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. The challenge: Anatomy of a dual-write migration 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. That’s because each DAO requires: A dedicated MutationConverter class mapping complex domain models to Spanner schema columns Dual-write branch handling and rollback or error-reporting logic A suite of unit tests verifying both primary and Spanner writes using fake time sources and test doubles (FakeTimeSource) Performing these identical, high-precision code changes across 30+ DAOs by hand would have taken months of engineering time. The solution: Standardized mutation converter patterns To verify that our automation pipeline could reliably generate clean code, we first standardized our DAO refactoring pattern around a decoupled MutationConverter interface. Instead of embedding raw Spanner table names and column assignments directly inside core DAO business logic, we isolate Spanner schema translation into dedicated converter units: code_block <ListValue: [StructValue([('code', '// Example of the standardized pattern generated by our pipeline\r\n\r\ntype BpcTransferAmountsMutationConverter interface {\r\n ToInsertMutation(entity *model.BpcTransferAmount) (*spanner.Mutation, error)\r\n ToUpdateMutation(entity *model.BpcTransferAmount) (*spanner.Mutation, error)\r\n}\r\n\r\ntype bpcTransferAmountsMutationConverterImpl struct {\r\n tableName string\r\n}\r\n\r\nfunc (c *bpcTransferAmountsMutationConverterImpl) ToInsertMutation(entity *model.BpcTransferAmount) (*spanner.Mutation, error) {\r\n if entity == nil {\r\n return nil, errors.New("entity cannot be nil")\r\n }\r\n \r\n // Map domain fields to Cloud Spanner table schema\r\n cols := []string{"TransferId", "AmountCents", "CurrencyCode", "LastModifiedTimestamp"}\r\n vals := []interface{}{\r\n entity.TransferId,\r\n entity.AmountCents,\r\n entity.CurrencyCode,\r\n spanner.CommitTimestamp, // Use Spanner commit timestamps\r\n }\r\n \r\n return spanner.Insert(c.tableName, cols, vals), nil\r\n}'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f874c0fca10>)])]> By establishing a rigid, deterministic contract between the DAO and the Spanner SDK (spanner.Mutation), we created an exact target specification that an AI codi
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