Postgres Checkpoint Tuning: The Latency Spike Nobody Sees Coming
Checkpoint problems show up as periodic latency spikes. The work is smoothing writes, sizing WAL, and proving checkpoints are the thing users feel.
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.
Checkpoint problems show up as periodic latency spikes. The work is smoothing writes, sizing WAL, and proving checkpoints are the thing users feel.
Postgres handles time-series data better than people assume. The decision to add a specialized extension should come from observed limits, not preemptive optimization.
Postgres materialized views refresh a result set; they do not give you automatic incremental aggregation. For high-churn dashboards, you need deltas, watermarks, and late-data rules.
When Postgres CPU is high, the question is not whether the server is fast enough. It is which queries are using the cycles and whether they should be.
High I/O is not a storage verdict. It is a workload question: which query, table, index, vacuum, checkpoint, or spill is reading and writing the bytes?
A Postgres OOM kill is one of the few crashes that is almost always preventable. The pattern is consistent enough to have a checklist.
Temp files are hidden disk work. They explain slow sorts, hash joins, and aggregations that look fine until work_mem runs out under real concurrency.
Bad plans usually start with bad row estimates. Fix the first wrong estimate and the rest of the plan often stops looking mysterious.
Slow inserts are rarely just inserts. They are usually index maintenance, constraint and trigger work, WAL/checkpoint pressure, or a transaction pattern that makes every row pay retail.
Slow deletes usually come from the work around the row: foreign keys, triggers, indexes, WAL, vacuum debt, and transaction size. The fix starts by finding what each deleted row has to pay for.