PostgreSQL is powerful but requires tuning for optimal performance. Our development team at Softechinfra shares techniques to make your database faster.
Understanding Performance
⏱️ Query Execution
Measure query execution time with EXPLAIN ANALYZE
📊 Throughput
Track queries/second and concurrent connections
💾 Cache Ratio
Monitor buffer cache hit ratio (target 95%+)
💿 Disk I/O
Minimize disk reads with proper indexing
Identifying Issues
pg_stat_statements
SELECT query, calls, total_time, mean_time
FROM pg_stat_statements
ORDER BY total_time DESC
LIMIT 10;EXPLAIN ANALYZE
EXPLAIN ANALYZE
SELECT * FROM orders
WHERE customer_id = 123
AND created_at > '2022-01-01';Configuration Tuning
Memory Settings
# postgresql.conf# Shared memory buffer
shared_buffers = 25% of RAM (e.g., 4GB for 16GB RAM)
# Work memory per operation
work_mem = 64MB
# Maintenance operations
maintenance_work_mem = 1GB
# Effective cache size (OS cache estimate)
effective_cache_size = 75% of RAM
Connection Settings
# Maximum connections
max_connections = 200# Consider using PgBouncer for connection pooling
Write Performance
# WAL settings
wal_buffers = 64MB
checkpoint_completion_target = 0.9
max_wal_size = 4GBIndexing Strategies
Index Types
B-tree (default)
CREATE INDEX idx_orders_customer
ON orders (customer_id);Partial Index
CREATE INDEX idx_orders_pending
ON orders (created_at)
WHERE status = 'pending';Composite Index
CREATE INDEX idx_orders_customer_date
ON orders (customer_id, created_at DESC);GIN Index (for JSONB)
CREATE INDEX idx_users_metadata
ON users USING GIN (metadata);Index Maintenance
-- Check index usage
SELECT indexrelname, idx_scan, idx_tup_read
FROM pg_stat_user_indexes;-- Reindex
REINDEX INDEX idx_orders_customer;
-- Remove unused indexes
DROP INDEX unused_index;
Query Optimization
Common Patterns
Use EXISTS instead of IN
-- Slow
SELECT * FROM orders
WHERE customer_id IN (SELECT id FROM customers WHERE active);-- Faster
SELECT * FROM orders o
WHERE EXISTS (
SELECT 1 FROM customers c
WHERE c.id = o.customer_id AND c.active
);
Avoid SELECT *
-- Bad
SELECT * FROM orders;-- Good
SELECT id, customer_id, total FROM orders;
Use LIMIT for pagination
-- With cursor-based pagination
SELECT * FROM orders
WHERE id > :last_id
ORDER BY id
LIMIT 20;Join Optimization
Table Maintenance
VACUUM
-- Regular vacuum
VACUUM ANALYZE orders;-- Full vacuum (blocks table)
VACUUM FULL orders;
Auto-vacuum Tuning
autovacuum_vacuum_scale_factor = 0.1
autovacuum_analyze_scale_factor = 0.05Table Partitioning
CREATE TABLE orders (
id SERIAL,
created_at TIMESTAMP,
...
) PARTITION BY RANGE (created_at);CREATE TABLE orders_2022
PARTITION OF orders
FOR VALUES FROM ('2022-01-01') TO ('2023-01-01');
Connection Pooling
PgBouncer
Configuration
[databases]
mydb = host=localhost dbname=mydb[pgbouncer]
pool_mode = transaction
max_client_conn = 1000
default_pool_size = 20
- Benefits
- Reduce connection overhead
- Handle many clients
- Improve performance
Monitoring
Key Queries
Cache hit ratio
SELECT
sum(heap_blks_hit) / (sum(heap_blks_hit) + sum(heap_blks_read)) as ratio
FROM pg_statio_user_tables;Long running queries
SELECT pid, now() - pg_stat_activity.query_start AS duration, query
FROM pg_stat_activity
WHERE state != 'idle'
ORDER BY duration DESC;Tools
Conclusion
"PostgreSQL performance tuning combines configuration, indexing, query optimization, and maintenance. Monitor continuously and optimize iteratively."— Rishikesh Baidya, Lead Developer
Database performance is critical for application responsiveness. We've optimized PostgreSQL databases for projects like AppliedView and Radiant Finance using our backend development expertise. See our database migration guide for more.
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