PostgreSQL and MySQL are the two most widely used open-source relational databases. Both are production-ready, have active communities, and can support most applications without issues. The difference lies in the details, and those details are very important depending on what you are creating.
The developer community has been voting with its keyboards. The Stack Overflow Developer Survey 2025 shows that PostgreSQL is used by 55.6% of all respondents and 58.2% of professional developers, compared with MySQL's 40.5% and 39.6%. PostgreSQL has also been the most admired and most desired database in its category every year since 2023.
MySQL remains widely used, powering WordPress, the LAMP stack, and substantial legacy infrastructure. It is still the preferred choice for these workloads. However, for new projects, the engineering community increasingly selects PostgreSQL as the default.
This guide outlines the architectural differences between PostgreSQL and MySQL, highlights their respective performance strengths, identifies key features for various workloads, and provides a straightforward decision framework.
A widely cited Hacker News thread on PostgreSQL vs MySQL sums up the design philosophy difference well: "PostgreSQL was built with priorities being: correctness first, then features, then performance. MySQL was built with: performance, then features, then correctness." That framing still holds today and explains most of the real-world differences engineers run into.
TL;DR
PostgreSQL is the better default for modern applications due to stronger data integrity, advanced query capabilities, and built-in support for AI, analytics, and multi-tenant security. MySQL remains a strong choice for simple, read-heavy workloads and legacy LAMP or CMS-based systems.
When deciding which to use, focus on:
- Workload type: Simple indexed reads, and CMS apps suit MySQL. Complex queries, analytics, and multi-tenant SaaS are better with PostgreSQL.
- Team expertise: If your team specializes in MySQL, leverage it. For new projects, PostgreSQL has stronger developer momentum and tooling.
- Critical features: PostgreSQL offers stricter data integrity, extensions (AI/vector search, geospatial, time-series), and native row-level security.
- Ecosystem fit: MySQL fits existing LAMP and WordPress stacks. PostgreSQL is better for greenfield, data-heavy, or extensible applications.
PostgreSQL vs MySQL: What Each Database Is
PostgreSQL
PostgreSQL is an open-source object-relational database management system (ORDBMS) that originated from the POSTGRES project at UC Berkeley in the late 1980s. It is governed by the PostgreSQL Global Development Group, a nonprofit with no single corporate owner.
License: Permissive BSD/MIT-style, with no restrictions on commercial use or distribution. This provides a practical advantage for software vendors unable to comply with GPL requirements.
PostgreSQL is distinguished by its extensibility. In addition to standard SQL, it supports custom data types, user-defined functions in PL/pgSQL, PL/Python, and PL/Perl, table inheritance, and a mature extension framework. Production-grade extensions such as PostGIS (geospatial), pgvector (AI vector search), and TimescaleDB (time-series) are widely adopted.
MySQL
MySQL is a relational database first released in 1995 and has been owned by Oracle Corporation since its acquisition of Sun Microsystems in 2010. It is available under the GPL for open-source use and under a commercial license for organizations that require embedding MySQL in proprietary software without releasing their source code.
MySQL excels in speed for simple, predictable workloads and offers deep integration with the LAMP stack. WordPress, Drupal, Joomla, and most PHP-driven platforms are built and tested on MySQL, supporting a large and stable ecosystem.
A notable community concern is Oracle's ownership of MySQL. The uncertainty following Oracle's acquisition in 2010 contributed to the creation of MariaDB in 2009 by MySQL's original creator, Michael Widenius. While MySQL and MariaDB remain broadly compatible, they have diverged, especially in storage engines and replication features.
PostgreSQL vs MySQL: Adoption and Developer Trends
The most reliable data on PostgreSQL and MySQL adoption comes from two sources: the DB-Engines Ranking, which tracks industry adoption signals, and the Stack Overflow Developer Survey 2025, which reports developer usage.
| Database | DB-Engines Score (Mar 2026) | 1-Year Change | SO 2025 Usage (All) | SO 2025 Usage (Professionals) | Admired | Desired |
|---|---|---|---|---|---|---|
| PostgreSQL | 680.08 | +16.66 | 55.6% | 58.2% | 65.5% #1 | 46.5% #1 |
| MySQL | 858.34 | -129.79 | 40.5% | 39.6% | 43.2% | 17.6% |
| MS SQL Server | 711.47 | +3.33 (flat) | 30.1% | 30.9% | 44.9% | 15.2% |
| Oracle | 1182.46 | -70.62 | 10.6% | 10.4% | 39.4% | 4.9% |
| MongoDB | 383.58 | +4.85 | 24% | 24.3% | 45.7% | 20.5% |
| Snowflake | 211.24 | +49.46 | 4.1% | 4.2% | 47.2% | 6.4% |
Sources: DB-Engines.com, March 2026, and Stack Overflow Developer Survey 2025. Admired = used it and want to continue using it. Desired = have not used it but want to.
Two things stand out. First, MySQL's DB-Engines score dropped nearly 130 points in one year — a 147-point swing against PostgreSQL's +17. Second, MySQL's desired score is only 17.6%. That means fewer than 1 in 5 developers who haven't used MySQL want to try it. For PostgreSQL, that number is 46.5%. This is not a current usage gap; it is a momentum gap.
The survey directly states: "What is it like to be the most desired and most admired technology in your category? The answer lies with PostgreSQL, ranked highest for both since 2023." That is the Stack Overflow survey's own words, not editorial spin.
PostgreSQL vs MySQL: Architecture Differences
Many real-world differences in performance, safety, and behavior under load between PostgreSQL and MySQL result from architectural decisions made decades ago. Understanding these foundations clarifies much of their observed behavior.
Process model vs. thread model
PostgreSQL creates a new OS process per client connection. Each process has isolated memory. A crashed query cannot corrupt another session. The cost: each process uses roughly 5-10 MB of RAM. High-connection environments need a pooler — PgBouncer is the standard, Supavisor is a newer option.
MySQL uses threads instead of processes. Threads share memory within a single process. This is lighter and lets MySQL handle thousands of direct connections without a pooler. If your application opens many short-lived connections and you cannot add PgBouncer to your stack, MySQL has a practical operational advantage here.
ACID compliance
PostgreSQL is fully ACID-compliant in every configuration. There is no storage engine to choose from and no mode to enable. MySQL's ACID compliance depends on which storage engine you use. InnoDB (the default since MySQL 5.5) is fully ACID-compliant. MyISAM, which still exists and is used in some legacy systems, is not; it has no transactions and no foreign key support.
This matters most in older or shared-hosting environments where you may not control the MySQL configuration. The PostgreSQL wiki article on this is old, but the underlying risk in legacy MySQL deployments persists.
Transactional DDL
This architectural difference is frequently cited by engineers who have managed migrations between both databases.
In PostgreSQL, schema changes such as CREATE TABLE, ALTER TABLE, and DROP INDEX can be executed within a transaction and rolled back if necessary, ensuring the database remains unchanged after a failed migration. In MySQL, DDL statements trigger implicit commits before and after execution, so a failed migration can leave the schema in a partial state.
For example, if a migration adds three tables and an index but fails on the third step, PostgreSQL allows you to roll back so no changes are applied. In MySQL, the first two tables remain committed, requiring manual cleanup.
Default transaction isolation
PostgreSQL defaults to Read Committed isolation, while MySQL defaults to Repeatable Read. In high-write, concurrent workloads, MySQL's Repeatable Read can cause increased gap locking and more deadlocks. PostgreSQL's Serializable Snapshot Isolation (SSI), available since version 9.1, can automatically detect and abort write-skew. MySQL addresses conflicts by blocking transactions.
PostgreSQL vs MySQL: Feature Comparison
| Feature | PostgreSQL | MySQL |
|---|---|---|
| License | PostgreSQL License — BSD/MIT-style, no commercial restrictions | GPL (commercial license needed for ISVs) |
| ACID compliance | All configurations, always | InnoDB only; MyISAM is not ACID |
| Transactional DDL (schema rollback) | Yes — DDL can be rolled back | No — DDL triggers implicit commit |
| JSON support | JSONB: binary storage, GIN indexes, path operators | JSON type since 5.7; no native path indexing |
| CHECK constraints | Fully enforced, always | Enforced from MySQL 8.0.16 only |
| Row-Level Security (RLS) | Native, built-in since v9.5 | No native support — requires views/application logic |
| Extension ecosystem | Rich: PostGIS, pgvector, TimescaleDB, Citus, pg_partman | Plugin architecture; no comparable extension ecosystem |
| Geospatial | PostGIS — production-grade, widely deployed | Limited spatial types; no PostGIS equivalent |
| Vector / AI search | pgvector — HNSW and IVFFlat indexes | No native support |
| Logical replication | Pub/Sub, per-table, cross-version | Binary log-based; simpler but less flexible |
| Parallel query | Scans, joins, sorts, aggregations — all parallelized | Limited parallel read support since 8.0 |
| Full-text search | tsvector/tsquery with ranking, custom dictionaries | Supported but less capable |
| Object hierarchy | Databases > Schemas > Tables | Databases > Tables (no schemas) |
| Connection model | Process per connection (needs PgBouncer at scale) | Thread per connection (no pooler required) |
| MVCC maintenance | VACUUM required (autovacuum handles most cases) | InnoDB undo log auto-purged in background |
| Default isolation level | Read Committed | Repeatable Read |
| SQL standards compliance | Strict — no silent type coercion | Strict in 8.0+; older installs may be permissive |
PostgreSQL vs MySQL: Performance
There is no clear overall performance winner between PostgreSQL and MySQL. Each optimizes for different query patterns, so performance depends on your application's specific requirements.
Where MySQL is faster
MySQL performs best with simple primary key lookups and indexed reads. In OLTP-style benchmarks using tools such as sysbench, MySQL often outperforms PostgreSQL on straightforward read-only queries, largely due to its lower per-connection overhead and InnoDB’s clustered index, which stores row data directly in the B-tree to reduce lookup costs.
For applications like WordPress sites, product catalogs, or services that rely primarily on simple index reads, this performance difference can be significant at scale.
Common benchmarking tools include sysbench (for OLTP workloads), pgbench (PostgreSQL-native benchmarking), and TPC-H (for analytical queries), each measuring different performance characteristics. Results vary based on schema design, indexing strategy, and workload patterns, so real-world performance should be validated for your specific use case.
Where PostgreSQL is faster
PostgreSQL excels at complex queries involving joins, aggregations, window functions, and common table expressions (CTEs). Its advanced cost-based query planner optimizes execution strategies for multi-step analytical workloads.
In real-world benchmarks and production environments, PostgreSQL often outperforms MySQL on complex reporting queries, particularly as query complexity and dataset size grow. This advantage is most evident with multiple joins, nested subqueries, or advanced SQL features.
PostgreSQL supports parallel query execution across multiple CPU cores for scans, joins, aggregations, and sorts. While MySQL 8.x introduced limited parallel read capabilities, PostgreSQL’s parallel execution model is broader and more mature, enabling better scalability for analytical workloads.
In mixed read/write benchmarks, PostgreSQL demonstrates strong throughput and consistent latency under concurrent workloads, especially with complex queries and high write contention. As with all benchmarks, results depend on schema design, indexing, and workload characteristics.
The VACUUM tradeoff
PostgreSQL's MVCC retains old row versions within the table until VACUUM removes them. On tables with heavy UPDATE or DELETE activity, dead tuples accumulate, requiring autovacuum to work harder. While autovacuum is sufficient for most workloads, extremely write-heavy tables may require additional operational attention.
MySQL's InnoDB stores old row versions in a separate undo log, which is automatically purged by a background thread, requiring no manual intervention.
PostgreSQL vs MySQL performance by workload
| Workload | Better choice | Why |
|---|---|---|
| Simple indexed reads (web, CMS) | MySQL (slight edge) | Lower per-connection overhead; InnoDB clustered index is fast for this pattern |
| Complex queries, analytics, reporting | PostgreSQL (clear advantage) | Superior query planner for window functions, CTEs, multi-table joins |
| High concurrent writes | PostgreSQL | Read Committed default; less gap locking and contention than MySQL's Repeatable Read |
| JSON-heavy applications | PostgreSQL (significant edge) | JSONB with GIN indexes vs MySQL's limited JSON indexing via generated columns |
| Bulk loading (simple schema) | Both adequate | Test with your specific schema; results similar |
| High connection count (1000+), no pooler | MySQL (advantage) | Thread model handles direct connections; PostgreSQL needs PgBouncer |
| Geospatial, time-series, vector search | PostgreSQL (only real option) | PostGIS, TimescaleDB, pgvector have no MySQL equivalents |
Row-Level Security: A Categorical Difference
This feature represents a categorical difference between PostgreSQL and MySQL, rather than a difference of degree.
PostgreSQL Row-Level Security
PostgreSQL has offered native Row-Level Security (RLS) since version 9.5. RLS allows you to define security policies directly on tables, specifying which rows a user or role can SELECT, INSERT, UPDATE, or DELETE. These policies are enforced automatically, regardless of how the query is executed.
The official PostgreSQL RLS documentation covers how to define per-role, per-command policies. The most common production use case is multi-tenant SaaS: one database serves many customers, each customer only sees their own rows. RLS enforces this at the database layer, so a bug in application code that forgets a WHERE tenant_id = ? filter does not become a data leak.
Supabase has built its entire multi-tenancy model around PostgreSQL RLS. Their guide on RAG with permissions using pgvector and RLS shows how RLS integrates with vector similarity search, restricting which documents are returned in an AI query, by user, by tenant, by permission level.
MySQL and multi-tenancy
MySQL does not offer native RLS. Implementing row-level access control requires application-layer filtering, views with hard-coded conditions, or stored procedures. Each method introduces potential failure modes that RLS is designed to prevent.
For teams developing multi-tenant applications that handle regulated data such as healthcare, finance, or HR, this difference often determines the database selection.
Extensions and AI Readiness
PostgreSQL's extension framework is the primary feature that distinguishes it from MySQL for modern application development.
pgvector and AI applications
pgvector is a PostgreSQL extension that enables vector similarity search. It supports HNSW and IVFFlat indexes for approximate nearest-neighbor queries and integrates with PostgreSQL's RLS, query planner, and transaction system.
Teams building RAG (Retrieval-Augmented Generation) pipelines can store documents, embeddings, and metadata in a single PostgreSQL database, apply RLS to vector searches, and run hybrid queries that combine semantic and relational filters, eliminating the need for a separate vector database. The Supabase RAG with permissions guide provides a production-ready example.
MySQL does not offer a native vector type or similarity search. While PostgreSQL gained pgvector through the open-source community, MySQL users must rely on Oracle's product roadmap or use a separate system for vector search capabilities.
PostGIS, TimescaleDB, and Citus
PostGIS extends PostgreSQL with hundreds of spatial functions and geometry types, making it the industry standard for geospatial database work in areas such as government mapping, logistics, urban planning, and location services. MySQL offers basic geometry support but is not a production substitute for PostGIS.
TimescaleDB adds time-based partitioning, continuous aggregates, and compression to PostgreSQL, supporting use cases such as IoT sensor data, financial ticks, and application metrics. Citus, now part of Microsoft Azure, transforms a standard PostgreSQL instance into a horizontally sharded distributed database while maintaining full SQL compatibility.
PostgreSQL vs MySQL: Which Should You Use?
The choice ultimately depends on your workload, existing technology stack, and your team's expertise.
Choose PostgreSQL when
- You are starting a new project and want the database choice with the strongest developer momentum in 2026
- Your application runs complex queries, analytics, or reporting, query planner quality will matter
- You need JSONB for semi-structured data, PostGIS for geospatial, pgvector for AI/vector search, or TimescaleDB for time-series
- You are building a multi-tenant SaaS application and need row-level security enforced at the database layer.
- You are building AI-powered features and need vector similarity search alongside relational data within a single system.
- You need strict data integrity, no silent type coercions, transactional DDL, and enforced constraints.
- You need flexible replication topologies or are planning a CDC pipeline for real-time data integration.
- You are developing commercial software, and compliance with the GPL license is a concern.
Choose MySQL when
- You are running WordPress, Drupal, Joomla, or any CMS that is designed and tested for MySQL.
- You are building or maintaining a LAMP-stack application where MySQL is the standard.
- Your workload is mostly simple indexed reads with no complex joins or analytical queries.
- Your team has deep MySQL expertise and production tooling (Orchestrator, ProxySQL, Percona Toolkit) already in place.
- You are integrating with the existing MySQL infrastructure, and switching would add complexity without a clear gain.
- You need to handle thousands of direct connections without adding a connection pooler to your architecture.
Many organizations operate MySQL for legacy products and PostgreSQL for new services. The primary challenge is synchronizing data between the two systems, particularly for cross-system reporting and analytics. Real-time CDC pipelines address this need without manual scripting or batch delays.
Migrating Between PostgreSQL and MySQL
Migrations between PostgreSQL and MySQL are well-understood. The primary tasks involve schema translation and query adjustments rather than data movement. Both migration directions are well documented and supported by tooling.
MySQL to PostgreSQL
Common schema changes required:
- AUTO_INCREMENT becomes SERIAL or GENERATED ALWAYS AS IDENTITY
- Backtick identifier quoting becomes double-quote quoting.
- IFNULL() becomes COALESCE()
- Review any queries that relied on MySQL's permissive type coercion under non-strict mode.
- Full-text search syntax differs: MySQL uses MATCH() AGAINST(), PostgreSQL uses tsvector and tsquery
Full step-by-step guide with code examples: How to Migrate from MySQL to PostgreSQL
PostgreSQL to MySQL
This migration is less common but relevant when standardizing on a MySQL ecosystem or integrating with a CMS. Key considerations include the lack of a direct MySQL equivalent for JSONB data, required adjustments for PostgreSQL-specific window function syntax in MySQL 8.x, and the need to redesign any logic using PostgreSQL extensions such as pgvector or PostGIS.
Full walkthrough: How to Migrate from PostgreSQL to MySQL
Sync or Migrate Between PostgreSQL and MySQL
Whether you are operating both databases, performing a migration, or integrating with data warehouses such as Snowflake or BigQuery, a reliable pipeline eliminates manual effort.
Estuary is a right-time data integration platform with native connectors for both PostgreSQL and MySQL, including CDC (change data capture) support. CDC reads row-level changes from the transaction log, not scheduled queries, so downstream systems stay in sync without adding load to production databases.
Common use cases:
- Sync MySQL operational data into a PostgreSQL analytics database for queries that MySQL handles slowly
- Replicate PostgreSQL into Snowflake, BigQuery, or Databricks via CDC for low-latency pipelines.
- Run a live MySQL-to-PostgreSQL migration with both databases in sync during the cutover window.
- Consolidate data from multiple MySQL or PostgreSQL instances into a single reporting destination.
Need help choosing between PostgreSQL and MySQL or building data pipelines? We’re here to help.
Summary: PostgreSQL vs MySQL
PostgreSQL is the preferred default for new projects. It offers stricter data integrity, a superior query planner for complex workloads, native row-level security, and an extension ecosystem supporting AI vector search, geospatial, and time-series use cases within the primary database. Developer adoption and momentum both favor PostgreSQL.
MySQL remains the optimal choice for WordPress, LAMP-stack applications, and simple read-heavy workloads managed by experienced MySQL teams. It offers a genuine performance advantage on simple queries and its thread model supports high connection counts without extra infrastructure.
Most applications will function well on either database. The decision is most critical for edge cases: complex query workloads and multi-tenant security requirements favor PostgreSQL, while LAMP-stack or CMS deployments are best served by MySQL. For AI and vector search, PostgreSQL is the only viable option.
FAQs
Is PostgreSQL harder to use than MySQL?
PostgreSQL has a slightly steeper initial setup, but the difference in day-to-day usage is small. PostgreSQL is stricter about data types and SQL standards, which catches bugs earlier. MySQL's permissiveness feels easier at first and causes hard-to-debug data issues later.
Can I run both PostgreSQL and MySQL together?
Yes, and many organizations do. A common setup is MySQL for legacy applications and PostgreSQL for new services. The main challenge is synchronizing data between the two for reporting or analytics — which is where a CDC pipeline like Estuary becomes useful.
What is the main difference between PostgreSQL and MySQL?
PostgreSQL is an object-relational database with a richer feature set: stricter SQL compliance, transactional DDL, native row-level security, JSONB, and a large extension ecosystem. MySQL is a pure relational database optimized for speed on simple workloads and deep integration with the LAMP stack. PostgreSQL is better for complex applications and data integrity; MySQL is better for simple web apps and CMS platforms.
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