The Ultimate Guide to PostgreSQL Performance Tuning

• Pradip
• 03 May, 2025
• 0 Comments
• 7 Mins Read

The Ultimate Guide to PostgreSQL Performance Tuning

If you’ve ever faced slow queries, high CPU usage, or database bottlenecks, I know how frustrating it can be. I’ve been there too, running queries that take forever, watching the system struggle under heavy load, and wondering, “Why is my database so slow?”

The good news? PostgreSQL is incredibly powerful, but to get the best performance out of it, you need to tune it properly.

So, let’s sit down and go through PostgreSQL performance tuning step by step. I’ll explain not just what to do, but why it works, with real-world scenarios to help you relate. Whether you’re a DBA, developer, or data engineer, this guide will give you actionable steps to make your database run like a Ferrari instead of a bullock cart.

---

Step 1: Find the Bottlenecks with EXPLAIN ANALYZE

Before we start optimizing, let’s first find out what’s slowing things down.

Think of your database like a city with traffic. If vehicles (queries) are stuck in traffic jams, we need to figure out where the congestion is happening. PostgreSQL gives us a powerful tool for this:

sql
EXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 101;

This command tells you exactly how PostgreSQL processes the query and where the delays are.

Real-world Example

Imagine an e-commerce platform with millions of orders. A customer logs in and expects to see their order history instantly. But if the database is scanning every single order instead of fetching just the relevant ones, the page will load painfully slow.

When you run EXPLAIN ANALYZE, you might see something like this:

pgsql
Seq Scan on orders (cost=0.00..4352.89 rows=5000 width=100)

That “Seq Scan” (Sequential Scan) means PostgreSQL is scanning the entire table instead of using an index. That’s bad news for performance!

Let’s fix it in the next step.

---

Step 2: Use Indexing for Faster Searches

Indexes are like indexes in a book—instead of flipping through every page, you can jump directly to the information you need.

If you frequently search for customer_id, let’s create an index:

sql
CREATE INDEX idx_orders_customer ON orders(customer_id);

Now, when a customer checks their order history, PostgreSQL can instantly locate relevant rows instead of scanning everything.

How to Check if an Index is Helping?

Run EXPLAIN ANALYZE again. If it now says Index Scan instead of Seq Scan, we’ve won! 🎉

---

Step 3: Optimize Joins for Large Tables

If you’re dealing with millions of records and performing JOIN operations, things can slow down. Let’s say you need to fetch a customer’s name along with their orders:

sql
SELECT o.order_id, c.customer_name
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id;

By default, PostgreSQL might scan both tables completely before joining them—terribly slow!

Scenario

A fintech company was struggling with slow queries in their fraud detection system. They were joining a transactions table (with 1 billion records) with a users table (50 million records). The queries took minutes to execute.

Solution

1. Ensure that both customer_id fields are indexed.
2. Use ANALYZE to update table statistics:

sql
ANALYZE orders;
ANALYZE customers;

3. Use Materialized Views to precompute data for reporting:

sql
CREATE MATERIALIZED VIEW customer_orders AS 
SELECT customer_id, COUNT(order_id) AS total_orders 
FROM orders GROUP BY customer_id;

Now, instead of recalculating every time, we query the precomputed summary—and it’s blazing fast!

---

Step 4: Increase Memory for Faster Queries (work_mem)

Have you ever seen a query take forever because it’s writing temporary files to disk? This happens when PostgreSQL runs out of memory.

Let’s check the current memory limit:

sql
SHOW work_mem;

By default, it’s 4MB—which is ridiculously low for complex queries.

Fix: Increase Memory

sql
SET work_mem = '128MB';

For permanent changes, modify postgresql.conf:

ini
work_mem = 128MB

Then restart PostgreSQL. Now, queries will run in memory instead of disk, making them at least 3-5x faster!

---

Step 5: Enable Auto Vacuum to Prevent Table Bloat

PostgreSQL doesn’t delete old rows immediately—instead, it marks them as dead tuples. Over time, this creates table bloat, which slows everything down.

Scenario

A healthcare startup had a patients table growing 10GB every month. They weren’t running VACUUM, so over time, their queries became 10x slower.

Fix: Enable Auto Vacuum

sql
SHOW autovacuum;

If it’s off, enable it:

ini
autovacuum = on

Run manual cleanup:

sql
VACUUM ANALYZE orders;

This removes dead tuples and updates query optimizations!

---

Step 6: Partition Large Tables

If your sales table has 100M+ rows, queries will slow down. Instead of keeping all data in one table, partition it by year:

sql
CREATE TABLE sales ( sale_id SERIAL, 
sale_date DATE NOT NULL, 
amount NUMERIC, 
PRIMARY KEY (sale_id, sale_date) 
) PARTITION BY RANGE (sale_date); 
CREATE TABLE sales_2023 PARTITION OF sales 
FOR VALUES FROM ('2023-01-01') TO ('2023-12-31');

Now, when querying sales for 2023, PostgreSQL will only scan the relevant partition—making queries 10x faster.

---

Step 7: Monitor Performance Like a Pro

Finally, let’s monitor PostgreSQL to catch slow queries before they become a disaster.

Find Slow Queries

sql
SELECT query, calls, total_exec_time FROM pg_stat_statements 
ORDER BY total_exec_time DESC LIMIT 5;

If you see a query taking 10+ seconds, optimize it!

Check Disk Usage

sql
SELECT relname, pg_size_pretty(pg_total_relation_size(relname::regclass)) 
FROM pg_catalog.pg_statio_user_tables 

ORDER BY pg_total_relation_size(relname::regclass) DESC;

If a table is too large, consider partitioning or indexing!

---

Step 8: Use Connection Pooling for High Traffic Applications

If your application makes thousands of connections per second, PostgreSQL can struggle to handle them efficiently. Each new connection consumes memory and CPU, leading to performance degradation.

Imagine you’re running a banking application that processes thousands of transactions per second. If every query opens a new connection, your database will choke under the load.

Solution: Use Connection Pooling with PgBouncer

Instead of opening new connections every time, we use a connection pooler like PgBouncer to reuse existing connections.

Steps to Enable Connection Pooling:

1. Install PgBouncer:

arduino
sudo apt-get install pgbouncer

1. Configure pooling in /etc/pgbouncer/pgbouncer.ini:

ini
[databases]
mydb = host=127.0.0.1 port=5432 dbname=mydb
[pgbouncer]
pool_mode = transaction
max_client_conn = 500
default_pool_size = 100

3. Start PgBouncer:

sql
systemctl start pgbouncer

Now, instead of 500 users opening 500 connections, they will share a smaller pool of connections, improving both performance and scalability.

---

Step 9: Leverage Query Parallelism for Faster Execution

PostgreSQL can execute queries in parallel, utilizing multiple CPU cores to speed up performance significantly.

Scenario

A data analytics company was running complex SUM and AVG queries on a 1TB dataset. Without parallel execution, each query took 45 seconds to run. After enabling parallelism, execution time dropped to 8 seconds.

How to Enable Parallel Queries?

Check the current setting:

sql
SHOW max_parallel_workers_per_gather;

If it’s low (default is 2), increase it:

sql
ALTER SYSTEM SET max_parallel_workers_per_gather = 4;

Now, PostgreSQL will split large queries across multiple CPU cores, drastically reducing execution time.

---

Final Recap: PostgreSQL Performance Tuning Checklist

Step 1:Identify slow queries with EXPLAIN ANALYZE

Step 2: Use indexesfor faster lookups

Step 3: Optimize joins and aggregations

Step 4: Increase memory (work_mem)

Step 5: Run VACUUMto prevent bloat

Step 6: Partition large tables

Step 7:Monitor slow queries and database performance

Step 8: Implement connection pooling with PgBouncer

Step 9: Enable query parallelism for faster execution

By implementing these steps, you’ll unlock the full power of PostgreSQL and make your database lightning fast!

---

Final Thoughts: Master PostgreSQL Performance Tuning with Learnomate Technologies

PostgreSQL is an incredibly powerful database, but to get the best out of it, performance tuning is key. By following the techniques we discussed—indexing, query optimization, partitioning, memory tuning, connection pooling, and parallel processing—you can make your database run faster, more efficiently, and at scale. Whether you’re a DBA, developer, or data engineer, these optimizations will help you handle real-world challenges with ease.

At Learnomate Technologies, we provide expert training on PostgreSQL covering everything from basics to advanced performance tuning. Our industry-focused training ensures you not only learn theory but also work on real-world scenarios, making you job-ready.

👉 For hands-on tutorials and deeper insights, check out our YouTube channel: 🔗 www.youtube.com/@learnomate

👉 Want to master PostgreSQL with expert guidance? Explore our detailed training program here: 🔗 PostgreSQL Training at Learnomate

👉 Connect with me on LinkedIn for more insights on databases and tech careers: 🔗 Ankush Thavali – LinkedIn

👉 If you want to read more about different technologies, check out our blog posts: 🔗 Learnomate Blog

Let me know in the comments what PostgreSQL performance challenges you’ve faced and how you’ve tackled them!

Happy Reading

ANKUSH😎