MongoDB to PostgreSQL Migration Checklist
A readiness checklist for migrating MongoDB documents to a relational PostgreSQL model. It covers collection profiling, target-model choice between normalized tables and JSONB, query rewriting, and reconciliation. Use it to map flexible documents to structured data without surprises.
When to Use This Checklist
Use this checklist when migrating from MongoDB, a document database, to PostgreSQL, a relational database. Teams make this move to gain strong consistency, joins, transactions, and SQL tooling, or to consolidate on a single engine. The core challenge is reconciling MongoDB's flexible, schemaless documents with PostgreSQL's structured tables, although PostgreSQL's JSONB type offers a useful middle ground.
How to Use This Checklist
Start with discovery: profile collections to learn how much documents actually vary, since real-world MongoDB data is often less uniform than expected. Then choose a target model. Fully normalized tables maximize relational benefits; JSONB columns preserve flexibility; a hybrid is common. Map embedded documents and arrays deliberately, and plan for inconsistent fields rather than assuming uniformity. Treat reconciliation and rollback as required gates before cutover.
What Good Looks Like
A well-run migration is built on accurate collection profiling, so the target model fits the real data, not an idealized version. Embedded structures and arrays are mapped intentionally to relations or JSONB, primary keys and constraints are introduced where the source had none, and inconsistent fields are handled explicitly. Queries are rewritten and benchmarked, JSONB columns are indexed with GIN where needed, and document counts reconcile to the target. A parallel run and rollback plan protect the cutover.
Common Pitfalls
The biggest trap is assuming documents are uniform; hidden field variations break a rigid relational mapping. Forcing full normalization onto naturally document-shaped data can hurt both performance and developer ergonomics, when JSONB would serve better. Teams underestimate query-rewrite effort from MongoDB's query language to SQL. Missing GIN indexes make JSONB queries slow. Finally, skipping reconciliation lets dropped or malformed documents reach production unnoticed.
Related Resources
Use schema-evolution-schema-registry discipline as you impose structure, and apply domain-driven-design-ddd to model entities well. Migrate incrementally with the expand-and-contract-migration-pattern and verify parity with data-quality-management.