PostgreSQL vs MySQL: Which to Choose

PostgreSQL and MySQL are both excellent relational databases, and both will handle the vast majority of workloads. The differences that matter are in the details: JSON handling, extensions, replication topology, and the ecosystem around each. This guide compares them on the dimensions that actually affect production decisions.

The Short Answer

• Choose PostgreSQL for complex queries, JSON workloads, advanced data types, and when you want one database that does everything (including vectors with pgvector).
• Choose MySQL for simple, high-throughput OLTP workloads, when your team already knows it, and when you are on managed platforms like AWS RDS where MySQL is the default comfort zone.

Feature Comparison

Feature	PostgreSQL	MySQL
License	PostgreSQL License (permissive)	GPL / commercial
JSON support	jsonb: indexed, queryable	JSON: valid but weaker queries
Full-text search	Built-in, strong	Built-in, weaker
Extensions	Rich (PostGIS, pgvector, ...)	Limited
Data types	Arrays, ranges, enums, hstore	Standard + JSON
Indexes	B-tree, GIN, GiST, BRIN, HNSW	B-tree, hash, full-text
Replication	Streaming, logical, cascading	Replica sets, group replication
Stored procedures	PL/pgSQL, PL/Python, PL/Perl	Stored procedures, events
Concurrency	MVCC, strong	MVCC (InnoDB), weaker isolation defaults

JSON: The Decisive Difference

For modern applications that mix structured and semi-structured data, PostgreSQL's jsonb is the standout feature:

-- PostgreSQL: index and query inside JSON
CREATE TABLE events (
    id BIGSERIAL PRIMARY KEY,
    payload jsonb NOT NULL
);

CREATE INDEX ON events USING GIN (payload);

-- Query inside the JSON document
SELECT * FROM events
WHERE payload @> '{"type": "checkout", "status": "failed"}';

-- Extract and aggregate
SELECT payload->>'country', COUNT(*)
FROM events
GROUP BY payload->>'country';

MySQL's JSON type stores JSON but querying inside it is weaker: no GIN-style indexing, and expressions like JSON_EXTRACT do not use indexes efficiently. If JSON is a first-class part of your data model, PostgreSQL wins clearly.

Extensions: PostgreSQL's Superpower

PostgreSQL extensions turn one database into many:

CREATE EXTENSION postgis;    -- geospatial
CREATE EXTENSION pgvector;   -- vector search for AI applications
CREATE EXTENSION timescaledb; -- time series
CREATE EXTENSION pg_cron;    -- scheduled jobs
CREATE EXTENSION pg_stat_statements; -- query analytics

The same database that stores your orders can serve vector search for your RAG application. MySQL has no equivalent extension ecosystem. If you want to avoid running a separate vector database or search engine, PostgreSQL with pgvector is the pragmatic choice.

Performance: The Honest Comparison

For simple point lookups and high write throughput, MySQL (InnoDB) is often faster out of the box. For complex queries, aggregations, and analytical workloads, PostgreSQL's optimizer is generally stronger.

-- Complex analytical query: PostgreSQL's optimizer shines
SELECT
    date_trunc('month', created_at) AS month,
    product_category,
    SUM(amount) AS revenue,
    COUNT(DISTINCT user_id) AS buyers
FROM orders
WHERE created_at > now() - interval '12 months'
GROUP BY 1, 2
ORDER BY 1, 3 DESC;

The practical guidance: benchmark on your own workload. Both databases are fast enough for most applications; the difference shows up at the extremes.

Replication and High Availability

PostgreSQL offers:

• Streaming replication: primary + read replicas with WAL shipping.
• Logical replication: selective table replication, cross-version upgrades.
• Cascading replication: replicas of replicas for geographic distribution.
• Failover: managed by Patroni, repmgr, or cloud providers.

MySQL offers:

• Replica sets: async and semi-sync replication.
• Group replication: multi-primary with conflict detection.
• Managed failover: built into RDS and other managed services.

Both are production-proven. The choice here is usually about your operational tooling, not the database itself.

Ecosystem and Team Familiarity

• MySQL is the default in the LAMP stack and has enormous mindshare. Most developers have used it. Managed offerings (RDS, Aurora) are mature.
• PostgreSQL has become the default for new projects in the last decade, especially in the Python, Node, and Go ecosystems. ORMs (Prisma, Drizzle, SQLAlchemy) treat it as a first-class citizen.

If your team knows one well and the workload does not demand the other's features, familiarity is a legitimate tiebreaker.

Migration Considerations

Moving between them is not trivial:

• Data types differ: SERIAL vs AUTO_INCREMENT, TEXT vs LONGTEXT, boolean handling.
• SQL dialect differs: ILIKE vs LIKE, LIMIT vs LIMIT, ON CONFLICT vs ON DUPLICATE KEY.
• Tooling differs: pg_dump vs mysqldump, different monitoring stacks.

Use a migration tool (pgloader, AWS DMS) and run both databases in parallel during cutover.

Implementation Checklist

• List your JSON and complex-query requirements first
• Check whether you need extensions (PostGIS, pgvector, TimescaleDB)
• Benchmark both on your actual workload, not synthetic tests
• Consider team familiarity as a real factor
• Plan replication topology before choosing
• Prototype the migration path if switching

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