Query Performance Tuning Tools

The Performance Tuning Process

Database performance tuning is a systematic process of identifying slow queries, understanding why they are slow, and making targeted improvements. It is not about guessing — every change should be informed by data from monitoring and profiling tools.

Step 1: Identify Slow Queries

PostgreSQL Slow Query Log

# postgresql.conf

log_min_duration_statement = 1000 # Log queries slower than 1 second

log_Queries = on

log_line_prefix = '%t [%p]: [%l-1] user=%u,db=%d,app=%a,client=%h '

log_checkpoints = on

log_lock_waits = on

-- Query log directly from the database

SELECT

query,

calls,

total_exec_time / calls AS avg_time_ms,

rows / calls AS avg_rows,

mean_exec_time,

max_exec_time,

stddev_exec_time

FROM pg_stat_statements

ORDER BY total_exec_time DESC

LIMIT 20;

MySQL Slow Query Log

# my.cnf

slow_query_log = 1

slow_query_log_file = /var/log/mysql/slow.log

long_query_time = 1

log_queries_not_using_indexes = 1

# Analyze slow query log

pt-query-digest /var/log/mysql/slow.log

# Output includes:

# - Worst queries by execution time

# - Worst queries by frequency

# - Query patterns and response time distribution

Step 2: Analyze Query Plans

EXPLAIN ANALYZE

EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON)

SELECT

u.name,

COUNT(o.id) AS order_count,

SUM(o.total) AS total_spent

FROM users u

LEFT JOIN orders o ON u.id = o.user_id

WHERE u.created_at > '2026-01-01'

GROUP BY u.id, u.name

HAVING COUNT(o.id) > 3

ORDER BY total_spent DESC

LIMIT 50;

Key Metrics to Analyze

| Metric | What It Tells You | |--------|-------------------| | `actual time` | Real execution time (first row, all rows) | | `rows` vs `rows est` | Accuracy of planner estimates (off by 10x+ needs ANALYZE) | | `buffers` | How much data was read (shared hit=from cache, read=from disk) | | `Seq Scan` | Full table scan (may need index) | | `Sort Method` | `quicksort` (memory) vs `external merge` (disk) | | `loops` | How many times a node executed (high loops on Nested Loop = problem) |

Example Analysis

-- Problematic query

EXPLAIN ANALYZE

SELECT *

FROM orders

WHERE status = 'pending'

ORDER BY created_at DESC;

Output:

Sort (cost=8921.4..9123.5 rows=80842 width=72)

Sort Key: created_at DESC

Sort Method: external merge Disk: 6128kB

-> Seq Scan on orders (cost=0.0..1234.5 rows=80842 width=72)

Filter: (status = 'pending')

**Diagnosis**: Sequential scan on 80K rows, sort spilling to disk.

**Fix**: Add a composite index:

CREATE INDEX idx_orders_status_created

ON orders(status, created_at DESC);

After fix:

Index Only Scan using idx_orders_status_created (cost=0.29..341.2 rows=80842 width=72)

Index Cond: (status = 'pending')

Step 3: Database Profiling Tools

pg_stat_statements (PostgreSQL)

-- Install extension

CREATE EXTENSION pg_stat_statements;

-- Reset statistics

SELECT pg_stat_statements_reset();

-- Find queries with highest total time

SELECT

queryid,

left(query, 80) AS query_preview,

calls,

round(total_exec_time::numeric, 1) AS total_ms,

round(mean_exec_time::numeric, 1) AS avg_ms,

round(shared_blks_hit::numeric / (shared_blks_hit + shared_blks_read + 1) * 100, 1) AS cache_hit_pct

FROM pg_stat_statements

WHERE query NOT LIKE '%pg_%'

ORDER BY total_exec_time DESC

LIMIT 10;

-- Find queries reading the most data

SELECT

queryid,

left(query, 80) AS query_preview,

calls,

shared_blks_read + local_blks_read AS blocks_read,

temp_blks_written AS temp_blocks -- spilled to disk

FROM pg_stat_statements

ORDER BY blocks_read DESC

LIMIT 10;

Performance Schema (MySQL)

-- Enable performance schema

-- my.cnf: performance_schema=ON

-- Find queries with full table scans

SELECT

digest_text,

count_star,

sum_rows_examined,

sum_rows_sent,

(sum_rows_examined / NULLIF(sum_rows_sent, 0)) AS exam_sent_ratio

FROM performance_schema.events_statements_summary_by_digest

ORDER BY exam_sent_ratio DESC

LIMIT 20;

-- Find queries with temporary tables

SELECT

digest_text,

sum_created_tmp_tables,

sum_created_tmp_disk_tables

FROM performance_schema.events_statements_summary_by_digest

WHERE sum_created_tmp_disk_tables > 0

ORDER BY sum_created_tmp_disk_tables DESC

LIMIT 10;

Step 4: Lock Monitoring

-- PostgreSQL: Blocked queries

SELECT

blocked_locks.pid AS blocked_pid,

blocked_activity.query AS blocked_query,

blocking_locks.pid AS blocking_pid,

blocking_activity.query AS blocking_query,

blocked_activity.wait_event_type,

blocked_activity.state,

now() - blocked_activity.query_start AS blocked_duration

FROM pg_catalog.pg_locks blocked_locks

JOIN pg_catalog.pg_stat_activity blocked_activity ON blocked_locks.pid = blocked_activity.pid

JOIN pg_catalog.pg_locks blocking_locks

ON blocking_locks.locktype = blocked_locks.locktype

AND blocking_locks.database = blocked_locks.database

AND blocking_locks.relation = blocked_locks.relation

AND blocking_locks.pid != blocked_locks.pid

JOIN pg_catalog.pg_stat_activity blocking_activity ON blocking_locks.pid = blocking_activity.pid

WHERE NOT blocked_locks.granted;

-- Check for long-running transactions

SELECT

pid,

state,

query,

now() - query_start AS query_duration,

now() - xact_start AS transaction_duration

FROM pg_stat_activity

WHERE state IN ('active', 'idle in transaction')

AND now() - query_start > interval '5 minutes'

AND query NOT LIKE '%pg_stat%'

ORDER BY query_duration DESC;

Step 5: Index Usage Analysis

-- PostgreSQL: Index usage statistics

SELECT

schemaname || '.' || relname AS table_name,

seq_scan,

seq_tup_read,

idx_scan,

idx_tup_fetch,

seq_tup_read / NULLIF(seq_scan, 0) AS avg_rows_per_seq_scan

FROM pg_stat_user_tables

WHERE seq_scan > 0

ORDER BY seq_scan DESC

LIMIT 20;

-- Find unused indexes

SELECT

schemaname || '.' || relname AS table,

indexrelname AS index_name,

idx_scan,

pg_size_pretty(pg_relation_size(indexrelid)) AS index_size

FROM pg_stat_user_indexes

WHERE idx_scan = 0

AND indexrelname NOT LIKE '%_pkey%'

ORDER BY pg_relation_size(indexrelid) DESC;

Automated Tuning Tools

| Tool | Database | Features | |------|----------|----------| | pgBadger | PostgreSQL | Log analysis with visual reports | | pgbadger | PostgreSQL | Query distribution, temp files, locks | | pg_stat_monitor | PostgreSQL | Enhanced pg_stat_statements with query groups | | MySQLTuner | MySQL | Configuration recommendations | | pt-query-digest | MySQL | Query analysis and pattern matching | | mongostat | MongoDB | Real-time MongoDB metrics |

# Generate a visual report with pgBadger

pgbadger /var/log/postgresql/postgresql.log -o report.html

# Analyze the report for:

# - Most time-consuming queries

# - Temporary file usage

# - Lock wait events

# - Checkpoint frequency

Performance Tuning Workflow

* **Establish baseline**: Measure current performance with representative queries.

2\. **Identify bottlenecks**: Use pg_stat_statements, slow query log, or APM tools. 3\. **Analyze specific queries**: Run EXPLAIN ANALYZE with buffers enabled. 4\. **Create targeted index**: Add or modify indexes based on the plan. 5\. **Test the fix**: Re-run the query and compare execution time. 6\. **Verify no regressions**: Check that other queries did not get slower. 7\. **Repeat**: Continue with the next slowest query.

Summary

Performance tuning is a data-driven process. Start with the slow query log or pg_stat_statements to identify problematic queries, use EXPLAIN ANALYZE to understand execution plans, monitor locks and contention, and track index usage. Focus on queries with the highest total execution time, not just the slowest individual queries. Always measure before and after making changes, and automate monitoring with tools like pgBadger for continuous visibility into query performance.