BigData / Apache Parquet Interview Questions
How does Parquet handle compression and encoding?
Parquet uses two complementary techniques to minimise storage:
Encoding — applied first, at the column level, to exploit data patterns:
- Dictionary Encoding — replaces repeated values with integer codes. Ideal for low-cardinality columns (e.g., status, country).
- Run-Length Encoding (RLE) — collapses consecutive identical values into (value, count) pairs.
- Delta Encoding — stores differences between successive integers; great for timestamps and monotonic IDs.
- Bit-packing — packs small integers into fewer bits.
Compression codec — applied after encoding to the byte stream:
- Snappy (default in Spark) — fast, moderate ratio (~2–3×).
- GZIP/Zlib — higher ratio (~4–6×) but slower CPU.
- ZSTD — best balance; recommended for most new deployments.
- LZO, LZ4, Brotli — specialised use cases.
Encoding and codec are configured independently per column, so hot columns can use fast codecs while archive columns use ZSTD for maximum compression.
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