Migrating from a relational database to a document database involves three main steps: schema redesign, data migration, and application adjustments. The process requires careful planning to account for differences in data modeling, query patterns, and transactional behavior between the two systems. Start by analyzing your existing relational schema and application requirements to determine how data should be structured in the document model.
First, redesign the schema to fit the document database’s strengths. Relational databases normalize data into tables, while document databases like MongoDB or Couchbase store nested, self-contained documents. For example, an e-commerce application’s orders table with related order_items might become a single document with an array of items. This reduces joins but may require denormalization. Identify entities that benefit from being grouped together, such as user profiles with embedded addresses. Avoid overcomplicating the schema—document databases tolerate flexible structures, but overly nested data can make queries inefficient. Tools like schema migration scripts or visual designers (e.g., MongoDB Compass) can help prototype the new structure.
Next, migrate the data. Use ETL (Extract, Transform, Load) tools or custom scripts to transfer data from the relational database to the document database. For instance, PostgreSQL’s pg_dump can export data, which can then be transformed into JSON documents using a Python script. Handle data type conversions explicitly—dates, binary data, or enums may need special handling. Validate the transformed data for consistency: ensure all required fields are present, and relationships (like foreign keys) are either embedded or replaced with references. For large datasets, perform the migration incrementally to minimize downtime. Test the migrated data with sample queries to verify performance and correctness.
Finally, update the application layer. Replace SQL queries with the document database’s query language (e.g., MongoDB’s aggregation pipeline) and adjust API calls. For example, a SQL JOIN might become a $lookup operation, but embedding data often eliminates the need for joins altogether. Modify transaction logic, as document databases may not support multi-document ACID transactions by default (though some, like MongoDB, offer limited support). Optimize indexing—document databases require indexes on frequently queried fields, similar to relational systems. Gradually roll out the new database alongside the old one, using feature flags or dual writes, to catch issues early. Monitor performance and adjust the schema or queries as needed post-migration.