Writing Parquet files using Haskell
2 days ago
- DataHaskell/Dataframe provides a simple `writeParquet` function for writing Parquet files with sensible defaults, and `writeParquetWithOptions` for fine-grained control over row group size, page size, compression, and other settings.
- Parquet format offers efficient storage, high compression ratios, and minimizes reading irrelevant data, making it superior to CSV/JSON for large-scale data interoperability in the Haskell ecosystem.
- A Parquet file consists of row groups, each containing column chunks composed of data pages; metadata at the end includes offsets, statistics, and bloom filters to support query optimizations.
- Key write options include `pageSize`, `rowGroupSize`, `batchRows`, `subBatchRows`, and `compressionCodec`, with targets considered best-effort; batches and sub-batches ensure consistent row counts across column chunks.
- Memory buffers are implemented using pinned `MutableByteArray`s to allow growth without fragmentation, with helper functions for writing primitive types and flushing buffers to disk or other buffers.
- The core writer loop is an effectful fold over the dataframe, maintaining state for file handle, column chunk buffers, scratch buffer, row group metadata, and row count; `writeBatch` and `rowWriterLoop` handle batching and page filling.
- Future work includes supporting more compression algorithms and encodings, adding concurrency, recording optional metadata (statistics, bloom filters), and implementing a two-pass strategy to reduce memory usage for large row groups.