All About Parquet Part 05 - Compression Techniques in Parquet

Parquet's columnar storage model enables highly effective compression, reducing storage costs while maintaining fast query performance. Compression is crucial for managing large datasets, as it saves storage space and improves query performance by reducing the amount of data that needs to be read from disk and transferred over networks. Parquet supports several widely-used compression algorithms, including Snappy, Gzip, Brotli, Zstandard, and LZO, each with its own strengths and weaknesses. Snappy is a popular choice due to its speed and reasonable compression ratio, making it ideal for real-time queries and analytics workloads. Gzip provides a high compression ratio but is slower than Snappy, making it suitable for archiving data or working with large, infrequently accessed datasets. Brotli offers higher compression ratios than Gzip with better performance, making it a good balance between file size reduction and read performance. Zstandard provides a balance between compression speed, decompression speed, and file size reduction, but requires more configuration. LZO is a lightweight compression algorithm that focuses on fast decompression, making it suitable for real-time analytics and streaming data processing. Choosing the right compression algorithm depends on the specific use case and the balance between compression efficiency and performance. Additionally, combining compression with encoding techniques, such as dictionary encoding or run-length encoding, can further optimize storage efficiency.