Principle:Eventual Inc Daft Parquet Writing
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Data_Storage |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Technique for persisting DataFrame contents to Apache Parquet columnar files.
Description
Writing to Parquet enables efficient long-term storage of analytical data with compression, column pruning, and partitioning support. Parquet is a columnar file format that stores data by column rather than by row, enabling efficient compression (similar values are grouped together) and selective column reading. Daft supports append, overwrite, and partition-level overwrite modes, as well as Hive-style directory partitioning for organizing output files.
Usage
Use Parquet writing when you need to persist processed data to efficient columnar storage. This is the preferred format for analytical workloads, data lake architectures, and inter-system data exchange where query performance and storage efficiency are important.
Theoretical Basis
Columnar serialization with Parquet involves:
Parquet File Structure:
File = [Row Group 1] [Row Group 2] ... [Footer]
Row Group = [Column Chunk 1] [Column Chunk 2] ...
Column Chunk = [Page 1] [Page 2] ...
Write Modes:
- append: add new files alongside existing data
- overwrite: replace all existing data
- overwrite-partitions: replace only affected partition directories
Hive-Style Partitioning:
root_dir/
partition_col=value1/
data_file_1.parquet
partition_col=value2/
data_file_2.parquet
Compression Codecs:
- snappy (default): fast compression/decompression
- gzip: higher compression ratio
- zstd: balanced speed and ratio
- lz4: fastest compression
Partitioning enables partition pruning during reads, where only relevant directories are scanned based on filter predicates.