PostgreSQL vs MySQL in 2026: Which Relational Database to Choose

Choosing between PostgreSQL and MySQL remains one of the most consequential infrastructure decisions a development team can make in 2026. Both databases have evolved dramatically over the past few years — PostgreSQL has surged to become the most popular database among professional developers, while MySQL continues to power millions of production applications and has shipped a wave of innovation releases with features like JSON duality views and native vector support.

This article is a comprehensive, data-driven comparison of PostgreSQL 17/18 and MySQL 9.x for backend engineers, database administrators, and engineering leads evaluating which relational database to adopt or migrate to in 2026. We cover architecture, performance benchmarks, data type support, replication, extensibility, cloud ecosystem, and provide concrete recommendations for different project types. All claims are backed by benchmark data and survey results from 2025-2026.

Current State and Adoption

PostgreSQL — The Developer Favorite

PostgreSQL has been in development since 1986, originating from the POSTGRES project at UC Berkeley. It is a fully open-source, community-driven project with no single corporate owner. Development is coordinated through the PostgreSQL Global Development Group, with contributions from companies including EDB, Crunchy Data, Microsoft, Google, and AWS.

In the 2025 Stack Overflow Developer Survey, PostgreSQL achieved what can only be described as a triple crown: it is the most used database (55.6% of professional developers, up from 48.7% in 2024), the most admired (65.5%), and the most wanted for three consecutive years. The gap with second-place MySQL has widened to 15 percentage points overall and 18.6 points among professional developers specifically.

PostgreSQL 17 was released in September 2024 with major improvements including the SQL/JSON JSON_TABLE command, incremental backup support, overhauled vacuum memory management, up to 2x faster COPY operations for bulk data loading, enhanced MERGE with RETURNING clause, and significant improvements to logical replication with failover slot synchronization. PostgreSQL 18, currently in development, introduces an asynchronous I/O (AIO) subsystem promising 2-3x improvements for sequential scans, bitmap heap scans, and vacuum operations.

MySQL — The Web Application Workhorse

MySQL was created in 1995 by MySQL AB and is now owned by Oracle Corporation. It remains one of the most deployed databases globally, forming the backbone of the traditional LAMP stack and powering platforms like WordPress, Shopify, and Facebook’s TAO storage layer.

MySQL has adopted a new release cadence starting with version 9.0: innovation releases ship every three months, while Long-Term Support (LTS) releases receive updates for eight years. MySQL 8.4 LTS remains the recommended production version for stability, while the innovation track (9.0 through 9.5 as of late 2025) delivers cutting-edge features.

Notable recent additions include a native VECTOR data type (MySQL 9.0) for AI and machine learning workloads, JavaScript stored programs in MySQL Enterprise Edition, JSON duality views (MySQL 9.4) that expose relational data as JSON documents, enhanced role management (MySQL 9.5), and encryption enabled by default for all replication connections. GTID mode is now enabled by default, simplifying replication configuration.

Architecture and Design Philosophy

The architectural differences between PostgreSQL and MySQL reflect fundamentally different design philosophies that influence nearly every aspect of their behavior.

Process Model vs Thread Model

PostgreSQL uses a multi-process architecture where each client connection spawns a dedicated backend process. These processes communicate via shared memory. This design provides strong isolation — a crash in one connection does not affect others — but consumes more memory per connection (typically 5-10 MB per idle connection). Connection pooling via PgBouncer or the built-in connection pooler in PostgreSQL 18 is recommended for high-connection-count workloads.

MySQL uses a multi-threaded architecture where each client connection is handled by a thread within a single server process. Threads share the same memory space, making MySQL more memory-efficient per connection. However, this means a severe bug in one thread can theoretically affect the entire server process.

MVCC Implementation

Both databases implement Multi-Version Concurrency Control (MVCC), but their approaches differ significantly:

PostgreSQL stores old row versions directly in the table heap. When a row is updated, the old version remains in the table until VACUUM reclaims it. This means updates effectively create a new row and mark the old one as dead. The advantage is that reads never block writes and vice versa. The disadvantage is table bloat requiring regular vacuuming — though PostgreSQL 17’s overhauled vacuum memory management has significantly improved this process.

MySQL (InnoDB) uses a rollback segment (undo log) to store old row versions. The current version stays in place, and the undo log allows the database to reconstruct previous versions when needed. This approach generally produces less table bloat but can lead to long undo history issues under heavy write loads with long-running transactions.

-- PostgreSQL: Check table bloat and dead tuples
SELECT relname, n_dead_tup, n_live_tup,
       round(n_dead_tup::numeric / NULLIF(n_live_tup, 0) * 100, 2) AS dead_ratio
FROM pg_stat_user_tables
WHERE n_dead_tup > 1000
ORDER BY n_dead_tup DESC;

-- MySQL: Check InnoDB undo log history
SELECT NAME, SUBSYSTEM, COUNT
FROM information_schema.INNODB_METRICS
WHERE NAME LIKE 'trx_rseg_history_len';

SQL Standards Compliance

PostgreSQL has historically prioritized strict SQL standards compliance. It supports a wider range of SQL:2023 features including MERGE, window functions with advanced frame specifications, lateral joins, common table expressions with recursive and materialized options, and full JSON_TABLE support as of version 17.

MySQL has improved its standards compliance significantly with version 8.0 and beyond, adding CTEs, window functions, and MERGE-like functionality via INSERT ... ON DUPLICATE KEY UPDATE. However, it still has MySQL-specific quirks such as implicit type conversions, non-standard GROUP BY behavior (the ONLY_FULL_GROUP_BY mode addresses this but is not enforced in all configurations), and differences in how NULL values are handled in unique indexes.

Performance Benchmarks

Performance comparisons between PostgreSQL and MySQL must be nuanced — the winner depends heavily on workload type, configuration, and data volume.

Read Performance

For simple point queries on indexed columns, MySQL has traditionally held a slight edge of 15-25% in synthetic benchmarks. However, recent benchmarks from 2025-2026 paint a more nuanced picture.

In testing conducted by BinaryIgor with properly configured instances, PostgreSQL achieved 23,441 queries per second (QPS) versus MySQL’s 6,300 QPS for high-volume read workloads — a 3.72x throughput advantage for PostgreSQL. Mean latency was 9.34x lower for PostgreSQL, and 99th percentile latency was 8.77x lower.

For complex analytical queries involving multiple joins, aggregations, and subqueries, PostgreSQL consistently outperforms MySQL due to its more sophisticated query optimizer and support for parallel query execution across multiple CPU cores.

Write Performance

For execution time on 1 million records, PostgreSQL’s execution time ranged from 0.6 ms to 0.8 ms, while MySQL’s ranged from 9 ms to 12 ms — making PostgreSQL approximately 13x faster in these specific tests.

Under concurrent workloads, PostgreSQL maintained stable performance with SELECT query latency of 0.7-0.9 ms during simultaneous inserts, while MySQL’s performance degraded to 7-13 ms under the same conditions.

Bulk Operations

PostgreSQL 17’s improved COPY command delivers up to 2x performance improvement when exporting large rows. The new ON_ERROR option allows imports to continue past individual row errors, which is particularly valuable for ETL pipelines.

-- PostgreSQL: Bulk load with error handling (PG 17+)
COPY large_table FROM '/data/import.csv'
WITH (FORMAT csv, HEADER true, ON_ERROR 'ignore');

-- MySQL: Bulk load equivalent
LOAD DATA INFILE '/data/import.csv'
INTO TABLE large_table
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;

Key Benchmark Summary

Metric	PostgreSQL	MySQL	Winner
Simple SELECT (indexed)	Competitive	Slight edge (15-25%)	MySQL (marginal)
Complex analytical queries	Superior optimizer, parallel execution	Single-threaded execution	PostgreSQL
High-volume reads (QPS)	23,441 QPS	6,300 QPS	PostgreSQL (3.72x)
Write throughput (1M rows)	0.6-0.8 ms	9-12 ms	PostgreSQL (13x)
Concurrent read+write stability	0.7-0.9 ms latency	7-13 ms latency	PostgreSQL
Bulk data loading (COPY/LOAD)	2x improvement in PG 17	Stable	PostgreSQL
Memory per connection	5-10 MB	1-4 MB	MySQL

Note: Benchmark results vary significantly based on hardware, configuration, data volume, and query complexity. Always benchmark with your specific workload.

Data Types and JSON Support

Native Data Types

PostgreSQL offers a significantly richer set of built-in data types:

• Array types: Native arrays of any data type
• JSONB: Binary JSON with indexing and operators
• hstore: Key-value store
• Range types: int4range, tsrange, daterange, etc.
• Network types: inet, cidr, macaddr
• Geometric types: point, line, polygon, circle
• UUID: Native UUID type with generation functions
• Full-text search types: tsvector, tsquery

MySQL’s data type system is more traditional, focusing on numeric, string, date/time, and spatial types. The VECTOR data type added in MySQL 9.0 is a notable recent addition.

JSON Support: JSONB vs JSON

This is one of the most significant differentiators in 2026. PostgreSQL offers two JSON types: json (stores text verbatim) and jsonb (stores decomposed binary format). MySQL offers a single JSON type that stores documents in an optimized binary format.

-- PostgreSQL: JSONB with GIN indexing
CREATE TABLE products (
    id SERIAL PRIMARY KEY,
    data JSONB NOT NULL
);

CREATE INDEX idx_products_data ON products USING GIN (data);

-- Query with containment operator (uses GIN index)
SELECT * FROM products WHERE data @> '{"category": "electronics"}';

-- Path query with jsonpath
SELECT * FROM products
WHERE data @? '$.tags[*] ? (@ == "sale")';

-- JSON_TABLE (PostgreSQL 17+)
SELECT jt.*
FROM products p,
     JSON_TABLE(p.data, '$.variants[*]' COLUMNS (
         sku TEXT PATH '$.sku',
         price NUMERIC PATH '$.price',
         stock INTEGER PATH '$.stock'
     )) AS jt;

-- MySQL: JSON with virtual column indexing
CREATE TABLE products (
    id INT AUTO_INCREMENT PRIMARY KEY,
    data JSON NOT NULL,
    category VARCHAR(100) GENERATED ALWAYS AS (data->>'$.category') VIRTUAL
);

CREATE INDEX idx_category ON products (category);

-- Query using generated column index
SELECT * FROM products WHERE category = 'electronics';

-- JSON_TABLE in MySQL
SELECT jt.*
FROM products p,
     JSON_TABLE(p.data, '$.variants[*]' COLUMNS (
         sku VARCHAR(50) PATH '$.sku',
         price DECIMAL(10,2) PATH '$.price',
         stock INT PATH '$.stock'
     )) AS jt;

-- MySQL 9.4: JSON duality views
CREATE JSON DUALITY VIEW product_json AS
SELECT JSON_OBJECT('id': id, 'name': name, 'price': price)
FROM products;

The critical difference is indexing. PostgreSQL supports GIN indexes directly on JSONB columns, enabling efficient queries with containment (@>), existence (?), and path operators without creating separate columns. MySQL requires virtual/generated columns to index specific JSON paths — you cannot index the JSON column itself. MySQL’s approach works well for known query patterns but lacks the flexibility of PostgreSQL’s GIN indexes for ad-hoc queries on semi-structured data.

MySQL 9.4’s JSON duality views are an interesting addition, allowing relational data to be exposed as JSON documents, but PostgreSQL’s JSONB remains more mature and performant for JSON-heavy workloads.

Replication and High Availability

PostgreSQL Replication

PostgreSQL supports multiple replication strategies:

• Streaming replication (physical): WAL-based byte-for-byte replication of the entire database cluster. Supports synchronous and asynchronous modes.
• Logical replication: Table-level selective replication that allows different schema versions on publisher and subscriber. PostgreSQL 17 added failover slot synchronization for seamless switchover.
• Cascading replication: Replicas can serve as upstream for other replicas, reducing load on the primary.
• Bidirectional replication (BDR): Available through extensions like pgEdge and EDB BDR for multi-master setups.

High availability solutions include Patroni (etcd-based automatic failover), repmgr, and pg_auto_failover.

# Example Patroni configuration for PostgreSQL HA
scope: postgres-cluster
namespace: /db/
name: node1

restapi:
  listen: 0.0.0.0:8008
  connect_address: 192.168.1.10:8008

etcd3:
  hosts: 192.168.1.20:2379,192.168.1.21:2379,192.168.1.22:2379

bootstrap:
  dcs:
    ttl: 30
    loop_wait: 10
    retry_timeout: 10
    maximum_lag_on_failover: 1048576
    postgresql:
      use_pg_rewind: true
      parameters:
        max_connections: 200
        shared_buffers: 4GB
        wal_level: replica
        max_wal_senders: 10
        max_replication_slots: 10
        hot_standby: "on"

MySQL Replication

MySQL offers its own set of replication options:

• Asynchronous replication: Traditional MySQL replication using binary logs. Simple to set up but risks data loss on primary failure.
• Semi-synchronous replication: At least one replica must acknowledge receipt of events before the primary commits. Balances safety and performance.
• Group Replication: MySQL’s built-in multi-master clustering solution with automatic conflict detection and resolution.
• InnoDB Cluster: A complete HA solution combining Group Replication, MySQL Shell, and MySQL Router for automatic failover and load balancing.
• InnoDB ClusterSet: Cross-datacenter disaster recovery by linking multiple InnoDB Clusters.

-- MySQL: Setting up InnoDB Cluster with MySQL Shell
-- On each node:
dba.configureInstance('root@node1:3306', {clusterAdmin: 'clusteradmin'});
dba.configureInstance('root@node2:3306', {clusterAdmin: 'clusteradmin'});
dba.configureInstance('root@node3:3306', {clusterAdmin: 'clusteradmin'});

-- Create the cluster from the primary
var cluster = dba.createCluster('myCluster');
cluster.addInstance('clusteradmin@node2:3306');
cluster.addInstance('clusteradmin@node3:3306');
cluster.status();

MySQL’s InnoDB Cluster provides a more integrated, out-of-the-box HA experience, while PostgreSQL’s HA ecosystem relies on third-party tools like Patroni but offers more flexibility in configuration.

Extensibility and Ecosystem

PostgreSQL Extensions

PostgreSQL’s extension system is arguably its greatest competitive advantage. Extensions can add new data types, index types, operators, functions, procedural languages, and even modify core behavior — all without forking the database.

Key extensions in 2026 include:

Extension	Purpose	Significance
pgvector	Vector similarity search	Essential for AI/ML applications; supports IVFFlat and HNSW indexes
PostGIS	Geospatial data processing	Industry standard for GIS; no MySQL equivalent of comparable depth
TimescaleDB	Time-series data	Turns PostgreSQL into a purpose-built time-series database
pg_cron	Job scheduling	Cron-like scheduling within the database
Citus	Distributed PostgreSQL	Horizontal sharding and distributed queries
pg_stat_statements	Query performance monitoring	Built-in query profiling and statistics
pg_partman	Partition management	Automated partition creation and maintenance
pgAudit	Audit logging	Session and object-level audit logging for compliance

-- PostgreSQL: Using pgvector for AI embeddings
CREATE EXTENSION vector;

CREATE TABLE documents (
    id SERIAL PRIMARY KEY,
    content TEXT,
    embedding vector(1536)  -- OpenAI ada-002 dimensions
);

CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

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

MySQL Plugins and Ecosystem

MySQL’s extension story is more limited. While MySQL supports plugins and storage engines, the extension API is narrower. Key ecosystem components include:

• MySQL Router: Connection routing and load balancing
• MySQL Shell: Advanced administration and scripting
• MySQL Enterprise features: audit, encryption, firewall (commercial license)
• HeatWave: Oracle’s cloud-native in-memory analytics and ML engine with vector processing capabilities
• ProxySQL: Third-party connection pooling and query routing

MySQL’s ecosystem strength lies more in the application layer — ORMs, drivers, and frameworks that have decades of MySQL optimization. WordPress, Drupal, Magento, and thousands of SaaS platforms are built on MySQL, creating a deep ecosystem of operational knowledge.

Cloud and Managed Services

Both databases are available as fully managed services from every major cloud provider:

Cloud Provider	PostgreSQL Service	MySQL Service
AWS	RDS for PostgreSQL, Aurora PostgreSQL	RDS for MySQL, Aurora MySQL
Google Cloud	Cloud SQL for PostgreSQL, AlloyDB	Cloud SQL for MySQL
Azure	Azure Database for PostgreSQL Flexible Server	Azure Database for MySQL Flexible Server
DigitalOcean	Managed PostgreSQL	Managed MySQL
Neon	Serverless PostgreSQL (branching)	N/A
PlanetScale	N/A	Serverless MySQL (branching, Vitess-based)
Supabase	Managed PostgreSQL + Auth + APIs	N/A
Aiven	Managed PostgreSQL	Managed MySQL

Notable trends in 2026:

• AWS Aurora supports both PostgreSQL and MySQL compatibility, with Aurora PostgreSQL gaining Limitless (distributed) mode.
• Google AlloyDB is a PostgreSQL-compatible database with significantly improved analytical performance.
• Neon and Supabase have built PostgreSQL-only platforms with developer-centric features like database branching and instant provisioning.
• PlanetScale offers MySQL-compatible serverless databases built on Vitess, providing horizontal scaling.

PostgreSQL has a slight edge in cloud innovation, with more specialized managed offerings (AlloyDB, Neon, Supabase) building on its architecture. However, MySQL’s massive installed base ensures continued investment from all cloud providers.

Security Features

PostgreSQL Security

PostgreSQL provides a comprehensive security model:

• Row-Level Security (RLS): Fine-grained access control at the row level, essential for multi-tenant SaaS applications.
• Column-level privileges: Grant SELECT on specific columns.
• Security labels: Integration with SELinux for mandatory access control.
• SSL/TLS: Full support with client certificate authentication.
• SCRAM-SHA-256: Default authentication method since PostgreSQL 14.
• pg_hba.conf: Flexible host-based authentication configuration.

-- PostgreSQL: Row-Level Security for multi-tenant application
ALTER TABLE orders ENABLE ROW LEVEL SECURITY;

CREATE POLICY tenant_isolation ON orders
    USING (tenant_id = current_setting('app.current_tenant')::int);

-- Each tenant only sees their own data
SET app.current_tenant = '42';
SELECT * FROM orders;  -- Only returns orders for tenant 42

MySQL Security

MySQL’s security features include:

• Roles: User role management (improved in MySQL 9.5 with mandatory roles).
• Data masking: Available in MySQL Enterprise Edition.
• Encryption: Table-level encryption (TDE) and replication encryption enabled by default in MySQL 9.5.
• Authentication plugins: Multiple authentication methods including caching_sha2_password (default).
• MySQL Firewall: Enterprise feature for SQL injection prevention.
• Audit logging: Enterprise feature for compliance.

MySQL’s notable limitation is the lack of native Row-Level Security comparable to PostgreSQL’s. Achieving similar functionality requires application-level logic or stored procedures, which is a significant drawback for multi-tenant applications.

Developer Experience and Tooling

Query Optimization and Debugging

PostgreSQL’s EXPLAIN ANALYZE is more detailed and informative. PostgreSQL 17 added I/O timing details (local read/write), and new SERIALIZE and MEMORY options:

-- PostgreSQL: Detailed query analysis
EXPLAIN (ANALYZE, BUFFERS, TIMING, SERIALIZE, MEMORY)
SELECT c.name, COUNT(o.id), SUM(o.total)
FROM customers c
JOIN orders o ON c.id = o.customer_id
WHERE o.created_at > '2025-01-01'
GROUP BY c.name
HAVING SUM(o.total) > 1000;

MySQL’s EXPLAIN ANALYZE (added in MySQL 8.0.18) provides similar but less granular information. MySQL 9.0 added the ability to save EXPLAIN ANALYZE output into a user variable.

Procedural Languages

PostgreSQL supports multiple procedural languages:

• PL/pgSQL (built-in)
• PL/Python
• PL/Perl
• PL/V8 (JavaScript, community extension)
• PL/Rust (via pgrx)

MySQL supports:

• MySQL stored procedures (SQL/PSM dialect)
• JavaScript stored programs (MySQL 9.0, Enterprise Edition only)

Client Tooling

Both databases have rich client ecosystems. PostgreSQL’s psql is a powerful interactive terminal with advanced features like \watch for repeated query execution, \crosstabview for pivot tables, and tab completion. MySQL’s mysql client is simpler but adequate, while MySQL Shell (mysqlsh) provides advanced scripting in JavaScript and Python.

Popular GUI tools like DBeaver, DataGrip, pgAdmin (PostgreSQL), and MySQL Workbench serve both ecosystems well.

When to Choose PostgreSQL

Choose PostgreSQL when your project requires:

1. Complex queries and analytics: PostgreSQL’s query optimizer, parallel query execution, CTEs, and window functions handle complex analytical workloads better.
2. JSON-heavy workloads: JSONB with GIN indexing provides superior performance for semi-structured data without separate indexing columns.
3. AI/ML integration: pgvector has become the default choice for embedding storage and similarity search. If you are building AI features, PostgreSQL saves you from running a separate vector database.
4. Geospatial applications: PostGIS is the industry standard and has no MySQL equivalent of comparable depth.
5. Multi-tenant SaaS: Row-Level Security makes tenant isolation straightforward at the database level.
6. Strict data integrity: PostgreSQL’s stricter type checking, constraint enforcement, and SQL standards compliance reduce data quality issues.
7. Extension-driven architectures: If you need TimescaleDB, Citus, or custom extensions, PostgreSQL’s extension system is unmatched.
8. New greenfield projects: With 55.6% developer adoption and growing, PostgreSQL is the safe default for new projects in 2026.

When to Choose MySQL

Choose MySQL when your project involves:

1. Legacy LAMP stack applications: WordPress, Drupal, Magento, and thousands of PHP frameworks are optimized for MySQL.
2. Simple, high-throughput read workloads: For straightforward indexed lookups with simple schemas, MySQL remains highly efficient and may edge out PostgreSQL.
3. Existing MySQL expertise: If your team has deep MySQL operational knowledge, the productivity advantage of staying with MySQL is real.
4. InnoDB Cluster for HA: MySQL’s integrated HA solution is simpler to deploy than PostgreSQL’s Patroni-based setups for teams without dedicated DBAs.
5. Oracle ecosystem integration: If you are already invested in Oracle’s ecosystem, MySQL Enterprise and HeatWave provide seamless integration.
6. PlanetScale or Vitess-based scaling: For applications requiring Vitess-style horizontal sharding with MySQL compatibility.
7. WordPress/WooCommerce at scale: The WordPress ecosystem is deeply tied to MySQL, and migration to PostgreSQL is nontrivial.

Comprehensive Comparison Table

Criteria	PostgreSQL 17/18	MySQL 9.x (InnoDB)
License	PostgreSQL License (permissive, MIT-like)	GPL v2 (Community) / Commercial (Enterprise)
Architecture	Multi-process	Multi-threaded
MVCC	Heap-based (requires VACUUM)	Undo log-based
SQL Compliance	Very high (SQL:2023)	Good (improved since 8.0)
JSON	JSONB with GIN indexing	JSON with virtual column indexing
Vector Search	pgvector (mature, HNSW+IVFFlat)	Native VECTOR type (9.0, basic)
Row-Level Security	Built-in	Not available natively
Extensions	Rich ecosystem (pgvector, PostGIS, TimescaleDB, Citus)	Limited plugin API
Replication	Streaming + Logical + BDR	Async + Semi-sync + Group Replication
Built-in HA	Requires Patroni/repmgr	InnoDB Cluster (integrated)
Parallel Queries	Yes (since PG 9.6)	Limited
Partitioning	Declarative (since PG 10)	Range, List, Hash, Key
Full-Text Search	Built-in (tsvector/tsquery)	Built-in (InnoDB FTS)
GIS Support	PostGIS (industry standard)	Basic spatial functions
SO 2025 Usage	55.6% (professional devs: 58.2%)	40.5% (professional devs: 39.6%)
Cloud Services	RDS, Aurora, AlloyDB, Neon, Supabase	RDS, Aurora, PlanetScale, Cloud SQL

Conclusion

In 2026, PostgreSQL has clearly become the default choice for new projects. The 2025 Stack Overflow Survey data — 55.6% overall adoption, 65.5% admiration rate, most-wanted database for three consecutive years — reflects a genuine shift in the industry, not just hype. PostgreSQL’s combination of strict SQL compliance, superior JSON handling, the unmatched extension ecosystem (pgvector for AI, PostGIS for GIS, TimescaleDB for time-series), and Row-Level Security makes it the more versatile and future-proof option.

However, MySQL is far from irrelevant. It powers a massive installed base, offers a more integrated HA story with InnoDB Cluster, and continues to innovate with JSON duality views, native vector types, and improved replication security. If you are maintaining existing MySQL applications, there is rarely a compelling reason to migrate away. MySQL’s ecosystem in the PHP and WordPress world remains dominant.

For new projects in 2026, our recommendation is straightforward: choose PostgreSQL unless you have a specific, concrete reason to choose MySQL (legacy compatibility, team expertise, or a specific MySQL-only platform like PlanetScale). PostgreSQL’s developer momentum, extensibility, and feature breadth give it a durable advantage that is only accelerating.

For existing MySQL deployments, evaluate migration on a case-by-case basis. The cost and risk of migration must be weighed against the benefits of PostgreSQL’s richer feature set. Tools like pgLoader, AWS DMS, and pgcopydb can streamline the process, but schema and query differences will require careful testing.

Sources

1. PostgreSQL 17 Release Notes — https://www.postgresql.org/about/news/postgresql-17-released-2936/
2. MySQL 9.0 Release Notes — https://dev.mysql.com/doc/relnotes/mysql/9.0/en/
3. MySQL 9.4 Release Notes — https://dev.mysql.com/doc/relnotes/mysql/9.4/en/news-9-4-0.html
4. Stack Overflow 2025 Developer Survey — Technology — https://survey.stackoverflow.co/2025/technology
5. MySQL vs PostgreSQL Performance Benchmarks by BinaryIgor — https://binaryigor.com/mysql-vs-postgresql-performance.html
6. PostgreSQL vs MySQL: Bytebase Comparison — https://www.bytebase.com/blog/postgres-vs-mysql/
7. PostgreSQL vs MySQL: JSON Support — https://www.bytebase.com/blog/postgres-vs-mysql-json-support/
8. PostgreSQL vs MySQL: EnterpriseDB In-depth Comparison — https://www.enterprisedb.com/blog/postgresql-vs-mysql-360-degree-comparison-syntax-performance-scalability-and-features