MongoDB to Postgres Migration: Proven Methods, Key Steps, and a Zero-Downtime Strategy

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Is Migrating from MongoDB to Postgres Right for Your Business?

Yes, migrating from MongoDB to Postgres is a strategic business decision. Consider migrating to Postgres for the reasons below.

• Data Integrity and Consistency: PostgreSQL enforces a strict schema and ACID compliance. This is useful in case your application handles financial transactions, healthcare records, and maintains strict consistency across mission-critical applications.
• Cost Optimization: Query performance will become efficient. This lower infrastructure requirement is reportedly expected to reduce database costs.
• Data validation: Postgres does data validation in the database layer itself. This lowers reliance on application-side checks and reduces data inconsistencies.
• Structured Data: Postgres provides better organization, validation, and querying performance for structured data when compared to MongoDB.
• Advanced Querying and Performance: Postgres excels at complex SQL queries, joins, and analytical functions. This feature improves the performance for relational workloads compared to MongoDB’s aggregation framework.
• Mature ecosystem and Community: Postgres poses a vast and mature ecosystem with extensive tooling, support, and a large active community. This offers robust resources for development and troubleshooting.

Comparing the Main Migration Methods

The two main migration methods for moving data from MongoDB to Postgres

1. Manual ETL Process: This method involves extracting, transforming, and loading data manually using command-line tools. Extract data using MongoDB’s built-in tools like mongoexport, typically in CSV or JSON format. Transform the data schema to fit the Postgres relational model and create tables matching the exported data structure. Using the Postgres copy command to load the exported files directly into new tables.
   Pros: 
   • Simple for small datasets or one-time migrations
   Cons: 
   • Due to human negligence, the chance of errors is there.

2. No-Code Data Integration Platforms: It means migrating, replicating, or capturing data from one database system to another without writing any code. They also provide a User Interface (UI) for easy interaction.
   Pros: 
   • Due to the intuitive interface, no coding is required.
   • Scales easily with data volume.
   Cons:
   • Requires platform setup
   • Licensing or service costs are more

Pre-Migration Assessment and Planning

Pre-migration assessment and planning are critical steps for a successful MongoDB to Postgres migration. 

1. Data compatibility and Schema Mapping: Evaluate your existing data structures, including document shapes, nested fields, and denormalized patterns of MongoDB collections. Design the Postgres design schema to optimize relational constraints, normalization, indexes, and data integrity rules.
2. Migration strategy: Decide on migration approach, big bang (full downtime) vs phased migration. Choose the right tools or platforms based on data size, complexity, and team expertise.
3. Risk Assessment and Mitigation: Neatly creates all the detailed test case that covers all data types, edge cases, and business logic validations. Document success criteria and create checkpoints for data accuracy, performance, and application functionality.

Step-by-Step MongoDB to Postgres Migration Process

The step-by-step MongoDB to Postgres Migration process involves the steps below.

1. Evaluate the MongoDB database: 

Understand the MongoDB structure, collections, document schemas, and data sizes. Hereby, we understand the relationships and data types.

2. Postgres Database Setup: 

Set up the Postgres database server and configure storage, compute, and networking based on the capacity planning assessment. Define tables, primary keys, and foreign key relationships. Create necessary tables with appropriate data types and constraints. Consider using Postgres JSONB types if some document flexibility is needed.

3. Choose Migration method: 

Consider choosing from any of the two migration methods, like manual scripts or ETL tools. For one-time migration, use manual scripts, and for bulk transfer, use ETL tools.

4. Data Migration: 

Use mongoexport to export data from MongoDB collections into JSON or CSV format. Flatten nested structures into related tables. Execute ETL pipeline to load a complete, initial snapshot of your data from MongoDB into Postgres.

5. Load data into Postgres:

Using the Postgres copy command, start importing data files. Insert transformed data into relational tables. Create required indexes to improve query execution. 

6. Verify and test data accuracy:

Verify that all the data has been accurately migrated and that relationships between tables are correctly established. Compare sample queries between the MongoDB and Postgres databases. If any mismatches occur, resolve them immediately.

7. Application code Updates:

Modify your application code to connect to the Postgres database. Rewrite the MongoDB-specific queries to use SQL syntax.

8. Plan Cutover:

Schedule a downtime period for the final cutover of the MongoDB. Closely monitor the Postgres and application after the cutover to identify and address any issues.

9. Monitor and Optimize:

Continuously monitor application and database performance. Track query performance, index utilization, and optimize the workload.

Common Data Mapping Patterns and Examples

MongoDB to Postgres migration needs careful consideration of data mapping between them. The common data patterns are

1. Flattening Nested Documents: MongoDB stores nested documents, arrays, and variable fields in a single collection. Postgres requires defined tables, primary keys, and relationships. A common pattern is normalizing nested documents into separate tables. 
   Example:
   A user profile document that contains multiple address objects would be split into a users table and an address table connected by a foreign key. This ensures consistency, prevents duplication, and enables relational queries.

2. Arrays to Join Tables: Arrays are commonly mapped to one-to-many relational tables in Postgres. Each element in the MongoDB array becomes a row in a related table.
   Example:
   For instance, a product document that includes an array of tags would become a product table and a separate product-tags table, where each tag is represented as an individual row.

3. Unstructured or Dynamic Data (JSONB): If certain fields in MongoDB have variable schemas, Postgres stores them as JSON blobs. Store such fields as JSONB to preserve flexibility while allowing indexing.
   Example:
   Postgres features like JSONB or semistructured fields. If certain document fields vary and do not justify full normalization, JSONB columns can preserve flexibility while still providing indexing and query capabilities.

4. Embedded Documents (Normalization): MongoDB often embeds related data in the same document. Postgres uses normalized referencing.
   Example: 
   Evaluate whether embedded data should be a separate table or kept in JSONB. Normalize for relational operations or keep embedding for performance.

Tools and Technologies for a Smooth Migration

Several tools and technologies facilitate smooth migration from MongoDB to Postgres.

• Built-in DB Tools: MongoDB command line utilities like mongoexport and mongodump will help to extract data in either JSON or CSV formats. Postgres tools like COPY command, psql, pg_dump, pg_restore are efficient for bulk data loading, dumping/restoring Postgres schema and data during finalizations.
• Data transformation and Initial Load (ETL): Extract, Transform, Load, and data integration platforms such as Airbyte, Fivetran, Talend Data Integration, Stitch, Hevo Data, and TapData are used for large, complex, or continuous data migrations. They help in automating data extraction, transformation, and loading while supporting schema conversion for nested documents.
• Change Data Capture (CDC): It is the technology that ensures the target Postgres database stays in sync with the MongoDB source during the migration. For ongoing synchronization and change-data capture, Kafka Connect and Debezium enable real-time screening.
• Specialized Migration: An open-source npm package (Node.js) that simplifies data migration by using Mongoose and Knex.js ORMS for data transfer. The Tyk Migration tool is used for the Tyk API gateway to migrate configuration and user data from MongoDB to PostgreSQL.

Testing, Validation, and Performance Tuning

Testing, validation, and Performance tuning are essential to ensure data integrity, application functionality, and business continuity. Testing is essential to ensure the accuracy and stability of the data. Run sample queries for both MongoDB and Postgres. 

Validation includes reviewing schema constraints, data types, and handling of values that may behave differently between document and relational databases. Perform a count comparison between the MongoDB collections and Postgres tables. Performance tuning is crucial because relational join operations in Postgres differ from those in MongoDB. Postgres features such as EXPLAIN ANALYZE and query statistics help to identify bottlenecks.

Common Risks and How Entrans Mitigates Them

Common risks in migrating from MogoDB to Postgres include challenges related to schema design, data transformation, data integrity, downtime, and query rewriting.

• Schema Design and Normalization Complexity: MongoDB’s flexible, schema-less JSON documents must be carefully normalized into Postgres’s strict relational tables, which requires handling nested documents, arrays, and many-to-many relationships correctly. To handle this, Entrans performs deep audits of MongoDB data, existing queries, and application dependencies. We design a detailed, normalized Postgres schema and develop robust transformation pipelines tailored to the business logic.
• Data Integrity and Completeness: During migration, some data can be lost. MongoDB and Postgres have different native data types. To handle this, we enforce relational integrity by helping define and apply Postgres constraints that were absent in MongoDB. It performs automated data cleansing and type conversions to ensure data conforms to the new schema’s rules.
• Downtime and Service Disruption: For a large volume of data scale migration, downtime of MongoDB is essential. If the loading process is slow or an error happens during migration, the business can face loss of revenue and customer frustration. To overcome this, Entrans utilizes CDC to capture real-time changes happening in the MongoDB source during the migration process.
• Rewriting Application Logic: When the data structure changes, the application code interacts with the database. The code needs to be rewritten for the new Postgres scheme and SQL query language. To overcome this, Entrans automatically generates initial boilerplate application code based on new PostgreSQL.

Cutover Planning and Zero-Downtime Tactics

Perfect planning in MongoDB to Postgres migration ensures zero downtime or near-zero downtime cutover to minimize business disruption. The migration begins with a staging environment to validate data mappings, relational schemas, and application query behavior. Ensure that Postgres is fully synchronized with MongoDB. Verify the data of both MongoDB and Postgres. Once Postgres is stable, change the application’s ability to handle traffic, and change the application’s configuration to read and write to Postgres.

The primary tactic for zero-downtime is continuous replication via CDC and a dual-write/read-pivot strategy. Utilize MongoDB’s oplog and Kafka for continuous incremental data capture and streaming to Postgres. Use Apache Spark to manage backpressure and schema transformations dynamically.

Post-Migration Optimization and Maintenance

A successful MongoDB to Postgres migration will become successful only when the post-migration steps are taken care of.

• Performance optimization: The priority is analyzing query performance, reviewing executing plans, and tuning indexes based on real usage patterns. Continuously monitor applications for bottlenecks and anomalies. Utilize the cloud-native features for better scalability.
• Postgres Configuration Tuning: Postgres settings are generic. Database administrators should review foreign key constraints, update statistics, and enable routine vacuuming.
• Backup: Regular backup and snapshot policies must be updated to reflect the new environment, along with automated alerting for query latency. Continuously monitoring ensures long-term performance stability and data reliability.

Why Choose Entrans for MongoDB to Postgres Migration

MongoDB’s drawbacks make us fall behind. Postgres is the preferred alternative and gives us optimized indexing and powerful extensions. At Entrans, we specialize in MongoDB and Postgres migration services. Our team of experts is dedicated to ensuring a seamless migration with minimal downtime and maximum efficiency. We offer customized solutions based on your business needs, so whether you require a fully managed migration or a more hands-on approach, we have the right expertise to support you.

Planning to migrate from MongoDB to Postgres, Entrans is here to help. Book a 20-minute Consultation today.