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Database / pgvector basics Interview Questions

What PostgreSQL configuration parameters affect pgvector performance?

Several PostgreSQL-level settings directly impact pgvector query and index performance. Tuning these appropriately for a vector workload can yield significant speedups.

Key PostgreSQL parameters for pgvector
ParameterDefaultRecommended (vector workload)Effect
maintenance_work_mem64MB2-8GBMemory for index builds - most impactful for HNSW build speed
max_parallel_maintenance_workers2CPU cores - 1Parallel workers for index build
work_mem4MB64-256MBMemory per query operation
effective_cache_size4GB~75% of RAMHelps query planner estimate index usage
shared_buffers128MB25% of RAMPostgreSQL shared memory cache
hnsw.ef_search4040-200 (based on recall needs)HNSW query-time recall/speed tradeoff
ivfflat.probes11-lists (based on recall needs)IVFFlat query-time recall/speed tradeoff
-- Permanently set in postgresql.conf or via ALTER SYSTEM:
ALTER SYSTEM SET maintenance_work_mem = '4GB';
ALTER SYSTEM SET max_parallel_maintenance_workers = 7;
ALTER SYSTEM SET work_mem = '128MB';
-- Apply config changes without full restart:
SELECT pg_reload_conf();

-- Set per-session (for index build or critical query):
SET maintenance_work_mem = '4GB';
SET max_parallel_maintenance_workers = 7;
CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops);
RESET maintenance_work_mem;
RESET max_parallel_maintenance_workers;

-- Per-query tuning:
SET hnsw.ef_search = 100;   -- increase recall for this query
SELECT * FROM items ORDER BY embedding <-> '[...]' LIMIT 5;
RESET hnsw.ef_search;

-- Verify current settings:
SHOW maintenance_work_mem;
SHOW hnsw.ef_search;
SHOW ivfflat.probes;

Which PostgreSQL configuration parameter has the greatest impact on HNSW index build speed?
What is the effect of increasing work_mem for pgvector queries?

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What is pgvector and what problem does it solve for developers? What are vector embeddings and why are they central to pgvector's use? How do you install and enable pgvector in PostgreSQL? What data types does pgvector provide and how do you define vector columns? What distance operators does pgvector provide and when do you use each? How do you insert and update vector data in pgvector? How do you perform a basic nearest-neighbour search with pgvector? What is the difference between exact and approximate nearest-neighbour search in pgvector? What is the HNSW index in pgvector and how do you create and tune it? What is the IVFFlat index in pgvector and how does it compare to HNSW? How do you use pgvector with Python and psycopg2? How do you use pgvector with SQLAlchemy and Python ORMs? How do you combine vector similarity search with SQL filters (hybrid search) in pgvector? How do you bulk-load vectors efficiently into pgvector? What operator classes must you use when creating pgvector indexes and why do they matter? How does pgvector fit into a RAG (Retrieval-Augmented Generation) pipeline? What is cosine distance vs cosine similarity and which does pgvector return? How do you check and monitor pgvector index creation progress? What is the pgvector maximum supported dimensions limit and how do you handle high-dimensional vectors? How do you use pgvector with LangChain for building AI applications? How does pgvector handle NULL values in vector columns? What are partial indexes in pgvector and when should you use them? How do you perform vector arithmetic and other vector functions in pgvector? How does pgvector compare to dedicated vector databases like Pinecone, Weaviate, and Qdrant? What is the difference between L1 and L2 distance in pgvector? How do you store and query vector embeddings with pgvector in a real schema design? What is halfvec and when should you use it to reduce storage costs? How do you handle vector dimensionality mismatches in pgvector? How do you use pgvector with Django? What are common performance tuning techniques for pgvector at scale? How do you implement semantic search with pgvector and a similarity threshold? How do you use pgvector with asyncpg or asyncio in Python? What is vector quantisation and how does pgvector support binary quantisation? How does pgvector integrate with managed PostgreSQL services? How do you use the inner product operator <#> with pgvector and when is it appropriate? How do you combine pgvector with full-text search (hybrid keyword + semantic search)? What PostgreSQL configuration parameters affect pgvector performance? How do you implement recommendation systems using pgvector? How do you use EXPLAIN and EXPLAIN ANALYZE to debug pgvector queries? What are best practices for a production pgvector deployment?
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