Downtime during a database migration can tank a production app—trust me, I’ve been there. As a solution design, I tackled this head-on, designing a cloud-native, event-driven solution using Kafka, AWS, and microservices. Stick around to see how I replaced DynamoDB with MongoDB without breaking a sweat.
Here’s how I tackled this at scale: a phased, zero-downtime migration using AWS, Kafka, and event-driven microservices. Check out this diagram for the flow:
The approach introduces an abstraction layer between services and databases, leveraging Kafka for event-driven reliability. Here’s how it works:
Phase 1: Current State
Data Flow:
- The Writer Service writes data to Amazon DynamoDB.
- Reader Services fetch data from DynamoDB.
- Archival Service moves older data to an Amazon S3 Bucket for storage.
Retention Policy: Data in DynamoDB is retained for 15 days before archival.
Phase 2: Transition (Dual Write & Validation)
Data Replication:
- Introduce a Kafka Sink Connector to sync data from Writer Service to MongoDB.
- Continue writing to DynamoDB while also storing data in MongoDB.
New Archival Process:
- A new S3 bucket is created to store data from MongoDB.
New Service Introduction:
- A New Service API is introduced to provide the same responses as the existing database.
- A toggle flag is implemented in the API to switch between DynamoDB and MongoDB.
Validation Steps:
- Validate responses from New Service against existing Reader Services.
- Ensure consistency in both S3 archival processes.
Phase 3: Full Migration & Decommissioning
Switching Data Source:
- Change the toggle flag to make Reader Services fetch data from MongoDB.
Enhancements & Optimization:
- Fully transition reading services to the New Service.
Decommissioning Old Infrastructure:
- Decommission DynamoDB and remove dependency on the old archival process.
Final Validation & Completion:
- Validate that all data is successfully migrated and all services function as expected.
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