Automated MySQL to PostgreSQL Sync Pipelines: CI/CD, Monitoring, and Rollback Plans

Overview

Automated MySQL→PostgreSQL sync pipelines move data continuously or in batches between systems with CI/CD-driven deployments, monitoring for data drift/errors, and rollback strategies to recover from failures.

Pipeline components

• Change capture: CDC via binlog readers (Debezium, Maxwell, custom binlog tailers) or timestamp-based incremental queries.
• Transformation: Type mapping (e.g., TINYINT→SMALLINT), SQL dialect changes, schema normalization, nullability and default handling, and data enrichment/validation.
• Load/replication: Stream writes (Kafka, streaming ETL), bulk loads (COPY, pg_bulkload), or direct upserts using idempotent SQL/UPSERT patterns.
• Schema management: Versioned migrations (Flyway, Liquibase, Sqitch) with separate MySQL and Postgres migration scripts and compatibility checks.
• Deployment/CI-CD: Pipeline repo with tests, linting, migration runners, and automated deploys (GitHub Actions, GitLab CI, Jenkins, Argo CD).
• Monitoring & alerting: Data consistency checks, lag/time-to-sync metrics, error rates, and observability (Prometheus + Grafana, ELK).
• Rollback & recovery: Point-in-time restores, replayable change logs, tombstones for deletes, and reversible migrations.

CI/CD best practices

• Single source of truth: Store schema, mappings, and transformation code in Git.
• Automated tests: Unit tests for transformations, schema validation tests, integration tests with a representative dataset, and end-to-end sanity checks.
• Staged deployments: Use environments (dev → staging → prod) with canary or blue/green for large schema changes.
• Schema compatibility checks: Static analysis to detect incompatible changes (column type shrink, dropped columns used downstream).
• Migration safety gates: Require manual approval for destructive migrations; run compatibility and backfill steps in staging first.

Monitoring & observability

• Key metrics: CDC lag, commit/transaction lag, throughput (rows/sec), failed writes, transformation error count, and schema drift incidents.
• Health checks: Row-count parity on critical tables, checksums (e.g., per-table hash), and sampled-record compare jobs.
• Alerting thresholds: High lag (> configurable seconds/minutes), error spike, checksum mismatch, or sustained throughput drop.
• Dashboards & logs: Dashboards for trends, detailed logs for failed events, and tracing for individual records through ETL paths.

Rollback and recovery strategies

• Idempotent writes: