Assisted MySQL → PostgreSQL Migration Preserving Primary Keys and Constraints

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We provide assisted MySQL to PostgreSQL migrations focused on integrity: preserving primary keys, foreign keys, UNIQUE and CHECK constraints. We map types, convert DDL with correct equivalents, and validate data after load. When appropriate, we plan a minimal cutover window using CDC and a pragmatic rollback plan.

When to hire assisted migration and who it's for

This service is for teams that need to move critical data from MySQL to PostgreSQL without losing integrity. It’s common when strict auditing, reliable reporting, or PostgreSQL features like JSONB and expressive constraints are required.

If you can’t risk duplicated IDs, broken relationships or invalid data, an integrity‑oriented migration that preserves primary keys and constraints reduces risk and speeds up the cutover with confidence.

Critical challenges that break integrity between MySQL and PostgreSQL

Differences in types, collation, null semantics, “invalid” dates and ID generation make migration sensitive. Below are common issues that affect keys and constraints — we address these explicitly in mapping and validation.

MySQL PostgreSQL Note
AUTO_INCREMENT GENERATED AS IDENTITY / SEQUENCE Adjust setval after load to keep the sequence at the correct position.
TINYINT(1) boolean Map 0/1 to false/true without losing semantics.
ENUM native ENUM or CHECK Avoid values outside the domain; document the decision.
UNSIGNED integer + CHECK Recreate limits with CHECK to prevent negatives.
0000-00-00 Invalid Normalize placeholders to NULL or valid dates.
ON UPDATE CURRENT_TIMESTAMP Trigger Replicate automatic timestamp updates.
Collation case-insensitive citext or appropriate collation Preserve uniqueness without breaking case-insensitive searches.
JSON JSONB Be mindful of semantics and GIN indexes.
`backticks` in identifiers "double quotes" Adjust names and reserved words in the DDL.

Working method: from analysis to safe cutover

We structure the migration into short, verifiable stages to reduce coupling and maximize risk visibility.

  • Discovery and inventory: schemas, sizes, relationships, problematic data.
  • Type and constraint mapping: target DDL with equivalent PK, FK, UNIQUE and CHECK definitions.
  • Proof of concept: convert a critical module or table and measure impacts.
  • DDL conversion: create the schema in PostgreSQL with decided names and collation.
  • Initial load: extract, transform and load with re-runnable logs.
  • Validation: counts, deterministic samples, and referential integrity checks.
  • Cutover planning: window, sequences, final reconciliation and documented rollback.

How we preserve primary keys and constraints in practice

Keeping primary key values prevents breakage in integrations that reference IDs. We create PKs in PostgreSQL with the same column sets and adjust sequences (setval) after load so writes don’t collide.

Foreign keys are recreated with exact column and cardinality matches. When bulk loading requires a specific order, we apply DEFERRABLE/INITIALLY DEFERRED FKs and validate them afterwards, ensuring consistency without accepting invalid data.

UNIQUE and CHECK constraints are translated to maintain the same business intent. For domains we evaluate native ENUM or CHECK, prioritizing clarity and maintainability.

  • PKs preserved and sequences synchronized.
  • FKs revalidated after load, with violation reports.
  • UNIQUE/indexes reviewed to keep natural and composite keys.
  • CHECKs used to replace UNSIGNED and simple business rules.

Validation, audit and automated tests

Integrity is proven, not assumed. We automate row‑counts, partitioned checksums, sample diffs and referential integrity queries. This reduces surprises at cutover and supports later audits.

We also structure migrations and rollbacks with disciplined versioning so you can reproduce environments and revert steps if needed. See related practices in the migrations and rollbacks guide and the database versioning best practices guide.

  • Row counts and checksums per table/partition.
  • Queries to detect orphans and duplicates.
  • Domain validation (ENUM/CHECK) and unexpected NULLs.
  • Deterministic sample comparison by primary key.

Windows, zero‑downtime and realistic rollback plan

When the cutover window must be short, we evaluate Change Data Capture (CDC) to keep PostgreSQL in sync during transition. Criteria include write volume, acceptable latency and operational complexity. Learn more about zero‑downtime migration using CDC.

We also plan an actionable rollback: snapshots, checkpoints and procedures to revert quickly if needed. Recovery practices benefit from point‑in‑time recovery; see our guide on recovering a table with PITR for related context.

Project deliverables and next steps

You receive artifacts that support the migration and future operations. The package is tailored to the identified scope and risks — we don’t promise unrealistic timelines or outcomes.

  • Migration plan with mappings and technical decisions.
  • Re-runnable, documented DDL/DML scripts.
  • Post-load validation and integrity reports.
  • Cutover and rollback playbook, step by step.
  • Operational checklist for production and initial monitoring.
  • Optional: post-migration performance recommendations; see PostgreSQL tuning for ERPs.

Frequently asked questions

Is it possible to migrate with no downtime?

It depends on volume and integrations. For minimal windows we assess CDC to synchronize changes until cutover. Even then, we plan a short final switch and a prepared rollback in case of unexpected issues.

What happens to AUTO_INCREMENT and current IDs?

We preserve existing primary key values. After loading, we synchronize the PostgreSQL sequence (setval) so new inserts won’t collide. We also verify foreign references to ensure consistency.

How do you handle invalid data like 0000-00-00 or strings that exceed limits?

We normalize such data before loading, applying clear rules: fix, replace with NULL when appropriate, or reject inconsistent records. PostgreSQL constraints help prevent reintroducing invalid values.

Do MySQL triggers, procedures and views migrate automatically?

No. Logic must be reviewed and, when necessary, rewritten in PL/pgSQL or an equivalent approach. We evaluate each case and prioritize preserving business rules with minimal impact.

How do you estimate migration time and cost?

It varies by scope: database size, number of integrations, data quality, write volume and downtime tolerance. We start with an analysis to size effort, risks and an execution plan.

Can I run MySQL and PostgreSQL in parallel for a while?

Yes — with CDC replication or carefully controlled dual‑write strategies. This lowers cutover risk but increases operational complexity. We evaluate pros and cons for your context.

Ready to evaluate your MySQL → PostgreSQL migration?

Tell us about your context, downtime constraints and integrity risks. Get a tailored migration plan with validations and a clear rollback strategy.

Discuss migration

See also