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Anatomy of a Parquet File
- Parquet files are produced using PyArrow, which allows for fine-tuned parameter tuning.
- Dataframes in Parquet files are stored in a columns-oriented storage format, unlike Pandas' row-wise approach.
- Parquet files are commonly stored in object storage databases like S3 or GCS for easy access by data pipelines.
- A partitioning strategy organizes Parquet files in directories based on partitioning keys like birth_year and city.
- Partition pruning allows query engines to read only necessary files, based on folder names, reducing I/O.
- Decoding a raw Parquet file involves identifying the 'PAR1' header, row groups with data, and footer holding metadata.
- Parquet uses a hybrid structure, partitioning data into row groups for statistics calculation and query optimization.
- Page size in Parquet files is a trade-off, balancing memory consumption and data retrieval efficiency.
- Encoding algorithms like dictionary encoding and compression are used for optimizing columnar format in Parquet.
- Understanding Parquet's structure aids in making informed decisions on storage strategies and performance optimization.
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