Principle:Apache Paimon FAISS Index Configuration
| Knowledge Sources | |
|---|---|
| Domains | Data_Lake, Vector_Search |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Mechanism for configuring approximate nearest neighbor (ANN) vector indexes using FAISS for efficient similarity search on data lake tables.
Description
FAISS (Facebook AI Similarity Search) index configuration defines the parameters for building and querying vector indexes on Paimon tables. Configuration includes selecting the distance metric (L2 Euclidean or Inner Product), index type (FLAT for exact search, HNSW for graph-based, IVF for cluster-based, IVF_PQ for compressed, IVF_SQ8 for scalar-quantized), and tuning parameters (nlist for cluster count, nprobe for search breadth, ef_search for HNSW depth). These settings control the trade-off between search accuracy, speed, and memory usage.
The configuration encompasses several interrelated dimensions:
- Distance Metric: L2 (Euclidean distance) measures geometric distance between vectors, while Inner Product (cosine similarity when vectors are normalized) measures directional alignment. The choice depends on how embeddings were trained.
- Index Type: Five supported types offer different trade-offs:
- FLAT: Exact brute-force search with perfect recall but linear time complexity.
- HNSW: Graph-based index with logarithmic search time and high recall, at the cost of higher memory usage.
- IVF: Cluster-based index that partitions the vector space into Voronoi cells for sub-linear search.
- IVF_PQ: Combines IVF clustering with Product Quantization for memory-efficient storage of large datasets.
- IVF_SQ8: Combines IVF clustering with scalar quantization (8-bit) for a balance of compression and accuracy.
- Tuning Parameters: Parameters like nlist, nprobe, ef_search, and m control the accuracy-speed-memory trade-off at both build and query time.
Usage
Use when setting up vector similarity search on Paimon tables containing embedding vectors. Choose index type based on dataset size and accuracy requirements:
- FLAT for small datasets (<100K vectors) where exact results are acceptable.
- HNSW for medium datasets with low latency needs and sufficient memory.
- IVF variants for large datasets (>1M vectors) where memory efficiency and scalability are priorities.
Key configuration decisions include:
- Select the distance metric that matches how embedding vectors were trained.
- Choose the index type based on dataset size, memory budget, and latency requirements.
- Tune index-specific parameters (nlist/nprobe for IVF, ef_search/m for HNSW) to balance accuracy and speed.
- Set size_per_index to control index sharding for parallel search.
Theoretical Basis
ANN search algorithms sacrifice exact accuracy for significant speed improvements. The theoretical foundations for each index type are:
Inverted File (IVF): Partitions the vector space into Voronoi cells using k-means clustering. At query time, only the nearest nprobe cells are searched, reducing complexity from O(n) to O(n/nlist * nprobe). Increasing nlist creates finer partitions (more clusters), while increasing nprobe searches more cells for higher recall at the cost of speed.
Hierarchical Navigable Small World (HNSW): Builds a multi-layer proximity graph where each layer is a navigable small world graph with decreasing density. Search begins at the top layer and greedily descends, achieving O(log n) search complexity. The m parameter controls the number of connections per node (affecting graph connectivity and memory), while ef_search controls the size of the dynamic candidate list during search (affecting recall).
Product Quantization (PQ): Compresses vectors by splitting them into pq_m sub-vectors and quantizing each independently into one of 2^pq_nbits centroids. This reduces memory from d*4 bytes to pq_m*pq_nbits/8 bytes per vector, enabling billion-scale search in memory. The trade-off is approximate distance computation.
Scalar Quantization (SQ8): Quantizes each vector dimension to 8-bit integers, reducing memory by 4x compared to float32 with minimal accuracy loss. SQ8 preserves the full dimensionality unlike PQ.
The nprobe and ef_search parameters are the primary knobs for controlling the accuracy-speed trade-off at query time without rebuilding the index.