PostgreSQL and MySQL are both excellent relational databases that power millions of production applications. The choice between them rarely comes down to one being objectively “better”—it’s about which fits your specific requirements, team experience, and operational constraints.
Having worked with both databases across different projects, we’ve found the decision usually hinges on a few key factors that generic comparisons tend to gloss over.
The Quick Answer
Choose PostgreSQL if: You need advanced SQL features, complex queries, JSON document storage alongside relational data, strong data integrity guarantees, or extensibility through custom types and functions.
Choose MySQL if: You need maximum read performance for simple queries, have a team experienced with MySQL, are building on a platform with strong MySQL integration (like many PHP frameworks), or need the specific replication and clustering options MySQL offers.
Either works well if: You’re building a typical web application with straightforward CRUD operations and don’t have specific requirements that favor one over the other.
Where PostgreSQL Excels
Standards Compliance and Advanced SQL
PostgreSQL takes SQL standards seriously. Features like window functions, common table expressions (CTEs), lateral joins, and full-text search work as documented in SQL standards, often with additional capabilities beyond the standard. If you’re writing complex analytical queries or need features that MySQL only partially supports, PostgreSQL is the safer choice.
This matters when queries get complex. Recursive CTEs for hierarchical data, window functions for running calculations, or sophisticated grouping operations work predictably and efficiently in PostgreSQL. MySQL has added many of these features over the years, but the implementations sometimes have limitations or behave differently than expected.
JSON and Document Storage
PostgreSQL’s JSONB data type offers genuinely powerful document storage within a relational database. You can store JSON documents, index specific fields within them, and query them efficiently—combining the flexibility of document databases with the reliability of PostgreSQL’s ACID guarantees.
This hybrid approach works well for applications where most data is relational but some entities have variable attributes. User preferences, product metadata with varying fields, or event data with evolving schemas can live in JSONB columns without requiring a separate document database.
MySQL has JSON support, but PostgreSQL’s implementation is more mature, with better indexing options and query performance for complex JSON operations.
Data Integrity and Constraints
PostgreSQL provides more sophisticated constraint options: check constraints that can reference other columns, exclusion constraints for complex uniqueness requirements, and deferrable constraints for situations where constraint checking needs to happen at commit time rather than statement time.
If data integrity is critical—financial applications, healthcare systems, anything where incorrect data has serious consequences—PostgreSQL’s stricter approach provides additional safety nets that MySQL doesn’t offer.
Extensibility
PostgreSQL’s extension system allows adding new data types, functions, operators, and index types without modifying the core database. PostGIS for geospatial data, pg_trgm for fuzzy text matching, TimescaleDB for time-series data—these extensions make PostgreSQL adaptable to specialized use cases while keeping the familiar PostgreSQL interface and reliability.
This extensibility means you can often add capabilities without introducing additional databases. Need full-text search? PostgreSQL has it built in. Need geospatial queries? PostGIS is mature and widely used. Need time-series storage? TimescaleDB extends PostgreSQL rather than replacing it.
Where MySQL Excels
Raw Read Performance
For simple read-heavy workloads—primary key lookups, straightforward SELECT queries, high-volume read operations—MySQL often outperforms PostgreSQL. The difference isn’t dramatic for typical applications, but for specific read-heavy patterns at scale, MySQL’s architecture can be faster.
This advantage is most pronounced when queries are simple and the working set fits in memory. MySQL’s query execution can be more efficient for basic operations, even if PostgreSQL handles complex queries better.
Replication and Clustering Options
MySQL’s replication capabilities are battle-tested at massive scale. MySQL replication powers some of the world’s largest web applications, with well-understood patterns for read replicas, multi-master setups, and geographic distribution.
MySQL also has multiple clustering options: Group Replication for synchronous multi-master, InnoDB Cluster for integrated high availability, and Galera Cluster (through Percona or MariaDB) for synchronous replication across multiple nodes. PostgreSQL has replication and high-availability options, but MySQL’s ecosystem in this area is more diverse.
Ecosystem and Tooling
MySQL has been the default database for web development for decades. Virtually every web framework has MySQL support baked in. Hosting providers universally offer MySQL. Managed database services often launched with MySQL first. This ubiquity means finding resources, tools, and expertise is straightforward.
For PHP applications in particular, MySQL integration is deeply embedded. WordPress, Laravel, Drupal—the entire PHP ecosystem assumes MySQL availability. Using PostgreSQL with these platforms is possible but requires more configuration and may encounter compatibility issues.
Operational Simplicity
MySQL can be simpler to operate, especially for teams without dedicated database expertise. Configuration is more straightforward, default settings are more reasonable for common use cases, and the operational patterns are well-documented in countless tutorials and guides.
PostgreSQL has historically required more tuning to achieve optimal performance, though this gap has narrowed significantly. For teams without database specialists, MySQL’s easier operational model can reduce ongoing maintenance burden.
Performance Realities
Benchmarks comparing PostgreSQL and MySQL are nearly meaningless without context. Both databases can be fast or slow depending on the workload, configuration, and hardware.
Write-heavy workloads: PostgreSQL’s MVCC implementation handles concurrent writes well. MySQL with InnoDB also handles concurrent writes effectively. The difference is rarely significant for typical applications.
Read-heavy workloads: MySQL may have an edge for simple queries at very high volume. PostgreSQL handles complex queries more efficiently. Most applications won’t notice a meaningful difference.
Mixed workloads: Both databases handle mixed read/write patterns well. Proper indexing matters more than database choice.
Complex queries: PostgreSQL generally executes complex analytical queries more efficiently, with a more sophisticated query planner that handles joins, subqueries, and aggregations better.
The honest answer is that neither database will be a performance bottleneck for the vast majority of applications. Proper schema design, appropriate indexing, and query optimization matter far more than the choice between PostgreSQL and MySQL.
Migration Considerations
Moving between PostgreSQL and MySQL isn’t trivial. SQL syntax differs in subtle ways: quoting, date handling, auto-increment behavior, default values. ORM layers abstract some differences but not all.
If you’re starting a new project, the switching cost is zero—pick what fits best. If you’re considering migration of an existing application, the effort required depends on how database-specific your code is. Applications that rely heavily on database-specific features face significant migration work. Applications with simple CRUD operations behind an ORM may migrate relatively easily.
The safest approach for new projects is avoiding database-specific features unless you need them. Standard SQL constructs work in both databases and preserve the option to switch later if requirements change.
Managed Database Services
Both databases have excellent managed offerings from major cloud providers:
PostgreSQL: Amazon RDS for PostgreSQL, Aurora PostgreSQL, Google Cloud SQL for PostgreSQL, Azure Database for PostgreSQL. Aurora PostgreSQL deserves special mention for providing PostgreSQL compatibility with Aurora’s performance and availability characteristics.
MySQL: Amazon RDS for MySQL, Aurora MySQL, Google Cloud SQL for MySQL, Azure Database for MySQL. Aurora MySQL is widely used and handles high-scale workloads effectively.
Managed services reduce the operational differences between the databases. Backups, replication, failover, and patching are handled for you. The choice becomes more about the database features and less about operational complexity.
Making the Decision
Start with your requirements, not your preferences:
Do you need specific PostgreSQL features? JSONB, advanced constraints, PostGIS, specific extensions? PostgreSQL is the clear choice.
Are you building on a MySQL-centric platform? WordPress, legacy PHP applications, systems with existing MySQL dependencies? MySQL avoids friction.
What does your team know? Expertise with either database has real value. The database your team knows well will perform better in production than the “superior” database your team is learning.
What’s your operational capacity? If you’re using managed services, this matters less. If you’re self-hosting, consider which database your operations team can support effectively.
What’s your cloud environment? Some platforms have better integration with one database. Check what’s available and well-supported in your environment.
For new projects without strong requirements pointing either direction, PostgreSQL is often the safer default. Its feature set is broader, standards compliance is better, and the direction of database technology trends favors PostgreSQL’s capabilities. But MySQL is a legitimate choice that powers countless successful applications—it’s not settling to choose MySQL when it fits your needs.
The Bottom Line
Both PostgreSQL and MySQL are production-ready databases that can handle demanding workloads. The “PostgreSQL is better” narrative that dominates some technical circles oversimplifies a nuanced decision.
Choose based on your specific requirements, team capabilities, and operational context. Either choice, properly implemented and maintained, will serve most applications well for years. The biggest mistakes we see aren’t choosing the “wrong” database—they’re poor schema design, missing indexes, and application-level inefficiencies that would cause problems regardless of which database was underneath.