Agent skill
postgresql-optimization
Expert in PostgreSQL performance tuning, query optimization, and database administration. Specializes in EXPLAIN analysis, indexing strategies, connection pooling, partitioning, and production-grade PostgreSQL operations.
Install this agent skill to your Project
npx add-skill https://github.com/curiositech/some_claude_skills/tree/main/.claude/skills/postgresql-optimization
Metadata
Additional technical details for this skill
- tags
-
postgresql sql performance indexing query-optimization database
- category
- database
- pairs with
-
[ { "skill": "database-design-patterns", "reason": "Schema design choices directly impact query performance and indexing effectiveness" }, { "skill": "supabase-admin", "reason": "Supabase runs PostgreSQL; optimization techniques apply directly to Supabase databases" }, { "skill": "performance-profiling", "reason": "Database query profiling identifies the slow queries that need PostgreSQL optimization" }, { "skill": "drizzle-migrations", "reason": "Index creation and schema changes through Drizzle must consider PostgreSQL performance impact" } ]
SKILL.md
PostgreSQL Optimization
Overview
Expert in PostgreSQL performance tuning, query optimization, and database administration. Specializes in EXPLAIN analysis, indexing strategies, connection pooling, partitioning, and production-grade PostgreSQL operations.
When to Use
- Diagnosing slow queries with EXPLAIN ANALYZE
- Creating optimal indexes for query patterns
- Designing database schemas for performance
- Configuring PostgreSQL for production workloads
- Implementing connection pooling (PgBouncer, Supavisor)
- Setting up partitioning for large tables
- Analyzing and reducing lock contention
- Migrating or upgrading PostgreSQL versions
Capabilities
Query Optimization
- EXPLAIN / EXPLAIN ANALYZE interpretation
- Query plan analysis and optimization
- Identifying sequential scans vs index scans
- Join optimization and query rewriting
- CTE vs subquery performance trade-offs
- Window function optimization
Indexing Strategies
- B-tree, GIN, GiST, BRIN index selection
- Partial indexes for filtered queries
- Expression indexes for computed values
- Covering indexes (INCLUDE clause)
- Index-only scans optimization
- Concurrent index creation
Schema Design
- Normalization vs denormalization trade-offs
- JSONB column design and indexing
- Array columns and operations
- Enum types vs lookup tables
- Foreign key cascade strategies
- Table inheritance and partitioning
Configuration Tuning
- Memory settings (shared_buffers, work_mem, effective_cache_size)
- Connection limits and pooling
- WAL and checkpoint tuning
- Autovacuum configuration
- Statistics collection settings
Advanced Features
- Partitioning (range, list, hash)
- Materialized views with refresh strategies
- Full-text search with tsvector/tsquery
- PostGIS geospatial queries
- Logical replication setup
- pg_stat_statements analysis
Dependencies
Works well with:
database-modeler- Schema design and ERD creationdata-pipeline-engineer- ETL and data processingsite-reliability-engineer- Database monitoring and alertingnextjs-app-router-expert- Full-stack data fetching
Examples
Reading EXPLAIN ANALYZE Output
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT u.*, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON o.user_id = u.id
WHERE u.created_at > '2024-01-01'
GROUP BY u.id;
-- Key metrics to look for:
-- - "Seq Scan" on large tables → needs index
-- - "Rows Removed by Filter" high → filter before join
-- - "Sort Method: external merge" → increase work_mem
-- - "Buffers: shared hit" vs "shared read" → cache efficiency
Creating Effective Indexes
-- Basic B-tree for equality and range queries
CREATE INDEX CONCURRENTLY idx_orders_user_created
ON orders (user_id, created_at DESC);
-- Partial index for common filter
CREATE INDEX CONCURRENTLY idx_orders_pending
ON orders (created_at)
WHERE status = 'pending';
-- GIN index for JSONB containment queries
CREATE INDEX CONCURRENTLY idx_products_metadata
ON products USING GIN (metadata jsonb_path_ops);
-- Covering index to enable index-only scans
CREATE INDEX CONCURRENTLY idx_users_email_covering
ON users (email) INCLUDE (name, created_at);
-- Expression index for case-insensitive search
CREATE INDEX CONCURRENTLY idx_users_email_lower
ON users (LOWER(email));
Optimizing N+1 Queries
-- BAD: N+1 pattern (1 + N queries)
SELECT * FROM posts WHERE user_id = $1;
-- Then for each post: SELECT * FROM comments WHERE post_id = $1;
-- GOOD: Single query with lateral join
SELECT p.*, c.comments
FROM posts p
LEFT JOIN LATERAL (
SELECT json_agg(c.*) as comments
FROM comments c
WHERE c.post_id = p.id
) c ON true
WHERE p.user_id = $1;
-- GOOD: Window function for aggregates
SELECT
p.*,
COUNT(*) OVER (PARTITION BY p.user_id) as user_post_count
FROM posts p
WHERE p.user_id = $1;
Table Partitioning
-- Create partitioned table by date range
CREATE TABLE events (
id BIGSERIAL,
event_type TEXT NOT NULL,
payload JSONB,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
PRIMARY KEY (id, created_at)
) PARTITION BY RANGE (created_at);
-- Create monthly partitions
CREATE TABLE events_2024_01 PARTITION OF events
FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');
CREATE TABLE events_2024_02 PARTITION OF events
FOR VALUES FROM ('2024-02-01') TO ('2024-03-01');
-- Automate partition creation with pg_partman
CREATE EXTENSION pg_partman;
SELECT partman.create_parent('public.events', 'created_at', 'native', 'monthly');
Connection Pooling Config (PgBouncer)
; pgbouncer.ini
[databases]
myapp = host=localhost dbname=myapp
[pgbouncer]
listen_addr = 0.0.0.0
listen_port = 6432
auth_type = md5
auth_file = /etc/pgbouncer/userlist.txt
; Pool settings
pool_mode = transaction ; Recommended for most apps
max_client_conn = 1000
default_pool_size = 20
reserve_pool_size = 5
; Timeouts
server_idle_timeout = 600
client_idle_timeout = 0
Performance Configuration
-- Check current settings
SHOW shared_buffers; -- ~25% of RAM
SHOW effective_cache_size; -- ~75% of RAM
SHOW work_mem; -- Per-operation, start small (64MB)
SHOW maintenance_work_mem; -- For VACUUM, CREATE INDEX (512MB-1GB)
-- Recommended production settings (for 32GB RAM server)
ALTER SYSTEM SET shared_buffers = '8GB';
ALTER SYSTEM SET effective_cache_size = '24GB';
ALTER SYSTEM SET work_mem = '64MB';
ALTER SYSTEM SET maintenance_work_mem = '1GB';
ALTER SYSTEM SET random_page_cost = 1.1; -- For SSD storage
ALTER SYSTEM SET effective_io_concurrency = 200; -- For SSD
-- Reload configuration
SELECT pg_reload_conf();
Finding Slow Queries
-- Enable pg_stat_statements
CREATE EXTENSION pg_stat_statements;
-- Top 10 slowest queries by total time
SELECT
round(total_exec_time::numeric, 2) as total_ms,
calls,
round(mean_exec_time::numeric, 2) as avg_ms,
round((100 * total_exec_time / sum(total_exec_time) OVER())::numeric, 2) as pct,
query
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 10;
-- Queries with most I/O
SELECT
round(shared_blks_read::numeric, 2) as disk_reads,
round(shared_blks_hit::numeric, 2) as cache_hits,
round(100.0 * shared_blks_hit / nullif(shared_blks_hit + shared_blks_read, 0), 2) as cache_hit_ratio,
query
FROM pg_stat_statements
ORDER BY shared_blks_read DESC
LIMIT 10;
Analyzing Table Bloat
-- Check table bloat
SELECT
schemaname,
tablename,
pg_size_pretty(pg_total_relation_size(schemaname || '.' || tablename)) as total_size,
pg_size_pretty(pg_relation_size(schemaname || '.' || tablename)) as table_size,
n_dead_tup,
n_live_tup,
round(100.0 * n_dead_tup / nullif(n_live_tup + n_dead_tup, 0), 2) as dead_pct
FROM pg_stat_user_tables
ORDER BY n_dead_tup DESC
LIMIT 10;
-- Manual VACUUM for critical tables
VACUUM (VERBOSE, ANALYZE) orders;
-- Reclaim space (requires exclusive lock)
VACUUM FULL orders; -- Use during maintenance window
Best Practices
- Always use EXPLAIN ANALYZE - Don't guess, measure actual query performance
- Create indexes CONCURRENTLY - Avoid blocking writes during index creation
- Partial indexes for hot paths - Index only the rows you query frequently
- Use connection pooling - PgBouncer or Supavisor for production
- Monitor pg_stat_statements - Track query performance over time
- Regular ANALYZE - Keep statistics current for query planner
- **Avoid SELECT *** - Only fetch columns you need
- Batch large updates - Process in chunks to avoid lock contention
- Use prepared statements - Reduce parsing overhead for repeated queries
Common Pitfalls
- Missing indexes - Check for sequential scans on large tables
- Over-indexing - Too many indexes slow down writes
- work_mem too low - Causes disk-based sorts and hash joins
- Connection exhaustion - Not using connection pooling
- Stale statistics - Autovacuum not running frequently enough
- Bloated tables - Not vacuuming after large deletes/updates
- N+1 queries - Fetching related data in loops instead of joins
- SELECT * everywhere - Fetching unnecessary columns
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