Moving a production MySQL database to PostgreSQL in 2026 is a schema and application rewrite disguised as a data transfer — the roadmap below sequences the work so you don't discover the hard parts during the cutover window.

TL;DR

• A MySQL to PostgreSQL migration in 2026 needs 6-10 weeks of planning before any data moves, schema mapping first and cutover last.
• pgloader handles bulk transfer for most schemas under 500GB; larger datasets need a phased logical replication approach instead.
• Stored procedures, triggers and MySQL-only functions rarely survive a straight port, budget 30-40% of migration time for rewrites.
• Cutting over without a 48-72 hour rollback window is the top cause of failed go-lives, per repeated field observations across 2025 and 2026 migrations.

Why this matters

MySQL and PostgreSQL look similar on the surface — both are relational, both speak SQL, both run on the same cloud instances. The similarity stops at syntax.

PostgreSQL enforces stricter typing, handles case sensitivity differently, and has no direct equivalent for MySQL's AUTO_INCREMENT, ENUM, or several string functions teams rely on daily. Teams that treat this as a data export/import job end up debugging broken sequences and silent data truncation in production, not in staging.

A migration roadmap for MySQL to PostgreSQL has to separate three concerns that get tangled otherwise: schema conversion, data movement, and application query rewrites. Each has its own failure mode and its own testing cycle. Mydbops PostgreSQL migration consulting runs this as a managed engagement precisely because the three tracks need to be staffed and sequenced together, not handed to whichever engineer is free that sprint.

What you'll need

• Full schema export from the source MySQL instance (mysqldump --no-data, plus a grants dump via pt-show-grants)
• A staging PostgreSQL instance on the target version — PostgreSQL 16 or 17 is the standard target in 2026
• pgloader or AWS DMS configured and licensed for the data volume involved
• Binlog access or a read replica for change-data-capture during the parallel-run phase
• A full inventory of raw SQL and ORM-generated queries hitting the database
• 3-6 weeks of calendar time for parallel validation before cutover
• A written rollback plan with a named decision-maker and a communicated cutover window

The steps

1. Audit the schema and flag MySQL-specific constructs

This step accomplishes one thing: it tells you how big the rewrite actually is before you commit a date. Pull every table definition and grep for ENUM columns, AUTO_INCREMENT primary keys, TINYINT(1) booleans, and any TEXT/BLOB columns using non-utf8mb4 collations.

Why it matters: PostgreSQL has no ENUM type that behaves the same way (it requires a CREATE TYPE statement per enum, not an inline column definition), and AUTO_INCREMENT needs to become a SERIAL or IDENTITY column with a matching sequence. Expected outcome: a spreadsheet of every table with a flag column for "needs manual conversion." Common mistake: teams skip views and stored procedures in this audit and find them during data migration instead, weeks later.

2. Map data types and rewrite stored logic

Go table by table and write the PostgreSQL DDL by hand for anything flagged in step 1. TINYINT(1) becomes BOOLEAN, DATETIME becomes TIMESTAMP, and MySQL's implicit string truncation on overflow becomes a hard error in PostgreSQL — which is usually the correct behavior, but it will surface bad data you didn't know existed.

Stored procedures and triggers written in MySQL's procedural SQL don't port to PL/pgSQL automatically. Budget real engineering time here — for a schema with 15-20 stored procedures, expect two to three weeks of rewrite and unit testing, not a weekend.

3. Stand up a parallel PostgreSQL environment

Provision a PostgreSQL instance sized to match production load, not a scaled-down staging box. Undersized staging environments hide performance regressions that only show up under real concurrency. Managed PostgreSQL services fit teams that need 24/7 DBA coverage after cutover.

Apply the converted schema from step 2, then load a representative data sample — not the full production dataset yet — and run your slowest known queries through EXPLAIN ANALYZE. This is where you catch missing indexes early, before the full data load makes re-indexing expensive.

4. Migrate data with pgloader or CDC replication

For databases under roughly 500GB, pgloader can move schema and data in a single pass and handles most type conversions automatically. For anything larger, or for databases that can't tolerate extended downtime, set up change-data-capture off the MySQL binlog so PostgreSQL stays in near-real-time sync during the parallel-run window.

Run a row-count and checksum comparison after the initial load — table by table, not just a total row count. Silent data loss during charset conversion (utf8mb4 to UTF8) is common enough that it needs its own verification step, not a spot check.

5. Rewrite and test application queries

MySQL-only functions like IFNULL(), GROUP_CONCAT(), and non-standard string concatenation with + or || behave differently or don't exist in PostgreSQL. Pull every raw SQL query and ORM query log from the inventory in step one and run them against the staging instance.

Why this matters more than teams expect: an ORM configured for MySQL dialect will silently generate syntax PostgreSQL rejects, and you won't catch it until a specific code path fires in production. Expected outcome: a passing test suite against the PostgreSQL staging instance, not just a schema that loads. Common mistake: testing only the happy path and skipping edge-case queries buried in reporting or admin tools.

6. Run a parallel validation period

Keep MySQL as the system of record and mirror writes to PostgreSQL for 2-4 weeks minimum. Compare query results and application behavior side by side under real traffic, not synthetic load.

This is the step teams cut short under deadline pressure, and it's the one that catches the data type edge cases no schema audit finds — a NULL handled differently, a sort order that changed because of collation differences, a report that returns a different row count.

7. Execute the cutover with a rollback window

Schedule the cutover for the lowest-traffic window your business has, communicate it to stakeholders at least a week out, and keep MySQL live and read-only for 48-72 hours after cutover as a fallback.

If the rollback plan only exists as a Slack message, it doesn't exist. Write it down: who makes the call to roll back, what the trigger conditions are (error rate threshold, latency threshold), and how long the fallback window stays open.

8. Decommission MySQL and tune PostgreSQL

Once the fallback window closes clean, decommission the MySQL instance and shift tuning effort to PostgreSQL-specific configuration — shared_buffers, work_mem, and autovacuum settings that have no MySQL equivalent and need their own baseline.

Troubleshooting

• Sequences out of sync after data load — pgloader sets the sequence to the max existing value, but manual dumps often don't. Run SELECT setval() against the max ID for every converted AUTO_INCREMENT column before going live.
• Case-sensitivity breaks lookups — MySQL's default collation is case-insensitive; PostgreSQL is case-sensitive by default. Queries comparing strings that worked in MySQL will silently return zero rows. Add LOWER() comparisons or a citext column type where this matters.
• ENUM columns fail to insert — PostgreSQL enums are strict; a value not defined in the CREATE TYPE statement gets rejected outright, where MySQL would coerce it. Audit source data for stray enum values before conversion, not after.
• Character encoding mismatches — utf8mb4 data inserted into a UTF8-only PostgreSQL column can throw encoding errors on 4-byte characters (emoji, some CJK text). Confirm the target database is created with UTF8 encoding and the correct locale before the first load.
• Replication lag during CDC — binlog-based CDC can fall behind under high write volume, especially with large transactions. Monitor lag continuously during the parallel-run window and don't schedule cutover if lag exceeds a few seconds.
• JSON columns lose query performance — MySQL's JSON type and PostgreSQL's JSONB behave differently under the hood. Add GIN indexes on JSONB columns used in WHERE clauses, or query performance regresses noticeably against what MySQL delivered.

Tools and resources

• pgloader — schema and data conversion for most migration sizes
• AWS DMS — for CDC-based migrations with minimal downtime tolerance
• pgAdmin / EXPLAIN ANALYZE — query plan comparison between the two engines
• pt-show-grants (Percona Toolkit) — exporting MySQL grants for recreation in PostgreSQL
• If the database sits under compliance scope, confirm that audit logging, access controls, and retained evidence remain valid before the schema freeze.

What to do next

A MySQL to PostgreSQL migration rarely fails on the pgloader command — it fails on the stored procedure nobody flagged in week one, or the cutover window nobody rehearsed. Teams in regulated environments need a scoped migration assessment that validates audit evidence alongside the database changes, not a generic runbook.

“If the rollback plan only exists as a Slack message, it doesn't exist.”

FAQ

How long does a MySQL to PostgreSQL migration take in 2026?

Most migrations take 6-10 weeks end to end, including schema audit, data type conversion, application query rewrites, and a parallel validation period. Larger schemas with heavy stored procedure logic can run longer.

Is PostgreSQL better than MySQL for production workloads?

Neither engine is universally better; PostgreSQL handles complex queries, JSONB indexing, and strict data typing better, while MySQL remains simpler to operate for straightforward read-heavy workloads. The right choice depends on query patterns and existing application code.

Can pgloader migrate a MySQL database automatically?

pgloader automates schema and data conversion for most databases under roughly 500GB, including type mapping and encoding conversion. It does not convert stored procedures, triggers, or application-level SQL, which still need manual rewrite.

What breaks most often during a MySQL to PostgreSQL migration?

AUTO_INCREMENT to SERIAL sequence mismatches, ENUM type incompatibilities, and case-sensitivity differences in string comparisons cause the most post-cutover incidents. All three are catchable during the schema audit and parallel-run phases.

Do I need downtime to migrate from MySQL to PostgreSQL?

A CDC-based approach using binlog replication can bring downtime down to minutes during cutover, while a straight pgloader dump-and-load approach usually requires a maintenance window of several hours depending on data volume.

How much of the migration work is application code, not database work?

Query rewrites and ORM dialect fixes typically account for 30-40% of total migration effort, since MySQL-specific functions and syntax differences surface throughout the application layer, not just in the schema.

Should I migrate all databases at once or one at a time?

Migrate one database or service at a time when the architecture allows it. A phased approach isolates failures to a single service and keeps the rollback window manageable instead of risking every workload at once.

One last thing

The cutover date isn't the highest-risk moment in this migration — the third week of the parallel-run period is, because that's when teams get comfortable and start skipping the row-by-row checksum comparisons that catch silent data drift. Keep the validation discipline through the entire window, not just the first few days.

Related guides

• AWS DMS performance tuning for database migrations
• PostgreSQL WAL file retention and logical replication
• Avoid hidden duplicate key errors in MySQL utf8mb4 migrations

Get a migration assessment first

A schema audit before cutover catches most of the rewrite work early.