Advanced Query Tuning

Advanced⏱120 minutes📖3 min readLesson 5 of 10

Advanced Query Tuning

Master advanced EXPLAIN analysis, index hints, query rewrite techniques, statistics management, and systematic slow-query diagnosis

EXPLAIN Deep Dive

The query planner's execution plan reveals every step your database takes.

sql

EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON)
SELECT e.*, d.name
FROM employees e
JOIN departments d ON d.id = e.department_id
WHERE e.salary > 100000;

Key Plan Nodes

Node	Meaning	Red Flags
`Seq Scan`	Full table scan	Large tables without filter
`Index Scan`	Index lookup by value	High `rows=...` vs actual
`Index Only Scan`	All data in index	Rarely, index bloat
`Bitmap Heap Scan`	Bitmap index scan + heap fetch	High `lossy` blocks
`Nested Loop`	For each outer row, probe inner	Bad when outer is large
`Hash Join`	Build hash table on one side	Memory spill to disk
`Merge Join`	Sort both sides + merge	Sorting large datasets
`Sort`	Explicit sort	Memory: `external merge Disk`

Reading EXPLAIN Output

plaintext

Sort  (cost=184.32..189.45 rows=2050 width=36)
  Sort Key: e.salary DESC
  ->  Hash Join  (cost=45.12..78.23 rows=2050 width=36)
        Hash Cond: (e.department_id = d.id)
        ->  Seq Scan on employees e
              Filter: (salary > 100000)
        ->  Hash  (cost=30.10..30.10 rows=1200 width=18)
              ->  Seq Scan on departments d

cost: First number = start-up cost, second = total cost (arbitrary units).
rows: Estimated number of rows (compare with actual rows in ANALYZE).
width: Average row width in bytes.

⚠️Warning

Always use ANALYZE to get actual vs estimated rows. A large discrepancy indicates stale statistics or a poor cardinality estimate.

Statistics Management

sql

-- Update statistics
ANALYZE employees;

-- Update statistics for a specific column
ANALYZE employees (salary);

-- Set column statistics target (higher = more detailed histogram)
ALTER TABLE employees ALTER COLUMN salary SET STATISTICS 1000;

-- View statistics
SELECT tablename, attname, n_distinct, most_common_vals, histogram_bounds
FROM pg_stats
WHERE tablename = 'employees';

When Statistics Go Stale

sql

-- Check last analyzed time
SELECT schemaname, tablename, last_analyze, last_autoanalyze
FROM pg_stat_user_tables
WHERE tablename = 'orders';

-- Force analyze on a busy table
ANALYZE orders;

Index Strategies

Composite Index Column Order

Place the most selective column first, then the sort column.

sql

-- Good for: WHERE department_id = ? ORDER BY salary DESC
CREATE INDEX idx_dept_salary ON employees (department_id, salary DESC);

-- Bad: sort column first — cannot use index for equality + sort
CREATE INDEX idx_salary_dept ON employees (salary, department_id);

Partial Indexes

sql

-- Index only active orders
CREATE INDEX idx_active_orders ON orders (order_date)
WHERE status = 'active';

-- Query that uses it
SELECT * FROM orders
WHERE status = 'active' AND order_date > '2024-01-01';

Covering Indexes (Include Columns)

sql

-- Index covers the query without touching the table
CREATE INDEX idx_covering ON employees (department_id, salary)
INCLUDE (first_name, last_name);

-- Now this query can use Index Only Scan:
SELECT first_name, last_name, salary
FROM employees
WHERE department_id = 10;

Expression Indexes

sql

-- Speed up queries on transformed columns
CREATE INDEX idx_lower_email ON users (LOWER(email));

-- Query
SELECT * FROM users WHERE LOWER(email) = 'user@example.com';

Query Rewrite Techniques

1. Convert Subqueries to JOINs

sql

-- Slow
SELECT * FROM products
WHERE category_id IN (
    SELECT id FROM categories WHERE active = true
);

-- Fast (same semantics)
SELECT p.*
FROM products p
JOIN categories c ON c.id = p.category_id
WHERE c.active = true;

ℹ️Note

IN (subquery) can be slower than JOIN because the subquery is materialized. However, IN handles NULLs differently: test for semantic equivalence.

2. Use EXISTS Instead of COUNT(*)

sql

-- Slow
SELECT * FROM customers c
WHERE (SELECT COUNT(*) FROM orders o WHERE o.customer_id = c.id) > 0;

-- Fast
SELECT * FROM customers c
WHERE EXISTS (SELECT 1 FROM orders o WHERE o.customer_id = c.id);

3. Avoid Functions in WHERE Clauses

sql

-- Bad: DATE() on every row prevents index usage
SELECT * FROM orders
WHERE DATE(order_date) = '2024-01-15';

-- Good: range query uses index
SELECT * FROM orders
WHERE order_date >= '2024-01-15' AND order_date < '2024-01-16';

4. Use UNION ALL Instead of OR

sql

-- OR can't always use indexes efficiently
SELECT * FROM employees
WHERE department_id = 5 OR status = 'active';

-- UNION ALL with separate indexes
SELECT * FROM employees WHERE department_id = 5
UNION ALL
SELECT * FROM employees WHERE status = 'active' AND department_id <> 5;

Identifying Slow Queries

sql

-- PostgreSQL: current running queries
SELECT pid, now() - pg_stat_activity.query_start AS duration, query, state
FROM pg_stat_activity
WHERE state != 'idle'
ORDER BY duration DESC;

-- Longest running queries (cumulative)
SELECT queryid, query, calls, mean_exec_time, rows,
       shared_blks_hit, shared_blks_read, shared_blks_dirtied
FROM pg_stat_statements
ORDER BY mean_exec_time DESC
LIMIT 20;

Common Bottlenecks

Symptom	Likely Cause	Fix
`Seq Scan` on large table	Missing index	Add index on filter column
`Sort` with `external merge Disk`	Sort memory exceeded	Increase `work_mem`
`Nested Loop` with many iterations	Wrong join order	Increase stats target, rewrite query
`Hash Join` spilling to disk	Not enough `work_mem`	Increase `work_mem` or tune hash_mem_multiplier
`Bitmap Heap Scan` with many `lossy` pages	`work_mem` too low for bitmap	Increase `work_mem`

Practical Case Studies

Case 1: Pagination with OFFSET

sql

-- Slow for high offsets (OFFSET 100000)
SELECT * FROM orders ORDER BY id LIMIT 20 OFFSET 100000;

-- Keyset pagination (fast)
SELECT * FROM orders
WHERE id > 100000
ORDER BY id
FETCH FIRST 20 ROWS ONLY;

Case 2: Count Optimization

sql

-- Very slow on large tables
SELECT COUNT(*) FROM orders;

-- Use estimated counts for dashboards
-- PostgreSQL: approx count via stats
SELECT reltuples::BIGINT AS estimated_count
FROM pg_class WHERE relname = 'orders';

Case 3: Update with Join

sql

-- Slow: correlated subquery
UPDATE products p
SET last_sale_date = (
    SELECT MAX(sale_date)
    FROM sales s
    WHERE s.product_id = p.id
);

-- Fast: use a FROM clause
UPDATE products p
SET last_sale_date = t.max_date
FROM (
    SELECT product_id, MAX(sale_date) AS max_date
    FROM sales
    GROUP BY product_id
) t
WHERE t.product_id = p.id;

Configuration Tuning

sql

-- Work memory for sorts
SET work_mem = '64MB';

-- Effective cache size (for planner estimates)
SET effective_cache_size = '4GB';

-- Parallel query workers
SET max_parallel_workers_per_gather = 4;

-- Genetic query optimizer (GEQO) threshold
SET geqo_threshold = 12;

💡Tip

Settings like work_mem apply per-operation, per-query. A query with 10 sort operations uses 10× work_mem. Monitor for memory pressure.

Practice Questions

What does EXPLAIN (ANALYZE, BUFFERS) show that plain EXPLAIN does not?
Given EXPLAIN ANALYZE output with rows=1000 but actual rows=10, what is the likely problem?
Write a query to find the top 10 slowest queries in a PostgreSQL database using pg_stat_statements.
What is the difference between a partial index and a covering index? Give an example of each.
Rewrite this slow query for better performance: SELECT * FROM orders WHERE DATE(created_at) = CURRENT_DATE.
How would you identify and fix a query that uses a Seq Scan on a table with 10M rows filtering by status = 'active'?
Explain the trade-offs of composite index column ordering. Give an example of a good and bad order.
Convert this slow query using COUNT to EXISTS: SELECT * FROM users WHERE (SELECT COUNT(*) FROM orders WHERE user_id = users.id) > 5.
What is keyset pagination and why is it faster than OFFSET-based pagination?
You see Sort Method: external merge Disk: 2048kB in EXPLAIN ANALYZE. What does it mean and how do you fix it?

Progress50%

Recursive CTEs

Table Partitioning and Sharding

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Course Content

1. ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE()2. LAG(), LEAD(), FIRST_VALUE(), LAST_VALUE(), NTH_VALUE()3. PIVOT, UNPIVOT, and Crosstab Queries 4. Recursive CTEs 5. Advanced Query Tuning 6. Table Partitioning and Sharding 7. Stored Procedures and Triggers 8. JSON and JSONB Operations 9. User Roles, Permissions, and Security 10. Backup, Recovery, and High Availability