Adaptive Compression in Inverted Indexes: What Actually Happens Inside Lucene, Elasticsearch, and Tantivy

Based on your detailed explanation, it's clear that managing compression for inverted indexes involves understanding several key components and algorithms. Here’s a summary of the core points and some additional insights:

Key Components in an Inverted Index

1. Terms Dictionary: A sorted list of unique tokens.
2. Postings List: For each term, a list of document IDs (and optionally frequencies and positions).
3. Skip Data: Sparse index over postings lists for efficient query evaluation.

Compression Techniques

Static vs Adaptive Compression

• Static Compression:

  • Treats the whole postings file as a byte stream.
  • Uses general-purpose codecs like ZSTD or GZIP.
  • Pros: Simple, good for cold storage.
  • Cons: Needs to decompress entire segments for queries.

• Adaptive Compression:

  • Assigns a codec per block (e.g., 128 or 256 postings).
  • Each block stores a header indicating the codec used and bitwidth.
  • Pros: Random-access decompression, efficient query evaluation.
  • Cons: Slightly higher space overhead due to headers.

Core Algorithms

1. Variable-Byte (VByte):

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