Here are some considerations when planning a trial or actual migration from Cloud SQL To Cloud Spanner on GCP.

Design Spanner Schema

  • Adapt Schema for Spanner: Spanner does not support certain SQL features like joins and some types of foreign key constraints. Design your schema accordingly:
    • Denormalization: Consider denormalizing some tables to optimize for Spanner's distributed architecture.
    • Primary Keys: Define primary keys that include a globally unique identifier (e.g., a UUID) and a sharding key to optimize data distribution.
  • Data Types: Map SQL data types to Spanner-supported types (e.g., INTEGER to INT64, VARCHAR to STRING).

3. Set Up Cloud Spanner Instance

  • Create Spanner Instance: Go to the Google Cloud Console and create a new Spanner instance, selecting the appropriate configuration (regional or multi-regional).
  • Create Database: Within the instance, create a new database and set up the initial schema based on your design.

4. Data Migration

  • Choose a Migration Tool: Consider using one of the following tools for data migration:
    • Google Cloud Dataflow: Use Dataflow to read from Cloud SQL and write to Spanner.
    • Custom Scripts: Write custom ETL scripts to extract data from Cloud SQL and load it into Spanner.
    • Database Migration Service (DMS): This service can facilitate the migration while minimizing downtime, especially for large datasets.
  • Data Transfer: Execute the migration, ensuring data integrity:
    • For large datasets, perform the migration in batches to reduce load and avoid timeouts.
    • Monitor for errors and address any data type or constraint issues that arise during migration.

5. Testing and Validation

  • Data Validation: After the migration, validate that the data in Spanner matches the original data in Cloud SQL:
    • Compare row counts, checksums, and sample data.
  • Application Testing: Update your application to point to the Spanner instance and conduct thorough testing:
    • Test all critical queries and transactions.
    • Monitor performance and adjust queries as needed to optimize for Spanner.

6. Adjust Application Logic

  • Update Connection Strings: Modify your application to connect to Cloud Spanner instead of Cloud SQL.
  • Refactor Queries: Depending on how the application uses SQL, refactor queries to align with Spanner's capabilities, especially for joins and transactions.
  • Connection Pooling: Implement connection pooling to manage connections efficiently.

7. Final Cutover

  • Plan for Downtime: Schedule a maintenance window for the final cutover to minimize disruption.
  • Sync Data: If using a continuous replication method, ensure any changes made in Cloud SQL since the initial migration are synced to Spanner.
  • Switch Applications: Redirect your applications to the new Spanner database and monitor for issues.

8. Post-Migration Activities

  • Monitoring and Optimization: Use Google Cloud Monitoring to keep an eye on performance metrics. Optimize queries and indexes based on usage patterns.
  • Backup and Recovery: Set up automated backups for the Spanner database.
  • Decommission Cloud SQL: Once you're confident in Spanner's stability, decommission the Cloud SQL instance.

Best Practices

  • Documentation: Keep detailed documentation of the migration process, including any changes made to the schema and application logic.
  • Incremental Migration: If feasible, consider an incremental migration strategy, migrating parts of the database or specific functionalities gradually.
  • Rollback Plan: Have a rollback plan in case of critical issues during the cutover process.