Supported Databases › PostgreSQL
PostgreSQL Performance Investigation with MCP Workflows
PostgreSQL's flexibility and open architecture make it a strong choice for many workloads, but that same flexibility means performance problems can originate from many directions. DatabaseMCP gives teams a consistent MCP-oriented investigation approach for PostgreSQL—covering managed cloud instances and self-hosted deployments alike.
Common PostgreSQL performance problems
- Sequential scans on large tables: Queries hitting large tables without appropriate indexes generate full sequential scans. pg_stat_statements surfaces these quickly by tracking total execution cost and rows returned per query.
- Autovacuum lag: When autovacuum cannot keep up with table bloat and dead tuple accumulation, query performance degrades progressively. Table bloat on high-churn tables is a common silent degrader that becomes visible only when impact is already severe.
- Lock contention: Long-running transactions holding row or table locks block concurrent writes and schema changes. DDL operations (like adding a column) acquiring access exclusive locks can create cascading waits across the application.
- Connection exhaustion: PostgreSQL creates one process per connection. Without a connection pooler, high connection counts increase memory pressure and context-switch overhead. Connections waiting for locks further amplify the problem.
- Checkpoint and WAL pressure: Heavy write workloads can push checkpoint frequency beyond the configured interval, causing write spikes and I/O saturation that affects read performance as well.
- Planner estimate errors: Stale statistics or complex join conditions cause the query planner to choose inefficient plans. EXPLAIN ANALYZE output reveals where estimated and actual row counts diverge significantly.
MCP investigation workflow for PostgreSQL
- pg_stat_statements review: Identify top queries by total time, mean time, and row count. High total-time queries with low mean-time indicate frequency problems; high mean-time queries indicate individual query inefficiency.
- Lock and wait analysis: Review pg_locks and pg_stat_activity for blocking relationships, idle-in-transaction sessions, and long-running queries holding locks.
- Vacuum and bloat assessment: Check pg_stat_user_tables for n_dead_tup counts, last autovacuum timing, and tables with significant bloat relative to live rows.
- Resource utilisation review: Correlate CPU, memory, and disk I/O with query activity to determine whether bottlenecks are compute- or storage-bound.
- Connection and pooler health: Monitor active, idle, and idle-in-transaction connection counts to identify connection pressure and pooler misconfiguration.
Cloud PostgreSQL coverage
The same investigation approach applies across self-hosted PostgreSQL and managed cloud deployments. For cloud-specific guidance see:
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