How to Test Queries in PostgreSQL

Galaxy Glossary

How do I use EXPLAIN ANALYZE to test query performance in PostgreSQL?

The EXPLAIN and EXPLAIN ANALYZE commands reveal the execution plan and performance metrics of a query so you can identify slow steps before running it in production.

Sign up for the latest in SQL knowledge from the Galaxy Team!

Description

What does EXPLAIN do?

EXPLAIN displays the execution plan PostgreSQL intends to use. It lists operations (Seq Scan, Index Scan, Hash Join) and estimated cost/rows without touching the data.

How is EXPLAIN ANALYZE different?

EXPLAIN ANALYZE runs the statement, then compares real timing, rows, and memory usage against the estimates. Use it on non-production data because it executes the query.

Which options matter most?

VERBOSE shows internal columns. BUFFERS includes shared-buffer hits vs. reads. FORMAT JSON delivers a machine-readable plan for tooling.

When should I use COSTS OFF?

Set COSTS OFF to hide planner cost numbers when you only care about the tree structure, keeping plans short for code reviews.

How do I test an ecommerce join?

Combine Orders, OrderItems, and Products, then run EXPLAIN ANALYZE to check whether indexes on order_id and product_id are used. Add LIMIT 10 during development to cut runtime.

What are best practices?

Always ANALYZE tables first to update statistics. Wrap destructive tests in BEGIN; ROLLBACK; to avoid data changes. Use an isolated staging database for heavy ANALYZE tests.

Tip: save plans

Store JSON plans in Galaxy Collections so teammates can endorse the optimal version and avoid regressions.

Why How to Test Queries in PostgreSQL is important

How to Test Queries in PostgreSQL Example Usage


EXPLAIN (ANALYZE, VERBOSE)
SELECT c.email, SUM(o.total_amount) AS lifetime_value
FROM Customers c
JOIN Orders o ON o.customer_id = c.id
GROUP BY c.email
ORDER BY lifetime_value DESC
LIMIT 10;

How to Test Queries in PostgreSQL Syntax


EXPLAIN [ ( option [, ...] ) ] <query>  
Options:  
  ANALYZE | VERBOSE | BUFFERS | COSTS { ON | OFF } | FORMAT { TEXT | JSON }  

Example (ecommerce):
EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON)
SELECT c.name, o.order_date, p.name, oi.quantity
FROM Customers c
JOIN Orders o   ON o.customer_id = c.id
JOIN OrderItems oi ON oi.order_id = o.id
JOIN Products p  ON p.id = oi.product_id
WHERE o.order_date >= CURRENT_DATE - INTERVAL '30 days';

Common Mistakes

Running EXPLAIN ANALYZE on production tables—this executes the query and can lock rows. Fix by copying data to staging or using EXPLAIN without ANALYZE.
Ignoring outdated statistics—plans rely on stats. Always run ANALYZE or VACUUM ANALYZE before testing to avoid misleading estimates.

Frequently Asked Questions (FAQs)

Does EXPLAIN change data?

EXPLAIN alone never changes data. Only EXPLAIN ANALYZE executes the statement, so wrap destructive tests in a transaction you can roll back.

Why do estimated rows differ from actual?

Out-of-date statistics, data skew, or missing extended statistics cause row misestimates. Run ANALYZE and consider CREATE STATISTICS to improve accuracy.

Can I automate plan regression checks?

Yes. Save JSON plans, then diff them in CI pipelines. Flag cost or node changes to catch performance regressions before deploy.

Want to learn about other SQL terms?

dbt tests

dbt tests are declarative assertions in dbt that automatically validate data quality during model builds.

dbt utils

dbt utils is a community-maintained dbt package that adds time-saving utility macros for analytics engineers, such as generating surrogate keys, safely unioning models, and automating date spines.

dbt Tutorial Glossary: Build, Test & Deploy SQL Models

dbt is an open-source framework that lets data teams transform raw warehouse tables into tested, documented, production-ready datasets using version-controlled SQL.

Trusted by top engineers on high-velocity teams

Assort Health

Curri

Rubie