WAL Monitoring in Postgres: What to Watch Before Disk Becomes the Story
WAL problems usually look like disk problems too late. Monitor generation rate, checkpoints, archiving, replication lag, and slot retention before pg_wal owns the incident.
Notes for the problems that show up after launch: bad plans, awkward migrations, index debt, vacuum pressure, replica lag, and the small decisions that make PostgreSQL easier to operate.
WAL problems usually look like disk problems too late. Monitor generation rate, checkpoints, archiving, replication lag, and slot retention before pg_wal owns the incident.
Use NUMERIC when exactness is the product contract. Use DOUBLE PRECISION when measurement error is already part of the domain and speed matters more than decimal identity.
Postgres full-text search is great for the cases it covers and frustrating for the cases it doesn't. Knowing where the line is saves a year of trying to make it do what Elasticsearch does.
CITEXT is a good fit when a column is almost always compared case-insensitively. It is not a universal Unicode answer, and it is not free.
Range types model time windows, numeric ranges, and any "from-to" data. They support overlap queries natively and combine with EXCLUDE constraints to prevent invalid data.
Materialized views are excellent when stale-enough reads are worth a scheduled rebuild. They are painful when teams expect incremental freshness or forget the refresh lock model.
Rollup tables are pre-aggregated summaries of source data, kept up to date via triggers or scheduled jobs. They turn dashboard queries from seconds into milliseconds.
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?