We haven’t benchmarked FastLanes directly against LanceDB yet, but here’s a quick look at the compression side:
LanceDB supports:
FSST
Bit-packing
Delta encoding
Opaque block codecs: GZIP, LZ4, Snappy, ZLIB
So in that regard, it’s quite similar to Parquet — a mix of lightweight codecs and general-purpose block compression.
FastLanes, on the other hand, introduces Expression Encoding — a unified compression model that allows combining lightweight encodings to achieve better compression ratios. It also integrates multiple research efforts from CWI into a single file format:
LanceDB supports:
FSST
Bit-packing
Delta encoding
Opaque block codecs: GZIP, LZ4, Snappy, ZLIB
So in that regard, it’s quite similar to Parquet — a mix of lightweight codecs and general-purpose block compression.
FastLanes, on the other hand, introduces Expression Encoding — a unified compression model that allows combining lightweight encodings to achieve better compression ratios. It also integrates multiple research efforts from CWI into a single file format:
The FastLanes Compression Layout: Decoding >100 Billion Integers per Second with Scalar Code (VLDB '23) PDF: https://dl.acm.org/doi/pdf/10.14778/3598581.3598587
ALP (Adaptive Lossless Floating-Point Compression) — SIGMOD '24 https://ir.cwi.nl/pub/33334/33334.pdf
G‑ALP (GPU-parallel variant of ALP) — DaMoN '25 https://azimafroozeh.org/assets/papers/g-alp.pdf
White-box Compression (self-describing, function-based) — CIDR '20 https://www.cidrdb.org/cidr2020/papers/p4-ghita-cidr20.pdf
CCC (Exploiting Column Correlations for Compression) — MSc Thesis '23 https://homepages.cwi.nl/~boncz/msc/2023-ThomasGlas.pdf