Decomposing a core relational database inside a five-year-old monolith is one of the riskier operations an engineering team can undertake. In this architecture breakdown, we trace the exact steps taken to carve out our user authentication subsystem into an isolated service without taking a single second of scheduled maintenance window.
Designing the Dual-Write Synchronization Phase
Our first objective was decoupling read operations from the legacy monolith database. We deployed the new microservice alongside a replica database and configured a Change Data Capture pipeline using Debezium and Kafka. Every write to the legacy Postgres database streamed changes to the new service database in under forty milliseconds.
To verify data parity before routing real users, we ran a shadow validation worker that compared query outputs between both systems in real time. Discrepancies were logged without impacting live requests, allowing us to fix edge-case data synchronization bugs early.
Flipping Read and Write Traffic Safely
Once parity was sustained for seven consecutive days, we shifted read traffic over to the new service using feature flags incremented by five percent daily. If error rates surpassed zero point zero one percent, automated rollbacks were configured to revert routing instantly.
The final stage involved reversing the CDC pipeline to stream writes from the new service back to the legacy schema during the write cutover. This guaranteed that legacy services dependent on the old database table continued functioning while downstream callers updated their endpoints.
Lessons Learned on Data Boundaries
Decoupling services is primarily a data management challenge, not a network infrastructure exercise. Investing heavily in automated data parity validation and shadow traffic routing gave our team the confidence to switch primary data stores without customer disruption.