MySQL to PostgreSQL Migration: Complete 2026 Guide with Syntax Conversion

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Why Migrate from MySQL to PostgreSQL?

PostgreSQL has seen explosive growth, becoming the database of choice for many organizations. Common reasons for migration include:

Advanced Features: Better JSON support, full-text search, and extensibility
Standards Compliance: Closer adherence to SQL standards
Complex Queries: Superior handling of CTEs, window functions, and subqueries
Data Integrity: More robust constraint enforcement
Licensing: Truly open-source with no commercial restrictions
Cloud Native: First-class support on all major cloud platforms

MySQL vs PostgreSQL: Key Differences

Syntax Differences

Feature	MySQL	PostgreSQL
Auto-increment	`AUTO_INCREMENT`	`SERIAL` or `GENERATED AS IDENTITY`
String concat	`CONCAT()` or pipes disabled	`
Case sensitivity	Case-insensitive by default	Case-sensitive
Quotes	Backticks ` for identifiers	Double quotes `"` for identifiers
LIMIT with offset	`LIMIT 10, 5`	`LIMIT 5 OFFSET 10`
IFNULL	`IFNULL(a, b)`	`COALESCE(a, b)`
Current timestamp	`NOW()`	`NOW()` or `CURRENT_TIMESTAMP`
Boolean	`TINYINT(1)`	Native `BOOLEAN`
UUID	`UUID()` function	`gen_random_uuid()`

Data Type Mapping

MySQL Type	PostgreSQL Equivalent	Notes
`TINYINT`	`SMALLINT`	No TINYINT in PostgreSQL
`TINYINT(1)`	`BOOLEAN`	When used as boolean
`INT AUTO_INCREMENT`	`SERIAL` or `INT GENERATED BY DEFAULT AS IDENTITY`
`BIGINT AUTO_INCREMENT`	`BIGSERIAL`
`DOUBLE`	`DOUBLE PRECISION`
`FLOAT`	`REAL`
`DATETIME`	`TIMESTAMP`
`TINYTEXT`	`TEXT`	PostgreSQL TEXT has no size limit
`MEDIUMTEXT`	`TEXT`
`LONGTEXT`	`TEXT`
`TINYBLOB`	`BYTEA`
`BLOB`	`BYTEA`
`ENUM('a','b')`	Create custom TYPE or CHECK constraint
`SET`	`ARRAY` or separate table
`JSON`	`JSON` or `JSONB`	JSONB recommended for indexing

Common Syntax Conversions

1. Table Creation

MySQL:

CREATE TABLE users ( id INT AUTO_INCREMENT PRIMARY KEY, email VARCHAR(255) NOT NULL, name VARCHAR(100), status ENUM(‘active’, ‘inactive’, ‘pending’) DEFAULT ‘pending’, created_at DATETIME DEFAULT CURRENT_TIMESTAMP, is_admin TINYINT(1) DEFAULT 0, INDEX idx_email (email) )

PostgreSQL:

CREATE TYPE user_status AS ENUM (‘active’, ‘inactive’, ‘pending’);

CREATE TABLE users ( id SERIAL PRIMARY KEY, email VARCHAR(255) NOT NULL, name VARCHAR(100), status user_status DEFAULT ‘pending’, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, is_admin BOOLEAN DEFAULT FALSE );

CREATE INDEX idx_email ON users(email)

2. LIMIT and OFFSET

MySQL:

SELECT * FROM products LIMIT 20, 10; — Skip 20, get 10

PostgreSQL:

SELECT * FROM products LIMIT 10 OFFSET 20; — Same result

3. String Functions

MySQL:

SELECT CONCAT(first_name, ’ ’, last_name) AS full_name FROM users; SELECT IFNULL(nickname, username) AS display_name FROM users; SELECT GROUP_CONCAT(tag SEPARATOR ’, ’) FROM post_tags GROUP BY

PostgreSQL:

SELECT first_name || ’ ’ || last_name AS full_name FROM users; SELECT COALESCE(nickname, username) AS display_name FROM users; SELECT STRING_AGG(tag, ’, ’) FROM post_tags GROUP BY

4. Date Functions

MySQL:

SELECT DATE_FORMAT(created_at, ‘%Y-%m-%d’) FROM orders; SELECT DATE_ADD(order_date, INTERVAL 7 DAY) FROM orders; SELECT DATEDIFF(end_date, start_date) FROM projects; SELECT YEAR(created_at), MONTH(created_at) FROM

PostgreSQL:

SELECT TO_CHAR(created_at, ‘YYYY-MM-DD’) FROM orders; SELECT order_date + INTERVAL ‘7 days’ FROM orders; SELECT end_date - start_date FROM projects; — Returns interval SELECT EXTRACT(YEAR FROM created_at), EXTRACT(MONTH FROM created_at) FROM

5. INSERT with Conflict Handling

MySQL:

INSERT INTO users (email, name) VALUES (‘test@example.com’, ‘Test User’) ON DUPLICATE KEY UPDATE name = VALUES(name)

PostgreSQL:

INSERT INTO users (email, name) VALUES (‘test@example.com’, ‘Test User’) ON CONFLICT (email) DO UPDATE SET

6. JSON Operations

MySQL:

SELECT JSON_EXTRACT(data, ‘$.name’) FROM profiles; SELECT * FROM products WHERE JSON_EXTRACT(attributes, ‘$.color’) = ‘red’

PostgreSQL:

SELECT data->>‘name’ FROM profiles; — ->> returns text SELECT * FROM products WHERE attributes->>‘color’ = ‘red’; — Or with JSONB (recommended): SELECT * FROM products WHERE attributes @> ’{“color”: “red”}‘

7. Full-Text Search

MySQL:

SELECT * FROM articles WHERE MATCH(title, content) AGAINST(‘database optimization’ IN NATURAL LANGUAGE MODE)

PostgreSQL:

SELECT * FROM articles WHERE to_tsvector(‘english’, title || ’ ’ || content) @@ to_tsquery(‘database & optimization’)

Migration Strategies

Strategy 1: Big Bang Migration

Best for: Small databases, acceptable downtime

Put application in maintenance mode
Export MySQL data
Convert schema
Import to PostgreSQL
Update application connection strings
Test thoroughly
Go live

Strategy 2: Parallel Running

Best for: Critical systems, minimal risk tolerance

Set up PostgreSQL with converted schema
Implement dual-write in application
Migrate historical data
Run both databases in parallel
Validate data consistency
Switch reads to PostgreSQL
Disable MySQL writes
Decommission MySQL

Strategy 3: Incremental Migration

Best for: Large databases, microservices

Identify independent data domains
Migrate one domain at a time
Update relevant services
Validate and stabilize
Repeat for next domain

Step-by-Step Migration Process

Step 1: Schema Conversion

Option A: Manual Conversion

Review each table and convert syntax manually using the mappings above.

Option B: Automated Tools

Use tools like:

pgLoader
AWS DMS (Database Migration Service)
AI2sql MySQL to PostgreSQL converter

Step 2: Export MySQL Data

# Export schema mysqldump -u user -p —no-data database_name > schema.sql

# Export data mysqldump -u user -p —no-create-info

Step 3: Convert Schema

Apply conversions for:

Data types
Auto-increment to SERIAL
ENUM types
Index syntax
Engine-specific features

Step 4: Create PostgreSQL Database

createdb -U postgres new_database

# Import converted schema psql -U postgres -d new_database -f

Step 5: Migrate Data

Using pgLoader (recommended):

Using CSV export/import:

— MySQL export SELECT * FROM users INTO OUTFILE ‘/tmp/users.csv’ FIELDS TERMINATED BY ’,’ ENCLOSED BY ’”’ LINES TERMINATED BY ‘\n’;

— PostgreSQL import COPY users FROM ‘/tmp/users.csv’ WITH (FORMAT csv, HEADER false)

Step 6: Validate Migration

— Compare row counts — MySQL SELECT COUNT(*) FROM users;

— PostgreSQL SELECT COUNT(*) FROM users;

— Validate sample data — Check specific records match

Step 7: Update Application

Update connection strings:

Before (MySQL):

After (PostgreSQL):

Update ORM/driver configurations for PostgreSQL compatibility.

Common Migration Pitfalls

1. Case Sensitivity

MySQL is case-insensitive by default. PostgreSQL preserves case.

Solution: Standardize to lowercase or use consistent quoting.

2. Zero Dates

MySQL allows ‘0000-00-00’ dates. PostgreSQL does not.

Solution: Convert to NULL or valid dates before migration.

UPDATE orders SET order_date = NULL WHERE order_date = ‘0000-00-00’

3. Empty Strings vs NULL

MySQL sometimes treats empty strings and NULL differently.

Solution: Audit and standardize before migration.

4. GROUP BY Strictness

PostgreSQL requires all non-aggregated columns in GROUP BY.

MySQL (works):

SELECT name, department, SUM(salary) FROM employees GROUP BY

PostgreSQL (error):

— Must include name in GROUP BY or use aggregate SELECT name, department, SUM(salary) FROM employees GROUP BY name,

5. UNSIGNED Integers

PostgreSQL doesn’t support UNSIGNED.

Solution: Use appropriate larger type or add CHECK constraint.

CREATE TABLE products ( id INTEGER PRIMARY KEY CHECK (id >= 0), stock INTEGER CHECK (stock >= 0) )

Using AI2sql for Migration

AI2sql’s MySQL to PostgreSQL converter automatically handles:

Syntax translation
Data type mapping
Function conversion
Index recreation
Constraint conversion

Example Conversion

Input (MySQL):

SELECT IFNULL(u.nickname, u.username) as display_name, DATE_FORMAT(o.created_at, ‘%Y-%m-%d’) as order_date, GROUP_CONCAT(p.name SEPARATOR ’, ’) as products FROM users u JOIN orders o ON u.id = o.user_id JOIN order_items oi ON o.id = oi.order_id JOIN products p ON oi.product_id = p.id WHERE o.created_at >= DATE_SUB(NOW(), INTERVAL 30 DAY) GROUP BY u.id LIMIT 0, 20

Output (PostgreSQL):

SELECT COALESCE(u.nickname, u.username) as display_name, TO_CHAR(o.created_at, ‘YYYY-MM-DD’) as order_date, STRING_AGG(p.name, ’, ’) as products FROM users u JOIN orders o ON u.id = o.user_id JOIN order_items oi ON o.id = oi.order_id JOIN products p ON oi.product_id = p.id WHERE o.created_at >= NOW() - INTERVAL ‘30 days’ GROUP BY u.id, u.nickname, u.username LIMIT 20 OFFSET 0

Conclusion

Migrating from MySQL to PostgreSQL is a significant undertaking, but the benefits often justify the effort. PostgreSQL’s advanced features, standards compliance, and robust ecosystem make it an excellent choice for growing applications.

Key takeaways:

Plan thoroughly - Document all differences and test conversions
Use automated tools - Save time on repetitive conversions
Test extensively - Validate data integrity and application behavior
Have a rollback plan - Prepare for unexpected issues
Leverage AI assistance - Tools like AI2sql accelerate conversion

Need to convert MySQL to PostgreSQL? Try AI2sql’s converter - paste your MySQL query and get PostgreSQL syntax instantly.

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