The Tool Desk
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Choose exact or approximate search
By default, pgvector performs exact nearest-neighbor search, which provides perfect recall. An approximate index can reduce search time, but may return different neighbors. Keep exact search when missing a true nearest neighbor is unacceptable; otherwise, compare approximate results with exact results on representative queries and decide whether the speed gain is worth the recall change.
Choose HNSW or IVFFlat
The pgvector project describes HNSW as having a better query speed–recall tradeoff than IVFFlat, while costing more to build and using more memory. IVFFlat builds faster and uses less memory, but requires data to train its index. These are project-level comparisons, not workload-specific benchmark results.
| Consideration | HNSW | IVFFlat |
|---|---|---|
| Query speed–recall tradeoff | Better, according to the pgvector README | Lower than HNSW, according to the pgvector README |
| Build time and memory | Slower to build; uses more memory | Faster to build; uses less memory |
| Data needed before index creation | Can be created on an empty table | Create after loading data because the index has a training step |
| Main tuning controls | m, ef_construction, and hnsw.ef_search |
lists and ivfflat.probes |
Match the index to the distance operator
The index operator class must correspond to the distance operator used in the query. For L2 distance, use vector_l2_ops; for inner product, use vector_ip_ops; and for cosine distance, use vector_cosine_ops. For example, a cosine index and matching query pattern are:
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CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops);
SELECT *
FROM items
ORDER BY embedding <=> '[...]'
LIMIT 10;Replace the example vector with a vector of the column’s dimensions. The important pairing is the cosine operator class and the cosine distance operator in ORDER BY; an index for a different metric will not serve as the matching index for that ordering.
Tune index parameters against your workload
HNSW
The documented defaults are m = 16, ef_construction = 64, and hnsw.ef_search = 40. The first two are index construction options; hnsw.ef_search controls the search setting. Increasing construction effort can improve recall, but can also increase build time and insert cost. A larger search effort can explore more candidates, with corresponding query-cost trade-offs. Treat the defaults as starting points, not guarantees.
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IVFFlat
The project’s starting heuristics for lists are around rows divided by 1,000 for tables up to 1 million rows, and around the square root of the row count above 1 million. A starting heuristic for ivfflat.probes is the square root of the number of lists. More probes can improve recall at the cost of speed. Validate both settings on your own data and queries; these are not benchmark-derived promises.
Account for filters and tenant boundaries
With approximate search, a WHERE condition is applied after the index scan has considered candidates. A selective filter can therefore leave fewer qualifying rows than the requested limit. The pgvector README illustrates this with a condition matching 10% of rows and the default HNSW ef_search of 40: about four qualifying rows on average. This is an illustration of those values, not a general performance guarantee.
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- For exact search on a filtered subset, an ordinary index on the filter column may help PostgreSQL identify qualifying rows.
- For approximate search, iterative scans can continue scanning for qualifying results.
- For a few fixed filter values, partial vector indexes may be appropriate.
- For many distinct values, such as tenants, consider partitioning or separate tables. A shared approximate index can let one tenant’s vectors affect another tenant’s recall and speed.
Iterative scans
Iterative index scans are available starting with pgvector 0.8.0, according to the project README. They scan farther until enough qualifying results are found or a configured maximum is reached. Strict ordering preserves exact distance order; relaxed ordering permits slight deviations in distance order and may improve recall. Confirm your installed pgvector version supports the settings before using them.
Build indexes without disrupting loading or writes
The pgvector project recommends adding indexes after initial bulk loading for better loading performance. For IVFFlat, wait until the table contains sufficient data for the chosen list count: too little data relative to that count can reduce the number of results returned. HNSW can be created on an empty table, though deferring index creation until after a large initial load may improve loading throughput.
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For production systems where avoiding write blocking matters, consider PostgreSQL’s CREATE INDEX CONCURRENTLY form. For example:
CREATE INDEX CONCURRENTLY ON items USING hnsw (embedding vector_cosine_ops);Index creation can be monitored through PostgreSQL’s pg_stat_progress_create_index view. The progress phases differ between HNSW and IVFFlat, so interpret the view with the index method in mind.
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Verify that the index helps
Index creation alone does not establish that a query is faster or uses the intended plan. Inspect representative queries with EXPLAIN (ANALYZE, BUFFERS), which reports execution details and buffer activity. Compare query latency and returned neighbors with your exact-search baseline, including important filter and tenant cases. Adjust the index method or settings when the plan, speed, result count, or recall does not meet your needs.
When an index is too large
pgvector indexes do not have to fit in memory, although the project says performance is likely better when they do. If index size is a constraint, the README documents half-precision indexing and binary quantization as ways to reduce index size. Both can affect accuracy or recall, so validate the results against your required quality before adopting them.
All project-specific defaults, heuristics, version notes, and trade-offs above are documented in the pgvector README, Indexing section.
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