Migrating from PostgresToMysql: A Step‑by‑Step Guide

Fast and Reliable PostgresToMysql Migration StrategiesMigrating a production database from PostgreSQL to MySQL (PostgresToMysql) is a substantial undertaking that requires careful planning, tooling choices, validation, and rollback mechanisms. This article outlines practical strategies to ensure migrations are fast, reliable, and minimize downtime. It covers preparatory steps, schema and data conversion, tooling options, performance considerations, testing, and cutover techniques.

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When and why you might migrate PostgresToMysql

• Cost or licensing considerations: MySQL variants may offer different licensing or hosting costs.
• Ecosystem compatibility: Your application stack or third‑party tools may have better support for MySQL.
• Operational familiarity: Teams with more MySQL expertise might prefer its operational characteristics.
• Performance characteristics: For certain workloads (e.g., simple reads/writes, high-concurrency key-value access), MySQL engines may be advantageous.

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High-level migration approaches

Choose the approach that best balances downtime, complexity, and risk:

• Dump and restore (bulk): Fast for one-off migrations with acceptable downtime.
• Logical replication and sync: Minimize downtime by streaming changes from Postgres to MySQL during a cutover window.
• Dual writes (application-level): Application writes to both databases until the target is ready.
• ETL pipelines: Transform and load with tools like Apache NiFi, Airbyte, or custom scripts for complex transformations.

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Preparatory steps

1. Inventory and analyze:

   • List schemas, tables, indexes, constraints, triggers, views, stored procedures, extensions, data types, and client drivers.
   • Identify PostgreSQL-specific features in use (arrays, JSONB, custom types, window functions, partial indexes, full-text search with tsvector, etc.).

2. Define success criteria:

   • Acceptable downtime, data consistency levels, performance targets, and validation checks.

3. Choose target MySQL variant:

   • MySQL Community, MariaDB, or MySQL-compatible cloud offerings (Amazon Aurora MySQL). Variant affects feature set and migration path.

4. Test environment:

   • Create a staging environment that mirrors production data size and topology for rehearsal.

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Schema conversion: key differences and workarounds

• Data types:

  • PostgreSQL arrays, enums, and composite types need modeling in MySQL (JSON columns, separate tables, or VARCHAR).
  • Replace PostgreSQL SERIAL with MySQL AUTO_INCREMENT or use explicit sequences emulated via tables.
  • Map JSONB to JSON (MySQL 5.7+) but check behavior differences (indexing, functions).

• Indexes and constraints:

  • Partial and expression indexes in Postgres must be implemented via filtered queries, additional columns, or manual maintenance.
  • Full-text search differs: consider migrating to MySQL full-text or external search engines (Elasticsearch).

• Functions, stored procedures, and triggers:

  • Rewrite PL/pgSQL functions and triggers into MySQL equivalents (stored procedures in SQL/PSM), or move logic into application layer.

• Views:

  • Materialized views must be emulated (tables + refresh jobs) because MySQL historically lacks native mat. views (check your MySQL version).

• Extensions:

  • Replace Postgres extensions (like PostGIS) with MySQL-compatible alternatives (e.g., MySQL spatial extensions) or external services.

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Data migration strategies

1. Bulk dump and load (fast initial copy)

   • Use pg_dump –data-only –column-inserts or pg_dump –format=custom combined with tools like myloader or LOAD DATA INFILE where appropriate.
   • Pros: simple, fast for initial load. Cons: downtime proportional to load time unless you combine with sync.

2. Logical replication and CDC (minimal downtime)

   • Use logical decoding (pg_logical, wal2json) and a CDC tool (Debezium, Maxwell-like, or proprietary) to stream changes into MySQL.
   • Requires mapping of DDL and careful handling of transactions, large BLOBs, and type conversions.

3. Two-phase migration (bulk + sync)

   • Stage 1: Bulk copy most data during a maintenance window.
   • Stage 2: Start CDC to capture ongoing changes and apply them to MySQL until cutover.

4. Dual writes (application-level)

   • Have the application write to both databases during a transition. Use idempotent writes and reconcile differences later.
   • Risky: increases application complexity and potential for inconsistency.

5. Incremental ETL for large datasets

   • Use batch ETL processes partitioned by time or primary key ranges. Useful when tables are huge and can’t be copied in one shot.

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Tools and utilities

• Schema converters:

  • pgloader — automates much of schema/data conversion and supports live migrations with minimal downtime.
  • AWS SCT (Schema Conversion Tool) — helpful when migrating to Aurora MySQL.
  • Custom scripts (Python, Go) when logic is complex.

• CDC and replication:

  • Debezium (Kafka-based) — captures Postgres WAL via logical decoding and publishes change events, which can be consumed and applied to MySQL.
  • Maxwell or Bottled Water-style tools — lighter-weight options for streaming.
  • SymmetricDS — database replication with transformations.

• Data loading:

  • LOAD DATA INFILE / myloader for fast bulk loads.
  • Percona Toolkit for consistency checks.

• Testing and validation:

  • pt-table-checksum and pt-table-sync for comparing and syncing row-level data.
  • Custom checksums or hash-based comparisons for large tables.

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Mapping complex features: examples

• JSONB:

  • Map to MySQL JSON; rework queries to use MySQL JSON functions; reindex using generated columns for performance.

• Arrays:

  • Normalize into child tables or encode as JSON when appropriate.

• Sequences and SERIAL:

  • Use AUTO_INCREMENT or emulate sequences with a dedicated table and application logic.

• Partial indexes:

  • Add boolean flag columns and index those columns, maintaining them with triggers or application updates.

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Testing strategy

• Functional tests:
  • Run application test suite against the MySQL staging DB.
• Performance tests:
  • Benchmark critical queries; test load patterns with tools like sysbench, JMeter, or pgbench-equivalents.
• Consistency checks:
  • Row counts, checksums, sample queries, and pt-table-checksum.
• Chaos testing:
  • Simulate network partitions, failovers, and rollbacks.

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Cutover techniques (minimizing downtime)

• Read-only window:
  • Make Postgres read-only, run final sync, then switch application to MySQL. Downtime = time to apply final delta + DNS/connection switch.
• Rolling cutover:
  • Route a subset of traffic to MySQL, gradually increase while monitoring errors and performance.
• Blue-green:
  • Deploy new app instances pointing to MySQL while keeping old instances on Postgres; switch traffic at the load balancer once validated.
• Dual reads for validation:
  • Read from both DBs and compare results before fully switching writes.

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Validation and rollback

• Pre-cutover validation:
  • Run checksums, sample queries, and end-to-end tests.
• Post-cutover monitoring:
  • Track error rates, slow queries, replication lags, and application logs.
• Rollback plan:
  • Keep Postgres read-only snapshot; be ready to switch back within a defined window if critical issues arise.
• Data drift resolution:
  • Use pt-table-sync, manual reconciliation scripts, or replay CDC logs to fix discrepancies.

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Performance tuning after migration

• Rebuild and tune indexes according to MySQL optimizer behavior.
• Use EXPLAIN ANALYZE (MySQL) to identify slow queries; adjust schema (indexes, denormalization) or queries.
• Configure MySQL buffer pool, query cache (if applicable), and connection pool settings.
• Use generated columns and JSON indexes for JSON-heavy schemas.

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Security and compliance considerations

• Keep data encrypted in transit (TLS) and at rest.
• Ensure user privileges and roles are mapped securely; avoid overly broad grants.
• Audit logging and monitoring to meet compliance requirements.

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Example migration checklist (concise)

• Inventory features and size.
• Choose migration approach and tools.
• Convert schema and create staging MySQL DB.
• Perform bulk data copy; validate row counts and checksums.
• Start CDC to capture new changes.
• Test application against MySQL; run perf tests.
• Plan cutover window; communicate to stakeholders.
• Execute cutover; validate; monitor.
• Rollback if required; finalize decommissioning.

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Final notes

Successful PostgresToMysql migrations combine careful schema translation, reliable data-sync (CDC) strategies, thorough testing, and clear rollback plans. Prioritize minimizing downtime and preserving data integrity through verification at every stage.