Understanding Parquet Format for beginners

2024-04-11 · Source: DataExpert.io Newsletter · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, medium

Summary

Parquet is a columnar storage format that provides significant efficiency and performance gains, often underlying the benefits attributed to open table formats like Iceberg and Delta, primarily through "Column Pruning," "Predicate Pushdown," and "Massive Compression." The article explains Parquet's internal encoding techniques, "Run-Length Encoding" (RLE) for repeated values and "Dictionary Encoding" for low-cardinality columns, both of which prepare data for optimal compression. A practical comparison demonstrates Parquet's ability to reduce data size by approximately 6.6 times compared to CSV using these encodings, further enhanced to an 8-fold reduction when combined with external compression algorithms like zstd. While RLE contributes significantly to compression, its maximum effectiveness often requires data sorting, highlighting a critical compute-storage tradeoff in data optimization.

Key takeaway

Parquet's columnar format, leveraging Run-Length Encoding (RLE) and Dictionary Encoding, achieves up to 6.6x data compression over CSV, reaching 8x with zstd for optimal storage. This enables efficient column pruning and predicate pushdown, drastically cutting cloud storage costs for data-intensive workloads. However, maximizing RLE's 70-80% compression contribution often requires sorting, introducing a critical compute-storage tradeoff.

Topics

• Apache Parquet
• Data Compression
• Run-Length Encoding
• Dictionary Encoding
• Columnar Storage

Best for: Data Engineer, Data Scientist, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by DataExpert.io Newsletter.