A practical method for diagnosing and improving production PostgreSQL: memory, WAL, checkpoints, queries, indexes, autovacuum, connections, RDS, and safe validation.
Read MoreLearn to read PostgreSQL EXPLAIN ANALYZE output with examples of estimated versus actual rows, scans, joins, sorts, BUFFERS, and query-plan fixes.
Read MoreB-tree is the default for a reason, but it is the wrong tool for full-text search, JSONB and huge append-only tables. Here is how to pick — and size — the right index type.
Read MoreFind missing, redundant, and high-risk PostgreSQL indexes from real workload evidence. PG Monitoring quantifies impact, considers write overhead, and keeps DDL reviewable.
Read MoreTraditional PostgreSQL monitoring tools like pg_stat_statements, pgBadger, and pgWatch2 provide basic visibility but miss the bigger picture. They show you what happened, but not why. They alert when problems occur, but can't predict them.
PG Monitoring combines real-time metrics, AI-powered analysis, and predictive anomaly detection to give you complete database intelligence. From replication lag prediction to automated index recommendations, we solve the problems that keep DBAs awake at night.
Our blog explores real-world PostgreSQL challenges and demonstrates how PG Monitoring outperforms traditional tools in replication monitoring, query performance optimization, security auditing, and multi-instance management.