How to Optimize Queries in ParadeDB in PostgreSQL

How do I optimize ParadeDB queries in PostgreSQL?

Optimize ParadeDB queries by adding the right indexes, tuning index parameters, and inspecting query plans to reduce latency and CPU usage.

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Description

Example H2

Example H3

Why is my ParadeDB query slow?

Slow queries usually scan the entire table or use a sub-optimal index. Use EXPLAIN (ANALYZE, BUFFERS) to reveal whether a sequential scan, high shared hit count, or CPU-bound distance calculation causes the delay.

Which index speeds up ParadeDB vector search?

Create an ivfflat or hnsw index on the embedding column.ParadeDB offloads distance computation to these indexes, cutting query time from seconds to milliseconds for large product catalogs.

Recommended ivfflat index syntax

Use CREATE INDEX idx_prod_vec ON products USING ivfflat (embedding vector_cosine_ops) WITH (lists=100); for balanced recall and speed on ~1 M rows.

How do I inspect a ParadeDB query plan?

Run EXPLAIN (ANALYZE, BUFFERS) before and after adding an index.Look for an Index Scan using idx_prod_vec node and verify Total Runtime drops.

When should I tweak ivfflat parameters?

Raise lists when recall is too low; lower it when latency matters more. Start with lists = sqrt(table_rows) and benchmark.

Best practices for large ecommerce catalogs

Combine a vector index on embedding with a B-tree index on stock or price to filter first, then KNN search.Use materialized views for popular queries and schedule VACUUM ANALYZE.

Use parameterized queries

Parameterized SQL prevents replans and SQL injection, keeping the planner’s statistics accurate.

How to force index usage during testing?

Disable sequential scans with SET enable_seqscan = off;. Remember to revert the setting afterward.

What is the impact of table statistics?

Outdated stats mislead the planner. Run ANALYZE products; after bulk loads so ParadeDB picks the optimal path.

Why How to Optimize Queries in ParadeDB in PostgreSQL is important

How to Optimize Queries in ParadeDB in PostgreSQL Example Usage


-- Fetch five closest products that are in stock and under $100
SELECT p.id, p.name, p.price
FROM Products p
WHERE p.stock > 0 AND p.price < 100
ORDER BY p.embedding <-> '[0.12,0.05,0.77]'
LIMIT 5;

How to Optimize Queries in ParadeDB in PostgreSQL Syntax


-- Create vector index for ParadeDB KNN search
CREATE INDEX idx_prod_vec ON Products USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);

-- Inspect plan before optimization
EXPLAIN (ANALYZE, BUFFERS)
SELECT id, name
FROM Products
ORDER BY embedding <-> '[0.12,0.05,0.77]'
LIMIT 5;

-- Tune query planner for testing
SET enable_seqscan = off;

-- Re-analyze after bulk load
ANALYZE Products;

Common Mistakes

Mistake: Creating an ivfflat index but forgetting to run ANALYZE afterward. Without updated statistics the planner may still pick a sequential scan. Fix: run ANALYZE Products immediately after the index build.
Mistake: Setting lists too high (e.g., 1000) on a small table. This bloats the index and slows inserts. Fix: start with lists≈sqrt(row_count) and adjust based on benchmarks.