MySQL vs PostgreSQL: Which Database Should You Use in 2026?

Quick Comparison Table

Before diving into the details, here is a high-level comparison of MySQL and PostgreSQL across the features that matter most:

Feature	MySQL	PostgreSQL
License	GPL v2 (dual license with commercial)	PostgreSQL License (MIT-like, fully permissive)
Default Port	3306	5432
ACID Compliance	Yes (InnoDB engine)	Yes (built-in, all tables)
JSON Support	JSON type with functional indexes	JSON and JSONB with GIN indexes
Replication	Built-in async/semi-sync, Group Replication	Streaming replication, logical replication
Max DB Size	Limited by storage engine (InnoDB: 64 TB per table)	Unlimited (limited only by available storage)
Best For	Simple web apps, read-heavy workloads, CMS platforms	Complex queries, data integrity, analytics, JSONB workloads
Community	Oracle-managed, large legacy community	Community-driven, fastest-growing RDBMS

Both are excellent relational databases. The right choice depends on your workload, team expertise, and the features you need. Let us break down every difference that matters.

History and Philosophy

Understanding where MySQL and PostgreSQL came from explains why they make different design decisions today.

MySQL: speed and simplicity

MySQL was created in 1995 by Michael Widenius and David Axmark in Sweden. The name comes from Widenius's daughter, My. It was designed from the start to be fast, easy to set up, and practical for web applications. MySQL became the "M" in the LAMP stack (Linux, Apache, MySQL, PHP) and powered the first generation of the dynamic web.

Sun Microsystems acquired MySQL AB in 2008, and Oracle acquired Sun in 2010. Oracle's ownership has been controversial. Some developers forked it as MariaDB over concerns about Oracle's stewardship. In practice, Oracle has continued to invest in MySQL, adding features like window functions, CTEs, and JSON support in MySQL 8.0.

MySQL's philosophy: make the common case fast and simple. It sacrifices some SQL standard compliance and advanced features in favor of performance and ease of use.

PostgreSQL: standards and extensibility

PostgreSQL traces its roots to 1986 at UC Berkeley, where Professor Michael Stonebraker led the POSTGRES project as a successor to Ingres. The project became PostgreSQL in 1996 when SQL support was added. That is 40 years of continuous development.

PostgreSQL has always been community-driven with no single corporate owner. A core team of volunteers and contributors from companies like EnterpriseDB, Crunchy Data, and Supabase maintain it. This means no licensing concerns and no risk of a single company changing direction.

PostgreSQL's philosophy: do it correctly according to the SQL standard, then make it extensible. It implements nearly every feature in the SQL standard and adds powerful extensions like PostGIS, pg_trgm, and custom types. It is sometimes called "the most advanced open-source relational database" and that label has been earned.

Syntax Differences

If you are migrating between MySQL and PostgreSQL, or writing queries that need to work on both, these are the syntax differences you will encounter most often. For a full reference, see the MySQL cheat sheet and PostgreSQL cheat sheet.

Auto-incrementing primary keys

MySQL uses AUTO_INCREMENT. PostgreSQL uses SERIAL or the SQL-standard GENERATED ALWAYS AS IDENTITY:

-- MySQL
CREATE TABLE users (
    id INT AUTO_INCREMENT PRIMARY KEY,
    name VARCHAR(100) NOT NULL
);

-- PostgreSQL (traditional)
CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    name VARCHAR(100) NOT NULL
);

-- PostgreSQL (SQL standard, recommended)
CREATE TABLE users (
    id INT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
    name VARCHAR(100) NOT NULL
);

Identifier quoting

MySQL uses backticks for reserved words. PostgreSQL uses double quotes (the SQL standard):

-- MySQL
SELECT `order`, `group`, `select` FROM `table`;

-- PostgreSQL
SELECT "order", "group", "select" FROM "table";

NULL handling functions

MySQL has IFNULL(). PostgreSQL uses COALESCE() (which MySQL also supports, and is the SQL standard):

-- MySQL
SELECT IFNULL(phone, 'N/A') AS phone FROM customers;

-- PostgreSQL (and SQL standard)
SELECT COALESCE(phone, 'N/A') AS phone FROM customers;

String concatenation

-- MySQL
SELECT CONCAT(first_name, ' ', last_name) AS full_name FROM users;

-- PostgreSQL (both work)
SELECT first_name || ' ' || last_name AS full_name FROM users;
SELECT CONCAT(first_name, ' ', last_name) AS full_name FROM users;

GROUP_CONCAT vs STRING_AGG

-- MySQL: aggregate strings with GROUP_CONCAT
SELECT department,
       GROUP_CONCAT(name ORDER BY name SEPARATOR ', ') AS members
FROM employees
GROUP BY department;

-- PostgreSQL: use STRING_AGG
SELECT department,
       STRING_AGG(name, ', ' ORDER BY name) AS members
FROM employees
GROUP BY department;

LIMIT with offset

-- MySQL shorthand
SELECT * FROM products LIMIT 10, 20;  -- skip 10, take 20

-- PostgreSQL (and MySQL also supports this)
SELECT * FROM products LIMIT 20 OFFSET 10;

Boolean types

-- MySQL: BOOLEAN is alias for TINYINT(1)
-- Values: 0 and 1 (or TRUE/FALSE which map to 0/1)
SELECT * FROM users WHERE is_active = 1;

-- PostgreSQL: native BOOLEAN type
-- Values: TRUE, FALSE, NULL
SELECT * FROM users WHERE is_active = TRUE;

ENUM handling

-- MySQL: ENUM is a column type
CREATE TABLE orders (
    id INT AUTO_INCREMENT PRIMARY KEY,
    status ENUM('pending', 'shipped', 'delivered', 'cancelled')
);

-- PostgreSQL: ENUM is a custom type (created separately)
CREATE TYPE order_status AS ENUM ('pending', 'shipped', 'delivered', 'cancelled');
CREATE TABLE orders (
    id SERIAL PRIMARY KEY,
    status order_status
);

These syntax differences matter when migrating from MySQL to PostgreSQL or from PostgreSQL to MySQL. AI2SQL handles these dialect differences automatically when generating queries.

Data Types

Both databases cover the standard SQL types (INT, VARCHAR, TEXT, TIMESTAMP, DECIMAL), but PostgreSQL offers a significantly richer type system.

PostgreSQL advantages

JSONB. PostgreSQL's binary JSON type stores JSON in an efficient, indexable format. You can create GIN indexes on JSONB columns and query nested keys with blazing speed:

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

CREATE INDEX idx_events_data ON events USING GIN (data);

-- Query nested JSON efficiently
SELECT data->>'event_name' AS event,
       data->'properties'->>'source' AS source
FROM events
WHERE data @> '{"event_name": "signup"}'
  AND data->'properties'->>'country' = 'US';

Arrays. PostgreSQL supports array columns natively, which eliminates the need for junction tables in many cases:

-- PostgreSQL: array columns
CREATE TABLE articles (
    id SERIAL PRIMARY KEY,
    title TEXT NOT NULL,
    tags TEXT[] DEFAULT '{}'
);

INSERT INTO articles (title, tags)
VALUES ('MySQL vs PostgreSQL', ARRAY['database', 'comparison', 'sql']);

-- Query array contents
SELECT title FROM articles WHERE 'database' = ANY(tags);
SELECT title FROM articles WHERE tags @> ARRAY['sql', 'comparison'];

hstore. A key-value store within a column, useful for semi-structured metadata:

-- PostgreSQL: hstore for key-value data
CREATE EXTENSION hstore;

ALTER TABLE products ADD COLUMN attributes hstore;
UPDATE products SET attributes = 'color => red, size => large'
WHERE id = 1;

SELECT name, attributes->'color' AS color FROM products;

Network types. Native INET and CIDR types for IP addresses and network ranges:

-- PostgreSQL: network types
CREATE TABLE access_log (
    id SERIAL PRIMARY KEY,
    client_ip INET NOT NULL,
    subnet CIDR
);

-- Query by subnet
SELECT * FROM access_log WHERE client_ip << '192.168.1.0/24';

Custom composite types. Define your own structured types:

-- PostgreSQL: custom types
CREATE TYPE address AS (
    street TEXT,
    city TEXT,
    state VARCHAR(2),
    zip VARCHAR(10)
);

CREATE TABLE customers (
    id SERIAL PRIMARY KEY,
    name TEXT NOT NULL,
    home_address address,
    work_address address
);

MySQL advantages

ENUM and SET. MySQL's ENUM type is simpler to define (inline with the column) and SET allows multiple values from a predefined list:

-- MySQL: SET type (multiple values from a list)
CREATE TABLE user_preferences (
    id INT AUTO_INCREMENT PRIMARY KEY,
    notifications SET('email', 'sms', 'push', 'in-app')
);

INSERT INTO user_preferences (notifications) VALUES ('email,push');
SELECT * FROM user_preferences WHERE FIND_IN_SET('email', notifications);

Simpler type system. MySQL's type system is easier to learn. PostgreSQL's advanced types are powerful but add complexity. If your application only needs standard types, MySQL's simplicity is an advantage.

Performance

Performance comparisons between MySQL and PostgreSQL depend heavily on the workload. Blanket statements like "MySQL is faster" or "PostgreSQL is faster" are misleading. Here is what actually matters:

Read-heavy workloads: MySQL wins

MySQL was engineered for fast reads. Simple SELECT queries, point lookups by primary key, and basic WHERE clauses run marginally faster on MySQL. This is why MySQL dominates in CMS platforms like WordPress and Drupal, where the primary operation is reading pages from the database.

MySQL's query cache (deprecated in 8.0 but replaced by other optimizations) and InnoDB's buffer pool are highly tuned for read patterns. For a blog or content site with 95% reads and 5% writes, MySQL is an excellent choice.

Write-heavy and complex queries: PostgreSQL wins

PostgreSQL's MVCC (Multi-Version Concurrency Control) implementation handles concurrent writes more gracefully. It does not lock rows for reads, and its approach to transaction isolation is more sophisticated.

For complex queries involving multiple joins, subqueries, CTEs, and window functions, PostgreSQL's query planner is more advanced. It considers more execution strategies and often produces better plans for complex queries:

-- Complex analytical query: PostgreSQL's planner excels here
WITH monthly_revenue AS (
    SELECT
        date_trunc('month', created_at) AS month,
        SUM(amount) AS revenue,
        COUNT(DISTINCT customer_id) AS customers
    FROM orders
    WHERE status = 'completed'
    GROUP BY date_trunc('month', created_at)
)
SELECT
    month,
    revenue,
    customers,
    revenue / customers AS arpu,
    LAG(revenue) OVER (ORDER BY month) AS prev_month,
    ROUND((revenue - LAG(revenue) OVER (ORDER BY month))
        / LAG(revenue) OVER (ORDER BY month) * 100, 1) AS growth_pct
FROM monthly_revenue
ORDER BY month DESC;

Connection handling

MySQL uses one thread per connection, which is lighter on resources. PostgreSQL uses one process per connection, which is heavier but more isolated. For applications with thousands of connections, PostgreSQL needs a connection pooler like PgBouncer. MySQL handles high connection counts more naturally.

Indexing

Both support B-tree indexes (the default). PostgreSQL also supports GIN (for full-text search and JSONB), GiST (for geometric and range data), SP-GiST (for non-balanced data structures), and BRIN (for large sequential datasets like time-series). These specialized indexes give PostgreSQL a significant advantage for non-trivial access patterns:

-- PostgreSQL: specialized index types
-- GIN index for full-text search
CREATE INDEX idx_articles_search ON articles USING GIN (to_tsvector('english', title || ' ' || body));

-- BRIN index for time-series data (very compact)
CREATE INDEX idx_events_time ON events USING BRIN (created_at);

-- GiST index for geographic queries (PostGIS)
CREATE INDEX idx_locations_geom ON locations USING GIST (geom);

Advanced Features

This is where PostgreSQL pulls ahead significantly. While MySQL 8.0 closed the gap on many features, PostgreSQL still offers capabilities that MySQL does not have.

PostgreSQL-exclusive features

CTEs with DML (writable CTEs). PostgreSQL lets you use INSERT, UPDATE, and DELETE inside CTEs, which enables complex operations in a single statement:

-- PostgreSQL: writable CTE
WITH deleted_inactive AS (
    DELETE FROM users
    WHERE last_login < NOW() - INTERVAL '2 years'
    RETURNING id, email, last_login
)
INSERT INTO archived_users (original_id, email, last_login, archived_at)
SELECT id, email, last_login, NOW()
FROM deleted_inactive;

LATERAL JOIN. Allows a subquery in the FROM clause to reference columns from preceding tables. This is extremely powerful for top-N-per-group queries:

-- PostgreSQL: LATERAL JOIN for top 3 orders per customer
SELECT c.name, recent.total, recent.created_at
FROM customers c
CROSS JOIN LATERAL (
    SELECT o.total, o.created_at
    FROM orders o
    WHERE o.customer_id = c.id
    ORDER BY o.created_at DESC
    LIMIT 3
) recent;

Full-text search with tsvector. PostgreSQL has built-in full-text search that rivals dedicated search engines for many use cases:

-- PostgreSQL: built-in full-text search
ALTER TABLE articles ADD COLUMN search_vector tsvector;
UPDATE articles SET search_vector =
    to_tsvector('english', title || ' ' || body);
CREATE INDEX idx_search ON articles USING GIN (search_vector);

-- Search with ranking
SELECT title,
       ts_rank(search_vector, query) AS rank
FROM articles,
     to_tsquery('english', 'mysql & postgresql & comparison') AS query
WHERE search_vector @@ query
ORDER BY rank DESC;

Materialized views. Store the result of a query physically and refresh it on demand. Excellent for dashboard queries and analytics:

-- PostgreSQL: materialized view
CREATE MATERIALIZED VIEW monthly_stats AS
SELECT
    date_trunc('month', created_at) AS month,
    COUNT(*) AS total_orders,
    SUM(amount) AS revenue,
    AVG(amount) AS avg_order_value
FROM orders
GROUP BY date_trunc('month', created_at);

-- Refresh when needed
REFRESH MATERIALIZED VIEW CONCURRENTLY monthly_stats;

Table inheritance. A table can inherit columns from a parent table, useful for partitioning patterns and shared schemas:

-- PostgreSQL: table inheritance
CREATE TABLE audit_log (
    id SERIAL PRIMARY KEY,
    action TEXT NOT NULL,
    performed_at TIMESTAMP DEFAULT NOW()
);

CREATE TABLE user_audit_log (
    user_id INT REFERENCES users(id),
    old_values JSONB,
    new_values JSONB
) INHERITS (audit_log);

Foreign data wrappers (FDW). Query external data sources (other PostgreSQL instances, MySQL, CSV files, REST APIs) as if they were local tables:

-- PostgreSQL: query a remote MySQL database
CREATE EXTENSION mysql_fdw;
CREATE SERVER mysql_server FOREIGN DATA WRAPPER mysql_fdw
    OPTIONS (host 'mysql-host', port '3306');

CREATE FOREIGN TABLE remote_users (
    id INT, name TEXT, email TEXT
) SERVER mysql_server
OPTIONS (dbname 'app', table_name 'users');

-- Now query MySQL data from PostgreSQL
SELECT * FROM remote_users WHERE name LIKE 'A%';

MySQL advantages

Simpler replication setup. MySQL's built-in replication is easier to configure. Binary log replication, semi-synchronous replication, and Group Replication work out of the box with less configuration than PostgreSQL's streaming replication.

MySQL Shell. A powerful admin tool for managing MySQL instances, creating InnoDB Cluster, and running JavaScript/Python scripts against the database.

InnoDB Cluster. A complete high-availability solution with automatic failover, built into MySQL. PostgreSQL requires third-party tools like Patroni or pg_auto_failover for equivalent functionality.

Scalability

Both MySQL and PostgreSQL scale well, but they take different approaches to horizontal scaling.

MySQL scaling

Read replicas are easy to set up with MySQL's binary log replication. Most MySQL deployments use a primary-replica architecture where writes go to the primary and reads are distributed across replicas.

InnoDB Cluster and Group Replication provide built-in multi-primary and single-primary clustering with automatic failover. This is MySQL's answer to high availability without third-party tools.

Vitess (developed by YouTube, now a CNCF project) provides horizontal sharding for MySQL. It powers YouTube, Slack, and Square. If you need to scale MySQL beyond a single server for writes, Vitess is the proven solution.

PostgreSQL scaling

Streaming replication provides physical replication from primary to standby servers. Standbys can serve read queries. Configuration is more involved than MySQL but provides strong consistency guarantees.

Logical replication (added in PostgreSQL 10) replicates specific tables or databases rather than the entire server. This enables selective replication, cross-version replication, and multi-directional setups.

Citus (now part of Microsoft) turns PostgreSQL into a distributed database with horizontal sharding. You keep writing standard SQL, and Citus distributes the data and queries across nodes.

Native partitioning (declarative partitioning since PostgreSQL 10) handles time-series data, multi-tenant applications, and large tables efficiently:

-- PostgreSQL: declarative partitioning by range
CREATE TABLE events (
    id BIGINT GENERATED ALWAYS AS IDENTITY,
    event_name TEXT NOT NULL,
    created_at TIMESTAMP NOT NULL,
    data JSONB
) PARTITION BY RANGE (created_at);

CREATE TABLE events_2026_q1 PARTITION OF events
    FOR VALUES FROM ('2026-01-01') TO ('2026-04-01');
CREATE TABLE events_2026_q2 PARTITION OF events
    FOR VALUES FROM ('2026-04-01') TO ('2026-07-01');

Cloud and Ecosystem

The cloud ecosystem around each database has become a major factor in the MySQL vs PostgreSQL decision. Here is what is available in 2026:

MySQL cloud options

• AWS RDS for MySQL / Aurora MySQL - The most popular managed MySQL service. Aurora is MySQL-compatible with up to 5x throughput improvement and automatic storage scaling.
• PlanetScale - Serverless MySQL built on Vitess. Branching workflows (like Git for your database), non-blocking schema changes, and horizontal scaling. Popular with Next.js and Vercel developers.
• Vitess - Open-source horizontal sharding for MySQL. Powers some of the largest MySQL deployments in the world.
• Google Cloud SQL / Azure Database for MySQL - Standard managed MySQL offerings from Google and Microsoft.

PostgreSQL cloud options

• AWS RDS for PostgreSQL / Aurora PostgreSQL - Managed PostgreSQL with Aurora's performance improvements. Strong enterprise adoption.
• Supabase - "Firebase alternative" built on PostgreSQL. Provides auth, real-time subscriptions, storage, and edge functions on top of a full PostgreSQL database. Extremely popular for new projects.
• Neon - Serverless PostgreSQL with branching, autoscaling to zero, and instant database cloning. Designed for modern development workflows.
• CockroachDB - Distributed SQL database wire-compatible with PostgreSQL. Provides global distribution and strong consistency.
• YugabyteDB - Distributed SQL database compatible with PostgreSQL. Designed for high availability across zones and regions.
• Tembo, Xata, DigitalOcean - Growing ecosystem of PostgreSQL-based platforms.

The PostgreSQL ecosystem is growing faster. Supabase, Neon, and similar platforms are bringing PostgreSQL to developers who previously defaulted to Firebase or MongoDB. This is a trend worth noting: the PostgreSQL ecosystem in 2026 is significantly more vibrant than MySQL's.

When to Use MySQL

MySQL is the right choice in these specific scenarios:

• WordPress, Drupal, or other PHP CMS platforms. These are built for MySQL and tested against MySQL. While PostgreSQL plugins exist, MySQL is the path of least resistance.
• Simple web applications with read-heavy workloads. If your app is mostly reading data with simple queries, MySQL's optimizations for this pattern give you a small but real edge.
• Legacy systems and existing MySQL infrastructure. If your team has years of MySQL expertise, your monitoring is tuned for MySQL, and your deployment scripts are MySQL-specific, switching to PostgreSQL for a new microservice adds unnecessary complexity.
• Simpler replication needs. If you need primary-replica replication without the complexity of Patroni or similar tools, MySQL's built-in replication is easier to set up and manage.
• Teams familiar with MySQL. The best database is the one your team knows well. A well-tuned MySQL deployment outperforms a poorly configured PostgreSQL instance every time.

Check the MySQL cheat sheet for a complete syntax reference.

When to Use PostgreSQL

PostgreSQL is the right choice in these scenarios:

• Complex queries and analytics. If your application relies on window functions, recursive CTEs, LATERAL joins, or complex aggregations, PostgreSQL's query planner handles them better.
• Data integrity is critical. PostgreSQL's constraint system is more complete: CHECK constraints, EXCLUDE constraints, domain types, and deferrable foreign keys give you more control over data quality.
• JSONB workloads. If you are storing and querying JSON data, PostgreSQL's JSONB with GIN indexes is significantly more capable than MySQL's JSON support.
• GIS and geospatial data. PostGIS is the gold standard for geospatial data in relational databases. Nothing in the MySQL ecosystem comes close.
• Analytics and reporting. Materialized views, advanced indexing (BRIN for time-series), and the richer function library make PostgreSQL better for analytical workloads.
• Strict SQL standards compliance. If you want to write portable SQL or follow best practices, PostgreSQL adheres more closely to the ANSI SQL standard.
• New projects in 2026. The ecosystem momentum (Supabase, Neon, Citus) and the growing community make PostgreSQL the safer long-term bet for greenfield projects.

Check the PostgreSQL cheat sheet for a complete syntax reference.

Migrating Between MySQL and PostgreSQL

If you are considering migrating from one to the other, here are the key differences to watch for:

Schema changes

• AUTO_INCREMENT becomes SERIAL or GENERATED ALWAYS AS IDENTITY
• Backtick quoting becomes double-quote quoting
• MySQL's inline ENUM becomes a CREATE TYPE in PostgreSQL
• TINYINT(1) for booleans becomes native BOOLEAN
• UNSIGNED integers do not exist in PostgreSQL (use CHECK constraints instead)
• DATETIME becomes TIMESTAMP (PostgreSQL has no DATETIME type)

Function differences

• IFNULL() becomes COALESCE()
• GROUP_CONCAT() becomes STRING_AGG()
• NOW() works in both, but PostgreSQL also has CURRENT_TIMESTAMP as a keyword (no parentheses)
• LIMIT offset, count shorthand becomes LIMIT count OFFSET offset
• STR_TO_DATE() becomes TO_DATE() or TO_TIMESTAMP()

Migration tools

pgloader is the standard tool for migrating MySQL to PostgreSQL. It handles schema conversion, data type mapping, and data transfer in a single command. For the reverse direction, tools like mysql-workbench or custom scripts handle PostgreSQL to MySQL migration.

AI2SQL can help during migration by converting MySQL queries to PostgreSQL and converting PostgreSQL queries to MySQL. Describe what your query does, specify the target database, and get the correct syntax instantly.

How AI2SQL Helps

Whether you work with MySQL, PostgreSQL, or both, AI2SQL removes the friction from writing queries across different database dialects.

Dialect-aware query generation. Tell AI2SQL which database you are using, describe what you need in plain English, and get a query with the correct syntax. No need to remember whether it is IFNULL or COALESCE, GROUP_CONCAT or STRING_AGG, backticks or double quotes.

Cross-database conversion. Paste a MySQL query and convert it to PostgreSQL, or vice versa. AI2SQL handles the syntax differences, data type mappings, and function replacements automatically. This is invaluable during database migration.

Complex queries made simple. Window functions, CTEs, multi-table joins, and aggregations are difficult to write from scratch. Describe the business question, specify MySQL or PostgreSQL, and AI2SQL generates the correct query. Works for both beginners learning SQL and experienced developers who want to move faster.

Learn the differences. Every generated query includes an explanation. When AI2SQL uses STRING_AGG instead of GROUP_CONCAT for PostgreSQL, it explains why. You learn the dialect differences naturally while getting your work done.

If this comparison helped you decide between MySQL and PostgreSQL, try AI2SQL free and start writing queries for your chosen database in seconds. See the SQL cheat sheet for a universal reference that covers both dialects.

Frequently Asked Questions

What is the main difference between MySQL and PostgreSQL?

MySQL prioritizes speed and simplicity, making it ideal for read-heavy web applications. PostgreSQL prioritizes standards compliance, extensibility, and advanced features like JSONB, arrays, window functions, and custom types. MySQL is owned by Oracle and uses a dual-license model, while PostgreSQL is fully open-source and community-driven.

Is PostgreSQL faster than MySQL?

It depends on the workload. MySQL is generally faster for simple read-heavy operations and basic SELECT queries. PostgreSQL is faster for complex queries involving joins, subqueries, CTEs, and analytical workloads. PostgreSQL also handles concurrent writes better thanks to its MVCC implementation. For most modern applications with mixed workloads, the performance difference is negligible.

Should I use MySQL or PostgreSQL for a new project in 2026?

For most new projects in 2026, PostgreSQL is the recommended choice. It offers more features, better SQL standards compliance, superior JSON support with JSONB, and a rapidly growing ecosystem with platforms like Supabase and Neon. Choose MySQL if you are building a WordPress or Drupal site, need compatibility with legacy systems, or your team already has deep MySQL expertise.

Can I migrate from MySQL to PostgreSQL?

Yes, migration is possible but requires attention to syntax differences. Key changes include replacing AUTO_INCREMENT with SERIAL or GENERATED ALWAYS AS IDENTITY, replacing backtick quoting with double quotes, updating MySQL-specific functions like IFNULL to PostgreSQL equivalents like COALESCE, and adjusting data types like ENUM and TINYINT. Tools like pgloader can automate much of the schema and data migration.

Do MySQL and PostgreSQL both support JSON?

Both support JSON, but PostgreSQL has a significant advantage with its JSONB type. JSONB stores JSON in a binary format that supports indexing with GIN indexes, making queries on JSON data much faster. MySQL supports JSON with generated columns and functional indexes, but PostgreSQL's JSONB is more mature and performant for heavy JSON workloads.