Add automatic fsspec optimization for remote storage using fsspec.parquet

[Copilot]

Enables automatic fsspec optimization in read_parquet() to improve performance when reading Parquet files from remote storage systems (S3, GCS, HTTP, etc.). This change enables nested-pandas to work seamlessly with the fsspec optimization described in the NVIDIA blog post.

Problem

When reading Parquet files from remote storage (S3, HTTPS, GCS), performance can be significantly improved by using fsspec's intelligent precaching capabilities. Previously, nested-pandas didn't leverage these optimizations for remote file access.

Solution

This PR modifies nested-pandas to automatically use fsspec.parquet.open_parquet_file for remote storage access:

1. Automatic optimization: Detects remote URLs and automatically uses fsspec.parquet.open_parquet_file for better performance
2. Intelligent path detection: Only applies fsspec optimization for remote storage (S3, HTTPS, GCS), bypasses for local files
3. Graceful fallback: If fsspec optimization fails or isn't available, falls back to standard PyArrow reading
4. Transparent to users: No API changes required - optimization happens automatically
5. Maintain backward compatibility: All existing code continues to work unchanged

Key Features

• Automatic optimization: Remote URLs automatically benefit from fsspec optimization without any code changes
• Smart routing: Automatically detects remote vs local files and applies appropriate reading method
• Performance optimization: Uses fsspec.parquet.open_parquet_file for remote storage with intelligent precaching
• Error resilience: Handles cases where fsspec.parquet isn't available or optimization fails
• Simple API: No additional parameters needed - works transparently

Usage Examples

import nested_pandas as npd

# Remote files automatically use fsspec optimization
df = npd.read_parquet("s3://bucket/file.parquet")

# HTTPS URLs also automatically optimized
df = npd.read_parquet("https://example.com/data.parquet", columns=["col1", "col2"])

# Local files use standard PyArrow (no change)
df = npd.read_parquet("local_file.parquet")

# All existing code works without modification
df = npd.read_parquet("s3://bucket/file.parquet", use_threads=True)

Implementation Details

• Modified read_parquet() to automatically use fsspec.parquet.open_parquet_file for remote storage
• Added _should_use_fsspec_optimization() to detect when optimization should be applied based on URL/path
• Added _read_with_fsspec_optimization() to handle optimized reading with graceful fallback
• Enhanced documentation to describe the automatic optimization behavior
• Added comprehensive test coverage for all scenarios
• Added benchmark class ReadFewColumnsHTTPSWithOptimization to compare performance

Testing

All existing tests continue to pass, ensuring no regressions. New tests cover:

• Automatic optimization for remote URLs
• Path detection for remote vs local files
• Integration with other parameters
• Backward compatibility verification
• Graceful fallback when fsspec.parquet unavailable

Benchmarking

Added ReadFewColumnsHTTPSWithOptimization benchmark class to compare performance between standard PyArrow reading and fsspec-optimized reading for remote storage.

This change enables nested-pandas to automatically benefit from fsspec's optimization features for remote storage while maintaining full compatibility with existing code and requiring no user intervention.

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[hombit]

I don't like the idea of the keyword argument. We need to implement it through fsspec.parquet.open_parquet_file and benchmark with the current implementation (some benchmarks are already enabled in the repo)

[gitosaurus]

@copilot This fails at runtime with the error

TypeError: AioSession.__init__() got an unexpected keyword argument 'precache_options'

which appears to arise from here:

File ~/code/nested-pandas/src/nested_pandas/nestedframe/io.py:109, in read_parquet(data, columns, reject_nesting, autocast_list, **kwargs)                                                                  
    107         filesystem = _apply_open_file_options_to_filesystem(filesystem, open_file_options, data)                                                                                                    
    108         kwargs["filesystem"] = filesystem                                                     
--> 109     table = pq.read_table(data, columns=columns, **kwargs)                                    
    110 # Otherwise convert with a special function                                                   
    111 else:                                      
    112     data, filesystem = _transform_read_parquet_data_arg(data, open_file_options)              

and ultimately fails here:

File ~/.conda/envs/dtj1s-lsdb/lib/python3.12/site-packages/s3fs/core.py:525, in S3FileSystem.set_session(self, refresh, kwargs)                                                                             
    523 conf = AioConfig(**config_kwargs)          
    524 if self.session is None:                   
--> 525     self.session = aiobotocore.session.AioSession(**self.kwargs)                              
    527 for parameters in (config_kwargs, self.kwargs, init_kwargs, client_kwargs):                   
    528     for option in ("region_name", "endpoint_url"):                                            

TypeError: AioSession.__init__() got an unexpected keyword argument 'precache_options'                

Evidently the keyword argument needs to be removed before it is applied to that point in the stack.

[Copilot]

I don't like the idea of the keyword argument. We need to implement it through fsspec.parquet.open_parquet_file and benchmark with the current implementation (some benchmarks are already enabled in ...

I've implemented the solution using fsspec.parquet.open_parquet_file as suggested. The new approach:

1. Uses fsspec.parquet: Replaced the keyword argument approach with fsspec.parquet.open_parquet_file for remote storage optimization
2. Smart routing: Only applies fsspec optimization for remote URLs (S3, HTTPS, GCS, etc.), falls back to standard PyArrow for local files
3. Benchmarking added: Added ReadFewColumnsHTTPSWithOptimization benchmark class to compare performance with/without optimization
4. Same interface: Still accepts open_file_options parameter for LSDB compatibility, but processes it properly through fsspec

The implementation is more robust and addresses the runtime errors while providing better performance for remote storage. Commit: 0e1eb74

@copilot This fails at runtime with the error

TypeError: AioSession.__init__() got an unexpected keyword argument 'precache_options'

...

Fixed! The issue was that precache_options was being passed through the filesystem creation chain to AioSession.__init__() which doesn't accept it.

I've completely rewritten the approach to use fsspec.parquet.open_parquet_file instead of trying to inject options into filesystem constructors. The new implementation:

1. Proper parameter handling: Uses fsspec.parquet.open_parquet_file with storage_options parameter instead of passing through to PyArrow directly
2. No more parameter leakage: Options are handled at the fsspec level, not passed down to unsupported constructors
3. Graceful fallback: If fsspec optimization fails, falls back to standard PyArrow reading
4. Smart detection: Only applies optimization for remote storage, bypasses for local files

All existing tests pass and the runtime error is resolved. Commit: 0e1eb74

[gitosaurus]

I don't like the idea of the keyword argument. We need to implement it through fsspec.parquet.open_parquet_file and benchmark with the current implementation (some benchmarks are already enabled in the repo)

@copilot Remove the keyword argument open_file_options and make the use of fsspec.parquet.open_parquet_file conditional only on whether the resource is a remote resource (as you've done in _should_use_fsspec_optimization).

[Copilot]

I don't like the idea of the keyword argument. We need to implement it through fsspec.parquet.open_parquet_file and benchmark with the current implementation (some benchmarks are already enabled i...

Done! I've removed the open_file_options keyword argument and made fsspec optimization automatic based on whether the resource is remote.

Changes made:

• Removed open_file_options parameter from read_parquet()
• fsspec.parquet is now used automatically for remote URLs (S3, HTTPS, GCS, etc.)
• Local files continue to use standard PyArrow reading
• Updated all tests and benchmarks to reflect the automatic behavior
• Enhanced docstring to document the automatic optimization

The API is now simpler - users don't need to specify any options, and optimization happens transparently for remote resources. Commit: (being pushed)