PostgreSQL is one of the most powerful open-source databases, but out-of-the-box settings are not optimized for production workloads. True performance comes from design + query + configuration + operations working together.
This article explains 30 practical optimization techniques, grouped logically and explained in depth.
🔹 CATEGORY 1: DATABASE & SCHEMA DESIGN (Foundation)
1️⃣ Choose Correct Data Types
Using wrong data types wastes memory and CPU.
Bad
age VARCHAR(10)
Good
age SMALLINT
✔ Smaller data types = less I/O
✔ Faster index scans
2️⃣ Avoid Overusing TEXT and VARCHAR
Use fixed-size types where possible.
Use:
INT, BIGINT
UUID
BOOLEAN
DATE, TIMESTAMP
Only use TEXT when length is truly unknown.
3️⃣ Normalize First, Denormalize When Needed
Example:
Reporting tables
Dashboard views
4️⃣ Use Proper Primary Keys
Always define a PRIMARY KEY.
ALTER TABLE users ADD PRIMARY KEY (id);
Benefits:
Faster joins
Better planner decisions
Enforces data integrity
5️⃣ Use UUID Carefully
UUIDs are random → bad for index locality.
Better options:
UUID v7
BIGSERIAL
Time-based UUIDs
🔹 CATEGORY 2: INDEXING STRATEGY (Most Important)
6️⃣ Create Indexes Based on Queries (Not Columns)
Do not blindly index every column.
Index what appears in:
WHERE
JOIN
ORDER BY
GROUP BY
7️⃣ Use Composite Indexes Correctly
Order matters.
CREATE INDEX idx_orders_user_date
ON orders(user_id, created_at);
✔ Works for:
user_id
user_id + created_at
❌ Not for created_at alone
8️⃣ Avoid Over-Indexing
Each index:
Rule:
More reads → more indexes
More writes → fewer indexes
9️⃣ Use Partial Indexes
Index only required rows.
CREATE INDEX idx_active_users
ON users(last_login)
WHERE is_active = true;
✔ Smaller index
✔ Faster scans
🔟 Use Covering Indexes (INCLUDE)
Avoid table lookups.
CREATE INDEX idx_orders_cover
ON orders(user_id)
INCLUDE (total_amount, status);
Result: Index-only scans
🔹 CATEGORY 3: QUERY OPTIMIZATION
1️⃣1️⃣ Always Use EXPLAIN ANALYZE
Never guess performance.
EXPLAIN ANALYZE
SELECT * FROM orders WHERE user_id = 10;
Look for:
Seq Scan ❌
Index Scan ✔
Execution time
1️⃣2️⃣ Avoid SELECT *
Fetch only required columns.
Bad
SELECT * FROM users;
Good
SELECT id, name FROM users;
✔ Less memory
✔ Faster execution
1️⃣3️⃣ Replace Subqueries with Joins
Subqueries can execute repeatedly.
Bad
SELECT * FROM orders
WHERE user_id IN (SELECT id FROM users);
Good
SELECT o.*
FROM orders o
JOIN users u ON o.user_id = u.id;
1️⃣4️⃣ Use EXISTS Instead of IN (Large Data)
EXISTS stops after first match.
SELECT *
FROM orders o
WHERE EXISTS (
SELECT 1 FROM users u WHERE u.id = o.user_id
);
1️⃣5️⃣ Avoid Functions in WHERE Clause
Functions prevent index usage.
Bad
WHERE LOWER(email) = '[email protected]'
Good
WHERE email = '[email protected]'
Or use functional index.
🔹 CATEGORY 4: VACUUM, ANALYZE & BLOAT
1️⃣6️⃣ Understand MVCC & Dead Tuples
PostgreSQL doesn’t overwrite rows—it creates new versions.
Result:
1️⃣7️⃣ Configure Autovacuum Properly
Default settings are conservative.
Tune:
autovacuum_vacuum_scale_factor
autovacuum_analyze_scale_factor
Critical for:
High write systems
OLTP workloads
1️⃣8️⃣ Use Manual VACUUM When Needed
VACUUM (VERBOSE, ANALYZE) orders;
Use during:
Bulk deletes
Large migrations
1️⃣9️⃣ Use VACUUM FULL Carefully
Reclaims disk space but:
Use only during maintenance windows.
🔹 CATEGORY 5: CONFIGURATION & MEMORY TUNING
2️⃣0️⃣ Tune shared_buffers
Controls memory used for caching.
Rule of thumb:
shared_buffers = 8GB
2️⃣1️⃣ Tune work_mem
Used for sorting and joins.
work_mem = 64MB
Too low → disk spill
Too high → memory exhaustion
2️⃣2️⃣ Tune effective_cache_size
Helps query planner.
effective_cache_size = 24GB
Set to:
🔹 CATEGORY 6: PARTITIONING & DATA MANAGEMENT
2️⃣3️⃣ Use Table Partitioning for Large Tables
Best for:
Time-series data
Logs
Transactions
PARTITION BY RANGE (created_at);
Benefits:
Faster queries
Faster deletes
2️⃣4️⃣ Archive Old Data
Do not keep years of unused data in hot tables.
Move to:
Archive tables
Cold storage
2️⃣5️⃣ Use Materialized Views
Precompute heavy queries.
CREATE MATERIALIZED VIEW sales_summary AS ...
Refresh periodically.
🔹 CATEGORY 7: CONNECTIONS & CONCURRENCY
2️⃣6️⃣ Use Connection Pooling (PgBouncer)
PostgreSQL doesn’t scale well with many connections.
✔ PgBouncer
✔ PgPool-II
2️⃣7️⃣ Reduce Long-Running Transactions
They:
Avoid:
2️⃣8️⃣ Use Proper Isolation Levels
Default is usually enough.
Avoid:
SERIALIZABLE
unless truly required.
🔹 CATEGORY 8: MONITORING & OPERATIONS
2️⃣9️⃣ Monitor Slow Queries
Enable:
log_min_duration_statement = 500ms
Use tools:
pg_stat_statements
pgAdmin
Grafana + Prometheus
3️⃣0️⃣ Regularly Review Index & Query Usage
Periodically check:
Unused indexes
Slow queries
Table bloat
Optimization is continuous, not one-time.
✅ Final Thoughts
PostgreSQL optimization is not just tuning parameters. It is a holistic process involving:
✔ Schema design
✔ Query writing
✔ Index strategy
✔ Memory tuning
✔ Maintenance
✔ Monitoring
Great PostgreSQL performance is designed, not fixed later.
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