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How to Database Convert from MySQL to PostgreSQL — Common Pitfalls and FixesMigrating a database from MySQL to PostgreSQL is a common task when teams want advanced SQL features, stricter standards compliance, better concurrency, or simply prefer PostgreSQL’s ecosystem. The process can be straightforward for small, simple schemas but becomes complex when the database uses MySQL-specific SQL, storage-engine behaviors, or relies on application assumptions. This article walks through a practical, step-by-step migration plan, highlights frequent pitfalls, and gives concrete fixes and examples.

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Overview and migration strategy

Successful migrations follow these phases:

• Assessment — inventory schema, data types, queries, stored code, and integrations.
• Preparation — plan schema changes, pick tools, create a staging environment, and set rollback steps.
• Conversion — convert schema, data, and application SQL; migrate data with minimal downtime.
• Validation — verify data integrity, performance, and application behavior.
• Cutover and post-migration — switch production, monitor, and address issues.

Choose the migration approach based on downtime tolerance:

• Full downtime (simplest): stop writes, export/import data, then start PostgreSQL.
• Minimal downtime (recommended for many apps): use logical replication or dual-write strategies to keep systems in sync, then cut over.
• Zero-downtime (complex): use CDC (change data capture) and a careful cutover plan.

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Common pitfalls and fixes

Below are the frequent problems teams encounter during MySQL → PostgreSQL conversions and practical fixes.

1) Data type mismatches

Problem: MySQL and PostgreSQL have different types and defaults. Examples:

• MySQL TINYINT(1) commonly used for boolean values.
• MySQL ENUM has no direct PostgreSQL equivalent.
• Unsigned integers in MySQL have no native PostgreSQL unsigned type.
• DATETIME vs TIMESTAMP handling and timezone behavior.

Fixes:

• Booleans: convert TINYINT(1) or ENUM(‘0’,‘1’) to PostgreSQL BOOLEAN. Example ALTER:

  
  ALTER TABLE my_table ALTER COLUMN active TYPE boolean USING active::boolean; 

• ENUMs: convert to PostgreSQL ENUM types or to TEXT/VARCHAR with a CHECK constraint. PostgreSQL ENUM:

  
  CREATE TYPE mood AS ENUM ('happy','sad','angry'); ALTER TABLE person ALTER COLUMN mood TYPE mood USING mood::mood; 

  Or use VARCHAR + CHECK if you may need to alter values often.

• Unsigned integers: choose a larger signed type (e.g., INT UNSIGNED → BIGINT) or validate ranges in application.
• DATETIME/TIMESTAMP:
  • Decide whether timestamps should be timezone-aware. Prefer TIMESTAMP WITH TIME ZONE (timestamptz) for globally consistent times.
  • Convert values explicitly and test edge cases (NULLs, zero-dates). Example:

    
    ALTER TABLE events ALTER COLUMN created_at TYPE timestamptz USING created_at AT TIME ZONE 'UTC'; 

2) Auto-increment vs sequences

Problem: MySQL AUTO_INCREMENT behavior differs from PostgreSQL sequences.

Fixes:

• Replace AUTO_INCREMENT with SERIAL or IDENTITY (PostgreSQL 10+). For more control, create a sequence and set default. Example (prefer IDENTITY for SQL standard compliance):

  
  CREATE TABLE users ( id BIGINT GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY, name text ); 

• When importing existing data, set the sequence value to max(id)+1:

  
  SELECT setval(pg_get_serial_sequence('users','id'), COALESCE(MAX(id),1)) FROM users; 

3) SQL dialect and function differences

Problem: MySQL functions and SQL syntax can differ (LIMIT/OFFSET, IFNULL vs COALESCE, CONCAT behavior, string functions, GROUP BY extensions).

Fixes:

• Replace MySQL-specific functions with PostgreSQL equivalents:
  • IFNULL(a,b) → COALESCE(a,b)
  • CONCAT(a,b) works in PostgreSQL but be cautious with NULLs; prefer CONCAT_WS or COALESCE as needed.
  • GROUP BY: PostgreSQL requires all non-aggregated columns in GROUP BY (unless using extensions like DISTINCT ON or window functions).
• Conditional expressions: use CASE WHEN instead of MySQL IF(). Example:

  
  SELECT CASE WHEN col IS NULL THEN 'n/a' ELSE col END FROM t; 

• LIMIT with OFFSET: same in PostgreSQL, but for single-row retrieval prefer LIMIT 1.

4) Indexes, full-text search, and character sets

Problem: Full-text search, collation, and index types differ.

Fixes:

• Full-text search: MySQL fulltext differs from PostgreSQL tsvector/tsquery. Rebuild full-text indexes using built-in tsvector and GIN indexes. Example:

  
  ALTER TABLE articles ADD COLUMN tsv tsvector; UPDATE articles SET tsv = to_tsvector('english', coalesce(title,'') || ' ' || coalesce(body,'')); CREATE INDEX articles_tsv_idx ON articles USING GIN(tsv); 

  Or create a generated column (Postgres 12+):

  
  ALTER TABLE articles ADD COLUMN tsv tsvector GENERATED ALWAYS AS (to_tsvector('english', title || ' ' || body)) STORED; CREATE INDEX ON articles USING GIN(tsv); 

• Collations and charset: MySQL often uses utf8mb4; PostgreSQL uses UTF8. Ensure the target cluster is created with UTF8 encoding. For specific collations, create matching collations or test sorting behavior.
• Index types: consider BRIN, GiST, SP-GiST, GIN depending on use cases (range, geo, full-text).

5) Stored routines, triggers, and procedural code

Problem: MySQL stored procedures, triggers, and functions (written with MySQL dialect) are not directly portable. MySQL’s procedural language differs from PL/pgSQL.

Fixes:

• Translate logic manually to PL/pgSQL, PL/pgSQL-compatible functions, or to application code for complex logic.
• Recreate triggers using PostgreSQL trigger functions: Example trigger skeleton: “`sql CREATE FUNCTION audit_changes() RETURNS trigger AS $\( BEGIN INSERT INTO audit_table(table_name, changed_at, old_row, new_row) VALUES (TG_TABLE_NAME, now(), row_to_json(OLD), row_to_json(NEW)); RETURN NEW; END; \)$ LANGUAGE plpgsql;

CREATE TRIGGER my_table_audit AFTER UPDATE ON my_table FOR EACH ROW EXECUTE FUNCTION audit_changes();

- Consider using event scheduling: MySQL EVENTs should be ported to cron jobs or to PostgreSQL background workers or extensions. #### 6) Transactions and isolation behavior Problem: MySQL’s default storage engine (InnoDB) supports transactions, but there are differences in isolation levels, locking behavior, and implicit commit behaviors (e.g., DDL behavior). Fixes: - Familiarize yourself with PostgreSQL MVCC and how it handles locking (row-level, no gap locks). - For long-running migrations, avoid long open transactions in PostgreSQL because they can bloat MVCC visibility maps and prevent VACUUM from cleaning up. - Convert any logic that relied on MySQL-specific locking behavior (e.g., SELECT ... FOR UPDATE nuances). #### 7) NULL and empty string handling Problem: MySQL sometimes treats empty strings and zero dates specially; application code may rely on these behaviors. Fixes: - Audit columns where empty string is used instead of NULL and decide on a consistent policy. - Use CHECK constraints to enforce expected formats and defaults to avoid ambiguous values. #### 8) Privileges, users, and authentication Problem: MySQL user accounts and authentication plugins (e.g., caching_sha2_password vs mysql_native_password) don’t port. Fixes: - Recreate roles and grants in PostgreSQL using roles and GRANT statements. Map application users to a service account or connection pooler role.   Example:   ```sql   CREATE ROLE app_user LOGIN PASSWORD 'strong_password';   GRANT SELECT, INSERT, UPDATE, DELETE ON ALL TABLES IN SCHEMA public TO app_user; 

• Consider using a connection pooler (pgbouncer) and integrate with your auth solution.

9) Replication and synchronization

Problem: Keeping systems in sync during migration is hard if you need minimal downtime.

Fixes:

• Use logical replication (pg_logical, built-in logical replication) or third-party tools (Debezium, AWS DMS) to replicate changes from MySQL to PostgreSQL.
• Workflow: initial bulk load → set up CDC → sync until cutover → stop writes to MySQL → final sync and switch writes to PostgreSQL.
• Test lag, conflict resolution, data type mapping before cutover.

10) Performance differences and query plans

Problem: Queries may perform differently due to planner differences, indexing strategies, and optimizer behaviors.

Fixes:

• Analyze slow queries using EXPLAIN (ANALYZE) in PostgreSQL and add appropriate indexes (including expression and partial indexes).
• Use PostgreSQL features: partial indexes, covering indexes with INCLUDE, BRIN for large tables with physical ordering, and materialized views for expensive aggregations.
• Ensure statistics are up-to-date: run ANALYZE after loading data.

  
  VACUUM ANALYZE; 

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Tools and utilities to help the migration

• pgloader — popular for converting schema and bulk-loading from MySQL to PostgreSQL with built-in type mappings and transform hooks.
• AWS DMS — managed service for heterogeneous migrations with CDC (note vendor lock-in).
• Debezium + Kafka — CDC-based approach for low-downtime migrations.
• ora2pg (for Oracle→Postgres) — mentioned only for analogy; not for MySQL.
• Custom ETL scripts — using Python (psycopg2, SQLAlchemy), Go, or other languages for complex transforms.
• pg_dump / pg_restore — used post-conversion for PostgreSQL data handling.
• Tools for schema diffing and migration management: Sqitch, Flyway, Liquibase.

Example pgloader command (simplified):

LOAD DATABASE      FROM mysql://user:password@mysql_host/dbname      INTO postgresql://user:password@pg_host/dbname  WITH include drop, create tables, create indexes, reset sequences, foreign keys  SET work_mem to '16MB', maintenance_work_mem to '512 MB'  CAST type datetime to timestamptz drop default using zero-dates-to-null; 

pgloader can handle many data type casts and speed optimizations; test its defaults first.

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Step-by-step practical checklist

1. Inventory

   • List tables, row counts, indexes, constraints, triggers, stored code, foreign keys, and user accounts.
   • Extract slow queries and application SQL patterns.

2. Create a PostgreSQL test environment

   • Match encoding (UTF8) and set shared_buffers, work_mem roughly for expected workload.
   • Prepare access controls and hardware similar to production.

3. Convert schema

   • Translate types, constraints, and indexes.
   • Recreate sequences/identity columns.
   • Port functions and triggers to PL/pgSQL.

4. Migrate data

   • Small DB: use pgloader or dump/import.
   • Large DB: use chunked exports, parallel loading, or CDC tools.
   • Verify row counts, checksums, and statistics.

5. Convert application SQL

   • Replace dialect differences, test ORM compatibility (most ORMs support PostgreSQL).
   • Update connection strings and pooling.

6. Validate

   • Run integration tests, compare application behavior, check report outputs.
   • Run EXPLAIN ANALYZE for heavy queries and tune indexes.

7. Cutover

   • For minimal downtime: use CDC to replicate changes, schedule brief cutover window, switch application.
   • For full downtime: put app into maintenance, final sync, update connection strings, resume.

8. Post-migration

   • Monitor performance, slow queries, and errors.
   • Run VACUUM and tune autovacuum settings.
   • Review backups and disaster recovery plan for PostgreSQL.

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Example: Common fixes in practice

• Problem: ORDER BY returns different results. Fix: Specify explicit ORDER BY columns and collations. Ensure client and server collations match.

• Problem: A query using GROUP BY worked in MySQL but fails in PostgreSQL. Fix: Add all non-aggregated columns to GROUP BY or use aggregates/window functions.

• Problem: Zero DATETIME values (0000-00-00) imported as invalid. Fix: Use pgloader transform or pre-process to convert zero-dates to NULL or valid defaults.

• Problem: Application relies on last_insert_id() behavior. Fix: Use RETURNING in INSERT to get generated IDs:

  INSERT INTO users(name) VALUES ('Alice') RETURNING id; 

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Testing, rollback, and safety nets

• Keep a rollback plan: snapshots/backups of MySQL before stopping writes; ability to point app back to MySQL quickly.
• Use feature flags and staged rollout to limit exposure.
• Run parallel writes (dual-writing) for short periods only and validate divergence detection.
• Monitor replication lag, error rates, and data drift during CDC.

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Final notes

Migration from MySQL to PostgreSQL rewards careful planning and testing. Many pitfalls are avoidable by inventorying schema and SQL usage, converting data types intentionally, and choosing the right tooling for your downtime constraints. Expect iterative tuning after cutover — PostgreSQL’s optimizer and feature set can offer significant long-term advantages but require different trade-offs than MySQL.

If you want, I can:

• produce a migration checklist tailored to your schema (if you share table counts and key types),
• convert sample CREATE TABLE statements from MySQL to PostgreSQL,
• or give a pgloader config example for your database.