How we cut integration sync time by splitting the pipeline

Port redesigned its integration architecture by separating data extraction in Ocean from transformation, loading, reconciliation, and event handling in a new data-source processor (DSP), addressing the reliability and scaling constraints of its former single-process pipeline. Previously, expensive JQ transformations, catalog delays, memory failures, and API rate limits could stall or restart entire syncs, while changes to mappings required refetching third-party data. DSP stores raw batches in object storage, distributes processing through queue-driven workers, tracks run state in Postgres, and supports independent retries, dead-letter handling, observability, and replaying stored data when mappings change. Reconciliation occurs only after extraction ends and catalog writes finish, preventing deletions based on incomplete syncs, while apply-mapping workflows manage ordering with live webhook events. Built in Go after tests showed substantially faster JQ performance than TypeScript, the system was rolled out gradually behind feature flags; early customer integrations fell from roughly three to four hours to about 45 minutes, although integrations limited primarily by GitHub extraction can still take much longer.

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