How I Actually Find and Fix Slow PostgreSQL Queries
A practical slow-query incident workflow: find the fingerprint, prove the bottleneck, avoid the tempting wrong fix, and ship the smallest change that actually holds.
Notes for the problems that show up after launch: bad plans, awkward migrations, index debt, vacuum pressure, replica lag, and the small decisions that keep PostgreSQL, MySQL, MariaDB, and SQL Server easier to operate.
Find the queries dragging your Postgres down, read their plans, and fix them in order of impact.
Read the guide →Which indexes to add, which to drop, and how to spot redundant index debt before it costs you writes.
Read the guide →How autovacuum works, when it falls behind, and how to keep bloat from eating your storage and your buffer cache.
Read the guide →Sizing pools, PgBouncer modes, and how to stop idle-in-transaction sessions from exhausting max_connections.
Read the guide →Track lag, WAL throughput, and replication slots before they turn into stale reads or a full disk.
Read the guide →
Backups, restores, PITR, archive gaps, and recovery drills for PostgreSQL.
AWS RDS, Azure Database for PostgreSQL, Google Cloud SQL, AlloyDB, and managed PostgreSQL monitoring notes.
Connection storms, pool saturation, PgBouncer behavior, and max_connections pressure.
CPU, memory, disk, temp files, checkpoints, buffers, and workload pressure.
Index design, index debt, reindexing, constraint indexes, and evidence-driven DDL.
Lock waits, deadlocks, blocking chains, idle transactions, and transaction hygiene.
Galera clustering, InnoDB and Aria internals, replication, optimizer behavior, and MariaDB operations notes.
InnoDB internals, replication, locking, query performance, and MySQL operations notes.
HNSW, IVFFlat, recall, embedding search, and production RAG performance notes.
Replica lag, WAL growth, failover readiness, hot standby behavior, and replication slots.
Query plans, EXPLAIN ANALYZE, planner regressions, pagination, joins, and statistics.
Wait statistics, tempdb, Query Store, Always On availability groups, and SQL Server operations notes.
Autovacuum, dead tuples, bloat, wraparound, MVCC, freeze age, and visibility.
A practical slow-query incident workflow: find the fingerprint, prove the bottleneck, avoid the tempting wrong fix, and ship the smallest change that actually holds.
Two columns in a composite index can go in either order. Which order matters more than people expect, and "index everything both ways" is a worse answer than the right call.
Partial indexes work when the product mostly asks about a predictable slice of a table. They fail when the predicate is vague, parameterized, or not repeated by the query shape.
GIN and GiST cover the cases B-tree cannot. They overlap, they have very different cost profiles, and the docs are vague about which to use. Here is the practical answer.
BRIN indexes are 100x smaller than B-tree on the right data. They are also useless on the wrong data. The deciding factor is whether the table is naturally sorted.
Foreign key constraints check the parent side automatically. The child side is your problem. Forgetting this index is one of the most common silent performance bugs.
Expression indexes are powerful but exacting. If the query expression and index expression do not match, the index you trusted may not be used.
Partitioning solves specific problems. It also creates new ones. Here's an honest look at range, list, and hash partitioning — including the mistakes that will haunt you.
REINDEX is a repair tool, not an indexing strategy. Use it when bloat, corruption, or access pattern churn makes rebuilding cheaper than carrying the old index forward.
Both enforce uniqueness. They look identical in `\d`. The differences only show up when you try to alter the table or hit them with a partial uniqueness rule.