When Google's Finance Engineering team needed to modernize their legacy data layer, they chose Spanner , a globally distributed, strongly consistent, multi-model database with high availability capabilities. But migrating to Spanner without taking production services offline was a daunting engineering challenge: As the internal team responsible for the application, we needed to manually rewrite dual-write logic across dozens of Data Access Objects (DAOs), a process that is slow and prone to human error.
Further, doing so without disruption would have required implementing multi-phase dual-write architectures across every DAO in our codebase. To solve this, we took an alternative approach: We built an automated refactoring pipeline powered by Antigravity CLI in headless mode. This helped us accelerate our migration velocity significantly while maintaining strict data parity in our staging environments as we prepare for production. When migrating high-throughput production services where financial accuracy is essential, simple cutover scripts do not work.
You must verify that both the legacy datastore and Spanner receive identical writes simultaneously until all the historical data backfills and verifications are complete. We structured our migration across three distinct phases: Historical backfill: Copying existing historical records to Spanner while maintaining referential integrity. Dual-write / dual-read implementation: Modifying every DAO to write mutations to both the primary store and Cloud Spanner in parallel during the migration window.
Automated API verification and parity checking: Intercepting RPC traffic and verifying end-to-end that every write lands with byte-for-byte equivalence across both stores. The architectural pattern is clean, but at our scale, we began to encounter friction.