DATABASES Signal 322
Adaptive Lossless Floating-Point Encoding in Apache Parquet
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Apache Parquet now supports ALP encoding for FLOAT and DOUBLE columns, offering faster random access and decompression while maintaining compression ratios comparable to ZSTD.
ALP enables engineers to retrieve individual values from columnar data without full page decoding, reducing latency in analytics pipelines and lowering CPU usage during read operations. It also expands Parquet’s applicability to workloads that previously required heavier compression codecs.
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ALP encodes FLOAT and DOUBLE values using per-vector exponent and factor parameters, enabling random-access reads.
Compression ratios are similar to ZSTD but decompression is up to ten times faster, improving query latency.
ALP is unsuitable for data with wide exponent ranges or high precision requirements, such as vector embeddings.
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The change introduces a new encoding primitive that directly addresses the three limitations of prior Parquet FLOAT/DOUBLE handling: slow random access, high decode latency, and limited parallelism.
Adoption requires updating Parquet readers and writers to recognize ALP as a supported codec, which may involve version upgrades in downstream projects that rely on Parquet for storage.
Because ALP only benefits decimal-style floating-point data, workloads that store binary or high-precision scientific values must continue using alternative encodings, preserving existing compression trade-offs.
The performance gains are most pronounced when queries filter or project a small subset of columns, as individual values can be decoded without processing entire pages, thereby accelerating interactive analytics.
Engineers must evaluate the impact of slightly reduced compression ratios on storage costs versus the gains in CPU and I/O efficiency, particularly in environments where storage bandwidth is a bottleneck.
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