PostgreSQL vs MySQL: Database Comparison for 2026

PostgreSQL and MySQL power the majority of web applications worldwide. Both are mature, reliable, and free. But they're built on different philosophies: PostgreSQL prioritizes correctness, extensibility, and SQL standards compliance. MySQL prioritizes simplicity, read performance, and ease of use. At Pillai Infotech, we use both — MySQL for our CMD Center (straightforward CRUD, PHP ecosystem), PostgreSQL for analytics-heavy projects with complex queries. Here's how to decide for your project.

Head-to-Head Comparison

Performance Benchmarks

Performance depends heavily on workload type. Neither database is universally faster.

The practical takeaway: If your workload is primarily simple CRUD with some joins (typical web app), both perform comparably. If you're doing complex analytical queries, window functions, CTEs, or JSON-heavy operations, PostgreSQL's query planner and optimizer consistently outperform MySQL.

JSON and Document Support

Both databases support JSON, but the implementations differ significantly:

-- PostgreSQL JSONB — binary storage, indexable, powerful operators
CREATE TABLE products (
    id SERIAL PRIMARY KEY,
    name TEXT NOT NULL,
    attributes JSONB DEFAULT '{}'
);

-- GIN index for fast JSON queries
CREATE INDEX idx_products_attrs ON products USING GIN (attributes);

-- Query nested JSON efficiently
SELECT name, attributes->'specs'->>'weight' AS weight
FROM products
WHERE attributes @> '{"category": "electronics"}'
  AND (attributes->'specs'->>'weight')::numeric < 500;

-- JSONB path queries (SQL/JSON standard)
SELECT name FROM products
WHERE attributes @? '$.tags[*] ? (@ == "wireless")';

-- MySQL JSON — text storage, functional indexes
CREATE TABLE products (
    id INT AUTO_INCREMENT PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    attributes JSON
);

-- Functional index on JSON path
CREATE INDEX idx_category ON products (
    (CAST(attributes->>'$.category' AS CHAR(50)))
);

-- Query JSON
SELECT name, attributes->>'$.specs.weight' AS weight
FROM products
WHERE JSON_EXTRACT(attributes, '$.category') = 'electronics'
  AND CAST(attributes->>'$.specs.weight' AS DECIMAL) < 500;

Advanced Features

PostgreSQL's feature set is significantly broader. Features that PostgreSQL has and MySQL doesn't (or does differently):

• CTEs (WITH RECURSIVE): Both support CTEs, but PostgreSQL's implementation is more optimized. MySQL added CTEs in 8.0 and they've improved significantly
• Window functions: Both support them, but PostgreSQL supports more frame types and has better optimization for complex window queries
• Custom types: PostgreSQL lets you define custom data types, operators, and index methods. This powers extensions like PostGIS, pgvector, and TimescaleDB
• LISTEN/NOTIFY: PostgreSQL has built-in pub/sub for real-time notifications. No equivalent in MySQL without polling or external tools
• Partial indexes: Index only rows that match a condition. Dramatically reduces index size for filtered queries
• UPSERT (ON CONFLICT): Both support it, PostgreSQL's syntax is more flexible with ON CONFLICT DO UPDATE
• Array and range types: Native array columns and range types (integer ranges, date ranges) with operators and indexing

Replication and High Availability

MySQL's InnoDB Cluster and Group Replication provide a more integrated HA solution out of the box. PostgreSQL requires assembling components (Patroni + PgBouncer + etcd), but this composability gives you more control. For managed services (RDS, Cloud SQL, Supabase), HA is handled for you regardless of which database you choose. See our database scaling guide for more on replication strategies.

Ecosystem and Hosting

PostgreSQL's extension ecosystem is a major differentiator. pgvector for AI/ML embeddings, PostGIS for geospatial, TimescaleDB for time-series, and Citus for distributed queries — all run within PostgreSQL itself, no separate databases needed.

Decision Guide

Frequently Asked Questions