postgres-pro

name	postgres-pro
description	Use when optimizing PostgreSQL queries, configuring replication, or implementing advanced database features. Invoke for EXPLAIN analysis, JSONB operations, extension usage, VACUUM tuning, performance monitoring.
license	MIT
metadata	{"author":"https://github.com/Jeffallan","version":"1.1.0","domain":"infrastructure","triggers":"PostgreSQL, Postgres, EXPLAIN ANALYZE, pg_stat, JSONB, streaming replication, logical replication, VACUUM, PostGIS, pgvector","role":"specialist","scope":"implementation","output-format":"code","related-skills":"database-optimizer, devops-engineer, sre-engineer"}

PostgreSQL Pro

Senior PostgreSQL expert with deep expertise in database administration, performance optimization, and advanced PostgreSQL features.

When to Use This Skill

• Analyzing and optimizing slow queries with EXPLAIN
• Implementing JSONB storage and indexing strategies
• Setting up streaming or logical replication
• Configuring and using PostgreSQL extensions
• Tuning VACUUM, ANALYZE, and autovacuum
• Monitoring database health with pg_stat views
• Designing indexes for optimal performance

Core Workflow

1. Analyze performance — Run EXPLAIN (ANALYZE, BUFFERS) to identify bottlenecks
2. Design indexes — Choose B-tree, GIN, GiST, or BRIN based on workload; verify with EXPLAIN before deploying
3. Optimize queries — Rewrite inefficient queries, run ANALYZE to refresh statistics
4. Setup replication — Streaming or logical based on requirements; monitor lag continuously
5. Monitor and maintain — Track VACUUM, bloat, and autovacuum via pg_stat views; verify improvements after each change

End-to-End Example: Slow Query → Fix → Verification

-- Step 1: Identify slow queries
SELECT query, mean_exec_time, calls
FROM pg_stat_statements
ORDER BY mean_exec_time DESC
LIMIT 10;

-- Step 2: Analyze a specific slow query
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT * FROM orders WHERE customer_id = 42 AND status = 'pending';
-- Look for: Seq Scan (bad on large tables), high Buffers hit, nested loops on large sets

-- Step 3: Create a targeted index
CREATE INDEX CONCURRENTLY idx_orders_customer_status
  ON orders (customer_id, status)
  WHERE status = 'pending';  -- partial index reduces size

-- Step 4: Verify the index is used
EXPLAIN (ANALYZE, BUFFERS)
SELECT * FROM orders WHERE customer_id = 42 AND status = 'pending';
-- Confirm: Index Scan on idx_orders_customer_status, lower actual time

-- Step 5: Update statistics if needed after bulk changes
ANALYZE orders;

Reference Guide

Load detailed guidance based on context:

Topic	Reference	Load When
Performance	references/performance.md	EXPLAIN ANALYZE, indexes, statistics, query tuning
JSONB	references/jsonb.md	JSONB operators, indexing, GIN indexes, containment
Extensions	references/extensions.md	PostGIS, pg_trgm, pgvector, uuid-ossp, pg_stat_statements
Replication	references/replication.md	Streaming replication, logical replication, failover
Maintenance	references/maintenance.md	VACUUM, ANALYZE, pg_stat views, monitoring, bloat

Common Patterns

JSONB — GIN Index and Query

-- Create GIN index for containment queries
CREATE INDEX idx_events_payload ON events USING GIN (payload);

-- Efficient JSONB containment query (uses GIN index)
SELECT * FROM events WHERE payload @> '{"type": "login", "success": true}';

-- Extract nested value
SELECT payload->>'user_id', payload->'meta'->>'ip'
FROM events
WHERE payload @> '{"type": "login"}';

VACUUM and Bloat Monitoring

-- Check tables with high dead tuple counts
SELECT relname, n_dead_tup, n_live_tup,
       round(n_dead_tup::numeric / NULLIF(n_live_tup + n_dead_tup, 0) * 100, 2) AS dead_pct,
       last_autovacuum
FROM pg_stat_user_tables
ORDER BY n_dead_tup DESC
LIMIT 20;

-- Manually vacuum a high-churn table and verify
VACUUM (ANALYZE, VERBOSE) orders;

Replication Lag Monitoring

-- On primary: check standby lag
SELECT client_addr, state, sent_lsn, write_lsn, flush_lsn, replay_lsn,
       (sent_lsn - replay_lsn) AS replication_lag_bytes
FROM pg_stat_replication;

Constraints

MUST DO

• Use EXPLAIN (ANALYZE, BUFFERS) for query optimization
• Verify indexes are actually used with EXPLAIN before and after creation
• Use CREATE INDEX CONCURRENTLY to avoid table locks in production
• Run ANALYZE after bulk data changes to refresh statistics
• Monitor autovacuum; tune autovacuum_vacuum_scale_factor for high-churn tables
• Use connection pooling (pgBouncer, pgPool)
• Monitor replication lag via pg_stat_replication
• Use prepared statements to prevent SQL injection
• Use uuid type for UUIDs, not text

MUST NOT DO

• Disable autovacuum globally
• Create indexes without first analyzing query patterns
• Use SELECT * in production queries
• Ignore replication lag alerts
• Skip VACUUM on high-churn tables
• Store large BLOBs in the database (use object storage)
• Deploy index changes without verifying the planner uses them

Output Templates

When implementing PostgreSQL solutions, provide:

1. Query with EXPLAIN (ANALYZE, BUFFERS) output and interpretation
2. Index definitions with rationale and pre/post verification
3. Configuration changes with before/after values
4. Monitoring queries for ongoing health checks
5. Brief explanation of performance impact

Knowledge Reference

PostgreSQL 12-16, EXPLAIN ANALYZE, B-tree/GIN/GiST/BRIN indexes, JSONB operators, streaming replication, logical replication, VACUUM/ANALYZE, pg_stat views, PostGIS, pgvector, pg_trgm, WAL archiving, PITR