ForeverAngry opened a new pull request, #2459:
URL: https://github.com/apache/iceberg-python/pull/2459
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# Rationale for this change
Hey there! ๐ This PR introduces **native Vortex file format support** to
PyIceberg, making it the **first major data processing library** to support
this next-generation columnar format alongside Parquet! This has been a big
learning opportunity for me, to dive deeper into pyiceberg, as well as file
formats. I hope folks find this interesting!
## What is Vortex?
[Vortex](https://docs.vortex.dev/) is a cutting-edge columnar file format
designed for high-performance analytics, offering:
- **๐๏ธ 100x faster random access** reads vs. Parquet
- **โก 2.5x faster analytical queries** in our benchmarks
- **๐๏ธ Similar compression ratios** to Parquet
- **๐ Advanced filtering capabilities** with predicate pushdown
## What's Added
โ
**Complete Integration** - Native `.vortex` file support throughout
PyIceberg
โ
**Full Feature Parity** - All operations work identically to Parquet files
โ
**Production Ready** - Comprehensive error handling and optimization
โ
**Advanced Filtering** - Complete PyArrow-based filtering with predicate
pushdown
โ
**Streaming Support** - Optimized RecordBatchReader for large datasets
โ
**Schema Compatibility** - Seamless conversion between Iceberg and Vortex
schemas
### Key Implementation Features
- **Smart Batch Optimization**: Adaptive batch sizing based on dataset
characteristics
- **Multi-path Read Strategy**: Direct URL reading, direct file opening, and
fallback temp file copy
- **Memory Optimization**: Custom allocator optimizations for Vortex
performance
- **Streaming Architecture**: Uses official Vortex RecordBatchReader API for
large datasets
- **Comprehensive Caching**: File handle caching for repeated reads
## Performance Results ๐
Based on our extensive benchmark suite
([`tests/benchmark/README.md`](tests/benchmark/README.md)):
| Metric | Vortex Performance | vs Parquet |
|--------|-------------------|------------|
| **Write Speed** | ~1.07M rows/sec | Similar |
| **Read Speed** | ~66M rows/sec | **2.5x faster** |
| **Compression** | Similar ratios | Comparable |
| **Filtered Queries** | Superior performance | **1.2x - 2.4x faster** |
### Benchmark Commands
```bash
# Quick performance check
python tests/benchmark/vortex_benchmark.py --quick
# Full comprehensive benchmark
python tests/benchmark/vortex_benchmark.py --full
# Large scale test (15M+ rows)
python tests/benchmark/vortex_benchmark.py --large
```
## Implementation Details
### Core Files Added/Modified
- **vortex.py** - Complete Vortex I/O implementation
- **pyarrow.py** - Integration with PyArrow write pipeline
- **test_vortex.py** - Comprehensive test suite
- **benchmark** - Performance benchmarking suite
- **test_add_vortex_files.py** - Integration tests
### Architecture Highlights
```python
# Example usage - works just like Parquet!
table.add_files(["data.vortex"]) # Add Vortex files
table.scan().to_arrow() # Read with filtering/projection
```
The implementation follows official [Vortex API
documentation](https://docs.vortex.dev/api/python/io) patterns and includes
optimizations inspired by their RepeatedScan and Dataset APIs.
## Are these changes tested?
Absolutely! ๐งช This includes one of the most comprehensive file format test
suites:
- **โ
20+ unit tests** covering all read/write operations
- **โ
Integration tests** with real Iceberg tables
- **โ
Performance benchmark suite** with automated instrumentation
- **โ
Error handling tests** for edge cases and fallbacks
- **โ
Schema compatibility tests** for complex nested types
- **โ
Filtering and projection tests** with PyArrow expressions
### Test Coverage
```bash
# Run Vortex-specific tests
pytest tests/io/test_vortex.py -v
pytest tests/integration/test_add_vortex_files.py -v
# Run benchmarks
python tests/benchmark/vortex_benchmark.py --quick
```
## Are there any user-facing changes?
Yes, but they're all **additive**! ๐
### New Features for Users
1. **Native Vortex File Support**: Users can now use `.vortex` files
anywhere they use `.parquet`
2. **Performance Benefits**: Faster analytical queries, especially with
filtering
3. **New Installation Option**: `pip install 'pyiceberg[vortex]'`
### Backwards Compatibility
- **โ
Zero breaking changes** - all existing Parquet workflows unchanged
- **โ
Optional dependency** - Vortex support only active when `vortex-data`
installed
- **โ
Graceful fallbacks** - informative errors when Vortex unavailable
### Usage Examples
```python
# Works exactly like Parquet files!
from pyiceberg.catalog import load_catalog
catalog = load_catalog("my_catalog")
table = catalog.load_table("my_namespace.my_table")
# Add Vortex files (new!)
table.add_files(["s3://bucket/data.vortex"])
# All existing operations work
df = table.scan().to_pandas() # Full scan
df = table.scan().filter(...).to_pandas() # Filtered scan
df = table.scan().select("col1", "col2").to_pandas() # Projected scan
```
This makes PyIceberg the first major lakehouse library to offer users a
choice between Parquet and Vortex formats based on their performance needs! ๐
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