Migrate MySQL to Cloud SQL with Speed and Accuracy

Overview

Enterprise-Grade MySQL to Cloud SQL Migration Suite

CloudSQL Migrator, which many developers are not familiar with, is a complete MySQL-to-Cloud SQL migration suite to help you accomplish that easily. It gets the metadata into BigQuery, the schema and code objects into Cloud SQL, and then deploys Dataflow jobs to move table data and update migration status and dashboards.

The Problems It Solves

Eliminates manual, error‑prone MySQL → Cloud SQL migrations.

Centralizes metadata capture and dependency ordering to prevent missing‑object failures.

Automates schema and code object migration (views, triggers, SPs, UDFs).

Fault tolerant data movement across tables with retries.

Progress and key outcomes in dashboards and logs for easy monitoring.

What Does the Product Do?

Collects account‑ and database‑level MySQL metadata.

Builds a migration plan and dependency order from captured metadata.

Migrates schema objects into Cloud SQL.

Converts views, stored procedures, UDFs, triggers etc to Cloud SQL

Migrates code objects (views, stored procedures, UDFs, triggers).

Data migration through Dataflow jobs.

Leveraging BigQuery status and metrics can be tracked in dashboards.

Frequently asked questions

1. What types of MySQL source databases are supported?
Pretty much anything. Whether it's on-premises MySQL setups, running as a managed service like AWS RDS or Azure MySQL, or hosted on a virtual machine (EC2/GCE), you're good to go. It supports MySQL 5.7 and 8.0, and it safely checks your database settings before moving a single row.
2. Which code objects can be automatically migrated?
It automatically grabs your Views, Triggers, Stored Procedures, and User-Defined Functions. It also figures out the exact order they need to be loaded so you don't run into annoying missing object errors when setting up on Google Cloud SQL.
3. Where is my migration data and metadata stored?
During the planning and execution phases, the tool extracts account and database level MySQL metadata and stores it directly in Google BigQuery. This central location is used to analyze dependencies, build the migration plan, and support real time tracking dashboards and logs. The final destination for your database table data, schemas, and code objects is your target Google Cloud SQL for MySQL instance
4. Is there a size limit for the MySQL databases being migrated?
It handle large, enterprise workloads by shifting the actual data movement to Google Cloud Dataflow. Dataflow is a fully managed, serverless service that scales automatically. It provisions worker instances to process data in parallel, allowing the suite to efficiently migrate large terabyte datasets. While the tool does not impose a cap on database size, practical limits are determined by your infrastructure and cloud environment:
- Target Cloud SQL Limits: The final database size cannot exceed Google Cloud SQL’s maximum storage capacity for a single instance.
- Network Bandwidth: The speed and capacity of your network connection will affect how quickly large volumes of data can move from the source to Google Cloud.
5. Does this product support minimal-downtime or live migrations?
This suite mainly acts as a highly organized batch or offline migration toolkit rather than a continuous live replication engine. The system is optimized to maximize data throughput through Dataflow, significantly reducing the migration window. It captures a consistent point-in-time snapshot, migrates structural code, and uses parallel streams to transfer data.
6. How can I track the progress or troubleshoot a failed migration?
It offers detailed, real-time tracking dashboards and resilient pipeline behaviors to simplify operations. Progress data stored in BigQuery is displayed in unified monitoring dashboards. Engineers can track real-time transfer speeds and deployment logs. If a network issue or threshold error occurs, the orchestrator pinpoint which table failed. Instead of restarting the entire migration, it triggers isolated retries at the table level, resuming data movement from the point of failure to reduce time loss.

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