Driver 01
Transactional schema changes
MySQL commits DDL implicitly, so a failed migration can leave a schema half-changed; PostgreSQL runs DDL inside transactions.
Migration playbook: MySQL / MariaDB → PostgreSQL
MySQL to PostgreSQL migration and schema translation
Move MySQL databases to PostgreSQL for transactional DDL, richer indexing, JSONB and extensions such as PostGIS and pgvector, with live replication until cutover.
Migration drivers
Why teams make this move
Driver 02
Query and JSON capabilities
Partial and expression indexes, JSONB with GIN indexes, and a mature planner for complex queries.
Driver 03
Extensions
PostGIS for geospatial data and pgvector for embeddings, inside the same database.
Execution sequence
How the migration runs
Each phase ends with a check you can verify — data parity, error rates, latency — and the rollback path is agreed before any traffic moves.
- 01Phase
Schema translation and audit
Converting MySQL types (TINYINT(1), DATETIME, ENUM, unsigned integers) to PostgreSQL equivalents with pgloader and a manual review.
- 02Phase
Change data capture
Streaming live MySQL changes to PostgreSQL with Debezium and Kafka after the bulk load.
- 03Phase
Shadow read verification
Mirroring application reads to PostgreSQL to compare results and query plans.
- 04Phase
Write cutover
Pausing writes briefly, confirming replication has caught up, and switching connection strings.
Risk prevention
Pitfalls that derail this migration
Risk 01
Case-sensitivity mismatches
MySQL's case-insensitive collations hiding duplicates that PostgreSQL's unique constraints then reject.
Risk 02
Sequences out of sync
Forgetting to reset PostgreSQL sequences after bulk loading, which causes primary key collisions.
Risk 03
Implicit type coercion
MySQL leniently accepting invalid values (zero dates, empty strings as 0) that PostgreSQL rejects.
Before and after
What we measure
We take a baseline before any change and report the same numbers after cutover, from your own tools. They are the evidence of whether the migration worked — not figures promised in advance.
- Row parity
- Row counts and checksums per table before cutover
- Query latency
- Top queries by total time, compared on both databases
- Write freeze
- Measured in rehearsal, then at the real cutover
Questions
Frequently asked migration questions
Yes. We bulk-load historical data while change data capture streams new writes, so the final cutover is a short write freeze. We rehearse it first, so you know the real number for your data before the day.
They are rewritten as PL/pgSQL functions and triggers, reviewed for behavior differences (transactions, error handling, type coercion) and covered by tests before cutover.
Rehearse the cutover before the real one
Tell us about your data volume, traffic and timeline. An engineer will reply within one business day to set up a call about the migration plan and its rollback path.