PostgreSQL vs MySQL: Database Comparison

Why This Comparison Matters

PostgreSQL and MySQL have been the two dominant open-source relational databases for over two decades. MySQL powered the LAMP stack era and remains the database behind WordPress, which runs roughly 40% of the web. PostgreSQL was long considered the "advanced" option, favored by teams that needed complex queries, strict data integrity, or features MySQL lacked.

In 2025, the landscape has shifted. PostgreSQL has become the default choice for new projects in the startup and SaaS world. MySQL remains enormously popular but its mindshare among newer developers has declined. The question for teams making a database choice today isn't which one is "better" in the abstract, but which one fits their specific workload, team expertise, and scaling requirements.

We tested both databases on identical hardware with realistic workloads. Here's what we found.

SQL Standards and Data Integrity

PostgreSQL has historically been more standards-compliant than MySQL. It implements a larger subset of the SQL specification and enforces stricter data validation by default. This matters in ways that aren't obvious until you hit a data quality issue in production.

A concrete example: MySQL in its default configuration silently truncates strings that exceed a column's defined length. If you insert a 300-character string into a VARCHAR(255) column, MySQL truncates it to 255 characters without error. PostgreSQL rejects the insert with an error.

-- PostgreSQL: strict by default
CREATE TABLE users (name VARCHAR(10));
INSERT INTO users (name) VALUES ('Christopher');
-- ERROR: value too long for type character varying(10)

-- MySQL (default): silently truncates
CREATE TABLE users (name VARCHAR(10));
INSERT INTO users (name) VALUES ('Christopher');
-- Inserts 'Christophe' (truncated, no error)

MySQL's STRICT_TRANS_TABLES mode (enabled by default since MySQL 5.7) addresses some of these issues, but PostgreSQL's approach to data integrity is more comprehensive. PostgreSQL supports CHECK constraints, exclusion constraints, and deferred constraint checking, all of which help prevent invalid data from entering the database.

For applications where data correctness is non-negotiable (financial systems, healthcare, compliance-heavy domains), PostgreSQL's strictness is a genuine advantage.

JSON and Semi-Structured Data

Both databases support JSON, but the implementations are very different in depth and capability.

PostgreSQL offers two JSON types: json (stored as text) and jsonb (stored as decomposed binary). The jsonb type supports GIN indexes, which means you can query nested JSON fields with index-backed performance. PostgreSQL also supports JSON path queries (SQL/JSON standard), JSON aggregation functions, and the ability to update individual keys within a JSON document.

-- PostgreSQL: indexed JSON queries
CREATE INDEX idx_meta ON products USING GIN (metadata jsonb_path_ops);

-- Query nested JSON with index support
SELECT * FROM products
WHERE metadata @> '{"category": "electronics", "in_stock": true}';

-- JSON path query
SELECT jsonb_path_query(metadata, '$.specs.weight')
FROM products
WHERE id = 1;

-- Update a specific key
UPDATE products
SET metadata = jsonb_set(metadata, '{price}', '29.99')
WHERE id = 1;

MySQL's JSON support (added in 5.7) stores JSON in a binary format and supports generated columns for indexing specific JSON paths. The indexing approach works but is more manual: you create a virtual column for each JSON path you want to index, then index that column.

-- MySQL: JSON indexing via generated columns
ALTER TABLE products
ADD COLUMN category VARCHAR(50)
  GENERATED ALWAYS AS (JSON_UNQUOTE(JSON_EXTRACT(metadata, '$.category')));

CREATE INDEX idx_category ON products (category);

-- Multi-valued indexes (MySQL 8.0.17+)
CREATE INDEX idx_tags ON products ((CAST(metadata->'$.tags' AS CHAR(50) ARRAY)));

For applications that mix relational and document-style data (which is increasingly common), PostgreSQL's JSON support is substantially more capable. If your JSON needs are simple (storing configuration blobs, API response caching), MySQL's JSON type is sufficient.

Performance Benchmarks

Performance comparisons between PostgreSQL and MySQL are notoriously context-dependent. The answer changes based on workload type, hardware, configuration tuning, and data volume. Here are our results on a standardized test (16-core, 64GB RAM, NVMe storage, default-ish configurations tuned for the hardware).

The pattern: MySQL is faster for simple, read-heavy workloads and bulk operations. PostgreSQL is faster for complex queries, analytical workloads, and JSON operations. For standard OLTP workloads (the typical web application), MySQL has a slight edge.

These differences are smaller than most online benchmarks suggest. Both databases are fast. Unless you're operating at a scale where a 10-15% throughput difference matters, performance should not be the primary decision factor.

Advanced Features

PostgreSQL has a significant feature advantage over MySQL. Some features that PostgreSQL offers and MySQL does not (or offers in limited form):

• Common Table Expressions (recursive CTEs): Both support CTEs, but PostgreSQL's implementation is more mature and handles recursive queries more efficiently
• Window functions: Both support window functions. PostgreSQL supports more window frame types and the FILTER clause on aggregates
• Custom types: PostgreSQL lets you define composite types, enum types, range types, and domain types
• Array columns: PostgreSQL natively supports array columns with GIN indexing. MySQL has no equivalent
• Full-text search: Both support full-text search. MySQL's InnoDB FTS is simpler to set up. PostgreSQL's tsvector/tsquery system is more powerful and supports ranking, phrase search, and custom dictionaries
• Materialized views: PostgreSQL supports materialized views with REFRESH MATERIALIZED VIEW CONCURRENTLY. MySQL does not have materialized views
• Table inheritance and partitioning: PostgreSQL has native declarative partitioning. MySQL has partitioning but with more limitations
• Extensions: PostgreSQL's extension system (CREATE EXTENSION) allows adding functionality like PostGIS (geospatial), pg_trgm (trigram matching), and TimescaleDB (time series) without forking the database

-- PostgreSQL: range types (no MySQL equivalent)
CREATE TABLE reservations (
  id SERIAL PRIMARY KEY,
  room_id INT,
  during TSRANGE,
  EXCLUDE USING GIST (room_id WITH =, during WITH &&)
);
-- The exclusion constraint prevents double-booking automatically

-- PostgreSQL: array columns
CREATE TABLE articles (
  id SERIAL PRIMARY KEY,
  title TEXT,
  tags TEXT[]
);
SELECT * FROM articles WHERE tags @> ARRAY['postgres', 'tutorial'];

MySQL's feature set covers standard relational database needs well. Most web applications don't need range types or array columns. But when you do need these features, retrofitting them in MySQL means moving logic to the application layer, which increases complexity and reduces data integrity guarantees.

Replication and Scaling

MySQL's replication has been battle-tested at enormous scale. MySQL's built-in replication supports asynchronous, semi-synchronous, and group replication (multi-primary). The MySQL ecosystem includes mature tools for managing replication: Orchestrator, ProxySQL, MySQL Router, and Vitess (used by YouTube, Slack, and GitHub for horizontal sharding).

PostgreSQL's streaming replication is reliable for read replicas. Logical replication (added in PostgreSQL 10) enables selective table replication and is useful for zero-downtime migrations. For horizontal scaling, PostgreSQL relies on external tools: Citus (now part of Microsoft/Azure) for distributed tables, PgBouncer for connection pooling, and Patroni for high availability.

For pure horizontal scaling, MySQL has the edge. Vitess has proven itself at scales that PostgreSQL's ecosystem hasn't matched. For most applications that scale vertically first (bigger machine) and then add read replicas, both databases handle the progression equally well.

The managed database market has exploded for both. PostgreSQL has Supabase, Neon (serverless), and Tembo. MySQL has PlanetScale (Vitess-based, though they dropped their free tier) and traditional managed options. Aurora supports both PostgreSQL and MySQL wire protocols with proprietary storage engines.

Ecosystem and Tooling

MySQL's ecosystem is broader in terms of raw numbers. More hosting providers, more GUI tools, more legacy applications assume MySQL. phpMyAdmin, MySQL Workbench, and dozens of other tools have decades of development behind them.

PostgreSQL's ecosystem has grown rapidly, particularly in the developer tools space. pgAdmin, DBeaver, and DataGrip provide solid GUI experiences. The extension ecosystem is a major differentiator: PostGIS for geospatial queries, pgvector for AI embeddings and vector search, TimescaleDB for time-series data, and pg_cron for scheduled jobs. These extensions run inside the database process, avoiding the latency and complexity of external services.

-- pgvector: vector similarity search (AI embeddings)
CREATE EXTENSION vector;
CREATE TABLE documents (
  id SERIAL PRIMARY KEY,
  content TEXT,
  embedding vector(1536)
);
CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops);

SELECT content, embedding <=> '[0.1, 0.2, ...]' AS distance
FROM documents
ORDER BY distance
LIMIT 10;

The pgvector extension has made PostgreSQL the default database for AI applications that need vector search alongside relational data. MySQL has no equivalent built-in, though external tools like OpenSearch can supplement this.

ORM and driver support is excellent for both. Every major ORM (Prisma, Drizzle, SQLAlchemy, ActiveRecord, Hibernate) supports both databases. PostgreSQL-specific features like array columns and JSONB are increasingly well-supported in modern ORMs.

Operational Complexity

MySQL is generally easier to operate. Its configuration is simpler, its default settings are more reasonable out of the box, and its memory usage is more predictable. A MySQL instance with default settings will perform acceptably for most workloads.

PostgreSQL requires more tuning. The default shared_buffers setting (128MB) is far too low for production. work_mem, effective_cache_size, and maintenance_work_mem all need adjustment based on available RAM. The autovacuum process (which reclaims space from deleted/updated rows) needs monitoring and occasional tuning. Tools like PGTune help generate reasonable configurations, but PostgreSQL demands more operational attention than MySQL.

Backup strategies differ too. MySQL's mysqldump and xtrabackup are well-documented and reliable. PostgreSQL's pg_dump and pg_basebackup serve similar roles, with WAL archiving providing point-in-time recovery. Both are mature, but MySQL's backup tooling has more documentation and community resources.

Who Should Use What

Choose PostgreSQL if:

• You need advanced SQL features (CTEs, window functions, array types, range types)
• Your application uses JSON heavily alongside relational data
• You want vector search for AI/ML workloads (pgvector)
• Data correctness and constraint enforcement are critical
• You're building on Supabase, Neon, or similar PostgreSQL-native platforms

Choose MySQL if:

• You're running WordPress or a PHP application with existing MySQL dependencies
• You need proven horizontal scaling (Vitess)
• Your workload is read-heavy with simple queries
• Operational simplicity is a priority and your team has MySQL experience
• You need compatibility with legacy systems that assume MySQL

The Verdict

PostgreSQL is the better database for most new projects in 2025. Its feature set is broader, its JSON support is stronger, its extension ecosystem (especially pgvector) addresses modern requirements, and its standards compliance reduces surprises. The managed PostgreSQL ecosystem (Supabase, Neon, Aurora PostgreSQL) makes operational complexity less of a concern than it used to be.

MySQL remains the right choice for read-heavy web applications, WordPress sites, and organizations with deep MySQL expertise. It's not falling behind; MySQL 8.4 is a capable database. But PostgreSQL's momentum, feature development pace, and extension ecosystem give it a meaningful lead for teams starting fresh.

If you don't have a strong reason to choose one over the other, start with PostgreSQL. Its stricter defaults will catch more bugs early, and you're less likely to outgrow its feature set as your application evolves.