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Here you can find the materials for the "Data Engineering 3: Batch Jobs and APIs" course, part of the MSc in Business Analytics at CEU. For the previous editions, see 2017/2018, 2018/2019, 2019/2020, 2020/2021, 2021/2022, 2022/2023, and 2023/2024.

Table of Contents

Schedule

3 x 2 x 100 mins on April 28, May 5 and 12:

  • 13:30 - 15:10 session 1
  • 15:10 - 15:40 break
  • 15:40 - 17:20 session 2

Location

In-person at the Vienna campus (QS B-421).

Syllabus

Please find in the syllabus folder of this repository.

Technical Prerequisites

  1. You need a laptop with any operating system and stable Internet connection.
  2. Please make sure that Internet/network firewall rules are not limiting your access to unusual ports (e.g. 22, 8787, 8080, 8000), as we will heavily use these in the class (can be a problem on a company network). CEU WiFi should have the related firewall rules applied for the class.
  3. Join the Teams channel dedicated to the class at ba-de4-2024 with the symvo7f team code
  4. When joining remotely, it's highly suggested to get a second monitor where you can follow the online stream, and keep your main monitor for your own work. The second monitor could be an external screen attached to your laptop, e.g. a TV, monitor, projector, but if you don't have access to one, you may also use a tablet or phone to dial-in to the Zoom call.

Class Schedule

Week 1

Goal: learn how to run and schedule R jobs in the cloud.

Background: Example use-cases and why to use R in the cloud?

Excerpts from https://daroczig.github.io/talks

  • "A Decade of Using R in Production" (Real Data Science USA - R meetup)
  • "Getting Things Logged" (RStudio::conf 2020)
  • "Analytics databases in a startup environment: beyond MySQL and Spark" (Budapest Data Forum 2018)

Welcome to AWS!

  1. Use the following sign-in URL to access the class AWS account: https://657609838022.signin.aws.amazon.com/console

  2. Secure your access key(s), other credentials and any login information ...

    ... because a truly wise person learns from the mistakes of others!

    "When I woke up the next morning, I had four emails and a missed phone call from Amazon AWS - something about 140 servers running on my AWS account, mining Bitcoin" -- Hoffman said

    "Nevertheless, now I know that Bitcoin can be mined with SQL, which is priceless ;-)" -- Uri Shaked

    So set up 2FA (go to IAM / Users / username / Security credentials / Assigned MFA device): https://console.aws.amazon.com/iam

    PS probably you do not really need to store any access keys, but you may rely on roles (and the Key Management Service, and the Secrets Manager and so on)

  3. Let's use the eu-west-1 Ireland region

Getting access to EC2 boxes

Note: we follow the instructions on Windows in the Computer Lab, but please find below how to access the boxes from Mac or Linux as well when working with the instances remotely.

  1. Create (or import) an SSH key in AWS (EC2 / Key Pairs): https://eu-west-1.console.aws.amazon.com/ec2/v2/home?region=eu-west-1#KeyPairs:sort=keyName including using the Owner tag!

  2. Get an SSH client:

    • Windows -- Download and install PuTTY: https://www.putty.org

    • Mac -- Install PuTTY for Mac using homebrew or macports

      sudo brew install putty
sudo port install putty

    • Linux -- probably the OpenSSH client is already installed, but to use the same tools on all operating systems, please install and use PuTTY on Linux too, eg on Ubuntu:

      sudo apt install putty

  3. Convert the generated pem key to PuTTY formatNo need to do this anymore, AWS can provide the key as PPK now.

  4. Make sure the key is readable only by your Windows/Linux/Mac user, eg

    chmod 0400 key.ppk

Create and connect to an EC2 box

  1. Create an EC2 instance

    1. Optional: create an Elastic IP for your box
    2. Go the the Instances overview at https://eu-west-1.console.aws.amazon.com/ec2/v2/home?region=eu-west-1#Instances:sort=instanceId
    3. Click "Launch Instance"
    4. Provide a name for your server (e.g. daroczig-de3-week1) and some additional tags for resource tracking, including tagging downstream services, such as Instance and Volumes:
      • Class: DE3
      • Owner: daroczig
    5. Pick the Ubuntu Server 24.04 LTS (HVM), SSD Volume Type AMI
    6. Pick t3a.small (2 GiB of RAM should be enough for most tasks) instance type (see more instance types)
    7. Select your AWS key created above and launch
    8. Pick a unique name for the security group after clicking "Edit" on the "Network settings"
    9. Click "Launch instance"
    10. Note and click on the instance id

  2. Connect to the box

    1. Specify the hostname or IP address

    1. Specify the "Private key file for authentication" in the Connection category's SSH/Auth/Credentials pane
    2. Set the username to ubuntu on the Connection/Data tab
    3. Save the Session profile
    4. Click the "Open" button
    5. Accept & cache server's host key

Alternatively, you can connect via a standard SSH client on a Mac or Linux, something like:

chmod 0400 /path/to/your/pem
ssh -i /path/to/your/pem ubuntu@ip-address-of-your-machine

As a last resort, use "EC2 Instance Connect" from the EC2 dashboard by clicking "Connect" in the context menu of the instance (triggered by right click in the table).

Install RStudio Server on EC2

  1. Look at the docs: https://www.rstudio.com/products/rstudio/download-server

  2. First, we will upgrade the system to the most recent version of the already installed packages. Note, check on the concept of a package manager!

    Download Ubuntu apt package list:

    sudo apt update

    Optionally upgrade the system:

    sudo apt upgrade

    And optionally also reboot so that kernel upgrades can take effect.

  3. Install R

    sudo apt install r-base

    To avoid manually answering "Yes" to the question to confirm installation, you can specify the -y flag:

    sudo apt install -y r-base

  4. Try R

    R

    For example:

    1 + 4
hist(mtcars$hp)
# duh, where is the plot?!

    Exit:

    q()

    Look at the files:

    ls
ls -latr

  5. Install RStudio Server

    wget https://download2.rstudio.org/server/jammy/amd64/rstudio-server-2024.12.1-563-amd64.deb
sudo apt install -y gdebi-core
sudo gdebi rstudio-server-2024.12.1-563-amd64.deb

  6. Check process and open ports

    rstudio-server status
sudo rstudio-server status
sudo systemctl status rstudio-server
sudo ps aux | grep rstudio

sudo apt -y install net-tools
sudo netstat -tapen | grep LIST
sudo netstat -tapen

  7. Look at the docs: http://docs.rstudio.com/ide/server-pro/

Connect to the RStudio Server

  1. Confirm that the service is up and running and the port is open

    ubuntu@ip-172-31-12-150:~$ sudo netstat -tapen | grep LIST
tcp        0      0 0.0.0.0:8787            0.0.0.0:*               LISTEN      0          49065       23587/rserver
tcp        0      0 0.0.0.0:22              0.0.0.0:*               LISTEN      0          15671       1305/sshd
tcp6       0      0 :::22                   :::*                    LISTEN      0          15673       1305/sshd

  2. Try to connect to the host from a browser on port 8787, eg http://foobar.eu-west-1.compute.amazonaws.com:8787

  3. Realize it's not working

  4. Open up port 8787 in the security group, by selecting your security group and click "Edit inbound rules":

  5. Authentication: http://docs.rstudio.com/ide/server-pro/authenticating-users.html

  6. Create a new user:

     sudo adduser ceu

  7. Login & quick demo:

    1+2
plot(mtcars)
install.packages('fortunes')
library(fortunes)
fortune()
fortune(200)
system('whoami')

  8. Reload webpage (F5), realize we continue where we left the browser :)

  9. Demo the terminal:

    $ whoami
ceu
$ sudo whoami
ceu is not in the sudoers file.  This incident will be reported.

  10. Grant sudo access to the new user by going back to SSH with root access:

    sudo apt install -y mc
sudo mc
sudo mcedit /etc/sudoers
sudo adduser ceu admin
man adduser
man deluser

Note 1: might need to relogin / restart RStudio / reload R / reload page .. to force a new shell login so that the updated group setting is applied Note 2: you might want to add NOPASSWD to the sudoers file:

```sh
ceu ALL=(ALL) NOPASSWD:ALL
```

Although also note (3) the related security risks.

  1. Custom login page: http://docs.rstudio.com/ide/server-pro/authenticating-users.html#customizing-the-sign-in-page

  2. Custom port (e.g. 80): http://docs.rstudio.com/ide/server-pro/access-and-security.html#network-port-and-address

    echo "www-port=80" | sudo tee -a /etc/rstudio/rserver.conf
sudo rstudio-server restart

Update R

Note the pretty outdated R version ... so let's update R by using the apt repo managed by the CRAN team:

wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | sudo tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc
sudo add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/"

## fetch most recent list of packages and version, then auto-upgrade
sudo apt-get update && sudo apt-get -y upgrade

Week 2

Quiz: https://forms.office.com/e/SgME7mHPM9 (5 mins)

Recap on what we covered last week:

  1. 2FA/MFA in AWS
  2. Creating EC2 nodes
  3. Connecting to EC2 nodes via SSH/Putty (note the difference between the PPK and PEM key formats)
  4. Updating security groups
  5. Installing RStudio Server
  6. The difference between R console and Shell
  7. The use of sudo and how to grant root (system administrator) privileges
  8. Adding new Linux users, setting password, adding to group
  9. Messing up package updates and making RStudio Server unusable

Note that you do NOT need to do the instructions below marked with the 💪 emoji -- those have been already done for you, and the related steps are only included below for documenting what has been done and demonstrated in the class.

Amazon Machine Images

💪 Instead of starting from scratch, let's create an Amazon Machine Image (AMI) from the EC2 node we used last week, so that we can use that as the basis of all the next steps:

  • Find the EC2 node in the EC2 console
  • Right click, then "Image and templates" / "Create image"
  • Name the AMI and click "Create image"
  • It might take a few minutes to finish

Then you can use the newly created de3-week2 AMI to spin up a new instance for you:

  1. Go the the Instances overview at https://eu-west-1.console.aws.amazon.com/ec2/v2/home?region=eu-west-1#Instances:sort=instanceId
  2. Click "Launch Instance"
  3. Provide a name for your server (e.g. daroczig-de3-week2) and some additional tags for resource tracking, including tagging downstream services, such as Instance and Volumes:
    • Class: DE3
    • Owner: daroczig
  4. Pick the de3-week2 AMI
  5. Pick t3a.small (2 GiB of RAM should be enough for most tasks) instance type (see more instance types)
  6. Select your AWS key created above and launch
  7. Select the de3 security group (granting access to ports 22, 8000, 8080, and 8787)
  8. Click "Advanced details" and select ceudataserver IAM instance profile
  9. Click "Launch instance"
  10. Note and click on the instance id

💪 Create a user for every member of the team

We'll export the list of IAM users from AWS and create a system user for everyone.

  1. Attach a newly created IAM EC2 Role (let's call it ceudataserver) to the EC2 box and assign 'Read-only IAM access' (IAMReadOnlyAccess):

  2. Install AWS CLI tool (note that using the snap package manager as it was removed from the apt repos):

    sudo snap install aws-cli --classic

  3. List all the IAM users: https://docs.aws.amazon.com/cli/latest/reference/iam/list-users.html

    aws iam list-users

  4. Install R packages from JSON parsing and logging (in the next steps) from the apt repo instead of CRAN sources as per https://github.com/eddelbuettel/r2u

    wget -q -O- https://eddelbuettel.github.io/r2u/assets/dirk_eddelbuettel_key.asc | sudo tee -a /etc/apt/trusted.gpg.d/cranapt_key.asc
sudo add-apt-repository "deb [arch=amd64] https://r2u.stat.illinois.edu/ubuntu noble main"
sudo apt update

sudo apt-get install -y r-cran-jsonlite r-cran-logger r-cran-glue

    Note that all dependencies (let it be an R package or system/Ubuntu package) have been automatically resolved and installed.

    Don't forget to click on the brush icon to clean up your terminal output if needed.

    Optionally enable bspm to enable binary package installations via the traditional install.packages R function.

  5. Export the list of users from R:

    library(jsonlite)
users <- fromJSON(system('aws iam list-users', intern = TRUE))
str(users)
users[[1]]$UserName

  6. Create a new system user on the box (for RStudio Server access) for every IAM user, set password and add to group:

    library(logger)
library(glue)
for (user in users[[1]]$UserName) {

  ## remove invalid character
  user <- sub('@.*', '', user)
  user <- sub('.', '_', user, fixed = TRUE)

  log_info('Creating {user}')
  system(glue("sudo adduser --disabled-password --quiet --gecos '' {user}"))

  log_info('Setting password for {user}')
  system(glue("echo '{user}:secretpass' | sudo chpasswd")) # note the single quotes + placement of sudo

  log_info('Adding {user} to sudo group')
  system(glue('sudo adduser {user} sudo'))

}

Note, you may have to temporarily enable passwordless sudo for this user (if have not done already) :/

ceu ALL=(ALL) NOPASSWD:ALL

Check users:

readLines('/etc/passwd')

💪 Install Jenkins to schedule R commands

  1. Install Jenkins from the RStudio/Terminal: https://www.jenkins.io/doc/book/installing/linux/#debianubuntu

    sudo apt install -y fontconfig openjdk-17-jre

sudo wget -O /usr/share/keyrings/jenkins-keyring.asc \
  https://pkg.jenkins.io/debian-stable/jenkins.io-2023.key
echo deb [signed-by=/usr/share/keyrings/jenkins-keyring.asc] \
  https://pkg.jenkins.io/debian-stable binary/ | sudo tee \
  /etc/apt/sources.list.d/jenkins.list > /dev/null
sudo apt-get update
sudo apt-get install -y jenkins

# check which port is open by java (jenkins)
sudo ss -tapen | grep java

  2. Open up port 8080 in the related security group

  3. Access Jenkins from your browser and finish installation

    1. Read the initial admin password from RStudio/Terminal via

      sudo cat /var/lib/jenkins/secrets/initialAdminPassword

    2. Proceed with installing the suggested plugins

    3. Create your first user (eg ceu)

Note that if loading Jenkins after getting a new IP takes a lot of time, it might be due to not be able to load the theme.css as trying to search for that on the previous IP (as per Jenkins URL setting). To overcome this, wait 2 mins for the theme.css timeout, login, disable the dark theme: jenkinsci/dark-theme-plugin#458

💪 Update Jenkins for shared usage

Update the security backend to use real Unix users for shared access (if users already created):

And allow jenkins to authenticate UNIX users and restart:

sudo adduser jenkins shadow
sudo systemctl restart jenkins

Then make sure to test new user access in an incognito window to avoid closing yourself out :)

💪 Set up an easy to remember IP address

Optionally you can associate a fixed IP address to your box:

  1. Allocate a new Elastic IP address at https://eu-west-1.console.aws.amazon.com/ec2/v2/home?region=eu-west-1#Addresses:
  2. Name this resource by assigning a "Name" tag
  3. Associate this Elastic IP with your stopped box, then start it

💪 Set up an easy to remember domain name

Optionally you can associate a subdomain with your node, using the above created Elastic IP address:

  1. Go to Route 53: https://console.aws.amazon.com/route53/home

  2. Go to Hosted Zones and click on ceudata.net

  3. Create a new Record, where

    • fill in the desired Name (subdomain), eg de3.ceudata.net
    • paste the public IP address or hostname of your server in the Value field
    • click Create

  4. Now you will be able to access your box using this custon (sub)domain, no need to remember IP addresses.

💪 Configuring for standard ports

To avoid using ports like 8787 and 8080 (and get blocked by the firewall installed on the CEU WiFi), let's configure our services to listen on the standard 80 (HTTP) and potentially on the 443 (HTTPS) port as well, and serve RStudio on the /rstudio, and Jenkins on the /jenkins path.

For this end, we will use Nginx as a reverse-proxy, so let's install it first:

sudo apt install -y nginx

First, we need to edit the Nginx config to enable websockets for Shiny apps etc in /etc/nginx/nginx.conf under the http section:

  map $http_upgrade $connection_upgrade {
      default upgrade;
      ''      close;
    }

Then we need to edit the main site's configuration at /etc/nginx/sites-enabled/default to act as a proxy, which also do some transformations, eg rewriting the URL (removing the /rstudio path) before hitting RStudio Server:

server {
    listen 80;
    rewrite ^/rstudio$ $scheme://$http_host/rstudio/ permanent;
    location /rstudio/ {
      rewrite ^/rstudio/(.*)$ /$1 break;
      proxy_pass http://localhost:8787;
      proxy_redirect http://localhost:8787/ $scheme://$http_host/rstudio/;
      proxy_http_version 1.1;
      proxy_set_header Upgrade $http_upgrade;
      proxy_set_header Connection $connection_upgrade;
      proxy_read_timeout 20d;
    }
}

And restart Nginx:

sudo systemctl restart nginx

Find more information at https://support.rstudio.com/hc/en-us/articles/200552326-Running-RStudio-Server-with-a-Proxy.

Let's see if the port is open on the machine:

sudo ss -tapen|grep LIST

Let's see if we can access RStudio Server on the new path:

curl localhost/rstudio

Now let's see from the outside world ... and realize that we need to open up port 80!

Now we need to tweak the config to support Jenkins as well, but the above Nginx rewrite hack will not work (see https://www.jenkins.io/doc/book/system-administration/reverse-proxy-configuration-troubleshooting/ for more details), so we will just make it a standard reverse-proxy, eg:

server {
    listen 80;
    rewrite ^/rstudio$ $scheme://$http_host/rstudio/ permanent;
    location / {

    }
    location /rstudio/ {
      rewrite ^/rstudio/(.*)$ /$1 break;
      proxy_pass http://localhost:8787;
      proxy_redirect http://localhost:8787/ $scheme://$http_host/rstudio/;
      proxy_http_version 1.1;
      proxy_set_header Upgrade $http_upgrade;
      proxy_set_header Connection $connection_upgrade;
      proxy_read_timeout 20d;
    }
    location ^~ /jenkins/ {
      proxy_pass http://127.0.0.1:8080/jenkins/;
      proxy_set_header X-Real-IP  $remote_addr;
      proxy_set_header X-Forwarded-For $remote_addr;
      proxy_set_header Host $host;
    }
}

And we also need to let Jenkins also know about the custom path, so uncomment Environment="JENKINS_PREFIX=/jenkins" in /lib/systemd/system/jenkins.service, then reload the Systemd configs and restart Jenkins:

sudo systemctl daemon-reload
sudo systemctl restart jenkins

See more details at the Jenkins reverse proxy guide.

Optionally, replace the default, system-wide index.html for folks visiting the root domain without either the rstudio or jenkins path (note that instead of the editing the file, which might be overwritten with package updates, it would be better to create a new HTML file and refer that from the Nginx configuration, but we will keep it simple and dirty for now):

echo "Welcome to DE3! Are you looking for <code>/rstudio</code> or <code>/jenkins</code>?" | sudo tee /usr/share/nginx/html/index.html

Then restart Jenkins, and good to go!

It might be useful to also proxy port 8000 for future use via updating the Nginx config to:

server {
    listen 80;
    rewrite ^/rstudio$ $scheme://$http_host/rstudio/ permanent;
    location / {

    }
    location /rstudio/ {
      rewrite ^/rstudio/(.*)$ /$1 break;
      proxy_pass http://localhost:8787;
      proxy_redirect http://localhost:8787/ $scheme://$http_host/rstudio/;
      proxy_http_version 1.1;
      proxy_set_header Upgrade $http_upgrade;
      proxy_set_header Connection $connection_upgrade;
      proxy_read_timeout 20d;
    }
    location ^~ /jenkins/ {
      proxy_pass http://127.0.0.1:8080/jenkins/;
      proxy_set_header X-Real-IP  $remote_addr;
      proxy_set_header X-Forwarded-For $remote_addr;
      proxy_set_header Host $host;
    }
    location ^~ /8000/ {
      rewrite ^/8000/(.*)$ /$1 break;
      proxy_pass http://127.0.0.1:8000;
      proxy_set_header X-Real-IP  $remote_addr;
      proxy_set_header X-Forwarded-For $remote_addr;
      proxy_set_header Host $host;
    }
}

This way you can access the above services via the below URLs:

RStudio Server:

Jenkins:

Port 8000:

If you cannot access RStudio Server on port 80, you might need to restart nginx as per above.

Next, set up SSL either with Nginx or placing an AWS Load Balancer in front of the EC2 node.

Warmup exercises

Install ggplot2 or your preferred Python packages to replicate the below steps that we will automate later:

  1. Install the devtools R package and a few others (binary distribution) in the RStudio/Terminal:

    sudo apt-get install -y r-cran-devtools r-cran-data.table r-cran-httr r-cran-jsonlite r-cran-data.table r-cran-stringi r-cran-stringr r-cran-glue r-cran-logger r-cran-snakecase

  2. Switch back to the R console and install the binancer R package from GitHub to interact with crypto exchanges (note the extra dependency to be installed from CRAN, no need to update any already installed package):

    devtools::install_github('daroczig/binancer', upgrade = FALSE)

  3. First steps with live data: load the binancer package and then use the binance_klines function to get the last 3 hours of Bitcoin price changes (in USD) with 1-minute granularity -- resulting in an object like:

    > str(klines)
Classes ‘data.table’ and 'data.frame':  180 obs. of  12 variables:
    $ open_time                   : POSIXct, format: "2020-03-08 20:09:00" "2020-03-08 20:10:00" "2020-03-08 20:11:00" "2020-03-08 20:12:00" ...
    $ open                        : num  8292 8298 8298 8299 8298 ...
    $ high                        : num  8299 8299 8299 8299 8299 ...
    $ low                         : num  8292 8297 8297 8298 8296 ...
    $ close                       : num  8298 8298 8299 8298 8299 ...
    $ volume                      : num  25.65 9.57 20.21 9.65 24.69 ...
    $ close_time                  : POSIXct, format: "2020-03-08 20:09:59" "2020-03-08 20:10:59" "2020-03-08 20:11:59" "2020-03-08 20:12:59" ...
    $ quote_asset_volume          : num  212759 79431 167677 80099 204883 ...
    $ trades                      : int  371 202 274 186 352 271 374 202 143 306 ...
    $ taker_buy_base_asset_volume : num  13.43 5.84 11.74 7.12 15.24 ...
    $ taker_buy_quote_asset_volume: num  111430 48448 97416 59071 126493 ...
    $ symbol                      : chr  "BTCUSDT" "BTCUSDT" "BTCUSDT" "BTCUSDT" ...
    - attr(*, ".internal.selfref")=<externalptr>
    Click here for the code generating the above ... 
    library(binancer)
klines <- binance_klines('BTCUSDT', interval = '1m', limit = 60*3)
str(klines)
summary(klines$close)

  4. Visualize the data, eg on a simple line chart:

    Click here for the code generating the above ... 
    library(ggplot2)
ggplot(klines, aes(close_time, close)) + geom_line()

  5. Now create a candle chart, something like:

    Click here for the code generating the above ... 
    library(scales)
ggplot(klines, aes(open_time)) +
    geom_linerange(aes(ymin = open, ymax = close, color = close < open), size = 2) +
    geom_errorbar(aes(ymin = low, ymax = high), size = 0.25) +
    theme_bw() + theme('legend.position' = 'none') + xlab('') +
    ggtitle(paste('Last Updated:', Sys.time())) +
    scale_y_continuous(labels = dollar) +
    scale_color_manual(values = c('#1a9850', '#d73027')) # RdYlGn

  6. Compare prices of 4 currencies (eg BTC, ETH, BNB and XRP) in the past 24 hours on 15 mins intervals:

    Click here for the code generating the above ... 
    library(data.table)
klines <- rbindlist(lapply(
    c('BTCUSDT', 'ETHUSDT', 'BNBUSDT', 'XRPUSDT'),
    binance_klines,
    interval = '15m', limit = 4*24))
ggplot(klines, aes(open_time)) +
    geom_linerange(aes(ymin = open, ymax = close, color = close < open), size = 2) +
    geom_errorbar(aes(ymin = low, ymax = high), size = 0.25) +
    theme_bw() + theme('legend.position' = 'none') + xlab('') +
    ggtitle(paste('Last Updated:', Sys.time())) +
    scale_color_manual(values = c('#1a9850', '#d73027')) +
    facet_wrap(~symbol, scales = 'free', nrow = 2)

  7. Some further useful functions:

    • binance_ticker_all_prices()
    • binance_coins_prices()
    • binance_credentials and binance_balances

  8. Create an R script that reports and/or plots on some cryptocurrencies, ideas:

    • compute the (relative) change in prices of cryptocurrencies in the past 24 / 168 hours
    • go back in time 1 / 12 / 24 months and "invest" $1K in BTC and see the value today
    • write a bot buying and selling crypto on a virtual exchange

Schedule R commands

Let's schedule a Jenkins job to check on the Bitcoin prices every hour!

  1. Visit Jenkins using the /jenkins URL path of your instance's public IP address

  2. Use your UNIX username and a password to log in

    If logging in takes a long time, it might be due to the Dark Theme plugin trying to load a CSS file from an outdated location absed on the Jenkins URL configured at /jenkins/manage/configure. To overcome this issue, wait it out at the above URL and update the IP address to your new dynamic IP address, or disable the plugin at /jenkins/manage/pluginManager/installed and then restart Jenkins at the bottom of the page.

  3. Create a "New Item" (job):

    1. Enter the name of the job: get current Bitcoin price

    2. Pick "Freestyle project"

    3. Click "OK"

    4. Add a new "Execute shell" build step

    5. Enter the below command to look up the most recent BTC price

      R -e "library(binancer);binance_coins_prices()[symbol == 'BTC', usd]"

    6. Run the job

  4. Debug & figure out what's the problem ...

  5. Install R packages system wide from RStudio/Terminal (more on this later):

    Either start R in the terminal as the root user (via sudo R) and run the previous devtools::install_github command there, or with a one-liner:

    sudo Rscript -e "library(devtools);withr::with_libpaths(new = '/usr/local/lib/R/site-library', install_github('daroczig/binancer', upgrade = FALSE))"

  6. Rerun the job

Schedule R scripts

  1. Create an R script with the below content and save on the server, eg as /home/ceu/bitcoin-price.R:

    library(binancer)
prices <- binance_coins_prices()
paste('The current Bitcoin price is', prices[symbol == 'BTC', usd])

  2. Follow the steps from the Schedule R commands section to create a new Jenkins job, but instead of calling R -e "..." in shell step, reference the above R script using Rscript instead:

    Rscript /home/ceu/de3.R

    Alternatively, you could also install little R for this purpose:

    sudo apt install -y r-cran-littler
r /home/ceu/de3.R

    Note the permission error, so let's add the jenkins user to the ceu group:

    sudo adduser jenkins ceu

    Then restart Jenkins from the RStudio Server terminal:

    sudo systemctl restart jenkins

    A better solution will be later to commit our R script into a git repo, and make it part of the job to update from the repo.

  3. Create an R script that generates a candlestick chart on the BTC prices from the past hour, saves as btc.png in the workspace, and update every 5 minutes!

    Example solution for the above ... 
    library(binancer)
library(ggplot2)
library(scales)
klines <- binance_klines('BTCUSDT', interval = '1m', limit = 60)
g <- ggplot(klines, aes(open_time)) +
        geom_linerange(aes(ymin = open, ymax = close, color = close < open), size = 2) +
        geom_errorbar(aes(ymin = low, ymax = high), size = 0.25) +
        theme_bw() + theme('legend.position' = 'none') + xlab('') +
        ggtitle(paste('Last Updated:', Sys.time())) +
        scale_y_continuous(labels = dollar) +
        scale_color_manual(values = c('#1a9850', '#d73027'))
ggsave('btc.png', plot = g, width = 10, height = 5)

    1. Enter the name of the job: Update BTC candlestick chart

    2. Pick "Freestyle project"

    3. Click "OK"

    4. Add a new "Execute shell" build step

    5. Enter the below command to look up the most recent BTC price

      Rscript /home/ceu/plot.R

    6. Run the job

    7. Look at the workspace that can be accessed from the sidebar menu of the job.

ScheduleR improvements

  1. Use a git repository to store the R scripts and fetch the most recent version on job start:

    1. Configure the Jenkins job to use "Git" in the "Source Code Management" section, and use e.g. https://gist.github.com/daroczig/e5d3ee3664549932bb7f23ce8e93e472 as the repo URL, and specify the branch (main).
    2. Update the Execute task section to refer to the btcprice.R file of the repo instead of the hardcoded local path.
    3. Make edits to the repo, e.g. update lookback to 3 hours and check a future job output.

  2. Set up e-mail notifications via eg https://app.mailjet.com/signin

    1. 💪 Sign up, confirm your e-mail address and domain

    2. 💪 Take a note on the SMTP settings, eg

      • SMTP server: in-v3.mailjet.com
      • Port: 465
      • SSL: Yes
      • Username: ***
      • Password: ***

    3. 💪 Configure Jenkins at http://de3.ceudata.net/jenkins/configure

      1. Set up the default FROM e-mail address at "System Admin e-mail address": [email protected]

      2. Search for "Extended E-mail Notification" and configure

        • SMTP Server
        • Click "Advanced"
        • Check "Use SMTP Authentication"
        • Enter User Name from the above steps
        • Enter Password from the above steps
        • Check "Use SSL"
        • SMTP port: 465

    4. Set up "Post-build Actions" in Jenkins: Editable Email Notification - read the manual and info popups, configure to get an e-mail on job failures and fixes

    5. Configure the job to send the whole e-mail body as the deault body template for all outgoing emails

    ${BUILD_LOG, maxLines=1000}

  3. Look at other Jenkins plugins, eg the Slack Notifier: https://plugins.jenkins.io/slack

Intro to redis

We need a persistent storage for our Jenkins jobs ... let's give a try to a key-value database:

  1. 💪 Install server

    sudo apt install redis-server
ss -tapen | grep LIST

    Test using the CLI tool:

    redis-cli get foo
redis-cli set foo 42
redis-cli get foo
redis-cli del foo

  2. 💪 Install an R client

    Although we could use the RcppRedis available on CRAN, so thus also in our already added apt repo, but rredis provides some convenient helpers that we plan to use, so we are going to install that as well from a custom R package repository to also demonstrate how drat works:

    sudo apt install -y r-cran-rcppredis
sudo Rscript -e "withr::with_libpaths(new = '/usr/local/lib/R/site-library', install.packages('rredis', repos=c('https://ghrr.github.io/drat', 'https://cloud.r-project.org')))"

  3. Interact from R

    ## set up and initialize the connection to the local redis server
library(rredis)
redisConnect()

## set/get values
redisSet('foo', 'bar')
redisGet('foo')

## increment and decrease counters
redisIncr('counter')
redisIncr('counter')
redisIncr('counter')
redisGet('counter')
redisDecr('counter')
redisDecr('counter2')

## get multiple values at once
redisMGet(c('counter', 'counter2'))

## list all keys
redisKeys()

## drop all keys
redisDelete(redisKeys())

For more examples and ideas, see the rredis package vignette or try the interactive, genaral (not R-specific) redis tutorial.

  1. Exercises

    • Create a Jenkins job running every minute to cache the most recent Bitcoin and Ethereum prices in Redis
    • Write an R script in RStudio that can read the Bitcoin and Ethereum prices from the Redis cache
Example solution ... 
library(binancer)
library(data.table)
prices <- binance_coins_prices()

library(rredis)
redisConnect()

redisSet('username:price:BTC', prices[symbol == 'BTC', usd])
redisSet('username:price:ETH', prices[symbol == 'ETH', usd])

redisGet('username:price:BTC')
redisGet('username:price:ETH')

redisMGet(c('username:price:BTC', 'username:price:ETH'))
Example solution using a helper function doing some logging ... 
library(binancer)
library(logger)
library(rredis)
redisConnect()

store <- function(s) {
  ## TODO use the checkmate pkg to assert the type of symbol
  log_info('Looking up and storing {s}')
  value <- binance_coins_prices()[symbol == s, usd]
  key <- paste('username', 'price', s, sep = ':')
  redisSet(key, value)
  log_info('The price of {s} is {value}')
}

store('BTC')
store('ETH')

## list all keys with the "price" prefix and lookup the actual values
redisMGet(redisKeys('username:price:*'))

More on databases at the "Mastering R" class in the Spring semester ;)

Interacting with MS Teams

  1. Join the #bots-bots-bots channel in the DE3 course's MS Teams

  2. Click on "Connectors" in the channel's config and add an incoming webhook with your username and optional logo, store the URL for later use

  3. 💪 Install the teamr package from CRAN

    sudo apt install -y r-cran-teamr

  4. Use the webhook URL to send messages to the channel from R:

    library(teamr)
webhook_url <- "https://ceuedu.webhook.office.com/webhookb2/..."
# create new connector card
cc <- connector_card$new(hookurl = webhook_url)
# set the title and text of the message
cc$title("Hi from R!")
cc$text("This is a test message sent using the `teamr` package.")
# send the message
cc$send()

Week 3

Quiz: https://forms.office.com/e/wRAxGqirdV (5 mins)

Recap on what we covered last week:

  • Amazon Machine Image
  • Shared RStudio Server
  • Settup up Jenkins
  • Reverse proxy for more conenvient access to services
  • Scheduling R commands in Jenkins
  • Scheduling R script in Jenkins
  • Further Jenkins features, such as e-mail notifications and git integration
  • Introduction to Redis
  • Interacting with MS Teams

New server for this week:

  1. Go the the Instances overview at https://eu-west-1.console.aws.amazon.com/ec2/v2/home?region=eu-west-1#Instances:sort=instanceId
  2. Click "Launch Instance"
  3. Provide a name for your server (e.g. daroczig-de3-week3) and some additional tags for resource tracking, including tagging downstream services, such as Instance and Volumes:
    • Class: DE3
    • Owner: daroczig
  4. Pick the de3 AMI
  5. Pick t3a.small (2 GiB of RAM should be enough for most tasks) instance type (see more instance types)
  6. Select your AWS key created above and launch
  7. Select the de3 security group (granting access to ports 22, 8000, 8080, and 8787)
  8. Click "Advanced details" and select ceudataserver IAM instance profile
  9. Click "Launch instance"
  10. Note and click on the instance id

Storing the secret webhook URL

  1. Do NOT store the webhook URL in plain-text (e.g. in your R script)!

  2. Let's use Amazon's Key Management Service: https://github.com/daroczig/CEU-R-prod/raw/2017-2018/AWR.Kinesis/AWR.Kinesis-talk.pdf (slides 73-75)

  3. 💪 Instead of using the Java SDK referenced in the above talk, let's install boto3 Python module and use via reticulate:

    sudo apt install -y python3-boto3
sudo apt install -y r-cran-reticulate r-cran-botor

    Let's also let R know that we want to use the globally installed Python interpreter and its packages instead of setting up local virtual environments by adding the following to your /etc/R/Renviron.site file:

    RETICULATE_PYTHON=/usr/bin/python3

  4. 💪 Create a key in the Key Management Service (KMS): alias/de3

  5. 💪 Grant access to that KMS key by creating an EC2 IAM role at https://console.aws.amazon.com/iam/home?region=eu-west-1#/roles with the AWSKeyManagementServicePowerUser policy and explicit grant access to the key in the KMS console

  6. 💪 Attach the newly created IAM role if not yet done

  7. Use this KMS key to encrypt the Slack token:

    library(botor)
botor(region = 'eu-west-1')
kms_encrypt(webhook_url, key = 'alias/de3')

    Note, if R asks you to install Miniconda, say NO, as Python3 and the required packages have already been installed system-wide.

  8. Store the ciphertext and use kms_decrypt to decrypt later, see eg

    kms_decrypt("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")

  9. 💪 Alternatively, use the AWS Parameter Store or Secrets Manager, see eg https://eu-west-1.console.aws.amazon.com/systems-manager/parameters/?region=eu-west-1&tab=Table and granting the AmazonSSMReadOnlyAccess policy to your IAM role or user.

    ssm_get_parameter('/teams/daroczig')

  10. Store your own webhook in the Parameter Store and use it in your R script.

Job Scheduler exercises

  • Create a Jenkins job to alert if Bitcoin price is below $80K or higher than $100K.
  • Limit this job to alert only once per hour, but alert should kick off as soon as possible (so don't schedule to run hourly, instead use a state to track when the last alert was sent).
  • Create a Jenkins job to alert if Bitcoin price changed more than $200 in the past hour.
  • Create a Jenkins job to alert if Bitcoin price changed more than 5% in the past day.
  • Create a Jenkins job running hourly to generate a candlestick chart on the price of BTC and ETH.
Example solution for the first exercise ... 
## get data right from the Binance API
library(binancer)
btc <- binance_klines('BTCUSDT', interval = '1m', limit = 1)$close

## or from the local cache (updated every minute from Jenkins as per above)
library(rredis)
redisConnect()
btc <- redisGet('username:price:BTC')

## log whatever was retreived
library(logger)
log_info('The current price of a Bitcoin is ${btc}')

## get the last alert time
last_alert <- redisGet('username:alert:last')
if (is.null(last_alert)) {
  last_alert <- 0
}
since_last_alert <- as.numeric(
    difftime(Sys.time(), last_alert, 
    units = "secs"))

## send alert
if (since_last_alert >= 60 && (btc < 80000 | btc > 100000)) {
  library(botor)
  botor(region = 'eu-west-1')
  webhook_url <- ssm_get_parameter('/teams/username')
  library(teamr)
  cc <- connector_card$new(hookurl = webhook_url)
  cc$title('Bitcoin price alert!')
  cc$text(paste('The current price of a Bitcoin is:', btc))
  cc$send()
}

Make API endpoints

  1. 💪 Install plumber: rplumber.io

    sudo apt install -y r-cran-plumber

  2. Create an API endpoint to show the min, max and mean price of a BTC in the past hour!

    Create ~/plumber.R with the below content:

    library(binancer)

#* BTC stats
#* @get /btc
function() {
  klines <- binance_klines('BTCUSDT', interval = '1m', limit = 60L)
  klines[, .(min = min(close), mean = mean(close), max = max(close))]
}

    Start the plumber application wither via clicking on the "Run API" button or the below commands:

    library(plumber)
pr("plumber.R") %>% pr_run(host='0.0.0.0', port=8000)

  3. Add a new API endpoint to generate the candlestick chart with dynamic symbol (default to BTC), interval and limit! Note that you might need a new @serializer, function arguments, and type conversions as well.

    Example solution for the above ... 
    library(binancer)
library(ggplot2)
library(scales)

#* Generate plot
#* @param symbol coin pair
#* @param interval:str enum
#* @param limit integer
#* @get /klines
#* @serializer png
function(symbol = 'BTCUSDT', interval = '1m', limit = 60L) {
  klines <- binance_klines(symbol, interval = interval, limit = as.integer(limit)) # NOTE int conversion
  library(scales)
  p <- ggplot(klines, aes(open_time)) +
    geom_linerange(aes(ymin = open, ymax = close, color = close < open), size = 2) +
    geom_errorbar(aes(ymin = low, ymax = high), size = 0.25) +
    theme_bw() + theme('legend.position' = 'none') + xlab('') +
    ggtitle(paste('Last Updated:', Sys.time())) +
    scale_y_continuous(labels = dollar) +
    scale_color_manual(values = c('#1a9850', '#d73027')) # RdYlGn
  print(p)
}

  4. Add a new API endpoint to generate a HTML report including both the above!

    Example solution for the above ...

    💪 Update the markdown package:

    sudo apt install -y r-cran-markdown

    Create an R markdown for the reporting:

    ---
title: "report"
output: html_document
date: "`r Sys.Date()`"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning=FALSE)
library(binancer)
library(ggplot2)
library(scales)
library(knitr)

klines <- function() {
  binance_klines('BTCUSDT', interval = '1m', limit = 60L)
}
```

Bitcoin stats:

```{r stats}
kable(klines()[, .(min = min(close), mean = mean(close), max = max(close))])
```

On a nice plot:

```{r plot}
ggplot(klines(), aes(open_time, )) +
  geom_linerange(aes(ymin = open, ymax = close, color = close < open), size = 2) +
  geom_errorbar(aes(ymin = low, ymax = high), size = 0.25) +
  theme_bw() + theme('legend.position' = 'none') + xlab('') +
  ggtitle(paste('Last Updated:', Sys.time())) +
  scale_y_continuous(labels = dollar) +
  scale_color_manual(values = c('#1a9850', '#d73027'))
```

    And the plumber file:

    library(binancer)
library(ggplot2)
library(scales)
library(rmarkdown)
library(plumber)

#' Gets BTC data from the past hour
#' @return data.table
klines <- function() {
    binance_klines('BTCUSDT', interval = '1m', limit = 60L)
}

#* BTC stats
#* @get /stats
function() {
  klines()[, .(min = min(close), mean = mean(close), max = max(close))]
}

#* Generate plot
#* @get /plot
#* @serializer png
function() {
  p <- ggplot(klines(), aes(open_time, )) +
    geom_linerange(aes(ymin = open, ymax = close, color = close < open), size = 2) +
    geom_errorbar(aes(ymin = low, ymax = high), size = 0.25) +
    theme_bw() + theme('legend.position' = 'none') + xlab('') +
    ggtitle(paste('Last Updated:', Sys.time())) +
    scale_y_continuous(labels = dollar) +
    scale_color_manual(values = c('#1a9850', '#d73027')) # RdYlGn
  print(p)
}

#* Generate HTML
#* @get /report
#* @serializer html
function(res) {
   filename <- tempfile(fileext = '.html')
   on.exit(unlink(filename))
   render('report.Rmd', output_file = filename)
   include_file(filename, res)
}

    Run via:

    library(plumber)
pr('plumber.R') %>% pr_run(port = 8000)

Try to DRY (don't repeat yourself!) this up as much as possible.

R API containers

Why API? Why R-based API? Examples

  • adtech
  • healthtech

  1. Write an R script that provides 3 API endpoints (look up examples from past week!):

    • /stats reports on the min/mean/max BTC price from the past 3 hours
    • /plot generates a candlestick chart on the price of BTC from past 3 hours
    • /report generates a HTML report including both the above
    Example solution for the above ...

    💪 Update the markdown package:

    sudo apt install -y r-cran-markdown

    Create an R markdown for the reporting:

    ---
title: "report"
output: html_document
date: "`r Sys.Date()`"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning=FALSE)
library(binancer)
library(ggplot2)
library(scales)
library(knitr)

klines <- function() {
  binance_klines('BTCUSDT', interval = '1m', limit = 60L)
}
```

Bitcoin stats:

```{r stats}
kable(klines()[, .(min = min(close), mean = mean(close), max = max(close))])
```

On a nice plot:

```{r plot}
ggplot(klines(), aes(open_time, )) +
  geom_linerange(aes(ymin = open, ymax = close, color = close < open), size = 2) +
  geom_errorbar(aes(ymin = low, ymax = high), size = 0.25) +
  theme_bw() + theme('legend.position' = 'none') + xlab('') +
  ggtitle(paste('Last Updated:', Sys.time())) +
  scale_y_continuous(labels = dollar) +
  scale_color_manual(values = c('#1a9850', '#d73027'))
```

    And the plumber file:

    library(binancer)
library(ggplot2)
library(scales)
library(rmarkdown)
library(plumber)

#' Gets BTC data from the past hour
#' @return data.table
klines <- function() {
    binance_klines('BTCUSDT', interval = '1m', limit = 60L)
}

#* BTC stats
#* @get /stats
function() {
  klines()[, .(min = min(close), mean = mean(close), max = max(close))]
}

#* Generate plot
#* @get /plot
#* @serializer png
function() {
  p <- ggplot(klines(), aes(open_time, )) +
    geom_linerange(aes(ymin = open, ymax = close, color = close < open), size = 2) +
    geom_errorbar(aes(ymin = low, ymax = high), size = 0.25) +
    theme_bw() + theme('legend.position' = 'none') + xlab('') +
    ggtitle(paste('Last Updated:', Sys.time())) +
    scale_y_continuous(labels = dollar) +
    scale_color_manual(values = c('#1a9850', '#d73027')) # RdYlGn
  print(p)
}

#* Generate HTML
#* @get /report
#* @serializer html
function(res) {
   filename <- tempfile(fileext = '.html')
   on.exit(unlink(filename))
   render('report.Rmd', output_file = filename)
   include_file(filename, res)
}

    Run via:

    library(plumber)
pr('plumber.R') %>% pr_run(port = 8000)

  2. Try to DRY (don't repeat yourself!) this up as much as possible.

  3. Bundle all the scripts into a single Docker image:

    a. 💪 Install Docker:

    curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] \
  https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
sudo apt-get install -y docker-ce

    b. Create a new file named Dockerfile (File/New file/Text file to avoid auto-adding the R file extension) with the below content to add the required files and set the default working directory to the same folder:

    FROM rstudio/plumber

RUN apt-get update && apt-get install -y pandoc && apt-get clean && rm -rf /var/lib/apt/lists/
RUN install2.r ggplot2
RUN installGithub.r daroczig/binancer
ADD report.Rmd /app/report.Rmd
ADD plumber.R /app/plumber.R
EXPOSE 8000
WORKDIR /app

    Note the step installing the required R packages!

    c. Build the Docker image:

    sudo docker build -t btc-report-api .

    d. Run a container based on the above image:

    sudo docker run -p 8000:8000 -ti btc-report-api plumber.R

    e. Realize that it's not working, as we need to install rmarkdown package, so update the above Dockerfile on line 4, rebuild the image and run again.

    f. Test by visiting the 8000 port or the Nginx proxy at http://de3.ceudata.net/8000, e.g. Swagger docs at http://de3.ceudata.net/8000/__docs__/#/default/get_report or an endpoint directly at http://de3.ceudata.net/8000/report.

Docker registry

Now let's make the above created and tested Docker image available outside of the RStudio Server by uploading the Docker image to Elastic Container Registry (ECR):

  1. 💪 Create a new private repository at https://eu-west-1.console.aws.amazon.com/ecr/home?region=eu-west-1, call it de3-example-api

  2. 💪 Assign the EC2InstanceProfileForImageBuilderECRContainerBuilds policy to the ceudataserver IAM role so that we get RW access to the ECR repositories. Tighten this role up in prod!

  3. 💪 Let's login to ECR on the RStudio Server so that we can upload the Docker image:

    aws ecr get-login-password --region eu-west-1 | sudo docker login --username AWS --password-stdin 657609838022.dkr.ecr.eu-west-1.amazonaws.com

  4. 💪 Tag the already build Docker image for upload:

    sudo docker tag btc-report-api:latest 657609838022.dkr.ecr.eu-west-1.amazonaws.com/de3-example-api:latest

  5. 💪 Push the Docker image:

    sudo docker push 657609838022.dkr.ecr.eu-west-1.amazonaws.com/de3-example-api:latest

  6. Check the Docker repository at https://eu-west-1.console.aws.amazon.com/ecr/repositories/private/657609838022/de3-example-api?region=eu-west-1

Docker service

  1. Go to the Elastic Container Service (ECS) dashboard at https://eu-west-1.console.aws.amazon.com/ecs/home?region=eu-west-1#/

  2. Create a task definition for the Docker run:

    1. Task name: btc-api
    2. Container name: api
    3. Image URI: 657609838022.dkr.ecr.eu-west-1.amazonaws.com/de3-example-api
    4. Container port: 8000
    5. Command in the Docker configuration: plumber.R
    6. Review Task size, but default values should fine for this simple task

  3. Create a new cluster, call it BTC_API, using Fargate. Don't forget to add the Class tag!

  4. Create a Service in the newly created Cluster at https://eu-west-1.console.aws.amazon.com/ecs/v2/clusters/btc-api/services?region=eu-west-1

    1. Compute option can be "Launch type" for now
    2. Specify the Task Family as btc-api
    3. Provide the same as service name
    4. Use the de3 security group
    5. Create a load balancer listening on port 80 (would need to create an SSL cert for HTTPS), and specify /stats as the healthcheck path, with a 10 seconds grace period
    6. Test the deployed service behind the load balancer, e.g. https://btc-api-1417435399.eu-west-1.elb.amazonaws.com/report

Home assignment

The goal of this assignment is to confirm that you have a general understanding on how to build data pipelines using Amazon Web Services and R, and can actually implement a stream processing application (either running in almost real-time or batched/scheduled way) or R-based API in practice.

Tech setup

To minimize the system administration and some of the already-covered engineering tasks for the students, the below pre-configured tools are provided as free options, but students can decide to build their own environment (on the top of or independently from these) and feel free to use any other tools:

  • de3 Amazon Machine Image that you can use to spin up an EC2 node with RStudio Server, Shiny Server, Jenkins, Redis and Docker installed & pre-configured (use your AWS username and the password shared on Slack previously) along with the most often used R packages (including the ones we used for stream processing, eg botor, AWR.Kinesis and the binancer package)
  • de3 EC2 IAM role with full access to Kinesis, Dynamodb, Cloudwatch and the slack token in the Parameter Store
  • de3 security group with open ports for RStudio Server and Jenkins
  • lecture and seminar notes at https://github.com/daroczig/CEU-R-prod

Required output

Make sure to clean-up your EC2 nodes, security groups, keys etc created in the past weeks, as left-over AWS resources will contribute negative points to your final grade! E.g. the EC2 node you created on the second week should be terminated.

  • Minimal project (for grade up to "B"): schedule a Jenkins job that runs every hour getting the past hour's 1-minute interval klines data on ETH prices (in USD). The job should be configured to pull the R script at the start of the job either from a private or public git repo or gist. Then

    • Find the min and max price of ETH in the past hour, and post these stats in the #bots-bots-bots MS Teams channel. Make sure to set your username for the message, and use a custom emoji as the icon.
    • Set up email notification for the job when it fails.

  • Recommended project (for grade up to "A"): Deploy an R-based API in ECS (like we did on the last week) for analyzing recent Binance (or any other real-time) data. The API should include at least 4 endpoints using different serializers, and these endpoints should be other than the ones we covered in the class. At least one endpoint should have at least a few parameters. Build a Docker image, push it to ECR, and deploy as service in ECS. Document the steps required to set up ECR/ECS with screenshots, then delete all services after confirming that everything works correctly.

Regarding feedback: by default, I add a super short feedback on Moodle as a comment to your submission (e.g. "good job" or "excellent" for grade A, or short details on why it was not A). If you want to receive more detailed feedback, please send me an email to schedule a quick call. If you want early feedback (before grading), send me an email at least a week before the submission deadline!

Delivery method

  • Create a PDF document that describes your solution and all the main steps involved with low level details: attach screenshots (including the URL nav bar and the date/time widget of your OS, so like full-screen and not area-picked screenshots) of your browser showing what you are doing in RStudio Server, Jenkins, in the AWS dashboards, or example messages posted in MS Teams, and make sure that the code you wrote is either visible on the screenshots, or included in the PDF.

  • STOP the EC2 Instance you worked on, but don’t terminate it, so I can start it and check how it works. Note that your instance will be terminated by me after the end of the class.

  • Include the instance_id on the first page of the PDF, along with your name or student id.

  • Upload the PDF to Moodle.

Submission deadline

Midnight (CET) on May 31, 2025.

Extra: Stream processing using R and AWS

An introduction to stream processing with AWS Kinesis and R: https://github.com/daroczig/CEU-R-prod/raw/2017-2018/AWR.Kinesis/AWR.Kinesis-talk.pdf (presented at the Big Data Day Los Angeles 2016, EARL 2016 London and useR! 2017 Brussels)

This section describes how to set up a Kinesis stream with a few on-demand shards on the live Binance transactions read from its websocket -- running in a Docker container, then feeding the JSON lines to Kinesis via the Amazon Kinesis Agent.

💪 Setting up a demo stream

  1. Start a t3a.micro instance running "Amazon Linux 2 AMI" (where it's easier to install the Kinesis Agent compared to using eg Ubuntu) with a known key. Make sure to set a name and enable termination protection (in the instance details)! Use SSH, Putty or eg the browser-based SSH connection. Note that the default username is ec2-user instead of ubuntu.

  2. Install Docker (note that we are not on Ubuntu today, but using Red Hat's yum package manager):

    sudo yum install docker
sudo service docker start
sudo service docker status

  3. Let's use a small Python app relying on the Binance API to fetch live transactions and store in a local file:

    Usage:

    screen -RRd streamer
sudo docker run -ti --rm --log-opt max-size=50m daroczig/ceu-de3-docker-binance-streamer >> /tmp/transactions.json
## "C-a c" to create a new screen, then you can switch with C-a "
ls -latr /tmp
tail -f /tmp/transactions.json

  4. Install the Kinesis Agent:

    As per https://docs.aws.amazon.com/firehose/latest/dev/writing-with-agents.html#download-install:

    sudo yum install -y aws-kinesis-agent

  5. Create a new Kinesis Stream (called crypto) at https://eu-west-1.console.aws.amazon.com/kinesis. Don't forget to tag it (Class, Owner)!

  6. Configure the Kinesis Agent:

    sudo yum install mc
sudo mcedit /etc/aws-kinesis/agent.json

    Running the above commands, edit the config file to update the Kinesis endpoint, the name of the stream on the local file path:

    {
  "cloudwatch.emitMetrics": true,
  "kinesis.endpoint": "https://kinesis.eu-west-1.amazonaws.com",
  "firehose.endpoint": "",

  "flows": [
    {
      "filePattern": "/tmp/transactions.json*",
      "kinesisStream": "crypto",
      "partitionKeyOption": "RANDOM"
    }
  ]
}

    Note that extra star at the end of the filePattern to handle potential issues when file is copy/truncated (logrotate).

  7. Restart the Agent:

    sudo service aws-kinesis-agent start

  8. Check the status and logs:

    sudo service aws-kinesis-agent status
sudo journalctl -xe
ls -latr /var/log/aws-kinesis-agent/aws-kinesis-agent.log
tail -f /var/log/aws-kinesis-agent/aws-kinesis-agent.log

  9. Make sure that the IAM role (eg kinesis-admin) can write to Kinesis and Cloudwatch, eg by attaching the AmazonKinesisFullAccess policy, then restart the agent

    sudo service aws-kinesis-agent restart

  10. Check the AWS console's monitor if all looks good there as well

  11. Note for the need of permissions to cloudwatch:PutMetricData (see example cloudwatch-putmetrics policy).

  12. Optionally set up a cronjob to truncate that the file from time to time:

    5 * * * * /usr/bin/truncate -s 0 /tmp/transactions.json

  13. Set up an alert in Cloudwatch if streaming stops

A simple stream consumer app in R

As the botor package was already installed, we can rely on the power of boto3 to interact with the Kinesis stream. The IAM role attached to the node already has the AmazonKinesisFullAccess policy attached, so we have permissions to read from the stream.

First we need to create a shard iterator, then using that, we can read the actual records from the shard:

library(botor)
botor(region = 'eu-west-1')
shard_iterator <- kinesis_get_shard_iterator('crypto', '0')
records <- kinesis_get_records(shard_iterator$ShardIterator)
str(records)

Let's parse these records:

records$Records[[1]]
records$Records[[1]]$Data

library(jsonlite)
fromJSON(as.character(records$Records[[1]]$Data))

Parsing and structuring records read from the stream

Exercises:

  • parse the loaded 25 records into a data.table object with proper column types. Get some help on the data format from the Binance API docs!
  • count the overall number of coins exchanged
  • count the overall value of transactions in USD (hint: binance_ticker_all_prices() and binance_coins_prices())
  • visualize the distribution of symbol pairs
A potential solution that you should not look at before thinking ... 
library(data.table)
dt <- rbindlist(lapply(records$Records, function(record) {
  fromJSON(as.character(record$Data))
}))

str(dt)

setnames(dt, 'a', 'seller_id')
setnames(dt, 'b', 'buyer_id')
setnames(dt, 'E', 'event_timestamp')
## Unix timestamp / Epoch (number of seconds since Jan 1, 1970): https://www.epochconverter.com
dt[, event_timestamp := as.POSIXct(event_timestamp / 1000, origin = '1970-01-01')]
setnames(dt, 'q', 'quantity')
setnames(dt, 'p', 'price')
setnames(dt, 's', 'symbol')
setnames(dt, 't', 'trade_id')
setnames(dt, 'T', 'trade_timestamp')
dt[, trade_timestamp := as.POSIXct(trade_timestamp / 1000, origin = '1970-01-01')]
str(dt)

for (id in grep('_id', names(dt), value = TRUE)) {
  dt[, (id) := as.character(get(id))]
}
str(dt)

for (v in c('quantity', 'price')) {
  dt[, (v) := as.numeric(get(v))]
}

library(binancer)
binance_coins_prices()

dt[, .N, by = symbol]
dt[symbol=='ETHUSDT']
dt[, from := substr(symbol, 1, 3)]
dt <- merge(dt, binance_coins_prices(), by.x = 'from', by.y = 'symbol', all.x = TRUE, all.y = FALSE)
dt[, value := as.numeric(quantity) * usd]
dt[, sum(value)]

Actual stream processing instead of analyzing batch data

Let's write an R function to increment counters on the number of transactions per symbols:

  1. Get sample raw data as per above (you might need to get a new shard iterator if expired):

    records <- kinesis_get_records(shard_iterator$ShardIterator)$Record

  2. Function to parse and process it

    txprocessor <- function(record) {
  symbol <- fromJSON(as.character(record$Data))$s
  log_info(paste('Found 1 transaction on', symbol))
  redisIncr(paste('USERNAME', 'tx', symbol, sep = ':'))
}

  3. Iterate on all records

    library(logger)
library(rredis)
redisConnect()
for (record in records) {
  txprocessor(record)
}

  4. Check counters

    symbols <- redisMGet(redisKeys('^USERNAME:tx:*'))
symbols

symbols <- data.frame(
  symbol = sub('^USERNAME:tx:', '', names(symbols)),
  N = as.numeric(symbols))
symbols

  5. Visualize

    library(ggplot2)
ggplot(symbols, aes(symbol, N)) + geom_bar(stat = 'identity')

  6. Rerun step (1) and (3) to do the data processing, then (4) and (5) for the updated data visualization.

  7. 🤦

  8. Let's make use of the next shard iterator:

    ## reset counters
redisDelete(redisKeys('USERNAME:tx:*'))

## get the first shard iterator
shard_iterator <- kinesis_get_shard_iterator('crypto', '0')$ShardIterator

while (TRUE) {

  response <- kinesis_get_records(shard_iterator)

  ## get the next iterator
  shard_iterator <- response$NextShardIterator

  ## extract records
  records <- response$Record
  for (record in records) {
    txprocessor(record)
  }

  ## summarize
  symbols <- redisMGet(redisKeys('USERNAME:tx:*'))
  symbols <- data.frame(
    symbol = sub('^symbol:', '', names(symbols)),
    N = as.numeric(symbols))

  ## visualize
  print(ggplot(symbols, aes(symbol, N)) + geom_bar(stat = 'identity') + ggtitle(sum(symbols$N)))
}

Stream processor daemon

  1. So far, we used the boto3 Python module from R via botor to interact with AWS, but this time we will integrate Java -- by calling the AWS Java SDK to interact with our Kinesis stream, then later on to run a Java daemon to manage our stream processing application.

    1. 💪 First, let's install Java and the rJava R package:
    sudo apt install r-cran-rjava
    1. 💪 Then the R package wrapping the AWS Java SDK and the Kinesis client, then update to the most recent dev version right away:
    sudo apt install r-cran-awr.kinesis
sudo R -e "withr::with_libpaths(new = '/usr/local/lib/R/site-library', install.packages('AWR', repos = 'https://daroczig.gitlab.io/AWR'))"
sudo R -e "withr::with_libpaths(new = '/usr/local/lib/R/site-library', devtools::install_github('daroczig/AWR.Kinesis', upgrade = FALSE))"
    1. 💪 Note, after installing Java, you might need to run sudo R CMD javareconf and/or restart R or the RStudio Server via sudo rstudio-server restart :/
    Error : .onLoad failed in loadNamespace() for 'rJava', details:
  call: dyn.load(file, DLLpath = DLLpath, ...)
  error: unable to load shared object '/usr/lib/R/site-library/rJava/libs/rJava.so':
  libjvm.so: cannot open shared object file: No such file or directory
    1. And after all, a couple lines of R code to get some data from the stream via the Java SDK (just like we did above with the Python backend):
    library(rJava)
library(AWR.Kinesis)
records <- kinesis_get_records('crypto', 'eu-west-1')
str(records)
records[1]

library(jsonlite)
fromJSON(records[1])

  2. Create a new folder for the Kinesis consumer files: streamer

  3. Create an app.properties file within that subfolder

executableName = ./app.R
regionName = eu-west-1
streamName = crypto
applicationName = my_demo_app_sadsadsa
AWSCredentialsProvider = DefaultAWSCredentialsProviderChain
  1. Create the app.R file:
#!/usr/bin/Rscript
library(logger)
log_appender(appender_file('app.log'))
library(AWR.Kinesis)
library(methods)
library(jsonlite)

kinesis_consumer(

    initialize = function() {
        log_info('Hello')
        library(rredis)
        redisConnect(nodelay = FALSE)
        log_info('Connected to Redis')
    },

    processRecords = function(records) {
        log_info(paste('Received', nrow(records), 'records from Kinesis'))
        for (record in records$data) {
            symbol <- fromJSON(record)$s
            log_info(paste('Found 1 transaction on', symbol))
            redisIncr(paste('symbol', symbol, sep = ':'))
        }
    },

    updater = list(
        list(1/6, function() {
            log_info('Checking overall counters')
            symbols <- redisMGet(redisKeys('symbol:*'))
            log_info(paste(sum(as.numeric(symbols)), 'records processed so far'))
    })),

    shutdown = function()
        log_info('Bye'),

    checkpointing = 1,
    logfile = 'app.log')

  1. 💪 Allow writing checkpointing data to DynamoDB and CloudWatch in IAM

  2. Convert the above R script into an executable using the Terminal:

cd streamer
chmod +x app.R
  1. Run the app in the Terminal:
/usr/bin/java -cp /usr/local/lib/R/site-library/AWR/java/*:/usr/local/lib/R/site-library/AWR.Kinesis/java/*:./ \
    com.amazonaws.services.kinesis.multilang.MultiLangDaemon \
    ./app.properties
  1. Check on app.log

Shiny app showing the progress

  1. Reset counters

    library(rredis)
redisConnect()
keys <- redisKeys('symbol*')
redisDelete(keys)

  2. 💪 Install the treemap package

    sudo apt install r-cran-httpuv r-cran-shiny r-cran-xtable r-cran-htmltools r-cran-igraph r-cran-lubridate r-cran-tidyr r-cran-quantmod r-cran-broom r-cran-zoo r-cran-htmlwidgets r-cran-tidyselect r-cran-rlist r-cran-rlang r-cran-xml r-cran-treemap r-cran-highcharter

  3. Run the below Shiny app

    ## packages for plotting
library(treemap)
library(highcharter)

## connect to Redis
library(rredis)
redisConnect()

library(shiny)
library(data.table)
ui     <- shinyUI(highchartOutput('treemap', height = '800px'))
server <- shinyServer(function(input, output, session) {

    symbols <- reactive({

        ## auto-update every 2 seconds
        reactiveTimer(2000)()

        ## get frequencies
        symbols <- redisMGet(redisKeys('symbol:*'))
        symbols <- data.table(
            symbol = sub('^symbol:', '', names(symbols)),
            N = as.numeric(symbols))

        ## color top 3
        symbols[, color := 1]
        symbols[symbol %in% symbols[order(-N)][1:3, symbol], color := 2]

        ## return
        symbols

    })

    output$treemap <- renderHighchart({
        tm <- treemap(symbols(), index = c('symbol'),
                      vSize = 'N', vColor = 'color',
                      type = 'value', draw = FALSE)
        N <- sum(symbols()$N)
        hc_title(hctreemap(tm, animation = FALSE),
        text = sprintf('Transactions (N=%s)', N))
    })

})
shinyApp(ui = ui, server = server, options = list(port = 3838))

We will learn more about Shiny in the upcoming Data Visualization 4 class :)

Will be updated from week to week.

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R materials for the "Data Infrastructure in Production" class at CEU

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