Choosing MySQL vs PostgreSQL — sound familiar?
- ▸ Stack-decision blocking your roadmap — Engineering picked one, leadership wants the other — and the actual technical differences haven't been catalogued against your workload. Decision has stalled the release for 2 months.
- ▸ Inherited a DB and questioning the fit — Acquired a system on MySQL but everything else you run is PostgreSQL; or vice-versa. Migration cost and benefit isn't modelled.
- ▸ Hyper-claims on both sides — "PostgreSQL is always better." "MySQL is faster." Both are partially true — you need the actual nuance for your specific access pattern, not a Hacker News opinion.
JusDB DBAs run both in production. We'll give you the honest answer in 30 minutes — no vendor pitch. Book a comparison call →
MySQL vs PostgreSQL
MySQL and PostgreSQL are the two most-deployed open-source OLTP databases in 2026. They share enough wire-protocol overlap that the choice often feels arbitrary — but their internal architectures, replication models, extension ecosystems, and operational characteristics differ in ways that matter at scale. This guide is the production-DBA view: where each one actually wins, where the marketing material is misleading, and how to make the call for your specific workload.
Feature Matrix
MySQL vs PostgreSQL — side-by-side
| Feature | MySQL | PostgreSQL |
|---|---|---|
| ACID transactions | Yes (InnoDB) | Yes (MVCC, default) |
| Replication | Async + semi-sync; Group Replication; InnoDB Cluster | Streaming + Logical replication; built-in, no extra tools |
| HA tooling | Orchestrator, ProxySQL, MHA, InnoDB Cluster | Patroni + etcd/Consul, pg_auto_failover, repmgr |
| Sharding (native) | No (Vitess / TiDB / PlanetScale add it) | No (Citus extension adds it natively) |
| JSON support | JSON (binary) — read-optimised | JSONB (binary, indexable) — fully featured |
| Full-text search | Built-in (MATCH AGAINST) | Built-in (tsvector + GIN); pg_trgm extension |
| Vector search | No native (use ProxySQL + external) | pgvector extension (production-ready) |
| Time-series workloads | Capable with partitioning | TimescaleDB extension (purpose-built) |
| Extensions ecosystem | Storage engines (InnoDB, MyRocks, NDB) | 200+ extensions (postgis, pgvector, pgcron, citus, timescaledb...) |
| Stored procedures | SQL/PSM, PL/SQL-like | PL/pgSQL — richer, plus PL/Python, PL/Perl, PL/V8 |
| Window functions | Yes (8.0+) | Yes (always supported, more comprehensive) |
| Geospatial | Native ST_* functions | PostGIS — industry-leading geospatial |
| Concurrent index build | Yes (8.0+ online DDL) | Yes (CREATE INDEX CONCURRENTLY) |
| MVCC implementation | Undo log (InnoDB rollback segments) | Heap-based (row versioning, requires VACUUM) |
| Vacuum / autovacuum | Not needed (undo-based) | Required; autovacuum tuning critical |
| Connection model | Thread per connection (light) | Process per connection (heavier; needs pgBouncer at scale) |
| Default replication | Async (Statement / Row / Mixed) | Async streaming (logical or physical) |
| Logical replication | Async, manual | Built-in (publish/subscribe model) |
| Cloud-managed options | RDS MySQL, Aurora MySQL, GCP Cloud SQL, Azure Database, PlanetScale | RDS PostgreSQL, Aurora PG, GCP Cloud SQL, Azure Database, Supabase, Neon, Crunchy Bridge |
| Licensing | GPL v2 (Oracle); MariaDB fork is GPL v2 | PostgreSQL License (BSD-style, very permissive) |
When MySQL wins
Simpler operational model
Thread-per-connection model handles thousands of connections without an external pooler. Lower memory ceiling for high-concurrency workloads.
Storage engine flexibility
Swap InnoDB for MyRocks (write-heavy), NDB Cluster (sharded), or columnstore engines without changing the SQL layer.
Strong sharded-OLTP ecosystem
Vitess (YouTube/Slack), TiDB, PlanetScale — sharding battle-tested at largest scales for web platforms.
Default-encoding stability
Charset behaviour and collation predictability are simpler operationally than PG's ICU evolution.
Cheaper memory footprint
Same workload uses less RAM than PG at high connection counts — important for budget-constrained deployments.
When PostgreSQL wins
Richer feature surface
JSONB indexing, window functions, CTEs, materialized views, RLS, partitioning — all native and richer than MySQL equivalents.
Extension ecosystem
PostGIS, pgvector, TimescaleDB, Citus, pg_partman, pg_cron — first-class extensions purpose-built for specialised workloads.
Transaction-level rigor
True serializable isolation; MVCC with full snapshot isolation; constraint-deferred transactions.
Liberal license
PostgreSQL License is BSD-style — distribution and embedding freedom that GPL-based MySQL doesn't offer.
Analytical query capability
Window functions + lateral joins + materialized views make PG far more capable for OLAP workloads on top of OLTP.
Migration
Migration paths between MySQL and PostgreSQL
MySQL → PostgreSQL
pgloader for one-shot migrations; Debezium CDC for zero-downtime. Type-mapping gotchas: ENUM, AUTO_INCREMENT → SERIAL, TINYINT → SMALLINT.
PostgreSQL → MySQL
Less common; usually driven by ecosystem commitment. pgloader can run in reverse; type conversion is harder due to PG's richer types.
Self-hosted → Cloud (both)
Aurora MySQL / RDS MySQL on AWS; Aurora PG / RDS PG; GCP CloudSQL covers both; Azure has both.
FAQ
Common questions
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