Best PracticesPerformance Optimization Guide

Comprehensive best practices guide for pg_stat_insights. Performance optimization strategies, monitoring techniques, and production deployment recommendations.

Optimization strategies
Monitoring best practices
Production ready

Optimization Strategies

Proven strategies for improving PostgreSQL performance

Query Optimization

Index Strategy

Create and maintain optimal indexes

Best Practices
  • Analyze query patterns before creating indexes
  • Use composite indexes for multi-column WHERE clauses
  • Create partial indexes for common filter conditions
  • Monitor index usage and remove unused indexes
  • Consider covering indexes to avoid table lookups
Code Examples
-- Analyze query patterns
SELECT query, calls, total_time, mean_time
FROM pg_stat_statements 
WHERE query LIKE '%WHERE%'
ORDER BY total_time DESC;

-- Create composite index
CREATE INDEX idx_users_email_status 
ON users(email, status) 
WHERE status = 'active';

-- Monitor index usage
SELECT schemaname, tablename, indexname, idx_scan, idx_tup_read
FROM pg_stat_user_indexes 
WHERE idx_scan = 0;

Query Rewriting

Improve query structure and efficiency

Best Practices
  • Replace subqueries with JOINs where possible
  • Use EXISTS instead of IN for large datasets
  • Avoid SELECT * and specify only needed columns
  • Use appropriate data types to reduce storage
  • Consider window functions for analytical queries
Code Examples
-- Instead of subquery
SELECT * FROM users 
WHERE id IN (SELECT user_id FROM orders WHERE status = 'completed');

-- Use JOIN for better performance
SELECT DISTINCT u.* FROM users u
JOIN orders o ON u.id = o.user_id 
WHERE o.status = 'completed';

-- Use EXISTS for better performance
SELECT * FROM users u
WHERE EXISTS (
    SELECT 1 FROM orders o 
    WHERE o.user_id = u.id AND o.status = 'completed'
);

Statistics and Planning

Ensure optimal query planning

Best Practices
  • Keep table statistics up to date with ANALYZE
  • Adjust statistics targets for large tables
  • Use EXPLAIN ANALYZE to verify execution plans
  • Consider query hints for complex scenarios
  • Monitor and tune planner parameters
Code Examples
-- Update statistics regularly
ANALYZE users;
ANALYZE orders;

-- Increase statistics target for large table
ALTER TABLE large_table ALTER COLUMN important_column SET STATISTICS 1000;

-- Analyze query execution plan
EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON)
SELECT u.*, o.total 
FROM users u 
JOIN orders o ON u.id = o.user_id 
WHERE u.created_at > '2024-01-01';

Monitoring Strategy

Baseline Establishment

Create performance baselines for comparison

Best Practices
  • Document normal query performance ranges
  • Establish thresholds for different query types
  • Track performance before and after changes
  • Maintain historical performance data
  • Create performance regression tests
Code Examples
-- Create performance baseline
CREATE TABLE performance_baseline AS
SELECT 
    query,
    calls,
    total_time,
    mean_time,
    now() as baseline_date
FROM pg_stat_statements 
WHERE query NOT LIKE '%pg_stat_statements%';

-- Compare current vs baseline
SELECT 
    current.query,
    current.mean_time as current_avg,
    baseline.mean_time as baseline_avg,
    (current.mean_time - baseline.mean_time) as difference
FROM pg_stat_statements current
JOIN performance_baseline baseline ON current.query = baseline.query;

Alert Configuration

Set up intelligent alerting for performance issues

Best Practices
  • Create alerts for slow queries (> 1 second)
  • Monitor query frequency anomalies
  • Alert on high I/O operations
  • Set up capacity planning alerts
  • Implement escalation procedures
Code Examples
-- Slow query alert
SELECT query, mean_time, calls
FROM pg_stat_statements 
WHERE mean_time > 1000  -- 1 second
AND calls > 10
ORDER BY mean_time DESC;

-- High I/O alert
SELECT query, shared_blks_read, shared_blks_hit
FROM pg_stat_statements 
WHERE shared_blks_read > 1000
AND (shared_blks_read::float / (shared_blks_hit + shared_blks_read)) > 0.5;

Trend Analysis

Monitor performance trends over time

Best Practices
  • Track query performance over time
  • Identify seasonal patterns
  • Monitor growth in query volume
  • Analyze performance degradation
  • Plan capacity based on trends
Code Examples
-- Query performance trends
SELECT 
    DATE_TRUNC('hour', now() - interval '1 hour' * generate_series(0, 23)) as hour,
    COUNT(*) as query_count,
    AVG(mean_time) as avg_execution_time,
    MAX(mean_time) as max_execution_time
FROM pg_stat_statements 
WHERE query NOT LIKE '%pg_stat_statements%'
GROUP BY hour
ORDER BY hour;

Production Deployment

Security Configuration

Secure pg_stat_insights in production

Best Practices
  • Use dedicated database user with minimal privileges
  • Enable SSL/TLS for database connections
  • Implement proper authentication and authorization
  • Regular security updates and patches
  • Monitor access logs and audit trails
Code Examples
-- Create dedicated user
CREATE USER pgsentinel_monitor WITH PASSWORD 'secure_password';
GRANT pg_monitor TO pgsentinel_monitor;
GRANT SELECT ON pg_stat_statements TO pgsentinel_monitor;
GRANT SELECT ON pg_stat_user_tables TO pgsentinel_monitor;

-- Enable SSL
ALTER SYSTEM SET ssl = on;
SELECT pg_reload_conf();

Performance Tuning

Optimize pg_stat_insights for production

Best Practices
  • Configure appropriate connection pooling
  • Implement caching for frequently accessed data
  • Use read replicas for analytics queries
  • Optimize data retention policies
  • Monitor resource usage and scale accordingly
Code Examples
-- Connection pooling configuration
# pgbouncer.ini
[databases]
analytics = host=localhost port=5432 dbname=analytics

[pgbouncer]
pool_mode = transaction
max_client_conn = 100
default_pool_size = 20

-- Data retention policy
DELETE FROM pg_stat_statements 
WHERE query_start < now() - interval '7 days';

High Availability

Ensure reliable monitoring service

Best Practices
  • Deploy multiple monitoring instances
  • Use load balancers for API endpoints
  • Implement health checks and auto-recovery
  • Backup monitoring data and configurations
  • Plan for disaster recovery scenarios
Code Examples
-- Health check endpoint
GET /api/v1/health
{
  "status": "healthy",
  "database": "connected",
  "timestamp": "2024-01-15T10:30:00Z"
}

-- Load balancer configuration
upstream pgsentinel_backend {
    server backend1:8000;
    server backend2:8000;
    server backend3:8000;
}

Monitoring Best Practices

Establish effective monitoring routines and procedures

Regular Performance Reviews

Establish consistent performance monitoring routines

Weekly

Key Tasks

  • Review slow query reports
  • Analyze query performance trends
  • Check for new performance issues
  • Update optimization recommendations
  • Document performance changes

Key Metrics

  • Average query execution time
  • Query frequency patterns
  • I/O operation trends
  • Cache hit ratios
  • Connection usage patterns

Proactive Optimization

Continuously optimize based on monitoring data

Ongoing

Key Tasks

  • Implement optimization recommendations
  • Test changes in development environment
  • Monitor impact of optimizations
  • Rollback ineffective changes
  • Document optimization results

Key Metrics

  • Query performance improvements
  • Resource usage reduction
  • Error rate changes
  • User experience metrics
  • System stability indicators

Capacity Planning

Plan for future growth and resource needs

Monthly

Key Tasks

  • Analyze growth trends in query volume
  • Project future resource requirements
  • Plan infrastructure scaling
  • Review and update monitoring thresholds
  • Document capacity planning decisions

Key Metrics

  • Query volume growth rates
  • Resource utilization trends
  • Performance degradation patterns
  • Peak usage periods
  • Scaling trigger points

Team Training and Knowledge Sharing

Ensure team understands monitoring and optimization

Quarterly

Key Tasks

  • Conduct performance optimization training
  • Share monitoring insights and best practices
  • Document troubleshooting procedures
  • Review and update team guidelines
  • Conduct performance optimization workshops

Key Metrics

  • Team knowledge assessment scores
  • Time to resolution for performance issues
  • Number of optimization implementations
  • Team confidence in monitoring tools
  • Documentation completeness

Common Pitfalls

Avoid these common mistakes in performance optimization

Over-indexing

Creating too many indexes can hurt performance

The Problem

Each index adds overhead for INSERT, UPDATE, DELETE operations

The Solution

Monitor index usage and remove unused indexes regularly

Prevention

  • Analyze query patterns before creating indexes
  • Use partial indexes for common filter conditions
  • Monitor index usage with pg_stat_user_indexes
  • Regularly review and clean up unused indexes

Ignoring Statistics

Outdated statistics lead to poor query plans

The Problem

Query planner makes suboptimal decisions with stale statistics

The Solution

Implement regular ANALYZE schedules and monitor statistics age

Prevention

  • Set up automated ANALYZE jobs
  • Monitor statistics age with pg_stat_user_tables
  • Increase statistics targets for important columns
  • Analyze tables after bulk data changes

Query Complexity

Overly complex queries are hard to optimize

The Problem

Complex queries often have poor execution plans

The Solution

Break down complex queries into simpler, more manageable parts

Prevention

  • Use views to simplify complex queries
  • Consider materialized views for expensive aggregations
  • Break down complex JOINs into steps
  • Use CTEs to improve query readability

Insufficient Monitoring

Not monitoring the right metrics or frequently enough

The Problem

Performance issues go undetected until they become critical

The Solution

Implement comprehensive monitoring with appropriate alerting

Prevention

  • Set up monitoring for all critical queries
  • Configure alerts for performance thresholds
  • Monitor both current performance and trends
  • Regular review of monitoring effectiveness

Performance Checklist

Use this checklist to ensure optimal performance

Database Configuration

  • PostgreSQL extensions enabled (pg_stat_statements, etc.)
  • Statistics collection configured and up to date
  • Appropriate memory settings (shared_buffers, work_mem)
  • Logging configured for slow queries
  • Connection limits set appropriately

Query Optimization

  • Slow queries identified and optimized
  • Appropriate indexes created and maintained
  • Query plans reviewed and optimized
  • Statistics targets set for important columns
  • Query complexity reduced where possible

Monitoring Setup

  • pg_stat_insights properly configured
  • Monitoring dashboards set up and functional
  • Alerts configured for performance thresholds
  • Historical data collection working
  • Team trained on monitoring tools

Production Readiness

  • Security configuration implemented
  • Backup and recovery procedures tested
  • High availability setup configured
  • Performance baselines established
  • Documentation and runbooks created

Additional Resources

Continue learning with these additional resources and guides

Query Analytics

Deep dive into query performance analysis

API Reference

Complete API documentation and examples

Getting Started

Quick setup and first insights