MySQL to PostgreSQL Migration
Moving to PostgreSQL with logical replication and verified checksums, so cutover is a switch you can reverse rather than an evening you dread.
Database migrations are judged on one thing
Everything else about a database migration is recoverable. Losing rows is not. So the entire method is built around proving equivalence continuously rather than trusting a dump and restore.
Logical replication keeps PostgreSQL synchronised with live MySQL while the application still writes to MySQL. Reads move first, then writes, and at every stage row counts and checksums are compared automatically. If the numbers disagree, nothing progresses.
The genuinely tricky part is behavioural rather than structural. MySQL and PostgreSQL differ on collation, case sensitivity, zero dates, unsigned integers and implicit casting. Our migration guide covers the ones that cause silent damage.
Teams that start here often pair it with vector search, on-premise to cloud migration and monolith to serverless migration.
Every other migration risk is recoverable. Losing rows is not, which is why nothing progresses while the checksums disagree.
Where database migrations cause damage
Four differences that corrupt data quietly rather than loudly.
MySQL default collation is case-insensitive and PostgreSQL is not, so uniqueness constraints and lookups behave differently after the move.
MySQL accepts dates like 0000-00-00 which PostgreSQL rejects outright, so rows fail to import or arrive with substituted values.
PostgreSQL has no unsigned types, so ranges must be remapped deliberately rather than truncated by an automated tool.
MySQL silently coerces types in comparisons where PostgreSQL raises an error, which surfaces as application failures after cutover rather than during import.
What a database migration covers
Continuous verification from first sync to final cutover.
Every type, constraint, index and collation mapped explicitly, with the differences that change behaviour flagged for a decision.
Zero dates, invalid encodings and out-of-range values identified and resolved with you before they block an import.
Continuous sync from live MySQL to PostgreSQL, so the target stays current while the application is untouched.
Automated row counts and per-table checksums, run continuously, with cutover blocked while any table disagrees.
Application queries reviewed for MySQL-specific syntax and behaviour, tested against PostgreSQL before any traffic moves.
Reads move first with dual verification, then writes, with a tested rollback available at each stage.
From schema analysis to cutover through controlled rollout
Nothing moves while the numbers disagree.
Schema, types, collations and query patterns audited, with behavioural differences documented and decided.
Data issues that PostgreSQL will reject identified and resolved in MySQL first.
Logical replication established and left to run, with checksum verification reporting continuously.
Read traffic moved to PostgreSQL with results compared against MySQL in shadow.
Writes switched during a low-traffic window with replication reversed so rollback remains possible.
The stack behind our database migrations
Proven replication tooling and verification we can show you.
Change-data-capture keeps the target current without touching the application.
Row counts and checksums per table, continuously, because a sample proves nothing.
The features that usually justify the move in the first place.
Backups, restores and monitoring rehearsed before cutover, not after.
What this looks like in production
Four hundred million rows with no maintenance window
A fintech needed PostgreSQL for JSONB and partitioning, with four hundred million transaction rows and a regulatory obligation that ruled out any planned downtime.
Analysis found eleven thousand rows with zero dates and a case-insensitive unique index on email addresses that had allowed near-duplicate accounts for years. Both were resolved in MySQL first, which was the right order.
Replication ran for three weeks with checksums reported daily. Reads moved first and were shadow-compared for a fortnight. The write cutover took under a minute, with reverse replication live in case it was needed. It was not.
Migrations where equivalence is proven, not assumed
Checksums run continuously from the first sync to the final cutover, and nothing progresses while a single table disagrees.
We move one slice at a time behind a router, with both systems live, so every step is small and every step is reversible.
A working demo and a written note every Friday covering what shipped, what slipped and what it means for the date. No status theatre.
Nothing goes live in one jump. We run in parallel or behind a flag until the numbers say it is safe to switch over.
