n0l#dC1Q?_(wv_ycDr zso@9%gZ%U1-=kPMWKaTz4J!5yDhjc&F|mchxH|9}^S%)lw-k3aQB$x|<>jQeKOD=VoJJqlfacvN3b8aI$ceiD(|ez&yRRPFoqbKFuK~>nVDetU3oz{8xyDjiK~sZts}3i0M#Fl^Me0BTxOyY zgeUpuhu^{2lvnAE_&Wzt`k%NUD)B<8l@=&#bA;bwPKt=WMQvc|ZvC+Sqv2${;{-Y6NBPJ7T zlZVN21Os9DOD~O$c%c?hYm@)zxUePp$r?%}sA~Ji 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deleted file mode 100644 index f3ab622e..00000000 --- a/docs/tutorials/data-science/overview.md +++ /dev/null @@ -1,59 +0,0 @@ -# Real-time Machine Learning (ML) predictions - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this tutorial you will learn how to deploy a real-time **data science** application into a scalable self-maintained solution. You create a service that predicts bicycle availability in New York, by building the raw data ingestion pipelines, Extract Transform Load (ETL), and predictions. - -## Aim - -Quix enables you to harness complex, efficient real-time infrastructure in a quick and simple way. You are going to build an application that uses real-time New York bicycle data and weather data to predict the future availability of bikes in New York. - -You will complete all the typical phases of a data science application: - - - Build pipelines to gather bicycle and weather data. - - - Store the data efficiently. - - - Train ML models with historic data. - - - Deploy the ML models into production and create predictions in real time. - -This would traditionally take several people with a wide set of different skills (Data Engineers, Data Scientists and Developers) and weeks of work. However, you will complete this tutorial on your own in a fraction of the time using Quix. - -## Prerequisites - -This tutorial has the following prerequisites: - -1. You will need to know how to train an ML model. - - ???- example "Want to learn it?" - - If you don't already know how to train an ML model, follow our "How to train an ML model" tutorial [here](../../tutorials/train-and-deploy-ml/overview.md). - - We take you through the process of getting the code to access the data, running the code in a Jupyter notebook, training the model and uploading your pickle file to Quix. - -2. You will need a Quix account and be logged into [Quix](https://portal.cloud.quix.io/workspaces){target=_blank}. - - !!! tip - - Go [here](https://quix.io){target=_blank} to sign up if you need a free account. - -## The parts of the tutorial - -This tutorial is divided up into several parts, to make it a more manageable learning experience. The parts are summarized here: - -1. **Create a bikes data real-time stream**. Access real-time data from New York's CitiBikes API using a ready made Code Sample. - -2. **Create a weather forecast data stream**. Add weather data using a free weather API. - -3. **Visualize the data**. View real-time and historic data in the visualization tools. - -4. **Get data to train a model**. Use the built in tools to get training data. - -5. **Deploy pre-trained ML models and produce predictions in real time**. Use our pre-trained models to get CitiBike predictions based on historical bicycle availability and weather forecasts. You also use the built-in visualization tools to view the models prediction. - -6. **Conclusion**. In this [concluding](6-conclusion.md) part you are presented with a summary of the work you have completed, and also some next steps for more advanced learning about Quix. - -[Part 1 - CitiBike data stream :material-arrow-right-circle:{ align=right }](1-bikedata.md) diff --git a/docs/tutorials/matlab/images/matlab_console.png b/docs/tutorials/matlab/images/matlab_console.png deleted file mode 100644 index b81d6ee890842989777a8d3e7ac52f02b2d0a85a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 79094 zcmYhD1ymc|_P2vmye;mO;_mKJ++B;iyN0603j}v49^9QmaW4dlLveSvFMZ$pzxT_m zBx@xznVEC;*=O%(|4t%Rls=)O5TXD80CZUyNi_ff$r%8Ek3xopenS3geF=Sg<1C}= z1^{6E{QHDSX22kVeg%+~6w~m|I$H7aSGQO~Iy*GyRGbmoFJNNwbAn&^`e9uZzCR2O z2LX@QTM7mtK*Krz53ZybHkO#n7X>McA!ZBpMRC*>Mb`nLRS~5~HpO2JvTZG0xw}7- zk{*|LCtzToT(|kfy-oEed~7Nbg0L={8JtQ_amYiH~!+08MUoLxN_Nrm+A&6mAaLickg*vBDjvGz9T zkkwv8wb?^qG(QLrcHoBb;J7WarZN|I_R#~$X z+hY&5sf#cTaYv2l_tf<-?!pWH_uS=QEE6bZ1~JP|HOa54_oL?^x9_lQ#d+tEgL_~Y 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zLi;}&6o^dhb{m}Ii1XopPd+rcnr|5sgYs)gv^wDXWkCxM6Vs6)FCLuNu;Wht)o41A zTi11$Dr&QCqiUbk7P`z}*pBF`a!8+BujS}FORGO;&!I|Z6R^6C(aB%86Q_27JYf`i&*U@m}VQ9`-{8Ufhn>( z?fo?yG}+j0{}eJ~omcV&$r>-@=ZSPj2!D_0tLA?*TqZiBx)1*W1d*Xs*p<$gD+oATTE&ufXlN6N`sSwf+{6FCTt1$oo diff --git a/docs/tutorials/matlab/matlab-and-simulink.md b/docs/tutorials/matlab/matlab-and-simulink.md deleted file mode 100644 index efb061d5..00000000 --- a/docs/tutorials/matlab/matlab-and-simulink.md +++ /dev/null @@ -1,553 +0,0 @@ -# MATLAB and Simulink - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -MATLAB and Simulink by MathWorks play an important role in scientific computing and Model-based Design in academic and industrial applications of science and technology. Ability to deploy MATLAB and Simulink applications with Quix enables engineers, scientists and researchers to develop, test, deploy and orchestrate large clusters of stream processing applications at scale. - -The following sections describe how to deploy MATLAB functions and Simulink models to Quix to perform real-time analytics and simulations with MATLAB and Quix Streams. - -## MATLAB - -This section describes the steps for deploying a MATLAB function that rotates 2D vectors counter-clockwise by a specified angle. - -### Prerequisites - -* A Quix account. - -??? info "Start for free" - [Book a session](https://quix.io/book-a-demo) with us to start for free. - - We will create a time-limited free account for you, and our experts will help you get started with your specific use case. - -* It is assumed you have created a Quix project and environment in which to contain your application. Alternatively, you can use a legacy workspace, but this is not recommended. -* MathWorks licenses for MATLAB, MATLAB Compiler and the MATLAB Compiler SDK. - -This tutorial uses MATLAB R2023a. Please refer to the [Working with different MATLAB versions](#working-with-different-matlab-versions) section for information on how to use a different version of MATLAB. - -### Preparing a MATLAB function for deployment - -This section describes the process for packaging MATLAB functions for deployment. The application templates in Quix have pre-built MATLAB packages. To deploy the default packages without compiling them, go to [deploying a MATLAB function](#deploying-a-matlab-function) section. - - 1. In MATLAB, create a new `*.m` file with the following function and save it as `rot.m`: - - ``` - function M = rot(v, theta) - R = [cos(theta) -sin(theta); sin(theta) cos(theta)]; - M = R * v; - end - ``` - 2. In the MATLAB command window, type the following command to compile the MATLAB function for your preferred runtime environment. For more information on compiling MATLAB functions for deployment, please refer to [MATLAB compiler documentation](https://www.mathworks.com/help/compiler/mcc.html#d124e20858){target=_blank}: - - === "Python" - - ``` python - mcc -W python:quixmatlab rot.m -d py - ``` - - === "C\#" - - ``` cs - mcc -W 'dotnet:quixmatlab,api=matlab-data,framework_version=6.0' rot.m -d cs - ``` - -3. Above commands generate deployable packages for Python in `py` directory and `cs` for .NET: - - ![MATLAB](./images/matlab_console.png){width=600} - -### Deploying a MATLAB function - - 1. Sign in to your environment in [Quix](https://portal.cloud.quix.io/){target=_blank}. - - 2. Click on the `Code Samples` in the left navigation panel, and search for `matlab` in the search box on the top left to filter code samples. - - 3. Select the MATLAB template for your programming language of choice. Optionally, use the `LANGUAGES` filter on the left to filter templates based on the programming language. - - 4. Click on the `Preview code` button to open a preview of the application, and then click on the `Edit code` button to generate an application from the template. - - 5. Enter `Rotation Transform` as the application name. Enter `matlab-input` for input topic and `matlab-output` for output topic, and click `Save as Application.` - - 6. To use your own MATLAB packages, replace the contents of the `MATLAB` directory in the application with your assets by clicking on the upload icon in the application explorer. - - 7. Assign a tag to the application by clicking on the `add tag` icon and typing in a release tag such as `1.0`. Click on `Deploy` on the top right to open the deployment dialog: - - ![create a release tag](./images/matlab_deployment_tag.png){width=600} - - 8. In the deployment dialog, choose the release tag and ensure the environment variables are set correctly. In the `Deployment Settings`, choose `Service` for the deployment type, give at least 2 CPUs and sufficient memory (~4GB for the default template), and click on `Deploy`: - - ![deploy the model](./images/matlab_deployment_dialog.png){width=600} - -### Testing the MATLAB function - -This section describes the steps to test the MATLAB function by deploying a service to generate test data. In production environments, data from sensors or the output of another function or simulation takes the place of this service. - - 1. Click on the `Code Samples` on the left navigation, select `Python` under languages, and `Source` under the pipeline stage. Then type `starter` in the search box to filter the starter template for a data source. Follow the steps described in the previous section to create an application named `2D Vector Source` based on this template by clicking on `Preview code` followed by `Edit code`. - - 2. Set the output topic of the application to the input topic of the deployment containing the MATLAB transformation: - - ![starter application configuration](./images/matlab_starter_application_creation.png){width=600} - - 3. Replace the contents of the `main.py` file of the application with the following script, which generates a random stream of 2D unit vectors: - - ```python - import quixstreams as qx - import time - import datetime - import os - import random, math - - client = qx.QuixStreamingClient() - topic_producer = client.get_topic_producer(topic_id_or_name = os.environ["output"]) - - stream = topic_producer.get_or_create_stream("vectors") - index = 0 - while True: - xa = random.uniform(-1, 1) - ya = random.uniform(-1, 1) - vlen = math.sqrt(xa ** 2 + ya ** 2) - stream.timeseries \ - .buffer \ - .add_timestamp(datetime.datetime.utcnow()) \ - .add_value("x", xa / vlen) \ - .add_value("y", ya / vlen) \ - .publish() - time.sleep(0.5) - ``` - - 4. Create a tag and deploy the `2D Vector Source`. Click `Pipeline` in the main navigation to display your pipeline: - - ![pipeline](./images/matlab_pipeline_view.png){width=600} - - 5. Click on the `Rotation Transform` node to view the application logs and deployment details: - - ![deployment details](./images/matlab_deployment_details.png){width=600} - - 6. Next, click on `Data explorer` on the left-hand navigation, select `Live data` from the top menu, and select the output topic of the MATLAB transformation (for example, `matlab-output`), the stream, and the parameters as shown in the figure below to view the live transformation: - - ![data explorer](./images/matlab_data_explorer.png){width=600} - -## Simulink - -This section describes the steps for deploying a Simulink model of an [internal combustion engine](https://www.mathworks.com/help/simulink/slref/modeling-engine-timing-using-triggered-subsystems.html){target=_blank} developed by MathWorks to Quix. - -### Prerequisites - - - A Quix Cloud account. - -??? info "Start for free" - [Book a session](https://quix.io/book-a-demo) with us to start for free. - - We will create a time-limited free account for you, and our experts will help you get started with your specific use case. - - - MathWorks licenses for MATLAB, MATLAB Compiler, Simulink, Simulink Compiler and MATLAB Compiler SDK. - -This tutorial uses MATLAB R2023a. Please refer to the [Working with different MATLAB versions](#working-with-different-matlab-versions) section for information on how to use a different version of MATLAB. - -### Preparing a Simulink model for deployment - - 1. Download the MATLAB and Simulink assets for the internal combustion engine from the MathWorks [site](https://www.mathworks.com/help/simulink/slref/modeling-engine-timing-using-triggered-subsystems.html){target=_blank} or the `samples` directory in the Quix `Code Samples` for MATLAB. - 2. Convert the input and output of the Simulink model to environment variables. For other means of interacting with Simulink programmatically, refer to [How to Bring Data from MATLAB Into Simulink](https://www.youtube.com/watch?v=kM2qL__YxBQ){target=_blank} and [Simulate a Simulink® model from Python](https://github.com/mathworks/Call-Simulink-from-Python){target=_blank}. - - ![Simulink](./images/simulink_console.png){width=600} - - 3. Create a MATLAB function in `engine.m` file with the following content to bootstrap the Simulink model and prepare it for deployment: - - ``` - function R = engine(throttle_angle, time) - ta = timeseries(throttle_angle, time); - assignin("base", "throttle_angle", ta); - si = Simulink.SimulationInput('sldemo_engine'); - si = simulink.compiler.configureForDeployment(si); - sout = sim(si); - R = sout.engine_speed.Data(end); - end - ``` - 4. If you use environment variables to pass arguments to Simulink, create them in the environment before compiling the model. Run the following commands on the MATLAB command window to seed some input data and run the Simulink model using the `engine.m` function: - - ``` - throttle_a = [0.2, 0.23, 1.2, 4.2, 5.3 ]; - ts = [1, 2, 3, 4, 5]; - engine(throttle_a, ts); - ``` - - 5. On the MATLAB command window, type the following command to compile the MATLAB function to your preferred runtime environment. For information on compiling MATLAB functions for deployment, please refer to [MATLAB compiler documentation](https://www.mathworks.com/help/compiler/mcc.html#d124e20858){target=_blank}: - - === "Python" - - ``` python - mcc -W python:quixmatlab engine.m -d py - ``` - - === "C\#" - - ``` cs - mcc -W 'dotnet:quixmatlab,api=matlab-data,framework_version=6.0' engine.m -d cs - ``` - - 6. Above commands generate deployable packages for Python in `py` directory and `cs` for .NET. - -### Deploying a Simulink model - - 1. Sign in to the environment in [Quix](https://portal.cloud.quix.io/){target=_blank}. - - 2. Click on the `Code Samples` on the left navigation panel and search for `simulink` in the search bar on the top left to filter code samples (templates for MATLAB and Simulink are the same): - - 3. Click on the template for your programming language of choice. You can also use the `LANGUAGES` filters on the left to filter templates based on the programming language. - - 4. Click on the `Preview code` button to open a preview of the application, and then click on the `Edit code` button to generate a new application from the template. - - 5. Enter `Engine Model` for the application name. Enter `simulink-input` for input topic and `simulink-output` for output topic, and click `Save as Application.` - - 6. To deploy your own functions and models, replace the contents of the `MATLAB` directory in the application with your packages. - - 7. Replace the contents of the main function (`main.py` for Python, `Program.cs` for C#) with the following for your target programming language. They are responsible for calling the `engine` function with the correct arguments: - - === "Python" - - ```python - import quixstreams as qx - import os - import quixmatlab - import matlab - - qxmlm = quixmatlab.initialize() - - client = qx.QuixStreamingClient() - input_topic = client.get_topic_consumer(os.environ["input"]) - output_topic = client.get_topic_producer(os.environ["output"]) - - def on_data_received_handler(input_stream: qx.StreamConsumer, data: qx.TimeseriesData): - with data: - for ts in data.timestamps: - throttle_angles = matlab.double([ts.parameters["throttle_angle"].numeric_value]) - timestamps = matlab.double([ts.timestamp_milliseconds / 1000]) - rv = qxmlm.engine(throttle_angles, timestamps) - ts.add_value("engine_speed", rv) - print("throttle angle:{}, engine speed:{}".format(throttle_angles[0][0], rv)) - output_stream = output_topic.get_or_create_stream(input_stream.stream_id) - output_stream.timeseries.publish(data) - - def on_stream_received_handler(stream: qx.StreamConsumer): - print("New stream: {}".format(stream.stream_id)) - stream.timeseries.on_data_received = on_data_received_handler - - input_topic.on_stream_received = on_stream_received_handler - - print("Listening to streams. Press CTRL-C to exit.") - qx.App.run() - ``` - - === "C\#" - - ```cs - using System; - using MathWorks.MATLAB.Runtime; - using MathWorks.MATLAB.Types; - using MathWorks.MATLAB.Exceptions; - using QuixStreams.Streaming; - using QuixStreams.Streaming.Models; - - namespace Service - { - class Program - { - static void Main(string[] args) - { - dynamic matlab = MATLABRuntime.StartMATLAB("quixmatlab.ctf"); - - var quix = new QuixStreamingClient(); - using var producer = quix.GetTopicProducer(Environment.GetEnvironmentVariable("output")); - using var consumer = quix.GetTopicConsumer(Environment.GetEnvironmentVariable("input")); - consumer.OnStreamReceived += (_, stream) => - { - Console.WriteLine($"New stream received: {stream.StreamId}"); - stream.Timeseries.OnDataReceived += (_, args) => - { - foreach (var ts in args.Data.Timestamps) - { - var tin = new double[] { ts.TimestampMilliseconds / 1000.0 }; - var din = new double[] { ts.Parameters["throttle_angle"].NumericValue.Value }; - double rv = matlab.engine(din, tin); - ts.AddValue("engine_speed", rv); - Console.WriteLine($"throttle angle:{din[0]}, engine speed:{rv}"); - } - producer.GetOrCreateStream(stream.StreamId).Timeseries.Publish(args.Data); - }; - }; - Console.WriteLine("Listening for streams"); - App.Run(); - Console.WriteLine("Exiting"); - } - } - } - ``` - - 8. Assign a tag to the application by clicking on the `add tag` icon and typing in a release tag such as `1.0`, and clicking on `Deploy` on the top right to open the deployment dialog: - - ![create a release tag](./images/simulink_deployment_tag.png){width=600} - - 9. In the deployment dialog, choose the release tag or the commit reference and ensure the environment variables are set correctly. In the `Deployment Settings`, choose `Service` for the deployment type, give at least 2 CPU cores and sufficient memory (~4GB for the default template), and click on `Deploy` to deploy the model: - - ![deploy the model](./images/simulink_deployment_dialog.png){width=600} - -### Testing the Simulink model - -This section describes the steps to deploy a service to generate test data for the model. In production environments, data from sensors or output of another simulation takes the place of this service. - - 1. Click on the `Code Samples` on the left navigation, select `Python` under languages, and `Source` under the pipeline stage. Then type `starter` in the search box to filter the starter template for a data source. Follow the steps described in the previous section to create an application based on this template by clicking on `Preview code` followed by `Edit code`. - - 2. Set the output topic of the `Engine Data Source` to the input topic of the deployment containing the Simulink model, `simulink-input`. - - 3. Replace the contents of the `main.py` file in the `Engine Data Source` application with the following script, which randomly generates a throttle angle once every second: - - ```python - import quixstreams as qx - import time - import datetime - import os - import random - client = qx.QuixStreamingClient() - topic_producer = client.get_topic_producer(topic_id_or_name = os.environ["output"]) - stream = topic_producer.create_stream() - while True: - throttle_angle = random.uniform(8, 12) - data = qx.TimeseriesData() - data.add_timestamp(datetime.datetime.utcnow()) \ - .add_value("throttle_angle", throttle_angle) - stream.timeseries.publish(data) - time.sleep(1) - ``` - - 4. Follow steps 8 and 9 in the preceding section to deploy the `Engine Data Source.` - - ![pipeline](./images/pipeline_view.png){width=600} - - 5. Click on the `Engine Model` deployment to view the deployment details and application logs: - - ![deployment details](./images/simulink_deployment_details.png){width=600} - - 6. Next, click on `Data explorer` on the left navigation panel, select `Live data` from the top menu, and select the output topic, the stream, and the parameters as shown in the figure below to see the live simulation: - - ![data explorer](./images/simulink_data_explorer.png){width=600} - -## Working with different MATLAB versions - -Quix supports all versions of MATLAB with support for MATLAB Runtime API. To reduce build-time, Quix [Docker Hub](https://hub.docker.com/u/quixanalytics){target=_blank} maintains images with MATLAB R2023a, Python and .NET LTS versions pre-installed. Modify the following dockerfiles to use a different version of MATLAB, Python or .NET: - -=== "Python" - - ```python - FROM ubuntu:22.04 - ENV PYTHONUNBUFFERED=1 - ENV PYTHONIOENCODING=UTF-8 - - # Python 3.10 - ENV PATH /usr/local/bin:$PATH - ENV LANG C.UTF-8 - RUN set -eux; \ - apt-get update; \ - apt-get install -y --no-install-recommends \ - ca-certificates \ - netbase \ - tzdata \ - ; \ - rm -rf /var/lib/apt/lists/* - ENV GPG_KEY A035C8C19219BA821ECEA86B64E628F8D684696D - ENV PYTHON_VERSION 3.10.11 - RUN set -eux; \ - \ - savedAptMark="$(apt-mark showmanual)"; \ - apt-get update; \ - apt-get install -y --no-install-recommends \ - dpkg-dev \ - gcc \ - gnupg \ - libbluetooth-dev \ - libbz2-dev \ - libc6-dev \ - libdb-dev \ - libexpat1-dev \ - libffi-dev \ - libgdbm-dev \ - liblzma-dev \ - libncursesw5-dev \ - libreadline-dev \ - libsqlite3-dev \ - libssl-dev \ - make \ - tk-dev \ - uuid-dev \ - wget \ - xz-utils \ - zlib1g-dev \ - ; \ - \ - wget -O python.tar.xz "https://www.python.org/ftp/python/${PYTHON_VERSION%%[a-z]*}/Python-$PYTHON_VERSION.tar.xz"; \ - wget -O python.tar.xz.asc "https://www.python.org/ftp/python/${PYTHON_VERSION%%[a-z]*}/Python-$PYTHON_VERSION.tar.xz.asc"; \ - GNUPGHOME="$(mktemp -d)"; export GNUPGHOME; \ - gpg --batch --keyserver hkps://keys.openpgp.org --recv-keys "$GPG_KEY"; \ - gpg --batch --verify python.tar.xz.asc python.tar.xz; \ - gpgconf --kill all; \ - rm -rf "$GNUPGHOME" python.tar.xz.asc; \ - mkdir -p /usr/src/python; \ - tar --extract --directory /usr/src/python --strip-components=1 --file python.tar.xz; \ - rm python.tar.xz; \ - \ - cd /usr/src/python; \ - gnuArch="$(dpkg-architecture --query DEB_BUILD_GNU_TYPE)"; \ - ./configure \ - --build="$gnuArch" \ - --enable-loadable-sqlite-extensions \ - --enable-optimizations \ - --enable-option-checking=fatal \ - --enable-shared \ - --with-lto \ - --with-system-expat \ - --without-ensurepip \ - ; \ - nproc="$(nproc)"; \ - EXTRA_CFLAGS="$(dpkg-buildflags --get CFLAGS)"; \ - LDFLAGS="$(dpkg-buildflags --get LDFLAGS)"; \ - LDFLAGS="${LDFLAGS:--Wl},--strip-all"; \ - make -j "$nproc" \ - "EXTRA_CFLAGS=${EXTRA_CFLAGS:-}" \ - "LDFLAGS=${LDFLAGS:-}" \ - "PROFILE_TASK=${PROFILE_TASK:-}" \ - ; \ - rm python; \ - make -j "$nproc" \ - "EXTRA_CFLAGS=${EXTRA_CFLAGS:-}" \ - "LDFLAGS=${LDFLAGS:--Wl},-rpath='\$\$ORIGIN/../lib'" \ - "PROFILE_TASK=${PROFILE_TASK:-}" \ - python \ - ; \ - make install; \ - \ - cd /; \ - rm -rf /usr/src/python; \ - \ - find /usr/local -depth \ - \( \ - \( -type d -a \( -name test -o -name tests -o -name idle_test \) \) \ - -o \( -type f -a \( -name '*.pyc' -o -name '*.pyo' -o -name 'libpython*.a' \) \) \ - \) -exec rm -rf '{}' + \ - ; \ - \ - ldconfig; \ - \ - apt-mark auto '.*' > /dev/null; \ - apt-mark manual $savedAptMark; \ - find /usr/local -type f -executable -not \( -name '*tkinter*' \) -exec ldd '{}' ';' \ - | awk '/=>/ { print $(NF-1) }' \ - | sort -u \ - | xargs -r dpkg-query --search \ - | cut -d: -f1 \ - | sort -u \ - | xargs -r apt-mark manual \ - ; \ - apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false; \ - rm -rf /var/lib/apt/lists/*; \ - \ - python3 --version - RUN set -eux; \ - for src in idle3 pydoc3 python3 python3-config; do \ - dst="$(echo "$src" | tr -d 3)"; \ - [ -s "/usr/local/bin/$src" ]; \ - [ ! -e "/usr/local/bin/$dst" ]; \ - ln -svT "$src" "/usr/local/bin/$dst"; \ - done - ENV PYTHON_PIP_VERSION 23.0.1 - ENV PYTHON_SETUPTOOLS_VERSION 65.5.1 - ENV PYTHON_GET_PIP_URL https://github.com/pypa/get-pip/raw/0d8570dc44796f4369b652222cf176b3db6ac70e/public/get-pip.py - ENV PYTHON_GET_PIP_SHA256 96461deced5c2a487ddc65207ec5a9cffeca0d34e7af7ea1afc470ff0d746207 - RUN set -eux; \ - \ - savedAptMark="$(apt-mark showmanual)"; \ - apt-get update; \ - apt-get install -y --no-install-recommends wget; \ - \ - wget -O get-pip.py "$PYTHON_GET_PIP_URL"; \ - echo "$PYTHON_GET_PIP_SHA256 *get-pip.py" | sha256sum -c -; \ - \ - apt-mark auto '.*' > /dev/null; \ - [ -z "$savedAptMark" ] || apt-mark manual $savedAptMark > /dev/null; \ - apt-get purge -y --auto-remove -o APT::AutoRemove::RecommendsImportant=false; \ - rm -rf /var/lib/apt/lists/*; \ - \ - export PYTHONDONTWRITEBYTECODE=1; \ - \ - python get-pip.py \ - --disable-pip-version-check \ - --no-cache-dir \ - --no-compile \ - "pip==$PYTHON_PIP_VERSION" \ - "setuptools==$PYTHON_SETUPTOOLS_VERSION" \ - ; \ - rm -f get-pip.py; \ - \ - pip --version - - # MathWorks base dependencies - ENV DEBIAN_FRONTEND="noninteractive" TZ="Etc/UTC" - COPY --from=git /project/base-dependencies.txt /tmp/base-dependencies.txt - RUN apt-get update && apt-get install --no-install-recommends -y `cat /tmp/base-dependencies.txt` \ - && apt-get clean && apt-get -y autoremove && rm -rf /var/lib/apt/lists/* - RUN [ -d /usr/share/X11/xkb ] || mkdir -p /usr/share/X11/xkb - - # MATLAB runtime - WORKDIR /tmp - RUN wget https://ssd.mathworks.com/supportfiles/downloads/R2023a/Release/2/deployment_files/installer/complete/glnxa64/MATLAB_Runtime_R2023a_Update_2_glnxa64.zip -O MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ - && unzip MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ - && ./install -agreeToLicense yes -destinationFolder /opt/matlab \ - && rm -rf /tmp/* - ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/opt/matlab/R2023a/runtime/glnxa64:/opt/matlab/R2023a/bin/glnxa64:/opt/matlab/R2023a/sys/os/glnxa64:/opt/matlab/R2023a/sys/opengl/lib/glnxa64" - ``` - -=== "C\# (SDK)" - - ```cs - FROM mcr.microsoft.com/dotnet/sdk:6.0-jammy - - # MathWorks base dependencies - ENV DEBIAN_FRONTEND="noninteractive" TZ="Etc/UTC" - COPY --from=git /project/base-dependencies.txt /tmp/base-dependencies.txt - RUN apt-get update && apt-get install --no-install-recommends -y `cat /tmp/base-dependencies.txt` \ - && apt-get clean && apt-get -y autoremove && rm -rf /var/lib/apt/lists/* - RUN [ -d /usr/share/X11/xkb ] || mkdir -p /usr/share/X11/xkb - - # MATLAB runtime - WORKDIR /tmp - RUN wget https://ssd.mathworks.com/supportfiles/downloads/R2023a/Release/2/deployment_files/installer/complete/glnxa64/MATLAB_Runtime_R2023a_Update_2_glnxa64.zip -O MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ - && unzip MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ - && ./install -agreeToLicense yes -destinationFolder /opt/matlab \ - && rm -rf /tmp/* - ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/opt/matlab/R2023a/runtime/glnxa64:/opt/matlab/R2023a/bin/glnxa64:/opt/matlab/R2023a/sys/os/glnxa64:/opt/matlab/R2023a/sys/opengl/lib/glnxa64" - ``` - -=== "C\# (Runtime)" - - ```cs - FROM mcr.microsoft.com/dotnet/runtime:6.0-jammy - - # MathWorks base dependencies - ENV DEBIAN_FRONTEND="noninteractive" TZ="Etc/UTC" - COPY --from=git /project/base-dependencies.txt /tmp/base-dependencies.txt - RUN apt-get update && apt-get install --no-install-recommends -y `cat /tmp/base-dependencies.txt` \ - && apt-get clean && apt-get -y autoremove && rm -rf /var/lib/apt/lists/* - RUN [ -d /usr/share/X11/xkb ] || mkdir -p /usr/share/X11/xkb - - # MATLAB runtime - WORKDIR /tmp - RUN wget https://ssd.mathworks.com/supportfiles/downloads/R2023a/Release/2/deployment_files/installer/complete/glnxa64/MATLAB_Runtime_R2023a_Update_2_glnxa64.zip -O MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ - && unzip MATLAB_Runtime_R2023a_Update_2_glnxa64.zip \ - && ./install -agreeToLicense yes -destinationFolder /opt/matlab \ - && rm -rf /tmp/* - ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/opt/matlab/R2023a/runtime/glnxa64:/opt/matlab/R2023a/bin/glnxa64:/opt/matlab/R2023a/sys/os/glnxa64:/opt/matlab/R2023a/sys/opengl/lib/glnxa64" - ``` - -=== "Base dependencies" - - ``` - ca-certificates libasound2 libc6 libcairo2 libcairo-gobject2 libcap2 libcrypt1 libcrypt-dev libcups2 libdrm2 libdw1 libgbm1 libgdk-pixbuf2.0-0 libgl1 libglib2.0-0 libgomp1 libgstreamer1.0-0 libgstreamer-plugins-base1.0-0 libgtk-3-0 libice6 libnspr4 libnss3 libodbc1 libpam0g libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 libsndfile1 libsystemd0 libuuid1 libwayland-client0 libxcomposite1 libxcursor1 libxdamage1 libxfixes3 libxft2 libxinerama1 libxrandr2 libxt6 libxtst6 libxxf86vm1 linux-libc-dev locales locales-all make net-tools odbcinst1debian2 procps sudo unzip wget zlib1g - ``` \ No newline at end of file diff --git a/docs/tutorials/outdated-tutorial.md b/docs/tutorials/outdated-tutorial.md new file mode 100644 index 00000000..f1bc92a8 --- /dev/null +++ b/docs/tutorials/outdated-tutorial.md @@ -0,0 +1,16 @@ +--- +title: Outdated Tutorial +description: This tutorial has been removed. Explore our current tutorials instead. +--- + +# Tutorial No Longer Available + +The tutorial you were looking for has been removed as it was out of date and referenced deprecated features. + +## Current Tutorials + +We recommend exploring our current tutorials instead. You can find all available tutorials on our [tutorials overview page](./overview.md). + +## Need help? + +If you need help finding a tutorial for your specific use case, feel free to ask in the [Quix Community](https://quix.io/slack-invite){target=_blank}! \ No newline at end of file diff --git a/docs/tutorials/overview.md b/docs/tutorials/overview.md index 28700fa3..a4db9c22 100644 --- a/docs/tutorials/overview.md +++ b/docs/tutorials/overview.md @@ -45,63 +45,6 @@ Each tutorial is divided into parts, so that you can leave a tutorial at a conve - ## Need help? diff --git a/docs/tutorials/rss-tutorial/image1.png b/docs/tutorials/rss-tutorial/image1.png deleted file mode 100644 index b6cd8914bef1f66ad649f3d0279bc3c549adbaa8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 46565 zcmd42cQjn#_ctmLLeN~9|eATLjDwb^Tq4P=tnod zEGoTbh~CrJ&fHNv?;~OQ?RPA2*(Lj~Pv;^=j2@-d@Pu!yF zslxAjjJ|B)mF(;+$&HHs*S;ESoLBt!GUX=ky?;%ByYl|O3vBZLs>?KaX%Z&hMx!Zz 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z?7RQlKj|lcZPeGG{O>^VJVABV67?d_#Mg9BP#r%jBkr?i4?%5@Fh>GCY8Ng$5l?~J NvXTlCC2#co{y#8oko^Dv diff --git a/docs/tutorials/sentiment-analysis/overview.md b/docs/tutorials/sentiment-analysis/overview.md deleted file mode 100644 index 74a15b81..00000000 --- a/docs/tutorials/sentiment-analysis/overview.md +++ /dev/null @@ -1,190 +0,0 @@ -# Chat sentiment analysis - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this tutorial you learn about a real-time chat sentiment analysis application, using a [Quix template project](https://github.com/quixio/chat-demo-app){target=_blank}. - -![Sentiment analysis pipeline](../../images/project-templates/chat-sentiment-pipeline.png) - -Sentiment analysis is performed on chat messages. The project includes a chat UI, where you can type chat messages. You can also connect to Twitch and perform sentiment analysis on large volumes of messages. - -You learn how to get the project, try out the UI, look more deeply into the UI and the sentiment analysis service, and then customize the UI. - -

        - -## Technologies used - -Some of the technologies used by this template project are listed here. - -**Infrastructure:** - -* [Quix](https://quix.io/){target=_blank} -* [Docker](https://www.docker.com/){target=_blank} -* [Kubernetes](https://kubernetes.io/){target=_blank} - -**Backend:** - -* [Redpanda Cloud](https://redpanda.com/redpanda-cloud){target=_blank} -* [Quix Streams](https://github.com/quixio/quix-streams){target=_blank} -* [Flask](https://flask.palletsprojects.com/en/2.3.x/#){target=_blank} -* [pandas](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html){target=_blank} - -**Sentiment analysis:** - -* [Hugging Face](https://huggingface.co/){target=_blank} - -**Frontend:** - -* [Angular](https://angular.io/){target=_blank} -* [Typescript](https://www.typescriptlang.org/){target=_blank} -* [Microsoft SignalR](https://learn.microsoft.com/en-us/aspnet/signalr/){target=_blank} - -**Data warehousing:** - -* [BigQuery](https://cloud.google.com/bigquery/){target=_blank} - -## GitHub repository - -The complete code for this project can be found in the [Quix GitHub repository](https://github.com/quixio/chat-demo-app){target="_blank"}. - -## Getting help - -If you need any assistance while following the tutorial, we're here to help in the [Quix Community](https://quix.io/slack-invite){target="_blank"}. - -## Prerequisites - -Get a Quix Cloud account. - -??? info "Start for free" - [Book a session](https://quix.io/book-a-demo) with us to start for free. - - We will create a time-limited free account for you, and our experts will help you get started with your specific use case. - -### Twitch API key - -You'll also need an API key for the [Twitch](https://dev.twitch.tv/docs/api/) service (optional), if you want to try Twitch-related features. - -### BigQuery credentials - -If you want to use the Quix BigQuery service (optional), you'll need to provide your credentials for accessing [BigQuery](https://cloud.google.com/bigquery/){target=_blank}. - -### Git provider - -You also need to have a Git account. This could be GitHub, Bitbucket, GitLab, or any other Git provider you are familar with, and that supports SSH keys. The simplest option is to create a free [GitHub account](https://github.com){target=_blank}. - -!!! tip - - While this tutorial uses an external Git account, Quix can also provide a Quix-hosted Git solution using Gitea for your own projects. You can watch a video on [how to create a project using Quix-hosted Git](https://www.loom.com/share/b4488be244834333aec56e1a35faf4db?sid=a9aa124a-a2b0-45f1-a756-11b4395d0efc){target=_blank}. - - ??? "Transcript" - - 0:01 Hi there, welcome to this video on creating a quick project. I should point out before I get into this video that I'm using the beta development environment at Quix and so what you see may be slightly different when you're testing Quicks. - - 0:21 Okay, so having said that I'll get straight into creating a new project. You can see this button over here. I'm going to click that to create a new project. - - 0:33 The new workflow in Quix starts with creating a project. Everything is done inside a project. Generally speaking, I'm simplifying it quite a lot. - - 0:44 A project corresponds to a Git repository. So everything that you create, including your pipeline configuration and so on, will be stored in a Git repository. - - 0:59 So I'll give my project a name and I'll call it video project. And the first thing I need to do is specify where this Git repository is going to be. - - 1:12 Now, the simplest option is to get quick to do all the work for you and just create the Git repository. - - 1:21 And the second thing I need to do is a product called gitty to host the Git repository. So we can do that for you. - - 1:27 It all happens in the background. And as you'll see in later videos, you have complete control about what happens in that repository. - - 1:37 You can review pull requests and do, merges and all of those normal things that you would do in a Git workflow. - - 1:47 The other option is to use an external provider. So for example, you could use GitLab, GitHub, Bitbucket and so on. - - 1:57 You can use any provider that supports the use of an SS. H key. For the purposes of this video, I'm going to keep it simple for now and just use the Quix manage Git. - - 2:11 So I'm now ready to create the project. Now every project will have at least one environment and usually several environments. - - 2:28 So what is an environment? An environment roughly corresponds to a branch in your Git repository, but there's a specific for an environment. - - 2:41 As well as you'll see as we go through this dialogue. But the first thing we need to do is give our environment a name. - - 2:52 So I'm going to name it. Based on the typical development workflow, so usually we'd have production. Branch or environment and you might have staging and development. - - 3:09 So I'm going to create the production environment and as I was saying earlier, that's going to roughly correspond to a branch. - - 3:17 So in this next section, we're going to specify the, ,branch that this environment corresponds to and I want production to correspond to the main branch. - - 3:30 And so there's nothing really I need to do here. I could create a new branch if I wanted to, ,but I want to use the default branch which is main. - - 3:41 The other, thing that I can do here is protect this branch and what that means is that prevents developers from merging or committing content, making changes directly to the main branch. - - 3:58 In order to change the main branch you'll have to raise, a pull request, a git pull request and that would have to be reviewed and approved and then merged in the usual way. - - 4:13 So we definitely want that because for production we don't want changes being made directly. So I'm protecting that and you'll, We'll see later how we go about taking changes that we've made and say the develop branch or the dev branch and merge those into the main branch. - - 4:32 I'll cover that in another video. So for now let's just click continue. Now for each environment that you create you can specify how you want to host Kafka. - - 4:45 Now as you probably know Quix uses Kafka as its broker. And you have several options here. You if you want the simplest option and most convenient option is to just let Quix do all the hard work for you. - - 5:01 We will create them. Kafka broker and you don't need to do any configuration. We just do it all for you. - - 5:08 We make sure that scales nicely as well. We use Kubernetes and you know as I say it's the least. It's it's the quickest option especially. - - 5:22 If you're testing things out, but there are other options as you can see here. You can use your own self hosted Kafka or you can connect to a Confluent Cloud and we'll cover those options in later videos. - - 5:39 So for now, I'm just going to go with the simplest option and click. Continue. The other thing that you'll need to do here is specify the storage option for the environment. - - 5:51 So the key thing to point out at this point is we were talking about environments as corresponding to a branch in get. - - 6:02 That is true, but it's also these other things like the Kafka options that you select and also the storage options. - - 6:11 Now the storage option that you're selecting here is for any data that you might possess. So in Quix it's possible to persist the data that's published to topics. - - 6:27 You can store that using our something called the data catalog and if you store, if you persist your topics and store the messages that are in the topics. - - 6:43 In our storage facilities, then there's a small charge associated with that. However, you don't have to persist topics. You can use external database solutions for storage of your data. - - 7:01 So you can use. Some of our standard connectors or even write your own connector to connect to more or less any database technology that you want. - - 7:11 The other thing that's stored here is metadata associated with messages. So for now, I'm just going to choose the standard option and create. - - 7:23 The environment. Okay, that's it. We'll wait for the project and the environment to be created. And then we'll have a look at what's in there in subsequent videos. - - 7:42 Okay, thanks for watching. And see you in the next video. - -## The pipeline - -The main services in the [pipeline](https://portal.cloud.quix.io/pipeline?workspace=demo-chatappdemo-prod&token=eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6Ik1qVTBRVE01TmtJNVJqSTNOVEpFUlVSRFF6WXdRVFF4TjBSRk56SkNNekpFUWpBNFFqazBSUSJ9.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.Ndm0K2iNHPxDq1ohF-yb-6LzIqx_UY8Ptcq0kAwSNye12S3deX_eDkC4XqZqW2NoSLd3GsmWV9PZGetGGp2IlqshQFZtUMp6WP6hq917ZC1i8JFx93PAbY7NT_88nFDovVlaRcoTpWvI-03KbryLkAoB28c6qb3EFwjCWFBuy_yA4yjQ8uF0-AZ0R9Qi4IBaekXWqcgO0a91gVRg0oA_hnzJFoR-EnZ2G1ZSxtuVgnyyPuQTMUvzJuUT_IJTLzEB_kejX0pcXRZBIwHP8MWLB4mE5DtIdz4jm8WIA4eZJZ7ZCG4dk-adQwZ2BdkNknV5eEwRgRJL4ybaplkaDlR-dg){target_blank} are: - -1. *UI* - provides the chat UI, and shows the sentiment being applied to the chat messages. - -2. *Sentiment analysis* - uses the [Hugging Face](https://huggingface.co/) model to perform sentiment analysis on the chat messages. There is also a *Drafts sentiment analysis* service for messages being typed, but not yet sent. - -3. *Twitch data source* - An alternative to typing chat messages - you select a Twitch channel and then perform sentiment analysis on Twitch messages. - -## The parts of the tutorial - -This tutorial is divided up into several parts, to make it a more manageable learning experience. The parts are summarized here: - -1. [Get the project](get-project.md) - you get the project up and running in your Quix account. - -2. [Try the UI](try-the-ui.md) - you try the UI, typing in chat messages and observing the sentiment analysis in operation. - -3. [Explore the UI service](ui-service.md) - explore UI service and gateways... - -4. [Explore the sentiment analysis service](sentiment-analysis-service.md) - you take a closer look at the sentiment analysis service, its code, and messages. - -5. [Explore ther Twitch service](twitch-service.md) - you explore the service that interfaces Quix with Twitch using the [Twitch API](https://dev.twitch.tv/docs/api/){target=_blank}. - -6. [Customize the UI](customize-the-ui.md) - you carry out a simple customization to the chat UI on a feature branch, and then merge your changes onto the `tutorial` branch. - -7. [Summary](summary.md) - you are presented with a summary of the work you have completed. - -## 🏃‍♀️ Next step - -[Part 1 - Get the project :material-arrow-right-circle:{ align=right }](get-project.md) diff --git a/docs/tutorials/sentiment-analysis/sentiment-analysis-service.md b/docs/tutorials/sentiment-analysis/sentiment-analysis-service.md deleted file mode 100644 index d5ad4a81..00000000 --- a/docs/tutorials/sentiment-analysis/sentiment-analysis-service.md +++ /dev/null @@ -1,82 +0,0 @@ -# Sentiment analysis service - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this part of the tutorial you learn about the sentiment analysis service. - -This service uses the Hugging Face model to calculate sentiment for messages, and these are then displayed on the web UI. - -There are three deployed services here: - -1. Chat sentiment analysis -2. Drafts sentiment analysis -3. Twitch sentiment analysis - -## 💡 Key ideas - -The key ideas on this page: - -* Hugging Face model is used to generate sentiment values -* How to examine message formats - -## What it does - -The sentiment analysis service uses a prebuilt model from [Hugging Face](https://huggingface.co/){target=_blank} to analyze the sentiment of each message flowing through the service. - -Draft messages are messages while the user is typing them, before they are sent. These are used to generate sentiment while the user is typing. - -Both the messages and draft messages are generated by the web UI. - -The sentiment analysis service subscribes to the `chat-messages` topic and performs sentiment analysis using the Hugging Face model, publishing a sentiment value to the `chat-with-sentiment` topic. - -The drafts sentiment analysis service performs sentiment analysis on messages published to the `drafts` topic and publishes sentiment values to the `drafts_sentiment` topic. - -The UI subscribes to these topics, and can then display the sentiment values in the UI. - -## 👩‍🔬 Lab - Examine messages - -There are several ways to view live data. This lab shows one way to do it. - -1. Click on `Topics` in the main left-hand navigation. - -2. Where you see live data for the `chat-messages` topic, click in that area, as shown in the screenshot: - - ![View live data](./images/topics-view-live-data.png){width=80%} - - You are taken to the live view of the Quix Data Explorer. - -3. The `chat-messages` topic is preselected for you. The stream names are the user names that you entered, or are user names from the Twitch service. Select any one and then select the `chat-message` parameter. - -4. Click the `Messages` view, and then click on any real-time message displayed. In the message code view you see something similar to the following: - - ``` json - { - "Epoch": 0, - "Timestamps": [ - 1695303751958000000 - ], - "NumericValues": {}, - "StringValues": { - "chat-message": [ - "Can you check on my order please?" - ] - }, - "TagValues": { - "room": [ - "channel" - ], - "name": [ - "gabbybe" - ], - "role": [ - "Customer" - ] - } - } - ``` - -## 🏃‍♀️ Next step - -[Part 5 - Explore the Twitch service :material-arrow-right-circle:{ align=right }](twitch-service.md) \ No newline at end of file diff --git a/docs/tutorials/sentiment-analysis/summary.md b/docs/tutorials/sentiment-analysis/summary.md deleted file mode 100644 index b279fdfa..00000000 --- a/docs/tutorials/sentiment-analysis/summary.md +++ /dev/null @@ -1,27 +0,0 @@ -# Summary - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this tutorial you've: - -* Learned how to fork a GitHub repository and then integrate it with Quix -* Examined the code for several services in the pipeline -* Learned that web apps can read and write to Quix topics using the Quix Streaming APIs. -* Used the message view in the Quix UI to examine messages flowing on topics -* Created a customization to the UI on a feature environment (branch) and then seen how to merge that to the `tutorial` branch - -## Next Steps - -Here are some suggested next steps to continue on your Quix learning journey: - -* Try the [computer vision tutorial](../computer-vision/overview.md). - -* If you decide to build your own connectors and apps, you can contribute to the [Code Samples repository](https://github.com/quixio/quix-samples){target=_blank}. Fork our Code Samples repo and submit your code, updates, and ideas. - -What will you build? Let us know! We’d love to feature your application or use case in our [newsletter](https://www.quix.io/community/). - -## Get help - -If you have any further questions, we're here to help in the [Quix Community](https://quix.io/slack-invite){target="_blank"}. diff --git a/docs/tutorials/sentiment-analysis/try-the-ui.md b/docs/tutorials/sentiment-analysis/try-the-ui.md deleted file mode 100644 index 62640e4a..00000000 --- a/docs/tutorials/sentiment-analysis/try-the-ui.md +++ /dev/null @@ -1,55 +0,0 @@ -# Try the UI - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this part of the tutorial you try out the UI to get a feel for the project and what it does. - -!!! tip - - In the pipeline view, you can always determine a topic name by hovering over the connecting line that represents that topic. You can also click the connecting line, to see its name, and optionally to jump to the Data Explorer to view live data for the topic. - -## Prerequisites - -!!! tip - - If you just want to try out the UI without performing the following steps, you can do that in the [deployed project](https://sentimentdemoui-demo-chatappdemo-prod.deployments.quix.io/chat){target=_blank}. - -You need a [PAT](../../apis/streaming-reader-api/setup.md#personal-access-token-pat) to get the UI working. - -Once you have this, you'll need to create a secret, and then use this secret in the `bearer_token` environment variable. - -[Read about environment variables and secret management](../../deploy/environment-variables.md). - -## Try out the UI in your Quix account - -Now try out the UI you just deployed. To do this: - -1. In the pipeline view find the UI service tile: - - ![Deployed UI tile](./images/web-ui-pipeline-segment.png){width=200px} - -2. In the service tile, click the external link icon to launch the UI in a new tab. - -3. Enter your username (it can be anything) and then type in some messages. Note that the typing indicator displays the sentiment as you type your message. - -4. Type various messages and check the sentiment is as expected. Also note that the sentiment analysis is shown in the real-time graph display. - -5. Select a different source from the `Message source` dropdown, and observe the messages and corresponding sentiment analysis graph change in real time. - -## 👩‍🔬 Lab - examine the code - -There are various ways you can view the code for this service. For example: - -1. Click `Pipeline` in the left-hand navigation to go to the pipeline view. - -2. Click the UI code panel as shown: - - ![Code panel](./images/click-code-tile.png){width=60%} - -3. The code is displayed in the built-in editor. You can navigate the codebase using the file explorer. The code view also has Intellisense built in - hover over a code construct to see more details. - -## 🏃‍♀️ Next step - -[Part 3 - Explore the UI service :material-arrow-right-circle:{ align=right }](ui-service.md) \ No newline at end of file diff --git a/docs/tutorials/sentiment-analysis/twitch-service.md b/docs/tutorials/sentiment-analysis/twitch-service.md deleted file mode 100644 index 5d54dc31..00000000 --- a/docs/tutorials/sentiment-analysis/twitch-service.md +++ /dev/null @@ -1,73 +0,0 @@ -# Twitch service - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In the UI as well as using the chat interface to send messages, you can also select Twitch channels and perform sentiment analysis on the messages published there: - -![Twitch channels](./images/twitch-channels.png) - -## 💡 Key ideas - -The key ideas on this page: - -* A Quix service can use external APIs to retrieve data and then publish that data into a Quix topic -* Publish time series data - -## Twitch credentials - -To run the Twitch service you'll need to provide your Twitch API credentials. You can configure these as [secret variables](../../deploy/secrets-management.md). The credentials required are shown in the following screenshot: - -![Twitch credentials](../sentiment-analysis/images/twitch-credentials.png) - -## What it does - -The Twitch service uses the [Twitch API](https://dev.twitch.tv/docs/api/){target=_blank} to read messages from some of the most popular channels. It then publishes these messages to the output topic, `messages`. - -In the following code you can see that a time series object is created, with a timestamp, the chat message, and other data, and then published to the output stream: - -``` python -def publish_chat_message(user: str, message: str, channel: str, timestamp: datetime, role: str = "Customer"): - timeseries_data = qx.TimeseriesData() - timeseries_data \ - .add_timestamp(timestamp) \ - .add_value("chat-message", message) \ - .add_tags({"room": "channel", "name": user, "role": role}) - - stream_producer = topic_producer.get_or_create_stream(channel) - stream_producer.timeseries.publish(timeseries_data) -``` - -The message format on the output `messages` topic: - -``` python -{ - "Epoch": 0, - "Timestamps": [ - 1695378597074000000 - ], - "NumericValues": {}, - "StringValues": { - "chat-message": [ - "@CaalvaVoladora Boomerdemons is also up" - ] - }, - "TagValues": { - "room": [ - "channel" - ], - "name": [ - "benkebultsax" - ], - "role": [ - "Customer" - ] - } -} -``` - -## 🏃‍♀️ Next step - -[Part 6 - Customize the UI :material-arrow-right-circle:{ align=right }](customize-the-ui.md) - diff --git a/docs/tutorials/sentiment-analysis/ui-service.md b/docs/tutorials/sentiment-analysis/ui-service.md deleted file mode 100644 index b8fb7511..00000000 --- a/docs/tutorials/sentiment-analysis/ui-service.md +++ /dev/null @@ -1,70 +0,0 @@ -# UI service - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this part of the tutorial you learn about the web UI service. - -![Web UI pipeline](./images/web-ui-pipeline-segment.png) - -This provides the UI for you to interact with this app. - -## 💡 Key ideas - -The key ideas on this page: - -* How a web client can read data from a Quix topic using Quix Streaming Reader API -* WebSockets as a way of streaming data into a web client -* Microsoft SignalR is the WebSockets technology used in the Streaming Reader API -* Access an external web application from the pipeline view - -## What it does - -The UI is an Angular web client written using Typescript. - -The key thing this service does is provide a UI that implements the chat interface, a typing indicator with sentiment value, sentiment value and emoticon for each message, and a real-time chart showing sentiment. - -The most important thing to understand is how this service reads and writes data to and from the Quix pipeline. - -This is done through use of the [Quix Streaming Reader API](../../apis/streaming-reader-api/overview.md). - -The Streaming Reader is used to read the sentiment from the sentiment analysis service for both the sent messages and draft messages. - -The four topics involved are: - -* `chat-messages` - topic for the sent messages -* `drafts` - topic for draft messages -* `chat-with-sentiment` - topic with sentiment for sent messages -* `drafts_sentiment` - topic with sentiment for draft messages - -So, the web UI uses the Reader API to read from both `chat-with-sentiment` and `drafts_sentiment`. - -The Streaming Reader API has both an HTTP and WebSockets interface you can use to interface with a Quix topic. This web client uses the WebSockets interface. This enables data to be streamed from the Quix topic into the web client with good performance. This is a more efficient method than using the request-response method of HTTP. - -The WebSockets interface uses Microsoft SignalR technology. You can read more about that in the [Quix SignalR documentation](../../apis/streaming-reader-api/setup.md#set-up-signalr) for the Reader API. - -In essence the code to read a topic needs to: - -1. Connect to Quix SignalR hub. -2. The web UI reads parameter data rather than event data, as that is the format used for inbound data in this case. -3. Handle "parameter data received" events using a callback. - -So, simplifying, after connection to the Quix topic, on a `ParameterDataReceived` event, the corresponding callback (event) handler is invoked. There are other events that can be subscribed to. You can read more about events and subscription in the [subscription and event documentation](../../apis/streaming-reader-api/subscriptions.md). An example for the handler is shown here: - -``` typescript -// Listen for parameter data and emit -readerHubConnection.on("ParameterDataReceived", (payload: ParameterData) => { - this.paramDataReceived.next(payload); -}); -``` - -## See also - -For more information refer to: - -* [Quix Streaming Reader API](../../apis/streaming-reader-api/overview.md) - read about the API used by clients external to Quix to read data from a Quix topic. - -## 🏃‍♀️ Next step - -[Part 4 - Explore the sentiment analysis service :material-arrow-right-circle:{ align=right }](sentiment-analysis-service.md) diff --git a/docs/tutorials/slack-alerting/add-requests.png b/docs/tutorials/slack-alerting/add-requests.png deleted file mode 100644 index 6da8543edca9d09226bcfe9e2b820d960c777405..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 27846 zcmdqIcT`jBx;Kit6dMAf3k?Dyf{F-=6s1KW0#X-UL_nq2D1;VDR0M(uNEZ;Ip?B#m zfQS@9O6V=2mk=O80s%sjJJIFd=bnAezW0uC@A%IB{%|A#GM#h2&--i7GeiE;R%JU1 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zQmuO@!8x~?)(Wcx{kX2~JuoOC&8lRPP!mMp8JAf1Oh8JHgj#SXyjjuPI|M+ogSd9- z<;mSaUgpm}RLO#WVo~fpWgrqN{aD*$8owjnu$Dt}^;jqPqu+wcXx-fFtoru4$TU*P z#zi$dv{8iy0UcbDwx?Xxo^qgk$jJr@@Lpa^CRW8NI=stM?W~E&S#e>F1n-dUbVfKe z{Q&&Q#i72J!;Inf%%>dLj#EW1`_{D+9Ca(`TKBPr+uK7w>zd{Fi&;LGEx5sXB%kgx cQ!Etkt&&amE`Dv`Yx)lp19Nzp{;xOx36RqO(*OVf diff --git a/docs/tutorials/slack-alerting/slack-alerting.md b/docs/tutorials/slack-alerting/slack-alerting.md deleted file mode 100644 index fb6550a2..00000000 --- a/docs/tutorials/slack-alerting/slack-alerting.md +++ /dev/null @@ -1,283 +0,0 @@ -# Sending TFL BikePoint availability alerts to Slack - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -## Aim - -In this tutorial you will learn how to use Quix to create event driven notifications in real-time. In this example we’ll connect to the Transport for London BikePoint API and send availability alerts to Slack using Slacks Webhooks. - -We will start by sending the raw BikePoint data to Slack and then we'll show you how to alter the pre-built solution to refine the Slack message. - -By the end you will have: - - - Configured Slack to allow external services to send messages via WebHooks. - - - Built and deployed a Quix Service that streams data received from the TFL BikePoint API. - - - Received messages to your slack channel on the availability of bikes around London. - -!!! note - - If you need any help, please sign up to the [Quix Community](https://quix.io/slack-invite){target=_blank}. - -## Application Architecture - -![application architecture](architecture.png) - -The solution has 2 main elements: - - - A Quix Service to pull TFL BikePoint data. - - - A Slack Alerting Quix service. - -A Quix Service receives data from the Transport For London BikePoint API and streams the data onto the Quix message broker. Another service listens to the bike data stream. When the number of available bikes at a station falls below the desired level an alert is sent to the Slack channel via the Slack Webhook. - -## Prerequisites - -To proceed with this tutorial you need: - - - A Quix Cloud account. - -??? info "Start for free" - [Book a session](https://quix.io/book-a-demo) with us to start for free. - - We will create a time-limited free account for you, and our experts will help you get started with your specific use case. - - - Access to Slack, you’ll need to be an admin. - - - A TFL account and API keys. - - ???- info "Getting your TFL API key" - - You’ll need a free TfL account which you can register for here: [https://api-portal.tfl.gov.uk/](https://api-portal.tfl.gov.uk/){target=_blank} - - A rough guide to finding your TfL API key is as follows: - - 1. Register for an account. - - 2. Login and click the `Products` menu item. - - 3. You should have 1 product to choose from `500 Requests per min.` - - 4. Click `500 Requests per min.` - - 5. Enter a name for your subscription into the box, e.g. QuixFeed, and click `Register.` - - 6. You can now find your API Keys in the profile page. - -!!! tip - Check out the application's README.md later on in the tutorial if you need help creating a Slack WebHook - -## Overview - -This walk through covers the following steps: - -1. Creating a Slack App. - -2. Setup Webhooks. - -3. Deploy a service to pull TFL BikePoint data. - -4. Publish messages to Slack from Quix. - -5. Edit the code and send a custom Slack message. - -## Part one - -In part one we will guide you through the process of selecting and deploying the TFL BikePoint connector and the Slack connector. -The end result will be Slack alerts containing data from the BikePoint API. - -### TFL BikePoint data - -Obtaining data from TFL's BikePoint API is fairly straight forward. You need to make a request to the relevant API endpoint, passing the correct parameters and API keys. When data is returned, you'll need to process it and ensure it's in the correct format to publish to a Quix topic. - -However, there is a much easier way to achieve the same outcome. - -1. Navigate to the `Code Samples`. - -2. Search for `TFL Bikepoint` and click the tile. - - ![TFL BikePoint sample tile](tfl-bikepoint-library-tile.png){width=300px} - -3. Click `Deploy`. - -4. Paste your TFL API keys into the `tfl_primary_key` and `tfl_secondary_key` input fields. - -5. Click `Deploy`. - -!!! success - - You've deployed the TFL API microservice and it is publishing data to Quix - -### Configure Slack - -You'll need to be an admin on Slack for this. - -#### Slack App - -Ensure you’re logged into the [Slack web portal](https://api.slack.com/messaging/webhooks){target=_blank} here then create a new app. - -1. Click `Create your Slack app`. - -2. On the popup, select `From Scratch`. - -3. Enter a name and choose your environment. - -4. Click `Create App`. - -#### Webhooks - -With the app created you'll now need to setup a webhook. This will give you a URL that you can use to publish messages to a Slack channel. - -1. On the left hand menu under `Features` select the `Incoming Webhooks` menu item. - -2. Switch the slider to `On` to activate incoming Webhooks. - -3. Click the `Add New Webhook to Workspace` button. - -4. Select the channel you want to give access to and thus publish messages to. - -5. Click allow. - -6. Copy the `Webhook URL` near the bottom of the page. Keep this safe you will need it soon. - -### Integration - -The time has come to actually connect Quix and Slack. Once again, with the help of the `Code Samples`, this is a simple task. - -1. Navigate to the `Code Samples`. - -2. Search for `Slack`. - -3. Click `Deploy`. - -4. Ensure that the `input` is set to `tfl-bikepoint-data`. - -5. Past your Webhook URL into the `webhook_url` input box. - -6. Click `Deploy`. - -!!! success - - You will see messages arriving in your chosen slack channel - - ![Data in Slack](slack-data.png) - -## Part two - -In this part of the tutorial you will replace the current Slack connector with a new connector to send customized alerts. It's based on the existing connector, so most of the work has already been done. - -!!! note - Begin by stopping the existing Slack connector from the home page. - -### Slack connector application - -Follow these steps to save the connector code to your environment. - -1. Navigate to the `Code Samples` and search for `Slack`. - -2. Click `Preview code` on the tile. - You can preview the code here and read the readme. You can't edit the code right now. - -3. Click `Edit code`. - -4. Ensure that the `input` field is set to `tfl-bikepoint-data` and past your Slack WebHook URL into the appropriate field. - -5. Click `Save as Application`. - The code is now saved to your environment and you can now edit the code and make any modifications you need. - -### Customize the message - -Now that you have the code saved and can edit it, you can customize the message that's sent to Slack. - -1. Ensure you are viewing the file called `quix_function.py`. - -2. Locate the function named `on_pandas_frame_handler`. - -3. Replace the code inside the function with the following code: - - ```python - # iterate the data frame - for i, row in df.iterrows(): - # get the number of bikes - num_bikes = row["NbBikes"] - # get the location - bike_loc = row["Name"] - # print a message - print("{} has {} bikes available".format(bike_loc, num_bikes)) - ``` - -4. You can now run the code here in the development environment by clicking the `Run` button near the top right of the code editor. - - !!! success - - You will see messages in the output window showing how many bikes there are at each location - - ![Output showing the number of bikes at each location](console-output.png){width=350px} - -5. Click the run button again to stop the code - -6. Now add the following code to the function named `on_pandas_frame_handler` (Add this to the code you already added in the steps above) - - ```python - message = "" - # check the number of remaining bikes - if num_bikes < 3: - message = "Hurry! {} only has {} bike left".format(bike_loc, num_bikes) - else: - message = "{} has {} bikes available".format(bike_loc, num_bikes) - - # compose and send your slack message - slack_message = {"text": message} - requests.post(self.webhook_url, json=slack_message) - ``` - -7. Repeat the process of running the code in the editor (see step 4 above) - - !!! success - - You now have customized messages in your Slack channel. - - ![Slack messages](slack-messages.png){width=350px} - - Don't forget to stop the service before proceeding - -### Deploy the service - -Now that you have verified that the code works it's time to deploy it as a microservice to the Quix serverless environment. - -Follow these steps: - -1. Tag the code. - - ???- info "This is how you tag the code" - - ![How to tag your code](tag.gif){width=350px} - -2. Click `Deploy` near the top right corner of the code editor window. - -3. On the Deploy dialog select the version tag you just created. - -4. Click `Deploy`. - - The service will be built, deployed and started. - -!!! success - - You modified an existing sample and deployed a microservice. - - You should start seeing Slack messages as soon as the service starts. - -!!! note "Clean up" - - You can delete the `Slack Notifications - Destination` you deployed in part one of this tutorial - -## What’s Next - -There are many ways you can use this code, try enhancing it so it only alerts you about each location once, or once every 5 minutes. - -Using Quix, coupled with something like Slack, enables for automatic real-time alerting. 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file mode 100644 index ff911f35..00000000 --- a/docs/tutorials/train-and-deploy-ml/import-data.md +++ /dev/null @@ -1,86 +0,0 @@ -# Import data into Jupyter Notebook - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -From a Jupyter Notebook, you retrieve the data that was generated in Quix in the [previous part](./create-data.md), and which was persisted into the Quix data store. - -## Run Jupyter Notebook - -Make sure you have reviewed the [prerequisites](./overview.md#prerequisites), and have Jupyter Notebook already installed. - -1. Now run Jupyter Notebook by entering the following command into your terminal: - -``` shell -jupyter notebook -``` - -2. Navigate to `http://localhost:8888` with your web browser. - -3. Select `New` and then `Python 3 (ipykernel)` from the menu, as shown here: - - ![Jupyter Notebook Python 3 selection](./images/jupyter-python3-selection.png) - -!!! tip - - If you don’t see **Python 3** in the `New` menu, run the following commands in your Python environment: - - ``` python - pip install ipykernel - python -m ipykernel install --user - ``` - -## Obtain the training data - -In the [previous part](./create-data.md) you generated some real-time data in Quix. - -Quix has a code generator that can generate code to connect your Jupyter Notebook to Quix. To use the code generator to retrieve data from Quix and import it into your Notebook: - -1. Make sure you are logged into Quix. - -2. Select your Workspace (you probably only have one currently). - -3. Click `Data Explorer` in the left-hand sidebar. - -4. Click `Add Query` to add a query to visualize some data. - -5. Select the F1 Game stream in the `Add Query` wizard, and click `Next`. - -6. In the `Select parameters and events` step of the wizard, select the `Brake`, `WorldPositionX`, `Steer`, `Speed`, and `Gear` parameters. - -7. Click `Done`. - -8. Turn off aggregation using the slider button, as illustrated in the following screenshot: - - ![Turn aggregation slider button off](./images/aggregation-slider-off.png) - -9. Use the time slider to select about ten minutes of data, as shown in the following screenshot: - - ![Time slider](./images/time-slider.png) - - This is a precaution, as if your try to import too much data into Jupyter Notebook you may get a `IOPub data rate exceeded.` error. Alternatively, you can increase your capacity by setting the config variable `--NotebookApp.iopub_data_rate_limit` in Jupyter. - -10. Select the `Code` tab. - -11. Select `Python` from the the `LANGUAGE` dropdown. - - ![Generated code to retrieve data](./images/connect-python.png) - -12. Copy all the code in the `Code` tab to your clipboard. - -13. Paste the Python code from your clipboard to your Jupyter Notebook: - - ![Python code in Jupyter Notebook](./images/jupyter-python-code.png) - -14. Click `Run`. - - The code prints out the pandas data frame containing the retrieved data, as shown in the following screenshot: - - ![Results from data fetch](./images/jupyter-results.png) - -!!! tip - - If you want to use this generated code for more than 30 days, replace the temporary token with a [Personal Access Token](../../apis/portal-api/setup.md#personal-access-token-pat). - -[Train your ML model :material-arrow-right-circle:{ align=right }](./train-ml.md) \ No newline at end of file diff --git a/docs/tutorials/train-and-deploy-ml/overview.md b/docs/tutorials/train-and-deploy-ml/overview.md deleted file mode 100644 index 50b8970e..00000000 --- a/docs/tutorials/train-and-deploy-ml/overview.md +++ /dev/null @@ -1,45 +0,0 @@ -# Real-time Machine Learning (ML) pipelines - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this tutorial, you learn how to extract data from Quix to train your Machine Learning (ML) model in Jupyter Notebook. You then learn how to deploy this model to Quix, so ML can be used to process your data in real time. - -## Video - -If you'd like to watch a video before stepping through this tutorial, you can view the following video on the [Quix YouTube channel](https://www.youtube.com/@quix1570){target=_blank}: - - - -## Prerequisites - -To complete this tutorial you need the following: - -* A Quix Cloud account. - -??? info "Start for free" - [Book a session](https://quix.io/book-a-demo) with us to start for free. - - We will create a time-limited free account for you, and our experts will help you get started with your specific use case. - -* [Python3](https://www.python.org/downloads/){target=_blank} installed. -* [Jupyter Notebook](https://jupyter.org/){target=_blank} to train your model and load data for training. See [How to work with Jupyter Notebook](../../develop/integrate-data/jupyter-nb.md) for further information. - -There are some other libraries that need to be installed, but instructions on how to do this are given when required. - -## The parts of the tutorial - -This tutorial is divided up into several parts, to make the learning experience more manageable. The parts are summarized here: - -1. **Create your data** - You learn how to create some data to work with in the rest of the tutorial. - -2. **Import data** - You learn how Quix makes it easy to import your data into Jupyter Notebook, by providing you with ready-to-use code. - -3. **Train your ML model** - You learn how to train an ML model. For this tutorial, this is done in Jupyter, but could also be done in Quix. - -4. **Deploy your ML model** - You learn how to deploy your ML model to Quix. - -5. **Summary** - In this [concluding](conclusion.md) part you are presented with a summary of the work you have completed, and also some next steps for more advanced learning about Quix. - -[Get some data :material-arrow-right-circle:{ align=right }](./create-data.md) diff --git a/docs/tutorials/train-and-deploy-ml/train-ml.md b/docs/tutorials/train-and-deploy-ml/train-ml.md deleted file mode 100644 index ca43706b..00000000 --- a/docs/tutorials/train-and-deploy-ml/train-ml.md +++ /dev/null @@ -1,260 +0,0 @@ -# Train your ML model - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this part you learn how to train an ML model in Jupyter Notebook, using the data you imported in the [previous part](./import-data.md). - -You write code to train your model on this data. You save your model to a Pickle file, which you then deploy in the [next part](./deploy-ml.md) of this tutorial. - -![](./images/train.png) - -This is just one approach that you might use if you are already familiar with Jupyter Notebook. You might also train your ML model directly in Quix using live data, or data played back using the [replay feature](../../manage/replay.md). - -## Install the required libraries - -You now need to install some Python libraries on your system. This is the system where you are running Jupyter Notebook. If you do not have Jupyter Notebook installed, please refer to the [prerequisites](./overview.md#prerequisites) for this tutorial. - -The following libraries are required to create the ML model and visualize your data. To install the libraries enter the following commands into a terminal on the system where you are running Jupyter Notebook: - -``` shell -pip install seaborn -pip install scikit-learn -pip install mlflow -pip install matplotlib -``` - -!!! note - - If you get an 'Access Denied' error when installing `mlflow`, try adding `--user` to the install command, for example `python3 -m pip install mlflow --user`. Alternatively, run the installer from an Anaconda Powershell Prompt with `--user`. - -## Include required imports - -To run the code in this tutorial, you need to import the installed libraries. - -1. Add the following code to your Jupyter Notebook: - - ``` python - import math - import matplotlib.pyplot as plt - import mlflow - import numpy as np - import pandas as pd - import pickle - import seaborn as sns - - from sklearn import tree - from sklearn.model_selection import KFold - from sklearn.metrics import confusion_matrix, accuracy_score - from sklearn.tree import DecisionTreeClassifier - ``` - -2. Click `Run` and ensure there are no errors. If you receive any errors, make sure you have installed the required libraries. - -You have now installed all the required libraries and tested their presence using the import statements. - -## Preprocessing of parameters - -You now need to prepare data for training by applying some transformations to the retrieved data. In this case you convert the world X coordinates into continuous values. - -Run the following code in your Jupyter Notebook: - -``` python -## Convert motion to continuous values -df["Motion_WorldPositionX_sin"] = df["Motion_WorldPositionX"].map(lambda x: math.sin(x)) -df["Motion_WorldPositionX_cos"] = df["Motion_WorldPositionX"].map(lambda x: math.cos(x)) -``` - -## Convert the brake values to boolean - -Braking values are fully off `0`, or fully on `1`, or somewhere in between for partial braking. - -You can convert the braking value to a boolean value, using the Python `round()` function. Run the following code: - -``` python -## Conversion of label -df["Brake_bool"] = df["Brake"].map(lambda x: round(x)) -``` - -The lambda function applies rounding to all braking values in the data frame. - -## Generate advanced brake signal for training - -To train the model to predict braking 5 seconds ahead, you need to shift the braking values to 5 seconds ahead. Run the following code: - -``` python -## Offset dataset and trim it -NUM_PERIODS = -round(5e9/53852065.77281786) - -df["Brake_shifted_5s"] = df["Brake_bool"].shift(periods=NUM_PERIODS) -df = df.dropna(axis='rows') # clean out null values -``` - -To plot the resulting data, run the following code in your Jupyter Notebook: - -``` python -plt.figure(figsize=(15, 8)) -plt.plot(df["Brake_shifted_5s"]) -plt.plot(df["Brake_bool"]) -plt.legend(['Shifted', 'Unshifted']) -``` - -The resulting plot is as follows: - -![](./images/brake-shifted.png) - -You can see from the plot that the blue data (shifted) has been shifted 5 seconds ahead of the orange data (unshifted). - -## Fit, predict, and score a model - -You now calculate class weighting to gain accuracy by performing class balancing: - -``` python -Y = df["Brake_shifted_5s"] - -cw = {} -for val in set(Y): - cw[val] = np.sum(Y != val) - -print(cw) -``` - -This prints a value such as: - -``` -{0.0: 2859, 1.0: 19944} -``` - -## Training the model - -In the following code snippet you run an experiment using **MLflow**. Notice in last three lines that each experiment is logging **MLflow** metrics for comparison purposes. Run the following code in your Jupyter Notebook: - -``` python -model_accuracy = pd.DataFrame(columns=[ - 'Baseline Training Accuracy', - 'Model Training Accuracy', - 'Baseline Testing Accuracy', - 'Model Testing Accuracy', -]) - -kfold = KFold(5, shuffle=True, random_state=1) - -with mlflow.start_run(): - class_weight = None - max_depth = 5 - features = ["Motion_WorldPositionX_cos", "Motion_WorldPositionX_sin", "Steer", "Speed", "Gear"] - - mlflow.log_param("class_weight", class_weight) - mlflow.log_param("max_depth", max_depth) - mlflow.log_param("features", features) - mlflow.log_param("model_type", "DecisionTreeClassifier") - - X = df[features] - decision_tree = DecisionTreeClassifier(class_weight=class_weight, max_depth=max_depth) - - for train, test in kfold.split(X): - X_train = X.iloc[train] - Y_train = Y.iloc[train] - X_test = X.iloc[test] - Y_test = Y.iloc[test] - - # Train model - decision_tree.fit(X_train, Y_train) - Y_pred = decision_tree.predict(X_test) - - # Assess accuracy - train_accuracy = round(decision_tree.score(X_train, Y_train) * 100, 2) - test_accuracy = round(decision_tree.score(X_test, Y_test) * 100, 2) - - Y_baseline_zeros = np.zeros(Y_train.shape) - baseline_train_accuracy = round(accuracy_score(Y_train, Y_baseline_zeros) * 100, 2) - Y_baseline_zeros = np.zeros(Y_test.shape) - baseline_test_accuracy = round(accuracy_score(Y_test, Y_baseline_zeros) * 100, 2) - - model_accuracy_i = pd.DataFrame({ - "Baseline Training Accuracy": [baseline_train_accuracy], - "Model Training Accuracy": [train_accuracy], - "Baseline Testing Accuracy": [baseline_test_accuracy], - "Model Testing Accuracy": [test_accuracy]}) - model_accuracy = pd.concat([model_accuracy, model_accuracy_i]).reset_index(drop=True) - - mlflow.log_metric("train_accuracy", model_accuracy["Model Training Accuracy"].mean()) - mlflow.log_metric("test_accuracy", model_accuracy["Model Testing Accuracy"].mean()) - mlflow.log_metric("fit_quality", 1/abs(model_accuracy["Model Training Accuracy"].mean() - model_accuracy["Model Testing Accuracy"].mean())) -``` - -You can review the model accuracy of the experiment by typing the following Python into your Jupyter Notebook: - -``` python -model_accuracy -``` - -This displays data on the model accuracy: - -| Depth | Baseline Training Accuracy | Model Training Accuracy | Baseline Testing Accuracy | Model Testing Accuracy | -| ----- | -------------------------- | ----------------------- | ------------------------- | ---------------------- | -| 0 | 88.97 | 97.93 | 86.49 | 86.49 | -| 1 | 87.59 | 97.24 | 91.89 | 83.78 | -| 2 | 89.04 | 96.58 | 86.11 | 88.89 | -| 3 | 88.36 | 97.95 | 88.89 | 83.33 | -| 4 | 88.36 | 97.95 | 88.89 | 80.56 | - -## Prediction preview - -You can plot the actual braking against the predicted braking using the trained model with the following code: - -``` python -f, (ax1, ax2) = plt.subplots(2, 1, sharey=True, figsize=(50,8)) -ax1.plot(Y) -ax1.plot(X["Speed"]/X["Speed"].max()) - -ax2.plot(decision_tree.predict(X)) -ax2.plot(X["Speed"]/X["Speed"].max()) -``` - -Running this code in your Jupyter Notebook results in the following plot: - -![Braking prediction plot](./images/braking-prediction.png) - -The blue line is the predicted braking, the orange is the actual speed of the car. You can see the speed of the car slows, in correlation to the predicted braking. - -## Saving the model - -When you are confident with the results, save the model into a Pickle file: - -``` python -pickle.dump(decision_tree, open('./decision_tree_5_depth.sav', 'wb')) -``` - -!!! tip - - The Pickle file will be located in folder where Jupyter Notebook command was invoked. - -## MLflow - -To help you with managing your experiments, you can review them in MLflow: - -!!! warning - - MLflow works only on MacOS, Linux, or Windows Linux Subsystem (WSL). - -!!! tip - - To have some meaningful data, run the experiment with three different values for the `max_depth` parameter. - -In a terminal window, run the following command to launch the MLflow server: - -``` python -mlflow ui -``` - -In the UI you can select the experiments to compare: - -![Select experiment to compare](./images/select-experiments.png) - -You can also plot metrics from experiments: - -![Experiments compared](./images/compare-experiments.png) - -[Deploy your Machine Learning (ML) model :material-arrow-right-circle:{ align=right }](deploy-ml.md) diff --git a/mkdocs.yml b/mkdocs.yml index 11f994d8..5b1e2ed6 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -628,58 +628,6 @@ nav: # - '8. Printers dashboard': 'tutorials/predictive-maintenance/printers-dashboard.md' # - '8. Lab: Add phone alerts': 'tutorials/predictive-maintenance/phone-alerts.md' - '8. Summary': 'tutorials/predictive-maintenance/summary.md' - # - 'Clickstream analysis': - # - 'Overview': 'tutorials/clickstream/overview.md' - # - '1. Get the project': 'tutorials/clickstream/get-project.md' - # - '2. Clickstream producer': 'tutorials/clickstream/clickstream-producer.md' - # - '3. Data ingestion': 'tutorials/clickstream/data-ingestion.md' - # - '4. Data enrichment': 'tutorials/clickstream/data-enrichment.md' - # - '5. Data aggregation': 'tutorials/clickstream/data-aggregation.md' - # - '6. Event detection': 'tutorials/clickstream/event-detection.md' - # - '7. Webshop frontend': 'tutorials/clickstream/webshop-frontend.md' - # - '8. Real-time dashboard': 'tutorials/clickstream/realtime-dashboard.md' - # - '9. Lab: change offer': 'tutorials/clickstream/change-offer.md' - # - '10. Summary': 'tutorials/clickstream/summary.md' - # - 'Computer vision': - # - 'Overview': 'tutorials/computer-vision/overview.md' - # - '1. Get the project': tutorials/computer-vision/get-project.md - # - '2. TfL camera feed': tutorials/computer-vision/tfl-camera-feed.md - # - '3. Frame grabber': tutorials/computer-vision/tfl-frame-grabber.md - # - '4. Object detection': tutorials/computer-vision/object-detection.md - # - '5. Web UI': tutorials/computer-vision/web-ui.md - # - '6. Other services': tutorials/computer-vision/other-services.md - # - '7. Add service': tutorials/computer-vision/add-service.md - # - '8. Summary': tutorials/computer-vision/summary.md - # - 'Chat sentiment analysis': - # - 'Overview': 'tutorials/sentiment-analysis/overview.md' - # - '1. Get the project': tutorials/sentiment-analysis/get-project.md - # - '2. Try the UI': 'tutorials/sentiment-analysis/try-the-ui.md' - # - '3. UI service': 'tutorials/sentiment-analysis/ui-service.md' - # - '4. Sentiment analysis service': 'tutorials/sentiment-analysis/sentiment-analysis-service.md' - # - '5. Twitch service': 'tutorials/sentiment-analysis/twitch-service.md' - # - '6. Customize the UI': 'tutorials/sentiment-analysis/customize-the-ui.md' - # - '7. Summary': 'tutorials/sentiment-analysis/summary.md' - # - 'Real-time ML predictions': - # - 'Overview': 'tutorials/data-science/overview.md' - # - '1. Bicycle data': 'tutorials/data-science/1-bikedata.md' - # - '2. Weather data': 'tutorials/data-science/2-weatherdata.md' - # - '3. Data views': 'tutorials/data-science/3-data.md' - # - '4. Get training data': 'tutorials/data-science/4-train.md' - # - '5. Run the model': 'tutorials/data-science/5-run.md' - # - '6. Conclusion': 'tutorials/data-science/6-conclusion.md' - # - 'Real-time ML pipelines': - # - 'Overview': 'tutorials/train-and-deploy-ml/overview.md' - # - 'Create your data': 'tutorials/train-and-deploy-ml/create-data.md' - # - 'Import data into Jupyter': 'tutorials/train-and-deploy-ml/import-data.md' - # - 'Train your ML model': 'tutorials/train-and-deploy-ml/train-ml.md' - # - 'Deploy your ML model': 'tutorials/train-and-deploy-ml/deploy-ml.md' - # - 'Conclusion': 'tutorials/train-and-deploy-ml/conclusion.md' - # - 'Currency Alerting': 'tutorials/currency-alerting/currency-alerting.md' - #- 'MATLAB and Simulink': 'tutorials/matlab/matlab-and-simulink.md' - # These tutorials are commented out due to lack of updates / testing - warning has been added to overview - #- 'RSS Processing': 'tutorials/rss-tutorial/rss-processing-pipeline.md' - #- 'Data Stream Processing': 'tutorials/data-stream-processing/data-stream-processing.md' - #- 'Slack Alerting': 'tutorials/slack-alerting/slack-alerting.md' # Pulling this one as apparently the app is no longer available in Google Store #- 'Real-time event detection': # - 'Overview': 'tutorials/event-detection/overview.md' @@ -725,31 +673,83 @@ plugins: 'platform/glossary.md': 'kb/glossary.md' 'platform/quixtour/overview.md': 'quix-cloud/quixtour/overview.md' 'platform/tutorials/index.md': 'tutorials/overview.md' - 'platform/tutorials/computer-vision/index.md': 'tutorials/computer-vision/overview.md' - 'platform/tutorials/computer-vision/get-project.md': 'tutorials/computer-vision/get-project.md' - 'platform/tutorials/computer-vision/tfl-camera-feed.md': 'tutorials/computer-vision/tfl-camera-feed.md' - 'platform/tutorials/computer-vision/tfl-frame-grabber.md': 'tutorials/computer-vision/tfl-frame-grabber.md' - 'platform/tutorials/computer-vision/object-detection.md': 'tutorials/computer-vision/object-detection.md' - 'platform/tutorials/computer-vision/web-ui.md': 'tutorials/computer-vision/web-ui.md' - 'platform/tutorials/computer-vision/other-services.md': 'tutorials/computer-vision/other-services.md' - 'platform/tutorials/computer-vision/add-service.md': 'tutorials/computer-vision/add-service.md' - 'platform/tutorials/computer-vision/summary.md': 'tutorials/computer-vision/summary.md' - 'platform/tutorials/currency-alerting/currency-alerting.md': 'tutorials/currency-alerting/currency-alerting.md' - 'platform/tutorials/data-science/index.md': 'tutorials/data-science/overview.md' + # Outdated tutorial redirects - All removed tutorials point to landing page + # Computer Vision tutorial (all pages) + 'platform/tutorials/computer-vision/index.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/computer-vision/get-project.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/computer-vision/tfl-camera-feed.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/computer-vision/tfl-frame-grabber.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/computer-vision/object-detection.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/computer-vision/web-ui.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/computer-vision/other-services.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/computer-vision/add-service.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/computer-vision/summary.md': 'tutorials/outdated-tutorial.md' + 'tutorials/computer-vision/overview.md': 'tutorials/outdated-tutorial.md' + 'tutorials/computer-vision/get-project.md': 'tutorials/outdated-tutorial.md' + 'tutorials/computer-vision/tfl-camera-feed.md': 'tutorials/outdated-tutorial.md' + 'tutorials/computer-vision/tfl-frame-grabber.md': 'tutorials/outdated-tutorial.md' + 'tutorials/computer-vision/object-detection.md': 'tutorials/outdated-tutorial.md' + 'tutorials/computer-vision/web-ui.md': 'tutorials/outdated-tutorial.md' + 'tutorials/computer-vision/other-services.md': 'tutorials/outdated-tutorial.md' + 'tutorials/computer-vision/add-service.md': 'tutorials/outdated-tutorial.md' + 'tutorials/computer-vision/summary.md': 'tutorials/outdated-tutorial.md' + # Data Science tutorial (all pages) + 'platform/tutorials/data-science/index.md': 'tutorials/outdated-tutorial.md' + 'tutorials/data-science/overview.md': 'tutorials/outdated-tutorial.md' + 'tutorials/data-science/1-bikedata.md': 'tutorials/outdated-tutorial.md' + 'tutorials/data-science/2-weatherdata.md': 'tutorials/outdated-tutorial.md' + 'tutorials/data-science/3-data.md': 'tutorials/outdated-tutorial.md' + 'tutorials/data-science/4-train.md': 'tutorials/outdated-tutorial.md' + 'tutorials/data-science/5-run.md': 'tutorials/outdated-tutorial.md' + 'tutorials/data-science/6-conclusion.md': 'tutorials/outdated-tutorial.md' 'platform/tutorials/data-stream-processing/data-stream-processing.md': 'tutorials/data-stream-processing/data-stream-processing.md' 'platform/tutorials/event-detection/index.md': 'tutorials/event-detection/overview.md' - 'platform/tutorials/matlab/matlab-and-simulink.md': 'tutorials/matlab/matlab-and-simulink.md' - 'platform/tutorials/nocode-sentiment/nocode-sentiment-analysis.md': 'tutorials/sentiment-analysis/overview.md' - 'platform/tutorials/rss-tutorial/rss-processing-pipeline.md': 'tutorials/rss-tutorial/rss-processing-pipeline.md' - 'platform/tutorials/sentiment-analysis/index.md': 'tutorials/sentiment-analysis/overview.md' - 'platform/tutorials/sentiment-analysis/get-project.md': 'tutorials/sentiment-analysis/get-project.md' - 'platform/tutorials/sentiment-analysis/try-the-ui.md': 'tutorials/sentiment-analysis/try-the-ui.md' - 'platform/tutorials/sentiment-analysis/sentiment-analysis-service.md': 'tutorials/sentiment-analysis/sentiment-analysis-service.md' - 'platform/tutorials/sentiment-analysis/twitch-service.md': 'tutorials/sentiment-analysis/twitch-service.md' - 'platform/tutorials/sentiment-analysis/customize-the-ui.md': 'tutorials/sentiment-analysis/customize-the-ui.md' - 'platform/tutorials/sentiment-analysis/summary.md': 'tutorials/sentiment-analysis/summary.md' - 'platform/tutorials/slack-alerting/slack-alerting.md': 'tutorials/slack-alerting/slack-alerting.md' - 'platform/tutorials/train-and-deploy-ml/index.md': 'tutorials/train-and-deploy-ml/overview.md' + # Sentiment Analysis tutorial (all pages) + 'platform/tutorials/sentiment-analysis/index.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/sentiment-analysis/get-project.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/sentiment-analysis/try-the-ui.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/sentiment-analysis/sentiment-analysis-service.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/sentiment-analysis/twitch-service.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/sentiment-analysis/customize-the-ui.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/sentiment-analysis/summary.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/nocode-sentiment/nocode-sentiment-analysis.md': 'tutorials/outdated-tutorial.md' + 'tutorials/sentiment-analysis/overview.md': 'tutorials/outdated-tutorial.md' + 'tutorials/sentiment-analysis/get-project.md': 'tutorials/outdated-tutorial.md' + 'tutorials/sentiment-analysis/try-the-ui.md': 'tutorials/outdated-tutorial.md' + 'tutorials/sentiment-analysis/sentiment-analysis-service.md': 'tutorials/outdated-tutorial.md' + 'tutorials/sentiment-analysis/twitch-service.md': 'tutorials/outdated-tutorial.md' + 'tutorials/sentiment-analysis/customize-the-ui.md': 'tutorials/outdated-tutorial.md' + 'tutorials/sentiment-analysis/summary.md': 'tutorials/outdated-tutorial.md' + # Train and Deploy ML tutorial (all pages) + 'platform/tutorials/train-and-deploy-ml/index.md': 'tutorials/outdated-tutorial.md' + 'tutorials/train-and-deploy-ml/overview.md': 'tutorials/outdated-tutorial.md' + 'tutorials/train-and-deploy-ml/create-data.md': 'tutorials/outdated-tutorial.md' + 'tutorials/train-and-deploy-ml/import-data.md': 'tutorials/outdated-tutorial.md' + 'tutorials/train-and-deploy-ml/train-ml.md': 'tutorials/outdated-tutorial.md' + 'tutorials/train-and-deploy-ml/deploy-ml.md': 'tutorials/outdated-tutorial.md' + 'tutorials/train-and-deploy-ml/conclusion.md': 'tutorials/outdated-tutorial.md' + # Clickstream tutorial (all pages) + 'tutorials/clickstream/overview.md': 'tutorials/outdated-tutorial.md' + 'tutorials/clickstream/get-project.md': 'tutorials/outdated-tutorial.md' + 'tutorials/clickstream/clickstream-producer.md': 'tutorials/outdated-tutorial.md' + 'tutorials/clickstream/data-ingestion.md': 'tutorials/outdated-tutorial.md' + 'tutorials/clickstream/data-enrichment.md': 'tutorials/outdated-tutorial.md' + 'tutorials/clickstream/data-aggregation.md': 'tutorials/outdated-tutorial.md' + 'tutorials/clickstream/event-detection.md': 'tutorials/outdated-tutorial.md' + 'tutorials/clickstream/webshop-frontend.md': 'tutorials/outdated-tutorial.md' + 'tutorials/clickstream/realtime-dashboard.md': 'tutorials/outdated-tutorial.md' + 'tutorials/clickstream/change-offer.md': 'tutorials/outdated-tutorial.md' + 'tutorials/clickstream/summary.md': 'tutorials/outdated-tutorial.md' + # Other outdated tutorials (single pages) + 'platform/tutorials/currency-alerting/currency-alerting.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/matlab/matlab-and-simulink.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/rss-tutorial/rss-processing-pipeline.md': 'tutorials/outdated-tutorial.md' + 'platform/tutorials/slack-alerting/slack-alerting.md': 'tutorials/outdated-tutorial.md' + 'tutorials/currency-alerting/currency-alerting.md': 'tutorials/outdated-tutorial.md' + 'tutorials/matlab/matlab-and-simulink.md': 'tutorials/outdated-tutorial.md' + 'tutorials/rss-tutorial/rss-processing-pipeline.md': 'tutorials/outdated-tutorial.md' + 'tutorials/slack-alerting/slack-alerting.md': 'tutorials/outdated-tutorial.md' + # Outdated tutorial redirects END 'apis/streaming-reader-api/index.md': 'apis/streaming-reader-api/overview.md' 'apis/portal-api/index.md': 'apis/portal-api/overview.md' 'platform/connectors/index.md': 'connectors/index.md' From 7700491b862c1ecceccee087350fbc25c3efa7d1 Mon Sep 17 00:00:00 2001 From: Peter Nagy Date: Thu, 7 Aug 2025 17:18:31 +0100 Subject: [PATCH 05/10] Remove Data explore related documentation --- docs/blog/posts/stream-processing-glossary.md | 3 +- docs/develop/integrate-data/jupyter-nb.md | 70 ----- docs/develop/process/types-of-processing.md | 2 +- .../integrations/influxdb-data-explorer.png | Bin 162681 -> 0 bytes .../integrations/telegraf-data-explorer.png | Bin 330326 -> 0 bytes docs/images/manage/data-explorer.png | Bin 194802 -> 0 bytes .../databases/influxdb/quickstart.md | 4 +- docs/kb/what-is-quix.md | 34 +-- docs/manage/MLOps.md | 2 +- docs/manage/overview.md | 19 -- docs/quix-cloud/overview.md | 4 +- docs/quix-cloud/why-quix-cloud.md | 4 +- .../data-stream-processing/CarDemoGif.gif | Bin 1667663 -> 0 bytes .../data-stream-processing/architecture.png | Bin 173010 -> 0 bytes .../data-stream-processing.md | 272 ----------------- .../data-stream-processing/library-items.png | Bin 35986 -> 0 bytes .../data-stream-processing/phone.jpg | Bin 85182 -> 0 bytes .../data-stream-processing/track.png | Bin 241262 -> 0 bytes docs/tutorials/event-detection/conclusion.md | 21 -- .../event-detection/crash-detection-ui.md | 58 ---- .../event-detection/crash-detection.md | 259 ---------------- .../event-detection/data-acquisition.md | 279 ------------------ .../images/crash-detection-ui-tile.png | Bin 15708 -> 0 bytes .../images/qr-setting-share-ui.png | Bin 82773 -> 0 bytes .../images/qr-settings-share-service.png | Bin 13163 -> 0 bytes .../event-detection/images/token-menu.png | Bin 7568 -> 0 bytes docs/tutorials/event-detection/images/ui.png | Bin 415277 -> 0 bytes docs/tutorials/event-detection/overview.md | 33 --- mkdocs.yml | 12 +- 29 files changed, 21 insertions(+), 1055 deletions(-) delete mode 100644 docs/develop/integrate-data/jupyter-nb.md delete mode 100644 docs/images/integrations/influxdb-data-explorer.png delete mode 100644 docs/images/integrations/telegraf-data-explorer.png delete mode 100644 docs/images/manage/data-explorer.png delete mode 100644 docs/tutorials/data-stream-processing/CarDemoGif.gif delete mode 100644 docs/tutorials/data-stream-processing/architecture.png delete mode 100644 docs/tutorials/data-stream-processing/data-stream-processing.md delete mode 100644 docs/tutorials/data-stream-processing/library-items.png delete mode 100644 docs/tutorials/data-stream-processing/phone.jpg delete mode 100644 docs/tutorials/data-stream-processing/track.png delete mode 100644 docs/tutorials/event-detection/conclusion.md delete mode 100644 docs/tutorials/event-detection/crash-detection-ui.md delete mode 100644 docs/tutorials/event-detection/crash-detection.md delete mode 100644 docs/tutorials/event-detection/data-acquisition.md delete mode 100644 docs/tutorials/event-detection/images/crash-detection-ui-tile.png delete mode 100644 docs/tutorials/event-detection/images/qr-setting-share-ui.png delete mode 100644 docs/tutorials/event-detection/images/qr-settings-share-service.png delete mode 100644 docs/tutorials/event-detection/images/token-menu.png delete mode 100644 docs/tutorials/event-detection/images/ui.png delete mode 100644 docs/tutorials/event-detection/overview.md diff --git a/docs/blog/posts/stream-processing-glossary.md b/docs/blog/posts/stream-processing-glossary.md index 09734b4b..61fc97c6 100644 --- a/docs/blog/posts/stream-processing-glossary.md +++ b/docs/blog/posts/stream-processing-glossary.md @@ -387,8 +387,7 @@ allows a system to hold larger amounts of historical data than a stream alone. Persistent data can also be useful in a recovery from a catastrophic event or for correcting historical inaccuracies caused by bugs. -Quix offers persistence at the topic level. If you persist a topic, you'll be -able to explore data streamed to it via the data explorer. +Quix offers data monitoring at the topic level, enabling you to view data streamed to topics in real time. ### Pipeline diff --git a/docs/develop/integrate-data/jupyter-nb.md b/docs/develop/integrate-data/jupyter-nb.md deleted file mode 100644 index e004a3de..00000000 --- a/docs/develop/integrate-data/jupyter-nb.md +++ /dev/null @@ -1,70 +0,0 @@ -# Exporting Quix data to Jupyter Notebook - -In this documentation, you learn how to use Jupyter Notebook to analyze data from Quix. - -!!! danger "Legacy features" - - Some of the features on this page are not available to new users, including those related to the Quix Data Explorer and the topic Persistence feature. However, legacy users may still have access to these facilities. - -## Why this is important - -Although Quix is a real-time platform, to build real-time in-memory models and data processing pipelines, you need to understand data first. Quix enables you to analyze data for discovery and analysis. - -## Prerequisites - -You'll need some data stored in Quix. You can use any of the Quix [data sources](../../connectors/index.md) available in the Quix Code Samples. - -You can also follow the onboarding process after you login for the first time. The onboarding process helps you create your first `source`. - -??? info "Start for free" - [Book a session](https://quix.io/book-a-demo) with us to start for free. - - We will create a time-limited free account for you, and our experts will help you get started with your specific use case. - -You also need Python 3 environment set up in your local environment. - -Install Jupyter Notebook as directed [here](https://docs.jupyter.org/en/latest/install/notebook-classic.html){target=_blank}. - -### Create a new notebook file - -You can now run Jupyter from the Windows start menu, or with the following command in an Anaconda Powershell Prompt, or the equivalent for your operating system: - -``` shell -jupyter notebook -``` - -Then create a new Python 3 notebook: - -![Jupyter new file](../../images/jupyter-wb/new-file.png) - -## Connecting Jupyter Notebook to persisted data - -The Quix web application has a Python code generator to help you connect your Jupyter notebook with Quix. - -You need to be logged into Quix for this. To import persisted data: - -1. Select an environment. - -2. In the main left-hand navigation, click `Data explorer`. - -3. Add a query to visualize some data. Select parameters, events, aggregation and time range. - -4. Select the **Code** tab. - -5. Ensure **Python** is the selected language: - - ![Jupyter connect Python](../../images/jupyter-wb/connect-python.png) - -6. Copy the Python code to your Jupyter notebook and click `Run`: - - ![Jupyter results](../../images/jupyter-wb/jupyter-results.png) - -!!! tip - - If you want to use this generated code for a long time, replace the temporary token with a [Personal Access Token](../../apis/portal-api/setup.md#personal-access-token-pat). - -## Too much data - -If you find that the query results in more data than can be handled by Jupyter Notebook, then try using the aggregation feature to reduce the amount of data returned. - -For more info on aggregation you can watch this [short video](https://youtu.be/fnEPnIunyxA){target=_blank}. diff --git a/docs/develop/process/types-of-processing.md b/docs/develop/process/types-of-processing.md index aabf1165..7ca6c3f6 100644 --- a/docs/develop/process/types-of-processing.md +++ b/docs/develop/process/types-of-processing.md @@ -3,7 +3,7 @@ Types of processing: * Transformation - transform data from raw data to clean structured data tables (we expect this to happen in Quix, clean data before persisting it). -* Exploratory data analysis (EDA) - using Quix data explorer and external tools like Jupyter Notebooks to understand data and find insights (generally a batch operation using persisted data in Quix and getting it to Jupyter) +* Exploratory data analysis (EDA) - using external tools like Jupyter Notebooks to understand data and find insights * Feature engineering - using external tools like Jupyter Notebooks to derive new data columns by making calculations from actual data columns eg calculate distance from time and speed. 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zu|4wNpn_sI+a5X@*~LiZu?f9>nx2~4_>AtU1CU%hUIN{mMfxipMD5Wuk$V1Py2@s; zD;bMV_40Kk_*f_3=@rIQgtbJAJ}%3&R*%vx{1Mp39FuN+V;Zn2<?{Fi2U;m6g1J* zo8PnNBYAVm5KE<96@eyEpmH z#RpFErYOAHw6BBJH6a_&#^lO{oyDENcs^1!TY*OIRkD0J%!OEF3ErGebm)+n-*QO7 zA|K*lkp81Z`}@8OAvT(}u(IEi%}AbOBdFz8xmR{|s6z6U2Xe5CK;rT|Ja^-JGWYv$ zFY?WHj%_#aEbS~(GUmf5JkAZ5!FMN5%ly*p{rv>8QqPt{M~Ep*rMH?82g@*US6B`s z|7@Tq;T0tj+55xZxhC7es6=L@zEx>5rJaR*(z%11T%Tk)B5O{hHst*`Yi=>XYxcR` zc8H8)?i1GKSq|IX(d)r|Clhcx+aa%CyTtSr3fyB;%$xW$D;*?%4spZs&NI8rp5Xj* gU2oZroKoEIDd0is_4|xP(SV;jHz7BQR80N<55w<(RsaA1 diff --git a/docs/integrations/databases/influxdb/quickstart.md b/docs/integrations/databases/influxdb/quickstart.md index 625cad78..198cb41d 100644 --- a/docs/integrations/databases/influxdb/quickstart.md +++ b/docs/integrations/databases/influxdb/quickstart.md @@ -200,13 +200,13 @@ You can now explore data queried from InfluxDB and published to the Quix topic ` 3. You can now see the live messages. Click on a message to display it. -Data is being queried from InfluxDB by the Quix connector, and then published to the Quix topic, `influxdb`. The Quix data explorer is then used to display this data in real time. +Data is being queried from InfluxDB by the Quix connector, and then published to the Quix topic, `influxdb`. The Quix live data monitoring is then used to display this data in real time. ## Summary In this quickstart you have learned how to publish data to InfluxDB, and also how to read data from InfluxDB. You also learned the basics of adding a simple transform to your stream processing pipeline. -You also learned how to view the messages in a Quix topic in real time, using the Quix data explorer. +You also learned how to view the messages in a Quix topic in real time, using the Quix live data monitoring. ## Next steps diff --git a/docs/kb/what-is-quix.md b/docs/kb/what-is-quix.md index 5bcdb46f..d6be9bc3 100644 --- a/docs/kb/what-is-quix.md +++ b/docs/kb/what-is-quix.md @@ -93,7 +93,7 @@ To achieve these goals, the Quix UI includes the following features: * **Pipeline view**: Visualize your pipeline architecture with the information provided from the deployment variables. You can also monitor your pipeline in real time. -* **Data Explorer**: Explore live and historical data of your applications to test that your code is working as expected. +* **Live data monitoring**: Monitor your application data in real time to test that your code is working as expected. * **Command-line Interface (CLI)**: Powerful command-line tool for managing all aspects of your Python stream processing pipelines, including permissions. @@ -121,7 +121,7 @@ Read more about [selecting your broker option](../create/create-environment.md#b ### Docker integration -When you create an application from a template in Quix, a Dockerfile is provided for you in the `build` directory of the application. This uses a base image, and additions only need to be made in the case of special requirements, such as to include a library that is not part of the base image. The Dockerfile, combined with your code and configuration, is used to drive the Quix build service, which ensures the generated Docker image is registered with the Quix Docker registry. This image is then submitted to the Quix serverless engine, as part of the deployment process. All this is largely transparent to the developer - you simply click `Deploy` to build and deploy your application, and then monitor the deployment using the tools provided, such as the Data Explorer, logs, CPU monitor, and memory monitor. +When you create an application from a template in Quix, a Dockerfile is provided for you in the `build` directory of the application. This uses a base image, and additions only need to be made in the case of special requirements, such as to include a library that is not part of the base image. The Dockerfile, combined with your code and configuration, is used to drive the Quix build service, which ensures the generated Docker image is registered with the Quix Docker registry. This image is then submitted to the Quix serverless engine, as part of the deployment process. All this is largely transparent to the developer - you simply click `Deploy` to build and deploy your application, and then monitor the deployment using the tools provided, such as live data monitoring, logs, CPU monitor, and memory monitor. ### Infrastructure as code @@ -191,40 +191,12 @@ In Quix you create a project to contain your Python stream processing applicatio Quix provides a suite of tools to enable you to monitor and manage your data. These include: -* Data Explorer - The Data Explorer enables you to view your data graphically in real time. Graph, table and messages views. +* Live data monitoring - View your data in real time through topic-based data streams. * Logs - Real-time logging information is displayed in a console tab. You also have the option of downloading your logs. * CPU monitor - you can monitor the CPU utilization of your deployment in real time. * Memory monitor - you can monitor the memory usage of the deployment in real time. * CLI - a powerful command line interface, see the [CLI documentation](../quix-cli/overview.md). -[See the Data Explorer in action](https://www.loom.com/share/0e3c24fb5f8c48038fe5cf02859b7ebc?sid=743fbdf7-fad5-4c26-831d-b6dad78b9b06). - -??? "Transcript" - - 0:01 Hello, welcome to the Quix quick start. In this short video I'll give you a few tips to help you get through the quick start. - - 0:11 First thing I'll show you is how to get your streaming token which you'll need for your command line program that you're going to build. - - 0:19 So to do that we click on settings then APIs and tokens and then streaming token. Then copy your streaming token to the clipboard then you can paste that into your.env file for your command line program to read. - - 0:47 Then once you've done that you can run the command line program. So obviously I have run this program before the first time you run this program you will see that the topic is automatically. - - 1:00 Created for you. So now the command line program is printing out CPU load on my computer. And publishing that data to the CPU load topic inside the quix platform. - - 1:16 So if we go back to platform. And click on topics. You can see these green bars represent inbound data. Here's the topic name CPU load. - - 1:37 If I click in the view live data. You can see the tool tip comes up. I'm taken to the data explorer where I can view live data. - - 1:49 I can select a topic in this case. I only have one topic. The stream. There's one string in this case. - - 1:58 And the parameter that I want to look at. Once I've done that, you can see that the the waveform view I see a graph of the CPU load against time. - - 2:11 And down here in the bottom right corner, I can see that these green bars represent inbound data. The other thing I can do here is. - - 2:21 I can look at the table view, which shows me the CPU load and table format in real time. Or I can look at the messages view where I can see individual messages such as stream metadata message or the actual inbound data. - - 2:41 If I click here, you can see the message JSON. Okay, that's it for this short video. Thanks very much for watching and see you in the next video. - ## Next steps * [Quickstart](../quix-cloud/quickstart.md) - get data into Quix and display it in less than 10 minutes diff --git a/docs/manage/MLOps.md b/docs/manage/MLOps.md index b8a50104..5207d768 100644 --- a/docs/manage/MLOps.md +++ b/docs/manage/MLOps.md @@ -27,7 +27,7 @@ In Quix you can: ## Test models on live data -In Quix you can connect models to live input topics to test them against live data sources. You can then review the results in the Data Explorer. +In Quix you can connect models to live input topics to test them against live data sources. You can then review the results in the live data monitoring. ## Build a production pipeline diff --git a/docs/manage/overview.md b/docs/manage/overview.md index 15d7456d..43318d6f 100644 --- a/docs/manage/overview.md +++ b/docs/manage/overview.md @@ -23,31 +23,12 @@ There is also a button to enable you to download the logs for offline viewing. There is also a tab with messages view. This is described in a later section. -## Data explorer - -!!! danger "Legacy feature" - - This feature is not available to new users. However, legacy users may still have access to this functionality. - -When your pipeline is running, and the applications are generating data on topics, you can use the Data Explorer to view data in real time. - -You can select the topic you want to view data on, and then the stream within that topic, as well as the specific parameters and events you are interested in. These can be displayed in waveform, table, or message view. The following screenshot illustrates the waveform view: - -![Data explorer](../images/manage/data-explorer.png) - -Table view enables you to view the parameter data in a tabular format, and messages view enables you to view the raw message data. - -!!! tip - - You can access similar functionality by clicking `Topics` in the left-hand sidebar, and then clicking on the topic whose messages you want to view. - ## Message viewer The message view shows the raw message format. The message viewer is available next to the Build logs, and logs tabs, as shown in the following screenshot: ![Message viewer](../images/manage/messages.png) -The data explorer also has a message viewer tab, as can be seen in the [data explorer section](#data-explorer). ## Portal API diff --git a/docs/quix-cloud/overview.md b/docs/quix-cloud/overview.md index 82f87202..3f6954c7 100644 --- a/docs/quix-cloud/overview.md +++ b/docs/quix-cloud/overview.md @@ -16,7 +16,7 @@ The components that make up the Quix Cloud enable developers to: - Increase project visibility and assign permissions to various projects and environments. 3. **Monitor and Observe in Real-Time**: - - Gain deep insights into pipeline performance with real-time logs, metrics, and data explorers. + - Gain deep insights into pipeline performance with real-time logs, metrics, and live data monitoring tools. - Monitor real-time data metrics, consumer lag, and other critical metrics. 4. **Scale and Manage Flexibly**: @@ -32,7 +32,7 @@ The components that make up the Quix Cloud enable developers to: - Leverage support for DevContainers for enhanced development workflows. 7. **Explore and Visualize Data**: - - Use data explorers to query and explore data using waveform and table views. + - Use live data monitoring tools to query and explore data using waveform and table views. - Visualize messages in data topics and track metrics with real-time data monitoring. 8. **Implement Robust CI/CD Processes**: diff --git a/docs/quix-cloud/why-quix-cloud.md b/docs/quix-cloud/why-quix-cloud.md index 266f46b9..b60b1245 100644 --- a/docs/quix-cloud/why-quix-cloud.md +++ b/docs/quix-cloud/why-quix-cloud.md @@ -9,7 +9,7 @@ description: Uncover the streamlined features of Quix Cloud, designed for easy d - **Streamlined Development and Deployment**: Simplifies the development and deployment of data pipelines with integrated online code editors, CI/CD tools, and YAML synchronization features. - **Enhanced Collaboration**: Enables efficient collaboration with multiple users, permission management, and project visibility. -- **Comprehensive Monitoring and Observability**: Provides real-time logs, metrics, and data explorers for deep insights into pipeline performance. +- **Comprehensive Monitoring and Observability**: Provides real-time logs, metrics, and live data monitoring tools for deep insights into pipeline performance. - **Scalability and Flexibility**: Easily scales resources and manages multiple environments, suitable for both small teams and large organizations. - **Security and Compliance**: Securely manages secrets and ensures compliance with dedicated infrastructure and SLA options. - **Fully Managed Infrastructure**: Isolated cloud infrastructure, managed Kafka or BYO broker, topics management, and YAML-based infrastructure as code. @@ -45,7 +45,7 @@ description: Uncover the streamlined features of Quix Cloud, designed for easy d - **Dev Containers Support**: (Coming soon) Support for 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z+Rge2{?}(Kw5q!3RsV`tx1p4ORdtFlW%>QQdcl4n)mC^h4f#Z!1fSL_7=t>l1?sOj ze!SWJ0aV9-i@C4JrHca&KW>kldeep&Vzw1im>5(Q+~nDl3;i3RXeI z&9s+PfoqvSa5>F_h;Yv^%pfV}?4MbWW{0W_(KZ3k48!cALO)LD6>{=_Eqx|t3ujyZX?G5_)J}}x|lLd#(W|Ru+k>=k?&FK&zr8_QaZJ1B@qZ$_T0tzq(Tgz+Hm}le%`05D9VwG z|5oC+0-5ela)d|$HS%AH@~wyL)%WxA7h+Z1` { - if (data.streamId.endsWith("-car-game-input-control")) { ... } - } - ``` - - Look for those lines in the `create-page.js` file and explore what they do. - - For more about WebSockets see [this page](../../apis/streaming-reader-api/overview.md). - -## Fun - -With the microservices for control and input processing deployed along with the UI we can move on to trying the game. - -1. Click the `Streaming Demo - UI` tile on the home page - -2. Click the `Public URL` in the `Details` panel on the left - - A new tab will open and load the games first page. - -3. Scan the QR code with your phone and follow the link - -4. Ensure your phone is rotated either left or right depending on your device - -5. Click the `START` button on your phones screen - - !!! note - You might need to allow notifications (this is just for the buzzer/vibrate to work) - - !!! note - Notice that the page on your computer has also changed to the racing track. - - You can now use your phone as a steering wheel to drive the car around the track. - - -6. DRIVE! 🏎️🏁 - -### Phone screen -![What you'll see on the phone](phone.jpg){width=400px} - -### Game screen - -![What you'll see on your computer](track.png){width=400px} - -## Getting help - -If you need any help, please sign up to the [Quix Community](https://quix.io/slack-invite){target=_blank}. \ No newline at end of file diff --git a/docs/tutorials/data-stream-processing/library-items.png b/docs/tutorials/data-stream-processing/library-items.png deleted file mode 100644 index 112d52e0bd1f96889656580fbe8f36a6927ed653..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 35986 zcmeFYXH=6-+b*o40)kYf7XhV6N2C{#-ld6169FYsLkJMM(hU%LFVY1BrG*YkuK_|2 zy@uXGG1MKr@B4Y5XTRT%Z>?{Ad;i$`S_@cnv96gpk2&X z5$M`Al9sEV>m80?t*%`Q<4{$Q(|0%9YJmLUF@(%boR76?v?^${YAEnKOeT7R=odv? ztE@c*n18+HMD3snZ6Nb@!jC`i@AmAvRV}KGh#*rQ%QnAB*yPSqz;0;xI;`HXJ zgYyjkhWBxr=S*VsFWonF0WUA~_i-x|JDvjBRvs#ncn1v;@gH06oI 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zvaLaV$N{j-VPi$O&tw+p(d%{lRN~0CB6@PV8`-Ex_V7lkO^7_ZrjPtS>eJ-W;mjp))|-iuy0!t*(;qjYxIw`U2MB!4YOJveF~$2AadiamX7 zVM6{y?23gh4W>r>fy6j6NeIlUV&)1yufzP$!Ta4bmnoiTTP(LeTQr)nOO>`veLGvzZIlMn%7NJY1#{ z%(t(q&rp^Zuc3sGm%zbCd;y&*l5wV4&YH!yMHsep;`#^Ci#F(p@6U3d%RaGf8uoMx zY72G=VsSQ8tnta0Ph({5xpcTuTjZS!?_m)-eSV9`?AefCN5t%%f>vGKFi(30!*f|+!3h3BMH6$vXZ1!6CpljFNDDUDk{|YxJo2UMtpSO Date: Thu, 7 Aug 2025 17:22:56 +0100 Subject: [PATCH 06/10] Update platform to cloud --- docs/apis/portal-api/http-requests.md | 6 +++--- docs/apis/portal-api/setup.md | 4 ++-- docs/apis/streaming-reader-api/reading-data.md | 4 ++-- docs/apis/streaming-reader-api/setup.md | 6 +++--- docs/apis/streaming-reader-api/subscriptions.md | 12 ++++++------ docs/manage/overview.md | 2 +- docs/manage/troubleshooting.md | 2 +- 7 files changed, 18 insertions(+), 18 deletions(-) diff --git a/docs/apis/portal-api/http-requests.md b/docs/apis/portal-api/http-requests.md index 8f0eea95..d768d167 100644 --- a/docs/apis/portal-api/http-requests.md +++ b/docs/apis/portal-api/http-requests.md @@ -29,13 +29,13 @@ Read the documentation on how to obtain your environment ID. The API endpoint URL has the following format: ``` -https://-.platform.quix.io/ +https://-.cloud.quix.io/ ``` So, for example, the endpoint URL for the portal API might resemble the following: ``` -https://reader-demo-project-env.platform.quix.io/ +https://reader-demo-project-env.cloud.quix.io/ ``` Note that the Portal API is account-wide and doesn't require an environment ID in the URL. @@ -81,5 +81,5 @@ You should structure most of your requests to the API around this pattern: curl -H "Authorization: bearer ${token}" \ -H "Content-Type: application/json" \ -d "@data.json" \ - https://${api-name}-${environment-id}.platform.quix.io/ + https://${api-name}-${environment-id}.cloud.quix.io/ ``` diff --git a/docs/apis/portal-api/setup.md b/docs/apis/portal-api/setup.md index a0e64cf2..f0e14e2b 100644 --- a/docs/apis/portal-api/setup.md +++ b/docs/apis/portal-api/setup.md @@ -47,7 +47,7 @@ In the `APIs and tokens` dialog, you can click `Personal Access Tokens` to gener When using APIs you'll need to obtain an ID based on a specific environment. For example, endpoints for environment-specific APIs use a domain with the following pattern: - https://-${environment-id}.platform.quix.io/ + https://-${environment-id}.cloud.quix.io/ The environment ID is a combination of your organization and environment names, converted to URL friendly values. @@ -102,7 +102,7 @@ The URLs for the API references are specific to your environment, so you can eas | API | API reference URL (Swagger documentation)| |---|---| | Streaming Reader | No HTTP/REST interface - SignalR (WebSockets or Long Polling)| -| Portal | https://portal-api.platform.quix.io/swagger | +| Portal | https://portal-api.cloud.quix.io/swagger | Replace `` with your environment ID. diff --git a/docs/apis/streaming-reader-api/reading-data.md b/docs/apis/streaming-reader-api/reading-data.md index 3dfe442b..3c0b453c 100644 --- a/docs/apis/streaming-reader-api/reading-data.md +++ b/docs/apis/streaming-reader-api/reading-data.md @@ -27,7 +27,7 @@ const options = { }; const connection = new signalR.HubConnectionBuilder() - .withUrl("https://reader-YOUR_ENVIRONMENT_ID.platform.quix.io/hub", options) + .withUrl("https://reader-YOUR_ENVIRONMENT_ID.cloud.quix.io/hub", options) .build(); // Establish connection (1) @@ -57,5 +57,5 @@ connection.start().then(() => { Note that SignalR will confirm its connection method, with logging such as the following: ``` shell -[2023-10-09T15:23:27.993Z] Information: WebSocket connected to wss://reader-joeengland-apitests-testing.platform.quix.io/hub?id=ABC9Ctg5zdQ7aAzXYX. +[2023-10-09T15:23:27.993Z] Information: WebSocket connected to wss://reader-joeengland-apitests-testing.cloud.quix.io/hub?id=ABC9Ctg5zdQ7aAzXYX. ``` diff --git a/docs/apis/streaming-reader-api/setup.md b/docs/apis/streaming-reader-api/setup.md index dbb5818e..d1b939ce 100644 --- a/docs/apis/streaming-reader-api/setup.md +++ b/docs/apis/streaming-reader-api/setup.md @@ -47,7 +47,7 @@ In the `APIs and tokens` dialog, you can click `Personal Access Tokens` to gener When using APIs you'll need to obtain an ID based on a specific environment. For example, endpoints for environment-specific APIs use a domain with the following pattern: - https://-${environment-id}.platform.quix.io/ + https://-${environment-id}.cloud.quix.io/ The environment ID is a combination of your organization and environment names, converted to URL friendly values. @@ -102,7 +102,7 @@ The URLs for the API references are specific to your environment, so you can eas | API | API reference URL (Swagger documentation)| |---|---| | Streaming Reader | No HTTP/REST interface - SignalR (WebSockets or Long Polling)| -| Portal | https://portal-api.platform.quix.io/swagger | +| Portal | https://portal-api.cloud.quix.io/swagger | Replace `` with your environment ID. @@ -163,7 +163,7 @@ You should also replace `YOUR_ENVIRONMENT_ID` with your environment ID. }; const connection = new signalR.HubConnectionBuilder() - .withUrl("https://reader-YOUR_ENVIRONMNENT_ID.platform.quix.io/hub", options) + .withUrl("https://reader-YOUR_ENVIRONMNENT_ID.cloud.quix.io/hub", options) .build(); connection.start().then(() => console.log("SignalR connected.")); diff --git a/docs/apis/streaming-reader-api/subscriptions.md b/docs/apis/streaming-reader-api/subscriptions.md index 92c6e3cc..e00db991 100644 --- a/docs/apis/streaming-reader-api/subscriptions.md +++ b/docs/apis/streaming-reader-api/subscriptions.md @@ -74,7 +74,7 @@ const options = { }; const connection = new signalR.HubConnectionBuilder() - .withUrl("https://reader-joeengland-apitests-testing.platform.quix.io/hub", options) + .withUrl("https://reader-joeengland-apitests-testing.cloud.quix.io/hub", options) .build(); // Establish connection @@ -138,7 +138,7 @@ const options = { }; const connection = new signalR.HubConnectionBuilder() - .withUrl("https://reader-joeengland-apitests-testing.platform.quix.io/hub", options) + .withUrl("https://reader-joeengland-apitests-testing.cloud.quix.io/hub", options) .build(); // Establish connection @@ -194,7 +194,7 @@ const options = { }; const connection = new signalR.HubConnectionBuilder() - .withUrl("https://reader-joeengland-apitests-testing.platform.quix.io/hub", options) + .withUrl("https://reader-joeengland-apitests-testing.cloud.quix.io/hub", options) .build(); // Establish connection @@ -214,7 +214,7 @@ connection.start().then(() => { Might produce the following output: ``` shell -[2023-10-09T15:23:27.993Z] Information: WebSocket connected to wss://reader-joeengland-apitests-testing.platform.quix.io/hub?id=o9Ctg5zdQ7aAzdQ2Cz4eMw. +[2023-10-09T15:23:27.993Z] Information: WebSocket connected to wss://reader-joeengland-apitests-testing.cloud.quix.io/hub?id=o9Ctg5zdQ7aAzdQ2Cz4eMw. Connected to Quix. stream -----> { streamId: '0a23798f-7d75-413d-9031-8d8386c2f8c7', @@ -347,7 +347,7 @@ const options = { }; const connection = new signalR.HubConnectionBuilder() - .withUrl("https://reader-joeengland-apitests-testing.platform.quix.io/hub", options) + .withUrl("https://reader-joeengland-apitests-testing.cloud.quix.io/hub", options) .build(); // Establish connection @@ -395,7 +395,7 @@ const options = { }; const connection = new signalR.HubConnectionBuilder() - .withUrl("https://reader-joeengland-apitests-testing.platform.quix.io/hub", options) + .withUrl("https://reader-joeengland-apitests-testing.cloud.quix.io/hub", options) .build(); // Establish connection diff --git a/docs/manage/overview.md b/docs/manage/overview.md index 43318d6f..69ada44e 100644 --- a/docs/manage/overview.md +++ b/docs/manage/overview.md @@ -41,4 +41,4 @@ The Portal API enables you to programmatically control Quix, and most objects in * Topics * Users -You can read more about the [Portal API](../apis/portal-api/overview.md) in the docs, or view the [Swagger reference](https://portal-api.platform.quix.io/swagger/index.html){target=_blank}. +You can read more about the [Portal API](../apis/portal-api/overview.md) in the docs, or view the [Swagger reference](https://portal-api.cloud.quix.io/swagger/index.html){target=_blank}. diff --git a/docs/manage/troubleshooting.md b/docs/manage/troubleshooting.md index ccfdea84..7a290f42 100644 --- a/docs/manage/troubleshooting.md +++ b/docs/manage/troubleshooting.md @@ -116,7 +116,7 @@ signalrcore.hub.errors.UnAuthorizedHubError Quix provided APIs require a valid bearer token, such as the Portal API: -- https://portal-api.platform.quix.io/swagger/index.html +- https://portal-api.cloud.quix.io/swagger/index.html ## Service keeps failing and restarting From ec3bb29096c1f5d2d2e8f77d77473c4120d8dedf Mon Sep 17 00:00:00 2001 From: Peter Nagy Date: Thu, 7 Aug 2025 17:27:16 +0100 Subject: [PATCH 07/10] Minor phrasing tweak --- docs/apis/portal-api/http-requests.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/apis/portal-api/http-requests.md b/docs/apis/portal-api/http-requests.md index d768d167..faf1b9e8 100644 --- a/docs/apis/portal-api/http-requests.md +++ b/docs/apis/portal-api/http-requests.md @@ -38,7 +38,7 @@ So, for example, the endpoint URL for the portal API might resemble the followin https://reader-demo-project-env.cloud.quix.io/ ``` -Note that the Portal API is account-wide and doesn't require an environment ID in the URL. +Note that the Portal API is not bound to environment and therefore doesn't require an environment ID in the URL. ## HTTP method From 296eed39b5016250c8c1cc3120c0e6e89d7f726d Mon Sep 17 00:00:00 2001 From: Peter Nagy Date: Thu, 7 Aug 2025 18:34:46 +0100 Subject: [PATCH 08/10] Fix some leftover links to removed tutorials --- docs/develop/process/timeseries-events.md | 2 +- docs/get-started/project-templates.md | 49 ------------------ .../chat-sentiment-pipeline.png | Bin 96807 -> 0 bytes .../clickstream-analysis-pipeline.png | Bin 90164 -> 0 bytes .../computer-vision-pipeline.png | Bin 65213 -> 0 bytes .../databases/influxdb/alerting.md | 2 +- docs/kb/why-stream-processing.md | 6 +-- docs/manage/MLOps.md | 3 +- docs/tutorials/influxdb-alerting/summary.md | 3 +- .../predictive-maintenance/summary.md | 5 +- mkdocs.yml | 3 +- 11 files changed, 9 insertions(+), 64 deletions(-) delete mode 100644 docs/get-started/project-templates.md delete mode 100644 docs/images/project-templates/chat-sentiment-pipeline.png delete mode 100644 docs/images/project-templates/clickstream-analysis-pipeline.png delete mode 100644 docs/images/project-templates/computer-vision-pipeline.png diff --git a/docs/develop/process/timeseries-events.md b/docs/develop/process/timeseries-events.md index 8802c9c0..0519ef9d 100644 --- a/docs/develop/process/timeseries-events.md +++ b/docs/develop/process/timeseries-events.md @@ -98,4 +98,4 @@ In the above code, the event generating code has been removed for simplicity. No * [Example code](https://github.com/quixio/tutorial-code/blob/main/generate-events/README.md){target=_blank} - the complete code for the example. * [Quix Streams](https://quix.io/docs/quix-streams/introduction.html) - documentation on data formats, publishing, and subscribing to topics. * [Quix Cloud Tour](../../quix-cloud/quixtour/overview.md) - generates processing based on threshold triggering. -* [Currency alerting](../../tutorials/currency-alerting/currency-alerting.md) - tutorial on generating events based on a threshold. +* [InfluxDB alerting tutorial](../../tutorials/influxdb-alerting/overview.md) - tutorial on generating events based on thresholds with real-time alerting. diff --git a/docs/get-started/project-templates.md b/docs/get-started/project-templates.md deleted file mode 100644 index c6cee1ac..00000000 --- a/docs/get-started/project-templates.md +++ /dev/null @@ -1,49 +0,0 @@ -# Project templates - -Project templates provide a way for you to examine your use case running in Quix. You don't need to sign up to see these projects running in Quix. - -Project templates also provide a starting point for your own projects - our project templates are hosted as open source in the [Quix GitHub repository](https://github.com/quixio), so you can fork them and import them into Quix, and use them to build out your own solution. - -

          - -- __Computer vision__ - - --- - - ![Computer vision pipeline](../images/project-templates/computer-vision-pipeline.png) - - A project template that implements a real-time computer vision application. - - [Explore :octicons-arrow-right-24:](../tutorials/computer-vision/overview.md) - -- __Chat sentiment analysis__ - - --- - - ![Chat sentiment analysis pipeline](../images/project-templates/chat-sentiment-pipeline.png) - - A project template that implements a chat application with sentiment analysis. - - [Explore :octicons-arrow-right-24:](../tutorials/sentiment-analysis/overview.md) - -- __Clickstream analysis__ - - --- - - ![Clickstream analysis pipeline](../images/project-templates/clickstream-analysis-pipeline.png) - - Clickstream analysis for online shop data. Features real-time dashboard and web shop user interface. - - [Explore :octicons-arrow-right-24:](../tutorials/clickstream/overview.md) - -- __Predictive maintenance__ - - --- - - ![Predictive maintenance pipeline](../images/project-templates/predictive-maintenance-pipeline.png) - - Predicts failures in 3D printers. - - [Explore :octicons-arrow-right-24:](../tutorials/predictive-maintenance/overview.md) - -
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zD-YgLwA--jrNq{Fg0Sv8rHr!gKi9>BBdB`B;$s*>`S;z0di(^=jga%z&nR560Ycvs z5}?en{ZgpdzOSl;e>jF=_kNwdID*Hovo1-mZjOr(F}ZsrKOqE*v#!HDRRsUd?yu_y_)T zkv=MaA_uzz{`&Yk)be`^d#oDl`y6F6H$=3QDf|7`VW~XqL1-&%=2FwbUvh0vbvlsr z0<10gF)#lgF7f*{?~-i>$d8V)mrk<(Y208+ZVgNGjdOd1{CnaKN{AI#bh-7v^L}1VUC%J@U0M~1UeSGcT_d-o`n-ZB)5oHbeP5-~=WWMPL=rc9+Nd|=H|C;XC zW??D^z_>o93Ge>>J1WSOARzhR{qTuD{Qj@mZ_8w(zmA$`5QzC?7lsAb p|1>V&DKPH;6BYJJ|OMlq-ZXA{F>iR#d-{ecjmezW_E4Zms|T diff --git a/docs/integrations/databases/influxdb/alerting.md b/docs/integrations/databases/influxdb/alerting.md index d6bd5150..5b743b0b 100644 --- a/docs/integrations/databases/influxdb/alerting.md +++ b/docs/integrations/databases/influxdb/alerting.md @@ -15,5 +15,5 @@ The alerting can be placed in the inbound pipeline, where you can detect issues * In-depth tutorial that takes you through setting up pipelines using Quix, Quix Streams, InfluxDB, and [PagerDuty for alerting](../../../tutorials/influxdb-alerting/overview.md). * Quix Cloud Tour demonstrates [building alerting](../../../quix-cloud/quixtour/serve-sms.md) from scratch with the Vonage APIs, which are simple to use. -* Currency alerting tutorial demonstrates using [Pushover to implement alerting](../../../tutorials/currency-alerting/currency-alerting.md#setting-up-the-pushover-destination). +* The [InfluxDB alerting tutorial](../../../tutorials/influxdb-alerting/overview.md) demonstrates implementing alerting with InfluxDB and PagerDuty. * The Predictive maintrenance project tutorial uses the Pushover service for [sending alerts](../../../tutorials/predictive-maintenance/phone-alerts.md). diff --git a/docs/kb/why-stream-processing.md b/docs/kb/why-stream-processing.md index c077c02e..bcf4725f 100644 --- a/docs/kb/why-stream-processing.md +++ b/docs/kb/why-stream-processing.md @@ -141,7 +141,7 @@ The main benefits of using stream processing in this scenario are: By implementing stream processing for real-time personalized marketing and inventory management, the retail company can gain a competitive edge in today's dynamic and fast-paced market environment. -See the [Clickstream analysis tutorial](../tutorials/clickstream/overview.md) for a step-by-step guide to building a similar project. +See our [tutorials overview](../tutorials/overview.md) for step-by-step guides to building real-time data processing projects. ## Use Case: Real-time website performance monitoring and personalization @@ -197,7 +197,7 @@ The main benefits of using stream processing in this scenario are: By leveraging real-time web analytics for performance monitoring and personalization, the company can drive higher user engagement, increase conversions, and ultimately achieve its business objectives more effectively in the competitive online marketplace. -See the [Clickstream analysis tutorial](../tutorials/clickstream/overview.md) for a step-by-step guide to building a similar project. +See our [tutorials overview](../tutorials/overview.md) for step-by-step guides to building real-time data processing projects. ## Use Case: Real-time fleet monitoring and optimization @@ -253,7 +253,7 @@ The main benefits of using stream processing in this scenario are: By implementing real-time telemetry for fleet monitoring and optimization, the transportation company can gain a competitive advantage in the market by offering faster, more efficient, and safer transportation services to its customers. -See the [Real-time Machine Learning predictions tutorial](https://quix.io/docs/tutorials/data-science/overview.html) for a related example. +See the [Predictive maintenance tutorial](../tutorials/predictive-maintenance/overview.md) for a related example of real-time ML. ## Use Case: Real-time process monitoring and anomaly detection diff --git a/docs/manage/MLOps.md b/docs/manage/MLOps.md index 5207d768..0600d4b5 100644 --- a/docs/manage/MLOps.md +++ b/docs/manage/MLOps.md @@ -53,5 +53,4 @@ In Quix, data teams can: Here are some suggested next steps to find out more about MLOps in Quix: * [Quickstart](../quix-cloud/quickstart.md) - get data into Quix and displayed in real time in under 10 minutes. -* [Building real-time ML pipelines tutorial](../tutorials/train-and-deploy-ml/overview.md) - train and run an ML model in Quix. -* [Building real-time ML predictions](../tutorials/data-science/overview.md) - use data science and ML to predict bicycle availability in New York. +* [Predictive maintenance tutorial](../tutorials/predictive-maintenance/overview.md) - build a complete ML pipeline for predicting equipment failures using real-time data. diff --git a/docs/tutorials/influxdb-alerting/summary.md b/docs/tutorials/influxdb-alerting/summary.md index 4ed24d8d..e8601b73 100644 --- a/docs/tutorials/influxdb-alerting/summary.md +++ b/docs/tutorials/influxdb-alerting/summary.md @@ -7,6 +7,5 @@ You have also seen how to add threshold detection to your pipeline, and built a ## Next steps * Quix Cloud Tour demonstrates [building alerting](../../quix-cloud/quixtour/serve-sms.md) from scratch with the Vonage APIs, which are simple to use. -* Currency alerting tutorial demonstrates using [Pushover to implement alerting](../../tutorials/currency-alerting/currency-alerting.md#setting-up-the-pushover-destination). -* The Predictive maintenance project tutorial uses the Pushover service for [sending alerts](../../tutorials/predictive-maintenance/phone-alerts.md). +* The Predictive maintenance project tutorial demonstrates [building alerting systems](../../tutorials/predictive-maintenance/overview.md) for equipment failure detection. * [Quix Integrations documentation](../../integrations/overview.md) diff --git a/docs/tutorials/predictive-maintenance/summary.md b/docs/tutorials/predictive-maintenance/summary.md index 0ff85432..75da97d7 100644 --- a/docs/tutorials/predictive-maintenance/summary.md +++ b/docs/tutorials/predictive-maintenance/summary.md @@ -17,10 +17,7 @@ Here are some suggested next steps to continue on your Quix learning journey: * Build something with our [Code Samples](../../develop/code-samples.md). - +* Try another tutorial from our [tutorials overview](../overview.md). ## Get help diff --git a/mkdocs.yml b/mkdocs.yml index 87a370ed..666ca090 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -23,8 +23,6 @@ nav: # - 'Produce data': 'get-started/produce.md' # - 'Consume data': 'get-started/consume.md' # - 'Process data': 'get-started/process.md' -# Mostly out of date -# - 'Project templates': 'get-started/project-templates.md' - 'Quix Streams': '!import https://github.com/quixio/quix-streams?branch=v3.21.0' - 'Quix Connectors': @@ -814,6 +812,7 @@ plugins: quix-streams/connectors/sources/file-source.md: quix-streams/connectors/sources/local-file-source.md 'quix-connectors/quix-streams/sources/coming-soon/AzureBlobStorage-source.md': 'quix-streams/connectors/sources/microsoft-azure-file-source.md' 'quix-connectors/quix-streams/sinks/coming-soon/AzureBlobStorage-sink.md': 'quix-streams/connectors/sinks/microsoft-azure-file-sink.md' + 'get-started/project-templates.md': 'tutorials/overview.md' theme: From e6c11d043719c2f4849a6a12eec4bf8800a7d954 Mon Sep 17 00:00:00 2001 From: Peter Nagy Date: Thu, 7 Aug 2025 18:49:05 +0100 Subject: [PATCH 09/10] Remove several legacy pages and redirect instead --- docs/get-started/consume.md | 64 -------------- docs/get-started/process.md | 86 ------------------ docs/get-started/produce.md | 67 -------------- docs/quix-cloud/quixtour/ingest.md | 128 --------------------------- docs/quix-cloud/quixtour/overview.md | 34 ------- mkdocs.yml | 25 +++--- 6 files changed, 11 insertions(+), 393 deletions(-) delete mode 100644 docs/get-started/consume.md delete mode 100644 docs/get-started/process.md delete mode 100644 docs/get-started/produce.md delete mode 100644 docs/quix-cloud/quixtour/ingest.md delete mode 100644 docs/quix-cloud/quixtour/overview.md diff --git a/docs/get-started/consume.md b/docs/get-started/consume.md deleted file mode 100644 index cdcd3a5b..00000000 --- a/docs/get-started/consume.md +++ /dev/null @@ -1,64 +0,0 @@ -# Consume data - -You'll now write some code that simply consumes data from a topic and prints it out. - -## Step 1: Write your consumer code - -Write your consumer code: - -``` python -from quixstreams import Application -from datetime import timedelta -import json - -# connect to your local Kafka broker -app = Application( - broker_address="localhost:9092", - consumer_group="consume-v1", - auto_offset_reset="earliest", -) - -# configure the input topic to subscribe to (you'll read data from this topic) -input_topic = app.topic("cpu-load") - -# consume (read) messages from the input topic -sdf = app.dataframe(topic=input_topic) - -# print every row -sdf = sdf.update(lambda row: print(json.dumps(row))) - -if __name__ == "__main__": - # run the application and process all inbound messages using the sdf pipeline - app.run(sdf) -``` - -Save this in a file names `consumer.py`. - -## Step 2: Run your consumer - -``` python -python3 consumer.py -``` - -You are now subscribing to data on the `cpu-load` topic. - -Each message received is printed out as JSON: - -``` json -{ - "cpu_load": 5.7, - "memory": { - "total": 0, - "used": 0, - "free": 0, - "percent": 0, - "sin": 90937131008, - "sout": 483672064 - }, - "timestamp": 1712238254512946000 -} -``` - -## Next step - -* [Process data](./process.md) - process streaming data in a Kafka topic in real time. diff --git a/docs/get-started/process.md b/docs/get-started/process.md deleted file mode 100644 index fa11cc2c..00000000 --- a/docs/get-started/process.md +++ /dev/null @@ -1,86 +0,0 @@ -# Process data - -The following processor code subscribes to messages on the `cpu-load` topic and processes the data in real time. In this case a tumbling window of twenty seconds is used to calculate the average CPU load. It then ublishes the transformed data to its output topic (`average-cpu-load`), where it can be persisted or processed further by additional stages in the pipeline. - -This assumes you still have your `producer.py` code running, but have shut down the consumer you ran in the [previous step](./consume.md). - -## Step 1: Create your processing code (transform) - -Create a new file containing the following code: - -``` python -from quixstreams import Application -from datetime import timedelta - -# connect to your kafka broker -app = Application( - broker_address="localhost:9092", - consumer_group="transform-v1", - auto_offset_reset="earliest", -) - -# consume data from the input topic -input_topic = app.topic("cpu-load") -# publish data to the output topic -output_topic = app.topic("average-cpu-load") - -# sdf reads messages from the input topic -sdf = app.dataframe(topic=input_topic) - -# calculate average cpu load over 20 seconds tumbling window -sdf = sdf.apply(lambda row: row["cpu_load"]) \ - .tumbling_window(timedelta(seconds=20)).mean().final() \ - .apply(lambda value: { - 'average-cpu-load': value['value'], - 'time': value['end'] - }) - -# print every row -sdf = sdf.update(lambda row: print(row)) - -# publish to output topic -sdf = sdf.to_topic(output_topic) - -if __name__ == "__main__": - # run the application and process all inbound messages using the sdf pipeline - app.run(sdf) -``` - -Save the code to a file named `transform.py`. - -??? "Explanation of the code" - - A transform subscribes to data from its input topic, processes the data, and then publishes the the transformed data to its output topic. The basic parts of this code are: - - * Connect to the Kafka broker. - * Configure the input topic to consume data from. - * Configure the output topic to publish processed data to. - * Define the Streaming DataFrame (SDF) processing pipeline: - * Read message from the input topic. - * Calculate the average CPU load over a 20 seconds tumbling window. - * Print every row. - * Write message to the output topic. - * Run the Quix app, using Streaming DataFrame (SDF) to process messages. - -## Step 2: Run your processor (transform) code - -In a **new shell tab**, run your transform code: - -``` -python3 transform.py -``` - -This will produce messages with the format on the output topic, `average-cpu-load`: - -``` json -{ - "average-cpu-load": 14.9, - "time": 1712149460000 -} -``` - -You'll notice the messages in the topic are in the JSON format. - -## Next step - -* [Quix CLI Quickstart](../quix-cli/cli-quickstart.md) - get started using the Quix CLI to build and run your first streaming data pipeline locally. diff --git a/docs/get-started/produce.md b/docs/get-started/produce.md deleted file mode 100644 index 13d0de4e..00000000 --- a/docs/get-started/produce.md +++ /dev/null @@ -1,67 +0,0 @@ -# Produce data - -Here you'll explore Quix Streams a bit more by creating a producer to publish messages to a topic in your local Kafka broker. You'll then create a consumer that subscribes to messages on that topic and prints the messages. - -This page assumes you have already [installed Quix Streams](./install.md). - -## Step 1: Set up a local Kafka broker - -Set up a local Kafka broker. You can follow [this quickstart](https://kafka.apache.org/quickstart){target=_blank} to install Apache Kafka. - -## Step 2: Write your producer code - -``` python -import psutil, time, json -from quixstreams import Application - -# connect to your local Kafka broker -app = Application(broker_address="localhost:9092", consumer_group="producer-v1") - -# configure the output topic to publish data to -output_topic = app.topic("cpu-load") - -# get the cpu load (and memory usage) -def get_cpu_load(): - cpu_load = psutil.cpu_percent(interval=1) - memory = psutil.swap_memory() - return { - "cpu_load": cpu_load, - "memory": memory._asdict(), - "timestamp": int(time.time_ns()), - } - -def main(): - # create a Producer to send data to the topic - with app.get_producer() as producer: - while True: - # Get the current CPU and memory usage - message = get_cpu_load() - print("CPU load: ", message["cpu_load"]) - - # publish a message to the output topic - producer.produce( - topic=output_topic.name, - key="server-1-cpu", - value=json.dumps(message) - ) - -if __name__ == '__main__': - try: - main() - except KeyboardInterrupt: - print('Exiting due to keyboard interrupt') -``` - -Save the code to a file named `producer.py`. - -## Step 3: Run your producer - -``` python -python3 producer.py -``` - -You are now publishing data into the `cpu-load` topic. You'll next create a consumer to consume these messages. - -## Next step - -* [Consume data](./consume.md) - subscribe to data in a Kafka topic. diff --git a/docs/quix-cloud/quixtour/ingest.md b/docs/quix-cloud/quixtour/ingest.md deleted file mode 100644 index 6581634b..00000000 --- a/docs/quix-cloud/quixtour/ingest.md +++ /dev/null @@ -1,128 +0,0 @@ -# Ingest data - -In this part of the tour you'll get some data flowing into your pipeline. You do this by reading CPU load on your service and publishing it to a topic. - -## Step 1: Sign in to Quix Cloud - -??? info "Start for free" - [Book a session](https://quix.io/book-a-demo) with us to start for free. - - We will create a time-limited free account for you, and our experts will help you get started with your specific use case. - -After signing up using the method of your choice, you'll be taken to the `Welcome to Quix` dialog, where you can create a new project, as shown in the next step. - -## Step 2: Create your first project - -You're now ready to create your first project. - -1. Give your project a suitable name, such as "Quix Cloud Tour". -2. Select the `Quix advanced configuration` option. -3. Click `Let's get started`. -4. Select `Quix managed Git`. -5. Click `Create project`. -6. Now create an environment. Enter an environment name of "PRODUCTION". -7. Make sure the `main` branch is selected. -8. Click `Continue`. -9. Select the Quix managed broker (which is the default option). -10. Click `Create environment`. After the environment is fully created you are taken to your pipeline view. - -## Step 3: Create a source - -To create a source: - -1. Click on your environment to be taken to its pipeline view. -2. Click `Code samples` on the left-hand sidebar. -3. Click `Starter Source` and then click `Save to your repo`. You can leave the default name and path. -4. You are taken to the code view. - -## Step 4: Write your code - -Replace the code in `main.py` with the following: - -``` python -import psutil, time, os, json -from quixstreams import Application - -app = Application() - -output_topic = app.topic(os.environ["output"]) - -def get_cpu_load(): - cpu_load = psutil.cpu_percent(interval=1) - memory = psutil.swap_memory() - return { - "cpu_load": cpu_load, - "memory": memory._asdict(), - "timestamp": int(time.time_ns()), - } - -def main(): - # Create a Producer to send data to the topic - with app.get_producer() as producer: - while True: - # Get the current CPU and memory usage - message = get_cpu_load() - print("CPU load: ", message["cpu_load"]) - - # Produce message to the topic - producer.produce( - topic=output_topic.name, - key="server-1-cpu", - value=json.dumps(message) - ) - -if __name__ == '__main__': - main() -``` - -Commit your changes. - -## Step 5: Set the output topic - -To set the output topic to which you publish data: - -1. Click your output variable edit menu and select `Edit variable`. - - ![Edit variable](../../images/edit-variable.png) - - The `Edit Variable` dialog is displayed. - -2. Click `new topic` to display the `New topic` dialog. Enter a `Name` of `cpu-load` and then click `Create`. - -## Step 6: Modify requirements.txt - -Edit the `requirements.txt` to make sure it includes `psutil`. Your file should be similar to the following: - -``` -quixstreams==2.5.0 -python-dotenv -psutil -``` - -Commit your changes. - -## Step 7: Run your code - -Run your code by clicking the `Run` button. Make sure there are no errors. After a moment the CPU load of your service is displayed. - -## Step 8: Deploy your code - -First, tag your code as `ingest-v1` by clicking the tag button: - -![Tag button](../../images/tag-button.png) - -Now, click the `Deploy` button, and `Deploy` again to deploy the service to your pipeline. - -## Step 9: Examine the messages - -In the deployments view, click on the `Messages` tab. There is only one topic for this service, so the output topic `cpu-load` is selected by default. - -Click on any message to see the JSON of the message: - -![Messages tab](../../images/messages-tab-json.png) - -Note the real-time stream of messages is no longer updated in real time. To resume real-time display of messages, click the play button. - -## 🏃‍♀️ Next step - -[Process your data :material-arrow-right-circle:{ align=right }](./process-threshold.md) diff --git a/docs/quix-cloud/quixtour/overview.md b/docs/quix-cloud/quixtour/overview.md deleted file mode 100644 index 54fc1b70..00000000 --- a/docs/quix-cloud/quixtour/overview.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -title: Quix Cloud Tour -description: Build a complete pipeline entirely in Quix Cloud. ---- - -# Quix Cloud Tour - -Welcome to the Quix Cloud Tour! - -The Quix Cloud Tour is designed to help you build a complete stream processing pipeline with Quix **in less than 30 minutes**. - -## Prerequisites - -To complete the Quix Cloud Tour you'll need the following: - -* (Optional) A free [Vonage API account](https://developer.vonage.com/sign-up). - -## CPU overload detection pipeline - -The pipeline you implement in this tour: - -1. **Ingest** - you create a source that publishes CPU load data from your source service into a Quix topic, using Quix Streams. - -2. **Process** - in this part, you process your data. Here you create a transform service that performs threshold detection. You publish messages to the transform's output topic. - -3. **Serve** - when you receive a message indicating CPU load has exceeded the threshold you (optionally) send an SMS to the system administrator. - -!!! note - - The components in the pipeline are connected together by [Kafka topics](../../kb/what-is-kafka.md). - -## 🏃‍♀️ Next step - -[Ingest data :material-arrow-right-circle:{ align=right }](./ingest.md) diff --git a/mkdocs.yml b/mkdocs.yml index 666ca090..14ed5da6 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -19,10 +19,6 @@ nav: - 'get-started/streaming.md' - 'get-started/stream-processing.md' - 'get-started/stream-processing-pipelines.md' -# - 'Quickstart': -# - 'Produce data': 'get-started/produce.md' -# - 'Consume data': 'get-started/consume.md' -# - 'Process data': 'get-started/process.md' - 'Quix Streams': '!import https://github.com/quixio/quix-streams?branch=v3.21.0' - 'Quix Connectors': @@ -217,11 +213,6 @@ nav: - 'Overview': 'quix-cloud/overview.md' - 'Quickstart': 'quix-cloud/quickstart.md' - 'Why Use Quix Cloud': 'quix-cloud/why-quix-cloud.md' -# - 'Quix Cloud Tour': -# - 'Overview': 'quix-cloud/quixtour/overview.md' -# - '1. Ingest': 'quix-cloud/quixtour/ingest.md' -# - '2. Process': 'quix-cloud/quixtour/process-threshold.md' -# - '3. Serve': 'quix-cloud/quixtour/serve-sms.md' - 'Quix Cloud Tour': - '1. Create your project': - 'Overview': 'create/overview.md' @@ -668,7 +659,7 @@ plugins: 'platform/quickstart.md': 'quix-cloud/quickstart.md' 'platform/what-is-quix.md': 'kb/what-is-quix.md' 'platform/glossary.md': 'kb/glossary.md' - 'platform/quixtour/overview.md': 'quix-cloud/quixtour/overview.md' + 'platform/quixtour/overview.md': 'quix-cloud/overview.md' 'platform/tutorials/index.md': 'tutorials/overview.md' # Outdated tutorial redirects - All removed tutorials point to landing page # Computer Vision tutorial (all pages) @@ -756,7 +747,7 @@ plugins: # Outdated tutorial redirects END 'apis/streaming-reader-api/index.md': 'apis/streaming-reader-api/overview.md' 'apis/portal-api/index.md': 'apis/portal-api/overview.md' - 'platform/connectors/index.md': 'connectors/index.md' + 'platform/connectors/index.md': 'quix-connectors/templates/index.md' 'connectors/index.md': 'quix-connectors/templates/index.md' 'connectors/confluentkafkasource-source.md': 'quix-connectors/templates/confluentkafkasource-source.md' 'connectors/environmentsource-source.md': 'quix-connectors/templates/environmentsource-source.md' @@ -789,10 +780,10 @@ plugins: 'integrations/influxdb/telegraf.md': 'integrations/databases/influxdb/telegraf.md' 'integrations/influxdb/replacing-flux.md': 'integrations/databases/influxdb/replacing-flux.md' 'platform/how-to/use-sdk-token.md': 'develop/authentication/streaming-token.md' - 'get-started/quixtour/ingest-push.md': 'quix-cloud/quixtour/external-source.md' + 'get-started/quixtour/ingest-push.md': 'develop/integrate-data/external-source.md' 'get-started/quickstart.md': 'quix-cloud/quickstart.md' - 'get-started/quixtour/overview.md': 'quix-cloud/quixtour/overview.md' - 'get-started/quixtour/external-source.md': 'quix-cloud/quixtour/external-source.md' + 'get-started/quixtour/overview.md': 'quix-cloud/overview.md' + 'get-started/quixtour/external-source.md': 'develop/integrate-data/external-source.md' 'get-started/quixtour/process-threshold.md': 'quix-cloud/quixtour/process-threshold.md' 'get-started/quixtour/serve-sms.md': 'quix-cloud/quixtour/serve-sms.md' 'get-started/what-is-quix.md': 'kb/what-is-quix.md' @@ -813,6 +804,12 @@ plugins: 'quix-connectors/quix-streams/sources/coming-soon/AzureBlobStorage-source.md': 'quix-streams/connectors/sources/microsoft-azure-file-source.md' 'quix-connectors/quix-streams/sinks/coming-soon/AzureBlobStorage-sink.md': 'quix-streams/connectors/sinks/microsoft-azure-file-sink.md' 'get-started/project-templates.md': 'tutorials/overview.md' + 'quix-cloud/quixtour/overview.md': 'quix-cloud/overview.md' + 'quix-cloud/quixtour/ingest.md': 'quix-cloud/overview.md' + 'get-started/produce.md': 'get-started/streaming.md' + 'get-started/consume.md': 'get-started/streaming.md' + 'get-started/process.md': 'get-started/streaming.md' + theme: From dca3646c82d3e5f17fd128291a333621d83195bc Mon Sep 17 00:00:00 2001 From: Peter Nagy Date: Thu, 7 Aug 2025 18:53:01 +0100 Subject: [PATCH 10/10] Remove legacy pages printer dashboard and phone alerts --- .../databases/influxdb/alerting.md | 2 +- .../images/library-pushover.png | Bin 48865 -> 0 bytes .../printers-dashboard-pipeline-segment.png | Bin 34214 -> 0 bytes .../images/printers-dashboard.png | Bin 314194 -> 0 bytes .../images/pushover-notification.png | Bin 74930 -> 0 bytes .../images/success-pushover.png | Bin 302326 -> 0 bytes .../predictive-maintenance/overview.md | 12 -- .../predictive-maintenance/phone-alerts.md | 193 ------------------ .../printers-dashboard.md | 21 -- mkdocs.yml | 11 +- 10 files changed, 3 insertions(+), 236 deletions(-) delete mode 100644 docs/tutorials/predictive-maintenance/images/library-pushover.png delete mode 100644 docs/tutorials/predictive-maintenance/images/printers-dashboard-pipeline-segment.png delete mode 100644 docs/tutorials/predictive-maintenance/images/printers-dashboard.png delete mode 100644 docs/tutorials/predictive-maintenance/images/pushover-notification.png delete mode 100644 docs/tutorials/predictive-maintenance/images/success-pushover.png delete mode 100644 docs/tutorials/predictive-maintenance/phone-alerts.md delete mode 100644 docs/tutorials/predictive-maintenance/printers-dashboard.md diff --git a/docs/integrations/databases/influxdb/alerting.md b/docs/integrations/databases/influxdb/alerting.md index 5b743b0b..f433e0a4 100644 --- a/docs/integrations/databases/influxdb/alerting.md +++ b/docs/integrations/databases/influxdb/alerting.md @@ -16,4 +16,4 @@ The alerting can be placed in the inbound pipeline, where you can detect issues * In-depth tutorial that takes you through setting up pipelines using Quix, Quix Streams, InfluxDB, and [PagerDuty for alerting](../../../tutorials/influxdb-alerting/overview.md). * Quix Cloud Tour demonstrates [building alerting](../../../quix-cloud/quixtour/serve-sms.md) from scratch with the Vonage APIs, which are simple to use. * The [InfluxDB alerting tutorial](../../../tutorials/influxdb-alerting/overview.md) demonstrates implementing alerting with InfluxDB and PagerDuty. -* The Predictive maintrenance project tutorial uses the Pushover service for [sending alerts](../../../tutorials/predictive-maintenance/phone-alerts.md). +* The [Predictive maintenance tutorial](../../../tutorials/predictive-maintenance/overview.md) demonstrates implementing alerting for real-time monitoring. diff --git a/docs/tutorials/predictive-maintenance/images/library-pushover.png b/docs/tutorials/predictive-maintenance/images/library-pushover.png deleted file mode 100644 index 35ab4799f5ddde299af8e1cdd92c3d360b7f4b35..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 48865 zcmeGEWkA&17e5LEqJT7rfb>u*(hWmNH;U3B4bnZdNOyyDhtgdGNSCy94&9wY-7n|- z&hh-8``mZ;<%QAt#_qNDUZ1u1XAz{NAc^_(#Zv?X1WaiuvG)iFNUjJ7h>56AfG6b9 zDNVqQj+v;alC-EOrIMYsv6+Pt0>aBE$4FM$cBN;2idFBc2*^65W#~i--YXNJB+Td+ 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Quix Cloud account. You'll need a [free InfluxDB](https://www.influxdata.com/products/influxdb-cloud/serverless/){target=_blank} account to try this out in your Quix account. - - ### Git provider You also need to have a Git account. This could be GitHub, Bitbucket, GitLab, or any other Git provider you are familar with, and that supports SSH keys. The simplest option is to create a free [GitHub account](){target=_blank}. diff --git a/docs/tutorials/predictive-maintenance/phone-alerts.md b/docs/tutorials/predictive-maintenance/phone-alerts.md deleted file mode 100644 index 0ce4dbbf..00000000 --- a/docs/tutorials/predictive-maintenance/phone-alerts.md +++ /dev/null @@ -1,193 +0,0 @@ -# 👩‍🔬 Lab: add phone alerts - -In this lab you use what you've learned so far, to add a new service, that can send alerts to your phone. - -You develop this change on a feature branch, and then you create a PR to merge your new feature into the `tutorial` branch after testing. - -This is a common pattern for development - you can test your new service on the feature branch, and then test again, before final integration into the production `main` branch. - -## Create an environment - -To create a new environment (and branch): - -1. Click `+ New environment` to create a new environment (note, your screen will look slightly different to the one shown here): - - ![New environment](./images/new-environment.png) - -2. Create a new environment called `Phone Alert`. - -3. Create a new branch called `phone-alert`. To do this, from the branch dropdown click `+ New branch` which displays the New branch dialog. - - !!! important - - Make sure you branch from the `tutorial` branch, not `main`, as you are going to merge your changes back to the `tutorial` branch. - -4. Complete creation of the environment using the default options. - -5. On the projects screen, click your newly created environment, `Phone Alert`. - -## Sync the environment - -You now see that the Quix environment is out of sync with the Git repository. You need to synchronize the Quix view of the environment, with that stored in the repository. - -To synchronize Quix with the repository: - -1. Click `Sync environment`: - - ![Sync environment](./images/sync-environment.png) - - The sync environment dialog is displayed, showing you the changes that are to be made to the `quix.yaml` file, which is the configuration file that defines the pipeline. - -2. Click `Sync environment`, and then `Go to pipeline`. - - In the pipeline view, you see the services building. Ensure all services are "Running" before continuing. - -## Add an alert filter tranformation service - -It would not be a good thing if your phone was alerting you for every alert produced by the alert service. For this reason, this service is used to filter alerts to only publish the ambient forecast over temperature alert. That is the alert produced when the ambient temperature goes above a safe level. - -To add a filter transformation service: - -1. Click on the `Code Samples` icon in the left-hand navigation. - -2. Select `Transformation` and `Basic Templates`, then click `Starter transformation`. - -3. Click `Edit code`, and set the `Application name` to `Alerts Filter`, and click `Save`. - -You'll now edit the code to implement the functionality you require. - -Modify your input topic to be `json-alerts`, and the output topic to be a new topic `ambient-alerts` (you'll need to use the `New topic` button to create this). - -You're only interested in forecast over temperature alerts, which have the message format: - -``` json -{ - "status": "over-forecast", - "parameter_name": "ambient_temperature", - "alert_temperature": 72.82717847141305, - "timestamp": "2024-04-16 10:42:40", - "message": "'Ambient temperature' is forecasted to go over 73ºC at 2024-04-16 10:42:40." -} -``` - -So you are looking for an `status` of `over-forecast`. - -You only want to publish the alert message to the output topic if it has the correct ID. Modify the event handler to the following: - -``` python -# if status == over-forecast then send alert -``` - -Commit your changes. - -### Test the service - -Now that you've modified the code, you'll now test your changes: - -1. Tag your changes, for example as `alerts-filter-v1`. - -2. Click the `Deploy` button and select your changed version from the `Version tag` dropdown. - -3. Once the `Alerts Filter` service is running, you are ready to proceed to the next step. - - -## Add the Pushover destination - -In this section you create a destination service that sends a push notification to your phone when an alert condition occurs. - -This service subscribes to the `ambient-alerts` topic and whenever a new message arrives, it sends a push notification to the Pushover app on your mobile phone. - -To set up the push notification service, follow these steps: - -1. Click on the `Code Samples` icon in the left-hand navigation. - -2. In the search box on the Code Samples page, enter "Pushover". - - You will see the `Pushover Output` sample appear in the search results: - - ![Pushover Notifications](./images/library-pushover.png "Pushover Notifications") - -3. Click the `Preview code` button, and on the page that appears, click the `Edit code` button. - -4. On the `Setup application` page, complete the following fields: - - | Field | Value | - | --- | --- | - | `Name` | Enter an application name or keep the default suggestion. | - | `Input` | Select the input topic. In this case, select `ambient-alerts` from the list. Every message will be read from this topic, and turned into a push notification. | - | `base_url` | Leave the default value, `https://api.pushover.net/1/messages.json?`. If you decide to use another push notification app, your can always update this value. | - | `api_token` | Enter the API token that you generated for this application in your Pushover dashboard. For example: `azovmnbxxdxkj7j4g4wxxxdwf12xx4`. | - | `user_key` | Enter the user key that you received when you signed up with Pushover. For example: `u721txxxgmvuy5dxaxxxpzx5xxxx9e` | - -5. Click the `Save as Application`. You now have a copy of the Pushover notification sample in your environment. - -6. Click the `Deploy` button. - -### Test the service - -You will shortly start receiving Pushover notifications on your phone, as shown here: - -![Pushover Notification Example](./images/pushover-notification.png){width=60%} - -!!! note - - In Pushover this application was named "3D Printers". - -Depending on the stage of the printing cycle, it might take a few minutes for you to get a notification. While you are waiting to receive a notification, you can inspect the logs, as shown previously. - -![Pushover Logs](./images/success-pushover.png) - -* Don't worry if the logs only show "_Listening to Stream_" initially — remember that the Alert Filter service only writes a message to the `ambient-alerts` topic when the ambient predicted temperature is over the threshold. -* This means that the `ambient-alerts` stream might be empty for a short while, until a threshold is triggered. - -## Merge the feature - -Once you are sure that the changes on your feature branch are tested, you can then merge your changes onto the `tutorial` branch. Here your changes undergo further tests before finally being merged into production. - -To merge your feature branch, `phone-alert` into `tutorial`: - -1. Select `Merge request` from the menu as shown: - - ![Merge request menu](./images/merge-request-menu.png) - -2. In the `Merge request` dialog, set the `phone-alert` branch to merge into the `tutorial` branch. - -You are going to create a pull request, rather than perform a direct merge. This enables you to have the PR reviewed in GitHub (or other Git provider). You are also going to do a squash and merge, as much of the feature branch history is not required. - -To create the pull request: - -1. Click `Create pull request`. You are taken to your Git provider, in this case GitHub. - -2. Click the `Pull request` button. - -3. Add your description, and then click `Create pull request`. - -4. Get your PR reviewed and approved. Then squash and merge the commits: - - ![Squash and merge](./images/squash-and-merge.png) - - You can replace the prefilled description by something more succinct. Then click `Confirm squash and merge`. - - !!! tip - - You can just merge, you don't have to squash and merge. You would then retain the complete commit history for your service while it was being developed. Squash and merge is used in this case by way of example, as the commit messages generated while the service was being developed were deemed to be not useful in this case. - -## Resync the environment - -You have now merged your new service into the `tutorial` branch in the Git repository. Your Quix view in the Tutorial environment is now out of sync with the Git repository. If you click on your Tutorial environment in Quix, you'll see it is now a commit (the merge commit) behind. - -You now need to make sure your Tutorial environment in Quix is synchronized with the Git repository. To do this: - -1. Click on `Sync environment`. The `Sync environment` dialog is displayed. - -2. Review the changes and click `Sync environment`. - -3. Click `Go to pipeline`. - -Your new service builds and starts in the Tutorial environment, where you can now carry out further testing. When you are satisfied this feature can be released to production, you would then repeat the previous process to merge your changes to Production `main`. - -## 🏃‍♀️ Next step - - -[Part 9 - Summary :material-arrow-right-circle:{ align=right }](summary.md) - diff --git a/docs/tutorials/predictive-maintenance/printers-dashboard.md b/docs/tutorials/predictive-maintenance/printers-dashboard.md deleted file mode 100644 index 8f09b613..00000000 --- a/docs/tutorials/predictive-maintenance/printers-dashboard.md +++ /dev/null @@ -1,21 +0,0 @@ -# Printers dashboard - -The printers dashboard service is a web app that displays the temperature of the selected printer in real time: - -![Printers dashboard](./images/printers-dashboard.png) - -The Quix [Streaming Reader API](../../apis/streaming-reader-api/overview.md) is used to subscribe to several topics, to read data into the web app. - -The topics subscribed to are: - -* `alerts` -* `forecast` -* `3d-printer-data` - -![Printers dashboard pipeline segment](./images/printers-dashboard-pipeline-segment.png) - -You can see the dashboard running [here](https://dash-demo-predictivemaintenance-production.deployments.quix.io/?_ga=2.114191879.1628320822.1701679251-1544698923.1686060578){target=_blank}. - -## 🏃‍♀️ Next step - -[Part 9 - Lab: Add phone alert :material-arrow-right-circle:{ align=right }](./phone-alerts.md) diff --git a/mkdocs.yml b/mkdocs.yml index 14ed5da6..87e19229 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -613,16 +613,7 @@ nav: - '5. Alerts': 'tutorials/predictive-maintenance/alert-service.md' - '6. InfluxDB - raw data': 'tutorials/predictive-maintenance/influxdb-raw-data.md' - '7. InfluxDB - alerts': 'tutorials/predictive-maintenance/influxdb-alerts.md' - # - '8. Printers dashboard': 'tutorials/predictive-maintenance/printers-dashboard.md' - # - '8. Lab: Add phone alerts': 'tutorials/predictive-maintenance/phone-alerts.md' - '8. Summary': 'tutorials/predictive-maintenance/summary.md' - # Pulling this one as apparently the app is no longer available in Google Store - #- 'Real-time event detection': - # - 'Overview': 'tutorials/event-detection/overview.md' - # - '1. Data acquisition': 'tutorials/event-detection/data-acquisition.md' - # - '2. Event detection': 'tutorials/event-detection/crash-detection.md' - # - '3. Demo UI': 'tutorials/event-detection/crash-detection-ui.md' - # - '4. Conclusion': 'tutorials/event-detection/conclusion.md' - Blog Index: - blog/index.md @@ -809,6 +800,8 @@ plugins: 'get-started/produce.md': 'get-started/streaming.md' 'get-started/consume.md': 'get-started/streaming.md' 'get-started/process.md': 'get-started/streaming.md' + 'tutorials/predictive-maintenance/printers-dashboard.md': 'tutorials/predictive-maintenance/overview.md' + 'tutorials/predictive-maintenance/phone-alerts.md': 'tutorials/predictive-maintenance/overview.md'

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zi%wns%<*|=XS`{V5?${ndP|-PT=FgtW(YUtXzj;Ibmbepwk5hOz>`)^<@ba_uAcuJ z+5UGCH98Z;^$|c)%L4(Zhy)5Rb<1yL{s`0S)h1fkWH)L|l;i>6j{9HR3WCf};J?1b z&3L(i`;&y;A&5f!C9CvsLDaNpqlr;Y{;k92K0ve~*qZqgPx0F?;2*K+wk$;Nos2kp z^KC#&>>J#Q7|iwb^;=s6{9R;@Q+T{$s?>p0Y!}%MjGve{v!yx+I9z_KU{9h}hSGIB z;X=m=0f1#w9#7cS?gs$R9~%wt&1FsbJA%HcGw#h~tqA@r0j?OEU&_C5^WlF2`e;a; diff --git a/docs/tutorials/train-and-deploy-ml/conclusion.md b/docs/tutorials/train-and-deploy-ml/conclusion.md deleted file mode 100644 index b2ca351b..00000000 --- a/docs/tutorials/train-and-deploy-ml/conclusion.md +++ /dev/null @@ -1,27 +0,0 @@ -# Conclusion - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this tutorial you've learned how to use Quix to generate real-time data. You've also learned how to import that data into Jupyter Notebook using the Quix code generator. You then saw how to deploy your ML model to Quix, and visualize its output in real time. - -![Data explorer](./images/visualize-result.png) - -The objective of the tutorial was not to create the most accurate model, but to step you through the overall process, and show you some of the useful features of Quix. It shows one possible workflow, where you train your ML model in Jupyter Notebook, and the integration between Quix and Jupyter was demonstrated. It is also possible to train your ML model directly in Quix, on live data. - -## Next Steps - -Here are some suggested next steps to continue on your Quix learning journey: - -* Visit the [Quix Code Samples GitHub](https://github.com/quixio/quix-samples){target=_blank}. If you decide to build your own connectors and apps, you can contribute something to the Quix Code Samples. Fork our Code Samples repo and submit your code, updates, and ideas. - -* [Chat sentiment analysis tutorial](../sentiment-analysis/overview.md) - In this tutorial you learn how to build a chat sentiment analysis pipeline, capable of analyzing real-time chat. - -* [Real-time Machine Learning (ML) predictions](../data-science/overview.md) - In this tutorial you use data science to build a real-time bike availability pipeline. - -What will you build? Let us know! We’d love to feature your application or use case in our [newsletter](https://www.quix.io/community/). - -## Getting help - -If you need any assistance, we're here to help in the [Quix Community](https://quix.io/slack-invite){target=_blank}. \ No newline at end of file diff --git a/docs/tutorials/train-and-deploy-ml/create-data.md b/docs/tutorials/train-and-deploy-ml/create-data.md deleted file mode 100644 index 5e73e2d7..00000000 --- a/docs/tutorials/train-and-deploy-ml/create-data.md +++ /dev/null @@ -1,48 +0,0 @@ -# Get your data - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -!!! danger "Legacy feature" - - This feature is not available to new users. However, legacy users may still have access to this functionality. - -In this part of the tutorial you learn how to obtain some real-time data to work with in the rest of the tutorial. You use a demo data source that generates Formula 1 race car data from a computer game. You use this data as the basis to build a ML model to predict braking patterns. - -## Create a persisted topic - -To make things a little easier, first create a **persisted topic** to receive the generated data. - -1. Log in to Quix. - -2. Click `Topics` on the left-hand menu. - -3. Click the `Add new`, which is located top right. - -4. Enter a topic name of `f1-data`. - -5. Leave other values in the `Create new topic` dialog at their defaults. - -6. Click `Done`. Now wait while the topic is created for you. - - -## Generate data from the demo data source - -Now generate the actual data for use later in the tutorial by completing the following steps: - -1. Click `Code Samples` on the left-hand sidebar. - -2. Find the `Demo Data` source. This service streams F1 Telemetry data into a topic from a recorded game session. - -3. Click the `Deploy` button in the `Demo Data` panel. - -4. You can leave `Name` as the default value. - -5. Make sure `Topic` is set to `f1-data` and then click `Deploy`. - -Once this service is deployed it will run as a [job](../../kb/glossary.md#job) and generate real-time data to the `f1-data`. - -This data is retrieved later in this tutorial using Python code generated for you by Quix. - -[Import data into Jupyter Notebook :material-arrow-right-circle:{ align=right }](./import-data.md) diff --git a/docs/tutorials/train-and-deploy-ml/deploy-ml.md b/docs/tutorials/train-and-deploy-ml/deploy-ml.md deleted file mode 100644 index a94d5196..00000000 --- a/docs/tutorials/train-and-deploy-ml/deploy-ml.md +++ /dev/null @@ -1,190 +0,0 @@ -# Deploy your ML model - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this part of the tutorial, you deploy the Pickle file [containing your ML model](./train-ml.md) to Quix. Your ML code then uses this model to predict braking in real time. - -![What you'll build](./images/run-live.png) - -You'll also use the Quix Data Explorer to visualize the braking prediction in real time. - -## Create a transform - -Ensure you are logged into Quix, then follow these steps to create a transform that uses your model: - -1. Click `Code Samples` in the left-hand sidebar. - -2. Filter the Code Samples by selecting `Python` under `LANGUAGES` and `Transformation` under `PIPELINE STAGE`. - -3. Locate the `Event Detection` item. - -4. Click `Preview code` in the `Event Detection` panel. You can browse the files to ensure you have the correct sample. - -5. Click `Edit code`. - -6. Change the name to `Prediction Model`. - -7. Ensure the input is `f1-data`. - -8. Leave output as `hard-braking` (its default value). - -9. Click `Save as Application`. The code is now saved to your environment. - - !!! tip - - You can see a list of applications at any time by clicking `Applications` in the left-hand navigation. - -## Upload the model - -Now you need to upload your ML model and edit your transform code to run the model. - -1. Click on `Applications` and select `Prediction Model` to display your application code. - -2. Click the `Upload File` icon at the top of the file list, as shown in the following screenshot: - - ![Upload file to application](./images/upload-file-to-application.png) - -3. Find the Pickle file containing your ML model. It's named `decision_tree_5_depth.sav` and is in the same directory as your Jupyter Notebook files. - - !!! warning - - When you click off the file, for example onto `quix_function.py`, the editor may prompt you to save the `.sav` file. **Click `Discard changes`**. - -4. Click `quix_function.py` in the file list (remember, **do not** commit changes to the model file). - -## Modify the transform code - -You need to modify your transform code to work with the ML model in the Pickle file. In the file `quix_function.py`: - -1. Add the following statements to import the required libraries: - - ``` python - import pickle - import math - ``` - -2. In the `__init__` function add the following lines to load the model: - - ``` python - ## Import ML model from file - self.model = pickle.load(open('decision_tree_5_depth.sav', 'rb')) - ``` - -3. Under the `__init__` function add the following new function to preprocess the data: - - ``` python - ## To get the correct output, we preprocess data before we feed them to the trained model - def preprocess(self, df): - - signal_limits = { - "Speed": (0, 400), - "Steer": (-1, 1), - "Gear": (0, 8), - "Motion_WorldPositionX": (-math.pi, math.pi), - "Brake": (0, 1), - } - - def clamp(n, minn, maxn): - return max(min(maxn, n), minn) - - for signal, limits in signal_limits.items(): - df[signal] = df[signal].map(lambda x: clamp(x, limits[0], limits[1])) - - df["Motion_WorldPositionX_sin"] = df["Motion_WorldPositionX"].map(lambda x: math.sin(x)) - df["Motion_WorldPositionX_cos"] = df["Motion_WorldPositionX"].map(lambda x: math.cos(x)) - - return df - ``` - -4. Replace the `on_dataframe_handler` function with the following code: - - ``` python - # Callback triggered for each new parameter data. - def on_dataframe_handler(self, stream_consumer: qx.StreamConsumer, df: pd.DataFrame): - - # if no speed column, skip this record - if not "Speed" in df.columns: - return df - - output_df = pd.DataFrame() - - # Preprocessing - df = self.preprocess(df) - - features = ["Motion_WorldPositionX_cos", "Motion_WorldPositionX_sin", "Steer", "Speed", "Gear"] - X = df[features] - - # Shift data into the future by 5 seconds. (Note that time column is in nanoseconds). - output_df["timestamp"] = df["timestamp"].apply(lambda x: int(x) + int((5 * 1000 * 1000 * 1000))) - output_df["brake-prediction"] = self.model.predict(X) - - print("Prediction") - print(output_df["brake-prediction"]) - - # Merge the original brake value into the output data frame - output_df = pd.concat([df[["timestamp", "Brake"]], output_df]).sort_values("timestamp", ascending=True) - - self.producer_stream.timeseries.buffer.publish(output_df) # Send filtered data to output topic - ``` - -5. Commit your changes to the `quix_function.py` file by clicking `Commit` or using ++ctrl+s++ or ++command+s++. - -## Update the `requirements.txt` file - -Click on the `requirements.txt` file and add `scikit-learn` on a new line. Commit your change. - -!!! success - - You have edited the code to load and run the model. - -## Run the code - -The simplest way to run the code is to click the `Run` button in the top right-hand corner. - -This will install any dependencies into a sandboxed environment and then run the code. - -The output console displays the result of the prediction. - -In the next few steps you deploy the code and then see a visualization of the output. - -## Deploy - -To deploy your transform: - -1. Click `Stop` if you haven't already done so. - -2. To deploy the code, click `Deploy`. - -3. In the dialog that appears click `Deploy`. - -Once the code has built, it is started automatically. - -!!! success - - Your code is now running in a production-ready ecosystem. - -## Visualize your data - -To see the output of your model in real time you can use the Data Explorer. To use the Data Explorer: - -1. Click the Data Explorer button on the left-hand sidebar. - -2. If it's not already selected click the `Live` data tab at the top. - -3. Ensure the `hard-braking` topic is selected from the `Select a topic` dropdown list. - -4. Select a stream (you should only have one). - -5. Select `brake-prediction` and `brake` from the parameters list. - -You now see a graphical output for the prediction being output by the model as well as the actual brake value, as illustrated in the following screenshot - -![Data explorer](./images/visualize-result.png) - -!!! note - - Don't forget the purpose of this tutorial was to show you how to deploy an ML model in Quix, rather than to train an accurate model. 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a/docs/integrations/databases/influxdb/replacing-kapacitor.md +++ b/docs/integrations/databases/influxdb/replacing-kapacitor.md @@ -7,7 +7,7 @@ search: # Replacing Kapacitor -It's possible to replace Kapacitor with Quix. The basic process is: +It's possible to replace Kapacitor with Quix. The basic process steps are: 1. Use Quix's prebuilt connectors to connect to InfluxDB v2 or InfluxDB v3 databases, as required. 2. Implement your Flux or TICKscript tasks in Python, using the Quix Streams client library. @@ -28,7 +28,7 @@ The following illustrates a typical processing pipeline running in Quix. | Integration with InfluxDB | Kapacitor seamlessly integrates with InfluxDB, enabling users to perform advanced analytics and processing on the time-series data stored in InfluxDB. | Quix connects to both InfluxDV v2 and InfluxDB v3 using prebuilt connectors. The v3 connectors include both source and sink. For v2, only source is currently available. If you want to migrate data from InfluxDB v2 to InfluxDB v3 then read [the tutorial](../../../tutorials/influxdb-migration/overview.md). | | Scalability | Kapacitor is designed to be horizontally scalable, enabling it to handle large volumes of data and scale alongside the rest of the TICK Stack components. | Quix was designed to be both vertically and horizontally scalable. It is based on [Kafka](../../../kb/what-is-kafka.md), using either a Quix-hosted broker, or an externally hosted broker. This means all the horizontal scaling features of Kafka, such as consumer groups, is built into Quix. Quix also enables you to configure the number of replicas, RAM, and CPU resources allocated on a per-service (deployment) basis, for accurate vertical scaling. | | High availability | Kapacitor supports high availability setups to ensure uninterrupted data processing and alerting even in the case of node failures. | As Quix uses a Kafka broker (including Kafka-compatible brokers such as Redpanda), it has all the high availability features inherent in a Kafka-based solution. In addition, Quix uses a Kubernetes cluster to seamlessly distribute and manage containers that execute your service's Python code. | -| Replay and backfilling | Kapacitor enables users to replay historical data or backfill missing data, enabling them to analyze past events or ensure data consistency. | Quix leverages Kafka's persistence capabilities. Once a topic is persisted, you can use Quix's built-in [replay](../../../manage/replay.md) facility, to replay persisted data into topics. This is useful for testing and evaluating processing pipelines, and examining historical data. This is also enhanced by the ability to use the built-in Quix Data Explorer, or connect to external tools, using Quix connectors. | +| Replay and backfilling | Kapacitor enables users to replay historical data or backfill missing data, enabling them to analyze past events or ensure data consistency. | Quix leverages Kafka's retention capabilities for data backfilling and analysis. You can process historical data stored in Kafka topics using standard Kafka consumer patterns. This is useful for testing and evaluating processing pipelines, and examining historical data. This is also enhanced by the ability to connect to external tools using Quix connectors. | | Extensibility | Kapacitor provides an extensible architecture, enabling users to develop and integrate custom functions, connectors, and integrations as per their specific requirements. | Quix is fully extensible using Python. Complex stream processing pipelines can be built out one service at a time, and then deployed with a single click. It is also possible to use a wide range of standard [connectors](../../../connectors/index.md) to connect to a range of third-party services. Powerful [integrations](../../overview.md) extend these capabilities. In addition, [REST and real-time APIs](../../../develop/apis-overview.md) are available for use with any language that supports REST or WebSockets. As Quix is designed around standard Git development workflows, it enables developers to collaborate on projects. | ## See also diff --git a/docs/manage/overview.md b/docs/manage/overview.md index 238b5e8e..15d7456d 100644 --- a/docs/manage/overview.md +++ b/docs/manage/overview.md @@ -49,34 +49,6 @@ The message view shows the raw message format. The message viewer is available n The data explorer also has a message viewer tab, as can be seen in the [data explorer section](#data-explorer). -## Persistence - -!!! danger "Legacy feature" - - This feature is not available to new users. However, legacy users may still have access to this functionality. - -While data in a Kafka topic is retained according to the topic retention time configured when you create a new topic: - -![New topic](../images/manage/new-topic.png) - -Quix also has an additional capability called persistence. Enabling persistence is done by simply activating a slider control: - -![Enable persistence](../images/manage/topic-persistence.png) - -Enabling this feature persists topic data into a database for later retrieval or for replay using the replay service. - -The replay service is used to play back persisted data into a topic. - -## Replay service - -!!! danger "Legacy feature" - - This feature is not available to new users. However, legacy users may still have access to this functionality. - -The replay service enables you to play persisted data back into a topic. - -You can read more about the [replay service](./replay.md) in the docs. - ## Portal API The Portal API enables you to programmatically control Quix, and most objects in it. Using the Portal API you can manage entities including: diff --git a/docs/manage/replay.md b/docs/manage/replay.md deleted file mode 100644 index 6178d624..00000000 --- a/docs/manage/replay.md +++ /dev/null @@ -1,49 +0,0 @@ -# How to replay data - -!!! danger "Legacy feature" - - This feature is not available to new users. However, legacy users may still have access to this functionality. - -Quix features a **replay service**. This service enables you to replay persisted data into a topic, as if it were live data. This is very useful for the following use cases: - -* Testing and debugging connectors and transforms -* [Stream reprocessing](https://quix.io/blog/intro-stream-reprocessing-python/?x-craft-preview=VDVjwJTquq&token=7oGSdC9yxYk0zECNUz2RtzJLtGqG-aZB){target=_blank} -* Testing and retraining ML models - -Once you have persisted data, it is possible to set up a replay service in your pipeline. Rather than using the live source of data, you can replay the persisted data into a topic in your pipeline, and monitor the processing. - -It is possible to configure the replay in various ways, for example: - -* You can edit the destination topic. This means you can play back the same persisted data into different topics for testing and debugging. -* You can replay the data at the original speed or at maximum speed. -* You can replay the data with either the original timestamps, or simulated timestamps. The simulated timestamps are based on present time, rather than the original timestamps. - -Once created, a replay service looks like any other service in your pipeline, and you can play or pause the streaming of data as required. - -## To replay persisted data into a topic - -You can only replay persisted data, so you need to persist a topic first. In your Quix environment, select `Topics` in the left-hand sidebar to list all your topics. Ensure the topic you want to persist has persistence switched on, as shown in the following screenshot: - -![Enable persistence](../images/replay/replay-add-persist-topic.png) - -Click `Pipeline` in your Quix environment, and then to create a new replay, click `Add new` in the top right corner: - -![Add replay](../images/replay/replay-add-new.png) - -Then select the `Replay` menu item. This displays the `Add replay` wizard. - -Select the topic with persisted data you want to replay from (only topics with persisted data will be shown), then select the stream whose data you want to replay: - -![Replay select stream](../images/replay/replay-select-stream.png) - -Then click `Next`. - -Now configure your replay according to your use case. For example, you can retain the data’s original timestamps, or select simulated timestamps for timestamps based on the present time. You also need to select the output topic (the topic the data is played into): - -![Configure replay](../images/replay/replay-configuration.png) - -Once you have completed your configuration, click `Add replay`. - -Switch to `Pipeline` view. The replay is created as a service in your pipeline. The service may take some time to load. You can then replay data into your pipeline using the play/pause control, as if it were live data: - -![Replay persisted data](../images/replay/replay-play-data.png) diff --git a/docs/tutorials/data-science/3-data.md b/docs/tutorials/data-science/3-data.md index 7cce58c7..ebbd3a23 100644 --- a/docs/tutorials/data-science/3-data.md +++ b/docs/tutorials/data-science/3-data.md @@ -10,7 +10,6 @@ With Quix it's easy to visualize your data in a powerful and flexible way, you can see the data in real time, as well as viewing historical data. -Quix was designed for real-time data, so if you want to see data-at-rest for any topic you must turn on data persistence for that specific topic. You'll do this in the [historical data](#historical-data) section. ## Real-time data @@ -40,22 +39,8 @@ Follow these steps to view real-time data as it arrives in your topics: Now you know how to observe data arriving into your topics. You can also explore the `Waveform` tab to see numeric data in a graphical form and the `Messages` tab to see the raw, [JSON format](https://en.wikipedia.org/wiki/JSON){target=_blank}, messages. -## Historical data +## Next step -In order to train a machine learning model on historical data, the live real-time data being ingested needs to be stored. However, topics are real time and therefore not designed for data storage. To solve this, Quix enables you to store the data going through a topic to an efficient real-time database if you need it. - -Enable persistence on your topics: - -1. Navigate to the `Topics` page using the left-hand navigation. - -2. Locate the topic(s) you want to store data for (in this case `bikes-topic` and `weather-topic`). - -3. For each topic, click the toggle in the `Persistence` column to `on`. - -4. Finally, go back to the `Pipeline` page. - - Now, to ensure there is some historical data stored in the weather topic, stop and then start the `VisualCrossing Weather` service. This will force the service to collect and publish fresh data to the `weather-topic` without waiting for 30 minutes. - - You will need this historical data in the next section, where you will learn how to retrieve data for training a model. +You will need data in the next section, where you will learn how to retrieve data for training a model. [Part 4 - Get data to train a model :material-arrow-right-circle:{ align=right }](4-train.md) diff --git a/docs/tutorials/train-and-deploy-ml/conclusion.md b/docs/tutorials/train-and-deploy-ml/conclusion.md index 8a9c1fed..b2ca351b 100644 --- a/docs/tutorials/train-and-deploy-ml/conclusion.md +++ b/docs/tutorials/train-and-deploy-ml/conclusion.md @@ -8,7 +8,7 @@ In this tutorial you've learned how to use Quix to generate real-time data. You' ![Data explorer](./images/visualize-result.png) -The objective of the tutorial was not to create the most accurate model, but to step you through the overall process, and show you some of the useful features of Quix. It shows one possible workflow, where you train your ML model in Jupyter Notebook, and the integration between Quix and Jupyter was demonstrated. It is also possible to train your ML model directly in Quix, on live data, or persisted data using the [replay functionality](../../manage/replay.md). +The objective of the tutorial was not to create the most accurate model, but to step you through the overall process, and show you some of the useful features of Quix. It shows one possible workflow, where you train your ML model in Jupyter Notebook, and the integration between Quix and Jupyter was demonstrated. It is also possible to train your ML model directly in Quix, on live data. ## Next Steps diff --git a/docs/tutorials/train-and-deploy-ml/create-data.md b/docs/tutorials/train-and-deploy-ml/create-data.md index c6e25ad8..5e73e2d7 100644 --- a/docs/tutorials/train-and-deploy-ml/create-data.md +++ b/docs/tutorials/train-and-deploy-ml/create-data.md @@ -26,9 +26,6 @@ To make things a little easier, first create a **persisted topic** to receive th 6. Click `Done`. Now wait while the topic is created for you. -7. Once the topic has been created, click the persistence slider button to ensure your data is persisted, as shown in the following screenshot: - - ![Enable topic persistence](./images/enable-topic-persistence.png) ## Generate data from the demo data source @@ -44,7 +41,7 @@ Now generate the actual data for use later in the tutorial by completing the fol 5. Make sure `Topic` is set to `f1-data` and then click `Deploy`. -Once this service is deployed it will run as a [job](../../kb/glossary.md#job) and generate real-time data to the `f1-data`, and this will be persisted. +Once this service is deployed it will run as a [job](../../kb/glossary.md#job) and generate real-time data to the `f1-data`. 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zg0diwfVwR)@H6FVJ}w9jHg4~3a7#@v5sw<;VI-Nnl*Sm8<0Yh@Qdp=gpV1MgJ)%KL4rT{<|v3aY+FTCKI{12Nd}&S3c6urfS3RO7G9jf!zFnrEptq}0MB3r+RJ#GB%^Y$&+0lJ?54q-r9fKQnFNw>1SE$YHrqEd8c{zbW#?1y;Vwx2c2O$ zlvVf(R=@#aAaupBj-o@+U`D30q0`j-26Vicr}tmo-fp0)5n45aVo7zQ&NsLjEl1xu zbO^}T;R5cm(}}mR>uX}-5UOFyFimf;i+R%BUam2m67o!|-RGTS%rcJMPbc0Bdc+*r= zVAQ!@u&N27=Cj_MsX}<^EScEnp_7)b)o;)q2P}vd#fYfLob?Hb8!H7hwf3t0yzj zdAZg@tO1Ga9jtEXYQdo|Q<~co1k7S?aMw0X1aCeB5NLvh62v34aq7=_`7#~<^Jx&B Wes9;rT>t0Zr(68DQKaNOdH)6F0#$JU diff --git a/mkdocs.yml b/mkdocs.yml index a70a8fc4..11f994d8 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -291,7 +291,6 @@ nav: - 'Troubleshooting': 'manage/troubleshooting.md' - 'Security': 'manage/security.md' - 'MLOps': 'manage/MLOps.md' - - 'Using replay service': 'manage/replay.md' - 'Testing multiple environments': 'manage/testing-environments.md' - 'Portal API': - 'Overview': 'apis/portal-api/overview.md' @@ -776,7 +775,6 @@ plugins: 'connectors/slacksink-destination.md': 'quix-connectors/templates/slacksink-destination.md' 'connectors/websocketdestination-destination.md': 'quix-connectors/templates/websocketdestination-destination.md' 'platform/how-to/personal-access-token-pat.md': 'develop/authentication/personal-access-token.md' - 'platform/how-to/replay.md': 'manage/replay.md' 'platform/how-to/create-project.md': 'create/create-project.md' 'client-library-intro.md': 'quix-streams/introduction.md' 'quix-streams/client-library-intro.md': 'quix-streams/introduction.md' From 032cd37e9d6af91af73e34573201b5a96daac5af Mon Sep 17 00:00:00 2001 From: Peter Nagy Date: Thu, 7 Aug 2025 16:44:19 +0100 Subject: [PATCH 04/10] Remove outdated tutorials and redirect them instead --- docs/tutorials/clickstream/change-offer.md | 149 ----- .../clickstream/clickstream-producer.md | 109 ---- .../tutorials/clickstream/data-aggregation.md | 148 ----- docs/tutorials/clickstream/data-enrichment.md | 109 ---- docs/tutorials/clickstream/data-ingestion.md | 71 --- 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Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this lab you use what you've learned so far, to modify the offer. You develop this change on a feature branch, and then you create a PR to merge your new feature into the `tutorial` branch after testing. This is a common pattern for development - you can test your new service on the feature branch, and then test again, before final integration into the production `main` branch. - -## Create an environment - -To create a new environment (and branch): - -1. Click `+ New environment` to create a new environment (note, your screen will look slightly different to the one shown here): - - ![New environment](./images/new-environment.png) - -2. Create a new environment called `Change Offer`. - -3. Create a new branch called `change-offer`. To do this, from the branch dropdown click `+ New branch` which displays the New branch dialog. - - !!! important - - Make sure you branch from the `tutorial` branch, not `main`, as you are going to merge your changes back to the `tutorial` branch. - -4. Complete creation of the environment using the default options. - -5. On the projects screen, click your newly created environment, `Change Offer`. - -## Sync the environment - -You now see that the Quix environment is out of sync with the Git repository. You need to synchronize the Quix view of the environment, with that stored in the repository. - -To synchronize Quix with the repository: - -1. Click `Sync environment`: - - ![Sync environment](./images/sync-environment.png) - - The sync environment dialog is displayed, showing you the changes that are to be made to the `quix.yaml` file, which is the configuration file that defines the pipeline. - -2. Click `Sync environment`, and then `Go to pipeline`. - - In the pipeline view, you see the services building. Ensure all services are "Running" before continuing. - -## Modify the code - -You now modify the code for the `Event detection` service. Currently the offer is triggered for the age ranges configured, and then by visiting Clothing -> Shoes -> Clothing. In this task you change the offer to be for a slightly different demographic (60 to 65 for both genders), and then get triggered by Shoes -> Clothing -> Shoes. - -In `behaviour_detector.py` modify the `transitions` dictionary as follows: - -``` python -transitions = { - "init": [ - { - "condition": lambda row, current_state: row["category"] == "shoes" - and ((row["gender"] == "M" and 60 <= row["age"] <= 65) - or (row["gender"] == "F" and 60 <= row["age"] <= 65)), - "next_state": "shoes_visited", - } - ], - "shoes_visited": [ - { - "condition": lambda row, current_state: row["category"] == "clothing", - "next_state": "clothes_visited" - }, - { - "condition": lambda row, current_state: row["category"] == "shoes", - "next_state": "shoes_visited" - } - ], - "clothes_visited": [ - { - "condition": lambda row, current_state: row["category"] == "shoes" - and row["productId"] != current_state["rows"][0]["productId"], - "next_state": "offer" - }, - { - "condition": lambda row, current_state: row["category"] == "shoes" - and row["productId"] == current_state["rows"][0]["productId"], - "next_state": "shoes_visited" - } - ] -} -``` - -Now that you've modified the code, you'll now test your changes: - -1. Tag your changes, for example as `change-offer-v1`. - -2. Click the `Redeploy` button and select your changed version from the `Version tag` dropdown. - -3. Once the `Event detection` service is running again, reload the web shop. - -4. Select an gender and age in the new range, for example, 65 and female. - -5. Now view shoes, clothing and shoes again in that order, and the offer is displayed. - -## Merge the feature - -Once you are sure that the changes on your feature branch are tested, you can then merge your changes onto the `tutorial` branch. Here your changes undergo further tests before finally being merged into production. - -To merge your feature branch, `change-offer` into `tutorial`: - -1. Select `Merge request` from the menu as shown: - - ![Merge request menu](./images/merge-request-menu.png) - -2. In the `Merge request` dialog, set the `change-offer` branch to merge into the `tutorial` branch. - -You are going to create a pull request, rather than perform a direct merge. This enables you to have the PR reviewed in GitHub (or other Git provider). You are also going to do a squash and merge, as much of the feature branch history is not required. - -To create the pull request: - -1. Click `Create pull request`. You are taken to your Git provider, in this case GitHub. - -2. Click the `Pull request` button. - -3. Add your description, and then click `Create pull request`. - -4. Get your PR reviewed and approved. Then squash and merge the commits: - - ![Squash and merge](./images/squash-and-merge.png) - - You can replace the prefilled description by something more succinct. Then click `Confirm squash and merge`. - - !!! tip - - You can just merge, you don't have to squash and merge. You would then retain the complete commit history for your service while it was being developed. Squash and merge is used in this case by way of example, as the commit messages generated while the service was being developed were deemed to be not useful in this case. - -## Resync the environment - -You have now merged your new feature into the `tutorial` branch in the Git repository. Your Quix view in the Tutorial environment is now out of sync with the Git repository. If you click on your Tutorial environment in Quix, you'll see it is now a commit (the merge commit) behind. - -You now need to make sure your Tutorial environment in Quix is synchronized with the Git repository. To do this: - -1. Click on `Sync environment`. The `Sync environment` dialog is displayed. - -2. Review the changes and click `Sync environment`. - -3. Click `Go to pipeline`. - -Your modified event detection service builds and starts in the Tutorial environment, where you can now carry out further testing. When you are satisfied this feature can be released to production, you would then repeat the previous process to merge your changes to Production `main`. - -## 🏃‍♀️ Next step - -[Part 10 - Summary :material-arrow-right-circle:{ align=right }](summary.md) - - - diff --git a/docs/tutorials/clickstream/clickstream-producer.md b/docs/tutorials/clickstream/clickstream-producer.md deleted file mode 100644 index 0dd3a486..00000000 --- a/docs/tutorials/clickstream/clickstream-producer.md +++ /dev/null @@ -1,109 +0,0 @@ -# Clickstream producer - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -In this project the clickstream data is read from a CSV file. This data represents fifteen days of shopping data gathered from a real online store. - -![pipline section](./images/clickstream-pipeline-section-producer.png) - -## Data published - -A subset of this data is then published to the `click-data` topic: - -* `ip` - the IP address of the customer -* `original_timestamp` - the Unix timestamp for the interaction -* `productId` - product ID (the product ID is embedded in the URL for the product viewed) -* `userAgent` - browser details for the client -* `userId` - UUID for the user - -This service runs continually, reloading the data from the CSV file. There's also an option (`keep_timing`) to keep the original timing of the data - this is the default in the ungated experience (the live demo). - -## Exploring the message format - -If you click `Topics` in the main left-hand navigation you see the topics in the environment: - -![Clickstream Topics](./images/clickstream-topics.png) - -Click in the `Data` area to view live data. This takes you into the Quix data explorer. You can then select the stream and parameter data you'd like to explore. You can then view this data in either the `Table` or `Messages` view. - -If you look at the messages in the `Messages` view, you'll see data has the following format: - -``` json -{ - "Epoch": 0, - "Timestamps": [ - 1699973201619326000 - ], - "NumericValues": { - "original_timestamp": [ - 1331802578 - ] - }, - "StringValues": { - "userId": [ - "F6F8B460-4204-4C26-A32C-B93826EDCB99" - ], - "ip": [ - "75.85.165.38" - ], - "userAgent": [ - "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8) AppleWebKit/534.52.7 (KHTML, like Gecko) Version/5.1.2 Safari/534.52.7" - ], - "productId": [ - "VD55179433" - ] - }, - "TagValues": {} -} -``` - -The Quix data explorer is a very useful tool for debugging and monitoring your pipeline. - -## Viewing the deployed application - -In the left-hand main navigation, click `Deployments` to see all the deployed services and jobs in the environment: - -![Clickstream deployments](./images/clickstream-deployments.png) - -Click `Clickstream producer` to select the deployment. This takes you to an extremely useful screen where you can: - -1. View the status of the deployment (such as CPU, memory usage, and replicas assigned). -2. See the live logs for the service. -3. See the topic lineage for the service. -4. Access Build logs (in case of errors when the service is built). -5. Access the Messages tab, where you can then see messages associated with the service in real time. - -## Viewing the application code - -There are many ways to view the code for the application (which is then deployed as a job or service). The quickest way from the current screen is to click the area shown: - -![Go to code view](./images/clickstream-deployment-code-view.png) - -You'll now be in the code view with the **version of the deployed code** displayed. - -If you review the code, you'll see that the CSV file is read on a background thread, and data published to streams based on the `userId`: - -``` python -stream_producer = producer_topic.get_or_create_stream(row['userId']) -stream_producer.timeseries.publish(df_row) -``` - -If you keep the original timing of the data, that is respected, although the delays are kept within 0 and 10 seconds. If `keep_timing` is `False` then a delay of 200ms is used between publishing data: - -``` python -if not keep_timing: - # Don't want to keep the original timing or no timestamp? That's ok, just sleep for 200ms - time.sleep(0.2) -``` - -Feel free to explore the code further. - -## Further reading - -* [Read a CSV file](../../develop/integrate-data/read-csv.md) - -## 🏃‍♀️ Next step - -[Part 3 - Data ingestion :material-arrow-right-circle:{ align=right }](./data-ingestion.md) diff --git a/docs/tutorials/clickstream/data-aggregation.md b/docs/tutorials/clickstream/data-aggregation.md deleted file mode 100644 index f6e32e5e..00000000 --- a/docs/tutorials/clickstream/data-aggregation.md +++ /dev/null @@ -1,148 +0,0 @@ -# Data aggregation - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -This service subscribes to the `enriched-click-data` topic. Any data received is aggregated, and the results written to Redis Cloud. These aggregations are then consumed by a Streamlit dashboard (Real-time dashboard) for visualization and analysis. [RocksDB](https://rocksdb.org/) is used to hold state for the aggregation calculations. - -The real-time dashboard displays: - -* Visitors in the last 15 minutes -* Sessions in the last 8 hours -* Devices connected right now (number and type) -* Top 10 viewed pages in the last hour -* Latest visitor details -* Category popularity in the last hour -* State machine log (useful for troubleshooting) -* Raw data view - -![Data aggregation](./images/data-aggregation-pipeline-segment.png) - -## Last hour of data - -For the last hour of data the data format is: - -``` python -columns = { - "timestamp": pd.Series(dtype='datetime64[ns]'), - "original_timestamp": pd.Series(dtype='int'), - "userId": pd.Series(dtype='str'), - "ip": pd.Series(dtype='str'), - "userAgent": pd.Series(dtype='str'), - "productId": pd.Series(dtype='str'), - "category": pd.Series(dtype='str'), - "title": pd.Series(dtype='str'), - "gender": pd.Series(dtype='str'), - "country": pd.Series(dtype='str'), - "deviceType": pd.Series(dtype='str'), - "age": pd.Series(dtype='int'), - "birthdate": pd.Series(dtype='datetime64[ns]'), - "datetime": pd.Series(dtype='datetime64[ns]') -} - -initial_df = pd.DataFrame(columns) -``` - -For each new dataframe received, the following is performed in the data handler routine: - -1. `last_hour_read` is read from the database (RocksDB). -2. Dataframe is concatenated on the accumulated data in `last_hour_data`. -3. `last_hour_read` is written back to the database (RocksDB). - -``` python -def on_dataframe_handler(stream_consumer: qx.StreamConsumer, df: pd.DataFrame): - ... - # Append data to last_hour_data and save to database - last_hour_data = db["last_hour_data"] - last_hour_data = pd.concat([last_hour_data, df], ignore_index=True) - db["last_hour_data"] = last_hour_data -``` - -## Last eight hours - -For the last eight hours of data, the data format is: - -``` python -db["eight_hours_aggregation"] = pd.DataFrame(columns=["datetime", "userId", "count"]) -``` - -For each new dataframe received, the following is performed in the data handler routine: - -1. `eight_hours_aggregation` is read from then database (RocksDB). -2. Data is aggregated (every 30 minutes based on datetime and userId) to give a count of sessions for each datetime, for example, Datetime: 15:00, Count: 159. -3. Concatenated onto `eight_hours_aggregation`. -4. `eight_hours_aggregation` is written back to the database (RocksDB). - -``` python -def on_dataframe_handler(stream_consumer: qx.StreamConsumer, df: pd.DataFrame): - ... - eight_hours_aggregation = db["eight_hours_aggregation"] - aggregated = df.groupby([pd.Grouper(key='datetime', freq='30min'), 'userId']).size().reset_index(name='count') - - # Add df_copy to eight_hours_aggregation. If the datetime is the same, add both counts - eight_hours_aggregation = (pd.concat([eight_hours_aggregation, aggregated]) - .groupby(['datetime', 'userId']).sum().reset_index()) - db["eight_hours_aggregation"] = eight_hours_aggregation -``` - -## Writing aggregations to Redis Cloud - -Aggregations are sent to Redis Cloud using a background Thread that runs every one second. The data written to Redis Cloud is: - -* Last hour of data -* Eight hours aggregation -* Visits in last fifteen minutes -* Products viewed in last hour -* Visitor details for the last ten minutes -* Popularity by category -* Device popularity - -This is done by the following code: - -``` python -def send_data_to_redis(): - while True: - try: - # Append data and discard data older than 1 hour - last_hour_data = db["last_hour_data"] - one_hour = pd.to_datetime(pd.Timestamp.now()) - pd.Timedelta(hours=1) - updated_last_hour_data = last_hour_data[last_hour_data["datetime"] > one_hour] - - if not updated_last_hour_data.equals(last_hour_data): - last_hour_data = updated_last_hour_data - db["last_hour_data"] = last_hour_data - - # This method uses its own rolling window, so we only have to pass the buffer - aggregate_eight_hours(db["eight_hours_aggregation"].copy()) - - # Get average visits in the last 15 minutes - calculate_visits_last_15min(last_hour_data.copy()) - - # Get top viewed productId in the last hour, keep only productId, category and count - calculate_products_last_hour(last_hour_data.copy()) - - # Get latest 10 visitors details (date and time, ip and country) - calculate_10_last_visitors(last_hour_data.copy()) - - # Get category popularity in the last hour - calculate_category_popularity(last_hour_data.copy()) - - # Get device type popularity in the last 10 minutes - calculate_device_popularity(last_hour_data.copy()) - - # Send all data - sorted_data = last_hour_data.sort_values(by='datetime', ascending=False) - r.set("raw_data", sorted_data.head(100).to_json()) - - # Sleep for 1 second - time.sleep(1) - except Exception as e: - print("Error in sender thread", e) -``` - -Note, there a simple Python function to calculate each of these items (see the code for more details). - -## 🏃‍♀️ Next step - -[Part 6 - Event detection :material-arrow-right-circle:{ align=right }](./event-detection.md) diff --git a/docs/tutorials/clickstream/data-enrichment.md b/docs/tutorials/clickstream/data-enrichment.md deleted file mode 100644 index fdfe2abf..00000000 --- a/docs/tutorials/clickstream/data-enrichment.md +++ /dev/null @@ -1,109 +0,0 @@ -# Data enrichment - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -This service is used to enrich the clickstream data with the product category, and the visitor's gender, birthday, and age. This data is obtained from Redis Cloud, which is populated by the data ingestion job. - -![Data enrichment](./images/data-enrichment-pipeline-segment.png) - -The service subscribes to the `click-data` topic, enriches the data, and then publishes it to the `enriched-click-data` topic. - -The enriched data format: - -``` json -{ - "Epoch": 0, - "Timestamps": [ - 1700147866722586000 - ], - "NumericValues": { - "original_timestamp": [ - 1331800486 - ], - "age": [ - 42 - ] - }, - "StringValues": { - "userId": [ - "8D0E437E-9249-4DDA-BC4F-C1E5409E3A3B" - ], - "ip": [ - "69.76.12.213" - ], - "userAgent": [ - "Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2) Gecko/20100101 Firefox/10.0.2" - ], - "productId": [ - "VD55177927" - ], - "category": [ - "clothing" - ], - "title": [ - "Deluxe Cashmere Sweater" - ], - "birthdate": [ - "1981-11-14" - ], - "country": [ - "United States" - ], - "deviceType": [ - "Desktop" - ], - "gender": [ - "F" - ] - }, - "TagValues": {} -} -``` - -You can see that the data format from `click-data` has been enriched with the following additional items read from Redis Cloud: - -* `category` -* `gender` -* `birthday` -* `age` - -## Main dataframe handler - -For each dataframe received by the service, the dataframe handler is invoked: - -``` python -# Callback triggered for each new timeseries data. This method will enrich the data -def on_dataframe_handler(stream_consumer: qx.StreamConsumer, df: pd.DataFrame): - # Enrich data - df['category'] = df['productId'].apply(get_product_category) - df['title'] = df['productId'].apply(get_product_title) - df['birthdate'] = df['userId'].apply(get_visitor_birthdate) - df['country'] = df['ip'].apply(get_country_from_ip) - df['deviceType'] = df['userAgent'].apply(get_device_type) - - # For synthetic data (from csv) we don't have age. For data generated from our live web, we have age and gender - if 'age' not in df.columns: - df['age'] = df['birthdate'].apply(calculate_age) - else: - df['age'] = df['age'].apply(convert_age_to_int) - - if 'gender' not in df.columns: - df['gender'] = df['userId'].apply(get_visitor_gender) - else: - df['gender'] = df['gender'].apply(get_first_letter_of_gender) - - # Create a new stream (or reuse it if it was already created). - # We will be using one stream per visitor id, so we can parallelise the processing - # because the partitioning key will be the stream id - producer_stream = producer_topic.get_or_create_stream(stream_consumer.stream_id) - producer_stream.properties.parents.append(stream_consumer.stream_id) - producer_stream.timeseries.buffer.publish(df) -``` - -The process is to enrich the data and build an enriched dataframe, and then publish the dataframe to the output topic, containing multiple streams - one for each visitor. - -## 🏃‍♀️ Next step - -[Part 5 - Data aggregation :material-arrow-right-circle:{ align=right }](./data-aggregation.md) diff --git a/docs/tutorials/clickstream/data-ingestion.md b/docs/tutorials/clickstream/data-ingestion.md deleted file mode 100644 index 5abf69f2..00000000 --- a/docs/tutorials/clickstream/data-ingestion.md +++ /dev/null @@ -1,71 +0,0 @@ -# Data ingestion - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -This application runs as a job and loads product and user data from JSON files and writes the information to Redis Cloud. The product data is used to populate the online store. User data is used later to trigger special offers targeting a specific demographic. - -![Data ingestion](./images/data-ingestion-pipeline-segment.png) - -## Data format - -You'll now investigate the format for the data ingested by the job. - -1. Click `Applications` in the main left-hand navigation. You'll see a list of Applications in the environment: - - ![Clickstream Applications](./images/clickstream-applications.png) - -2. Click `Data ingestion` to load the code view. - -3. In the `Application files` panel you can see all the files for the application. There are the two data files that are loaded: `users.json` and `products.json`. - -4. Click on a data file to investigate the format. - -Each product data item has the following format: - -``` json -{ - "id": "VD55181667", - "category": "books", - "title": "Chronicles of the Ancient Forest by John Smith", - "description": "Immerse yourself in the captivating world created by John Smith. Chronicles of the Ancient Forest takes you on a journey through an ancient forest filled with mystery and wonder. A must-read for fans of fantasy and adventure.", - "image": "VD55181667.png", - "price": 92.04 -} -``` - -Each user data item has the following format: - -``` json -{ - "userId":"0001BDD9-EABF-4D0D-81BD-D9EABFCD0D7D", - "gender":"F", - "birthDate":"1984-04-08" -} -``` - -The additional user data of gender and birthday is used later to trigger a special offer tailored to a specific demographic. - -Users are identified by a UUID. - -The code is relatively simple for this application. For example, here's the code to load the product data and write it to Redis: - -``` python -# Read products from products.tsv and store the category in Redis -def load_products(): - products = pd.read_json('products.json') - pipe = r.pipeline() - - for index, row in products.iterrows(): - key = f'product:{row["id"]}' - pipe.hset(key, 'cat', row['category']) - pipe.hset(key, 'title', row['title']) - - pipe.execute() - print(f"Imported {len(products)} products") -``` - -## 🏃‍♀️ Next step - -[Part 4 - Data enrichment :material-arrow-right-circle:{ align=right }](./data-enrichment.md) diff --git a/docs/tutorials/clickstream/event-detection.md b/docs/tutorials/clickstream/event-detection.md deleted file mode 100644 index b4d87b4a..00000000 --- a/docs/tutorials/clickstream/event-detection.md +++ /dev/null @@ -1,74 +0,0 @@ -# Event detection - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -This service monitors the user behavior in real time, using a simple state machine. The idea is the state machine detects when conditions for a offer should be triggered. The offer is tailored to the user demographic. - -![Event detection](./images/event-detection-pipeline-segment.png) - -## Offer logic - -The offer logic is implemented in the `process_dataframe` method, and first of all cleans data to avoid storing unnecessary data in memory - it cleans page refreshes and ignores all actions that are not considered for the offers. - -The conditions for triggering the offer are as follows: - -1. If, in the last 5 minutes, a female between 25 and 35, or a male between 36 and 45: -2. Views any item in the clothing category and then, -3. Views any item in the shoes category and then, -4. Views a different item in the clothing category then, -5. Show a special offer for that demographic - -These transitions need to occur within a 30 minute window. - -## Topics - -The service subscribes to the `enriched-click-data` topic. When an offer is detected an offer data message is published to `special-offers` - this is then used by the webshop frontend to display the tailored offer. - -## Offer data format - -For the male demographic `offer1` is generated: - -``` json -[ - { - "Timestamp": 1700221907989992000, - "Tags": {}, - "Id": "offer", - "Value": "offer1" - } -] -``` - -For the female demographic `offer2` is generated: - -``` json -[ - { - "Timestamp": 1700222075422209000, - "Tags": {}, - "Id": "offer", - "Value": "offer2" - } -] -``` - -These can then be used by the UI to display the correct offer popup. - -## State machine logs - -The service uses the Redis Cloud logging facility. The state machine code logs out state to Redis Cloud. From there the log messages can also be displayed in the UI, for example: - -``` -2023-11-17 11:54:35: [User sad-music-boxing triggered offer offer2] -2023-11-17 11:54:35: [User sad-music-boxing entered state offer][Event: clicked VD55177927][Category: clothing] -2023-11-17 11:54:32: [User sad-music-boxing entered state shoes_visited][Event: clicked VD55147564][Category: shoes] -2023-11-17 11:54:29: [User sad-music-boxing entered state clothes_visited][Event: clicked VD55129406][Category: clothing] -``` - -If the user falls within the age range for each gender, and transitions through the states clothes_visited -> shoes_visited -> clothing_visited within 30 minutes, then the tailored offer is triggered. - -## 🏃‍♀️ Next step - -[Part 7 - Webshop frontend :material-arrow-right-circle:{ align=right }](./webshop-frontend.md) diff --git a/docs/tutorials/clickstream/get-project.md b/docs/tutorials/clickstream/get-project.md deleted file mode 100644 index 81fdb304..00000000 --- a/docs/tutorials/clickstream/get-project.md +++ /dev/null @@ -1,31 +0,0 @@ -# Get the project - -!!! danger - - This tutorial is out of date. Please check the [tutorials overview](../overview.md) for our latest tutorials. - -While you can see the [deployed project running in Quix](https://portal.cloud.quix.io/pipeline?workspace=demo-clickstreamanalysis-prod&token=pat-b88b3caf912641a1b0fa8b47b262868b){target=_blank}, it can be useful to learn how to get a project up and running in your own Quix account. - -Once you have the project running in your Quix account, you can modify the project as required, and save your changes to your copy of the project. - -## Clone the project - -See the [clone a project documentation](../../create/clone-project.md) for further information on how to do this. - -## Add secrets - -You'll need to add the following secrets: - -| Environment Variable (secret) | Service(s) | Description| -|---|---|---| -| `redis_host` | Data ingestion, Real-time dashboard, Data aggregation, Data enrichment, Event detection | Host URL for Redis Cloud database | -| `redis_port` | Data ingestion, Real-time dashboard, Data aggregation, Data enrichment, Event detection | Port for the Redis Cloud database | -| `redis_username` | Data ingestion, Real-time dashboard, Data aggregation, Data enrichment, Event detection | Username for the Redis Cloud database - typically `default`| -| `redis_password` | Data ingestion, Real-time dashboard, Data aggregation, Data enrichment, Event detection | Password for the Redis Cloud database - found from Security section | -| `bearer_token` | Webshop frontend | A [PAT](../../develop/authentication/personal-access-token.md) that the web app uses to authenticate the Streaming Reader API | - -You need to [create secrets](../../deploy/secrets-management.md) for these and then assign them to the corresponding [environment variables](../../deploy/environment-variables.md). - -## 🏃‍♀️ Next step - -[Part 2 - Clickstream producer :material-arrow-right-circle:{ align=right }](clickstream-producer.md) diff --git a/docs/tutorials/clickstream/images/assign-secret-dropdown.png 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