To compress index metadata like pointers and graph links, developers can employ several strategies that balance space efficiency with access performance:
1. Efficient Integer Encoding: Storing raw 64-bit pointers is often wasteful. Instead, use smaller integer types (32-bit or 16-bit) if the dataset fits within their limits. For sequential or clustered IDs, delta encoding (storing differences between consecutive IDs) reduces magnitude, allowing variable-length encoding schemes like Simple-8b or Elias-Fano. For example, in a graph with sorted adjacency lists, Elias-Fano can compress links into compact bit vectors while supporting fast rank/select operations. Bit-packing further optimizes space by using the minimal bits per pointer (e.g., 14 bits for 16K nodes) and packing multiple pointers into a single 64-bit word. However, this adds overhead for bitwise extraction during queries.
2. Structural Compression: Hierarchical indices (e.g., HNSW, trees) can use succinct data structures. For trees, a LOUDS (Level-Order Unary Degree Sequence) representation encodes the topology using bit vectors, requiring only ~2 bits per node. For graph indices, prune low-impact edges (e.g., in HNSW, remove long-range links with minimal effect on recall) to reduce connectivity data. Similarly, group nodes into blocks (like in a B-tree) and store intra-block links as local offsets instead of global pointers. For example, a block of 256 nodes can use 8-bit offsets, cutting pointer size by 87.5%.
3. Lossy Compression and Shared Structures: Apply controlled lossy techniques, such as quantizing numerical metadata (e.g., storing approximate distances in 8 bits instead of 32-bit floats) or collapsing similar subgraphs into shared templates. For example, if multiple nodes share identical neighbor sets (common in grid-like graphs), store the set once and reference it via a small integer ID. Dictionary encoding works here but requires static datasets. Probabilistic structures like Bloom filters are less useful for pointers but can optimize auxiliary metadata like partition membership checks.
Trade-offs: Most techniques involve speed-space trade-offs. Delta encoding and bit-packing increase computational overhead during traversal, while pruning and quantization may reduce recall. Developers should profile these methods against their specific latency and accuracy requirements. For instance, combining 16-bit pointers with Elias-Fano encoding can reduce metadata size by 50-70% with minimal performance loss, making it a practical default choice for many graph-based indices.