Migrate from AWS to Google Cloud: Migrate from Amazon RDS for SQL Server to Cloud SQL for SQL Server

Last reviewed 2024-06-28 UTC

Google Cloud provides tools, products, guidance, and professional services to migrate from Amazon Relational Database Service (RDS) to Cloud SQL for SQL Server.

This document is intended for cloud and database administrators who want to plan, implement, and validate a database migration project. It's also intended for decision makers who are evaluating the opportunity to migrate and want an example of what a migration might look like.

This document focuses on a homogeneous database migration, which is a migration where the source and destination databases are the same database technology. The source is Amazon RDS for SQL Server, and the destination is Cloud SQL for SQL Server.

This document is part of a multi-part series about migrating from AWS to Google Cloud that includes the following documents:

For this migration to Google Cloud, we recommend that you follow the migration framework described in Migrate to Google Cloud: Get started.

The following diagram illustrates the path of your migration journey.

Migration path with four phases.

You might migrate from your source environment to Google Cloud in a series of iterations—for example, you might migrate some workloads first and others later. For each separate migration iteration, you follow the phases of the general migration framework:

  1. Assess and discover your workloads and data.
  2. Plan and build a foundation on Google Cloud.
  3. Migrate your workloads and data to Google Cloud.
  4. Optimize your Google Cloud environment.

For more information about the phases of this framework, see Migrate to Google Cloud: Get started.

To design an effective migration plan, we recommend that you validate each step of the plan, and ensure that you have a rollback strategy. To help you validate your migration plan, see Migrate to Google Cloud: Best practices for validating a migration plan.

Assess the source environment

In the assessment phase, you determine the requirements and dependencies to migrate your source environment to Google Cloud.

The assessment phase is crucial for the success of your migration. You need to gain deep knowledge about the workloads you want to migrate, their requirements, their dependencies, and about your current environment. You need to understand your starting point to successfully plan and execute a Google Cloud migration.

The assessment phase consists of the following tasks:

  1. Build a comprehensive inventory of your workloads.
  2. Catalog your workloads according to their properties and dependencies.
  3. Train and educate your teams on Google Cloud.
  4. Build experiments and proofs of concept on Google Cloud.
  5. Calculate the total cost of ownership (TCO) of the target environment.
  6. Choose the migration strategy for your workloads.
  7. Choose your migration tools.
  8. Define the migration plan and timeline.
  9. Validate your migration plan.

For more information about the assessment phase and these tasks, see Migrate to Google Cloud: Assess and discover your workloads. The following sections are based on information in that document.

The database assessment phase helps you choose the size and specifications of your target Cloud SQL database instance that matches the source for similar performance needs. Pay special attention to disk size and throughput, IOPS, and number of vCPUs. Migrations might struggle or fail due to incorrect target database instance sizing. Incorrect sizing can lead to long migration times, database performance problems, database errors, and application performance problems. When deciding on the Cloud SQL instance, keep in mind that disk performance is based on the disk size and the number of vCPUs.

The following sections rely on Migrate to Google Cloud: Assess and discover your workloads, and integrate the information in that document.

Build an inventory of your Amazon RDS instances

To define the scope of your migration, you create an inventory and collect information about your Amazon RDS instances. Ideally, the process should be automated, because manual approaches are prone to error and can lead to incorrect assumptions.

Amazon RDS and Cloud SQL might not have similar features, instance specifications, or operations. Some functionalities might be implemented differently or be unavailable. Areas of difference might include infrastructure, storage, authentication and security, replication, backup, high availability, resource capacity model and specific database engine feature integrations, and extensions. Depending on the database engine type, instance size, and architecture, there are also differences in the default values of database parameter settings.

Benchmarking can help you to better understand the workloads to be migrated and helps you define the right architecture of the migration target environment. Collecting information about performance is important to help estimate the performance needs of the Google Cloud target environment. Benchmarking concepts and tools are detailed in the Perform testing and validation phase of the migration process, but they also apply to the inventory building stage.

Tools for assessments

For an initial overview assessment of your current infrastructure, we recommend that you use Google Cloud Migration Center along with other specialized database assessment tools such as migVisor and Database Migration Assessment Tool (DMA).

With Migration Center, you can perform a complete assessment of your application and database landscape, including the technical fit for a database migration to Google Cloud. You receive size and configuration recommendations for each source database, and create a total cost of ownership (TCO) report for servers and databases.

For more information about assessing your AWS environment by using Migration Center, see Import data from other cloud providers.

In addition to Migration Center, you can use the specialized tool migVisor. migVisor supports a variety of database engines and is particularly suitable for heterogeneous migrations. For an introduction to migVisor, see the migVisor overview.

migVisor can identify artifacts and incompatible proprietary database features that can cause migration defaulting, and can point to workarounds. migVisor can also recommend a target Google Cloud solution, including initial sizing and architecture.

The migVisor database assessment output provides the following:

  • Comprehensive discovery and analysis of current database deployments.
  • Detailed report of migration complexity, based on the proprietary features used by your database.
  • Financial report with details on cost savings post migration, migration costs, and new operating budget.
  • Phased migration plan to move databases and associated applications with minimal disruption to the business.

To see some examples of assessment outputs, see migVisor - Cloud migration assessment tool.

Note that migVisor temporarily increases database server utilization. Typically, this additional load is less than 3%, and can be run during non-peak hours.

The migVisor assessment output helps you to build a complete inventory of your RDS instances. The report includes generic properties (database engine version and edition, CPUs, and memory size), as well as details about database topology, backup policies, parameter settings, and special customizations in use.

If you prefer to use open source tools, you can use data collector scripts with (or instead of) the mentioned tools. These scripts can help you collect detailed information (about workloads, features, database objects, and database code) and build your database inventory. Also, scripts usually provide a detailed database migration assessment, including a migration effort estimation.

We recommend the open source tool DMA, which was built by Google engineers. It offers a complete and accurate database assessment, including features in use, database logic, and database objects (including schemas, tables, views, functions, triggers, and stored procedures).

To use DMA, download the collection scripts for your database engine from the Git repository, and follow the instructions. Send the output files to Google Cloud for analysis. Google Cloud creates and delivers a database assessment readout, and provides the next steps in the migration journey.

Identify and document the migration scope and affordable downtime

At this stage, you document essential information that influences your migration strategy and tooling. By now, you can answer the following questions:

  • Are your databases larger than 5 TB?
  • Are there any large tables in your database? Are they larger than 16 TB?
  • Where are the databases located (regions and zones), and what's their proximity to applications?
  • How often does the data change?
  • What is your data consistency model?
  • What are the options for destination databases?
  • How compatible are the source and destination databases?
  • Does the data need to reside in some physical locations?
  • Is there data that can be compressed and archived, or is there data that doesn't need migration at all?

To define the migration scope, decide what data to keep and what to migrate. Migrating all your databases might take considerable time and effort. Some data might remain in your source database backups. For example, old logging tables or archival data might not be needed. Alternatively, you might decide to move data after the migration process, depending on your strategy and tools.

Establish data migration baselines that help you compare and evaluate your outcomes and impacts. These baselines are reference points that represent the state of your data before and after the migration and help you make decisions. It's important to take measurements on the source environment that can help you evaluate your data migration's success. Such measurements include the following:

  • The size and structure of your data.
  • The completeness and consistency of your data.
  • The duration and performance of the most important business transactions and processes.

Determine how much downtime you can afford. What are the business impacts of downtime? Are there periods of low database activity, during which there are fewer users affected by downtime? If so, how long are such periods and when do they occur? Consider having a partial write only downtime, while read-only requests are still served.

Assess your deployment and administration process

After you build the inventories, assess the operational and deployment processes for your database to determine how you need to adapt them to facilitate your migration. These processes are fundamental to how you prepare and maintain your production environment.

Consider how you complete the following tasks:

  • Define and enforce security policies for your instances: For example, you might need to replace Amazon Security Groups. You can use Google IAM roles, VPC firewall rules, and VPC Service Controls to control access to your Cloud SQL instances and constrain the data within a VPC. If you're planning on using Windows Authentication with Cloud SQL for SQL Server, you need to deploy Managed Microsoft AD and connect to your existing Active Directory infrastructure.
  • Patch and configure your instances: Your existing deployment tools might need to be updated. For example, you might be using custom configuration settings in Amazon parameter groups or Amazon option groups. Your provisioning tools might need to be adapted to use Google Cloud Storage or Secret Manager to read such custom configuration lists.
  • Manage monitoring and alerting infrastructure: Metric categories for your Amazon source database instances provide observability during the migration process. Metric categories might include Amazon CloudWatch, Performance Insights, Enhanced Monitoring, and OS process lists.

Complete the assessment

After you build the inventories from your Amazon RDS environment, complete the rest of the activities of the assessment phase as described in Migrate to Google Cloud: Assess and discover your workloads.

Plan and build your foundation

In the plan and build phase, you provision and configure the infrastructure to do the following:

  • Support your workloads in your Google Cloud environment.
  • Connect your source environment and your Google Cloud environment to complete the migration.

The plan and build phase is composed of the following tasks:

  1. Build a resource hierarchy.
  2. Configure Google Cloud's Identity and Access Management (IAM).
  3. Set up billing.
  4. Set up network connectivity.
  5. Harden your security.
  6. Set up logging, monitoring, and alerting.

For more information about each of these tasks, see the Migrate to Google Cloud: Plan and build your foundation.

Monitoring and alerting

Use Google Cloud Monitoring, which includes predefined dashboards for several Google Cloud products, including a Cloud SQL monitoring dashboard. Alternatively, you can consider using third-party monitoring solutions that are integrated with Google Cloud, like Datadog and Splunk. For more information, see About database observability.

Migrate Amazon RDS for SQL Server instances to Cloud SQL for SQL Server

To migrate your instances, you do the following:

  1. Choose the migration strategy: continuous replication or scheduled maintenance.

  2. Choose the migration tools, depending on your chosen strategy and requirements.

  3. Define the migration plan and timeline for each database migration, including preparation and execution tasks.

  4. Define the preparation tasks that must be done to ensure the migration tool can work properly.

  5. Define the execution tasks, which include work activities that implement the migration.

  6. Define fallback scenarios for each execution task.

  7. Perform testing and validation, which can be done in a separate staging environment.

  8. Perform the migration.

  9. Perform the production cut-over.

  10. Clean up the source environment and configure the target instance.

  11. Perform tuning and optimization.

Each phase is described in the following sections.

Choose the migration strategy

At this step, you have enough information to evaluate and select one of the following migration strategies that best suits your use case for each database:

  • Scheduled maintenance (also called one-time migration): This approach is ideal if you can afford downtime. This option is relatively lower in cost and complexity, because your workloads and services won't require much refactoring. However, if the migration fails before completion, you have to restart the process, which prolongs the downtime.

  • Continuous replication (also called trickle migration): For mission-critical databases, this option offers a lower risk of data loss and near-zero downtime. The efforts are split into several chunks, so if a failure occurs, rollback and repeat takes less time. However, setup is more complex and takes more planning and time. Additional effort is also required to refactor the applications that connect to your database instances. Consider one of the following variations:

    • Using the Y (writing and reading) approach, which is a form of parallel migration, duplicating data in both source and destination instances during the migration.
    • Using a data-access microservice, which reduces refactoring effort required by the Y (writing and reading) approach.

For more information about data migration strategies, see Evaluating data migration approaches.

The following diagram shows a flowchart based on example questions that you might have when deciding the migration strategy for a single database:

Flowchart to help you choose the migration strategy.

The preceding flowchart shows the following decision points:

  • Can you afford any downtime?

    • If no, adopt the continuous replication migration strategy.
    • If yes, continue to the next decision point.

  • Can you afford the downtime represented by the cut-over window while migrating data? The cut-over window represents the amount of time to take a backup of the database, transfer it to Cloud SQL, restore it, and then switch over your applications.

    • If no, adopt the continuous replication migration strategy.
    • If yes, adopt the scheduled maintenance migration strategy.

Strategies might vary for different databases, even when they're located on the same instance. A mix of strategies can produce optimal results. For example, migrate small and infrequently used databases by using the scheduled maintenance approach, but use continuous replication for mission-critical databases where having downtime is expensive.

Usually, a migration is considered completed when the switch between the initial source instance and the target instance takes place. Any replication (if used) is stopped and all reads and writes are done on the target instance. Switching when both instances are in sync means no data loss and minimal downtime.

For more information about data migration strategies and deployments, see Classification of database migrations.

Migration configurations that provide no application downtime require a more complicated setup. Find the right balance between setup complexity and downtime scheduled during low-traffic business hours.

Each migration strategy has a tradeoff and some impact associated with the migration process. For example, replication processes involve some additional load on your source instances and your applications might be affected by replication lag. Applications (and customers) might have to wait during application downtime, at least as long as the replication lag lasts before using the new database. In practice, the following factors might increase downtime:

  • Database queries can take a few seconds to complete. At the time of migration, in-flight queries might be aborted.
  • The cache might take some time to fill up if the database is large or has a substantial buffer memory.
  • Applications stopped in the source and restarted in Google Cloud might have a small lag until the connection to the Google Cloud database instance is established.
  • Network routes to the applications must be rerouted. Depending on how DNS entries are set up, this can take some time. When you update DNS records, reduce TTL before the migration.

The following common practices can help minimize downtime and impact:

  • Find a time period when downtime would have a minimal impact on your workloads. For example, outside normal business hours, during weekends, or other scheduled maintenance windows.
  • Identify parts of your workloads that can undergo migration and production cut-over at different stages. Your applications might have different components that can be isolated, adapted, and migrated with no impact. For example, frontends, CRM modules, backend services, and reporting platforms. Such modules could have their own databases that can be scheduled for migration earlier or later in the process.
  • If you can afford some latency on the target database, consider implementing a gradual migration. Use incremental, batched data transfers, and adapt part of your workloads to work with the stale data on the target instance.
  • Consider refactoring your applications to support minimal migration impact. For example, adapt your applications to write to both source and target databases, and therefore implement an application-level replication.

Choose your migration tools

The most important factor for a successful migration is choosing the right migration tool. Once the migration strategy has been decided, review and decide upon the migration tool.

There are many tools available, each optimized for certain migration use cases. Use cases can include the following:

  • Migration strategy (scheduled maintenance or continuous replication).
  • Source and target database engines and engine versions.
  • Environments in which source instances and target instances are located.
  • Database size. The larger the database, the more time it takes to migrate the initial backup.
  • Frequency of the database changes.
  • Availability to use managed services for migration.

To ensure a seamless migration and cut-over, you can use application deployment patterns, infrastructure orchestration, and custom migration applications. However, specialized tools called managed migration services can facilitate the process of moving data, applications, or even entire infrastructures from one environment to another. They run the data extraction from the source databases, securely transport data to the target databases, and can optionally modify the data during transit. With these capabilities, they encapsulate the complex logic of migration and offer migration monitoring capabilities.

Managed migration services provide the following advantages:

  • Minimize downtime: Services use the built-in replication capabilities of the database engines when available, and perform replica promotion.

  • Ensure data integrity and security: Data is securely and reliably transferred from the source to the destination database.

  • Provide a consistent migration experience: Different migration techniques and patterns can be consolidated into a consistent, common interface by using database migration executables, which you can manage and monitor.

  • Offer resilient and proven migration models: Database migrations are infrequent but critical events. To avoid beginner mistakes and issues with existing solutions, you can use tools from experienced experts, rather than building a custom solution.

  • Optimize costs: Managed migration services can be more cost effective than provisioning additional servers and resources for custom migration solutions.

The next sections describe the migration tool recommendations, which depend on the chosen migration strategy.

Tools for scheduled maintenance migrations

The following subsections describe the tools that can be used for one-time migrations, along with their limitations and best practices.

Built-in database engine backups

When significant downtime is acceptable, and your source databases are relatively static, you can use the database engine's built-in backup and restore capabilities.

Some effort is required for setup and synchronization, especially for a large number of databases, but database engine backups are usually readily available and straightforward to use. This approach is suitable for any database size, and it's usually more effective than other tools for large instances.

Database engine backups have the following general limitations:

  • Backups can be error prone, particularly if performed manually.
  • Data is unsecured if the backups are not properly managed.
  • Backups lack proper monitoring capabilities.

If you choose this approach, consider the following restrictions and best practices:

  • For backups of databases larger than 5 TB, use a striped backup (a backup of multiple files).
  • When using a striped backup, you can't back up or restore from more than 10 backup files at the same time.
  • You must back up to an Amazon S3 bucket in the same Amazon region of your source database instance.
  • Backups don't include the SQL Server logins, permissions, and server roles, because they're situated at the instance level. To transfer this type of data from the source instance to the target instance, use PowerShell scripts or tools like DBAtools.

For details about limitations and best practices, see Best practices for importing and exporting data with Cloud SQL for SQL Server and Cloud SQL for SQL Server Known Issues.

Other approaches for scheduled maintenance migrations

Using other approaches might provide more control and flexibility in your scheduled maintenance migration process.

For example, by using flat files to export and import your data (or by using custom scripts), you can do the following:

  • Perform transformations, checks, or other operations on your data during the migration. For example, validations, aggregation, or normalization and denormalization on your source data.
  • Choose which structures you want to migrate and which to leave behind. You might decide to extract just a subset of your tables in flat files for import.
  • Choose to filter out data based on domain, source, age, or other custom criteria. For example, you might exclude data that reaches an age threshold, and store it in files or the final backup of your source database, before the migration.
  • Refactor your database structures, and synchronize the incurred downtime with the migration downtime.
  • Consolidate multiple instances or databases into a single instance or database, to mitigate operational costs and ease your scalability issues. For example, you might want to change your approach from having one instance, database, or schema per customer to a single, multi-tenancy optimized database structure.

Other approaches include the following:

  • Use CSV or JSON files: With this approach, you extract the database's data into the files, and then import the files to your target instances.

    • While generally slower, this method helps you migrate a subset of tables (or data within a given table).
    • The CSV and JSON formats are understood by many tools.
    • If you automate the process, you have the option to transition to a batched continuous replication migration.

  • Use the SQL Server Import and Export Wizard from Microsoft: This tool uses SQL Server Integration Services (SSIS), and lets you import data from various sources, like other database engines or flat files.

  • Use the SQL Server Generate and Publish Scripts Wizard and bcp utility: This tool is a part of Microsoft SQL Server Management Studio.

    • This approach lets you create scripts for either your entire database schema or only parts of it.
    • The bcp utility lets you script the data and export it into files.

  • Use snapshot replication, if your source uses Amazon RDS Standard:

    • With this approach, you restore the SQL Server backup of the RDS instance to another standalone instance of SQL Server on Compute Engine. Then, use snapshot replication to migrate to Cloud SQL for SQL Server.
    • The snapshot generation keeps locks on source tables, which may impact your workloads.
    • The snapshot replication might introduce additional loads on your Amazon RDS server.
    • However, you can choose which objects get migrated or replicated, which provides flexibility.
    • For details, see Migrating data from SQL Server 2017 to Cloud SQL for SQL Server using snapshot replication.

Tools for continuous replication migrations

The following diagram shows a flowchart with questions that can help you choose the migration tool for a single database, when you use a continuous replication migration strategy:

Flowchart to help you choose a tool for continuous replication migrations.

The preceding flowchart shows the following decision points:

  • Do you prefer to use managed migration services?

    • If yes, continue to the next decision. If you can afford some minimal downtime and don't need data transformation or real-time synchronization, we recommend Database Migration Service. Otherwise, explore third party options.
    • If no, continue to the next decision. If database engine built-in replication is supported, we recommend using built-in replication. If not, we recommend exploring other migration options.

  • Can you afford minimal downtime, and migrate without data transformation or real-time synchronization?

    • If yes, we recommend Database Migration Service.
    • If no, explore third-party options.

  • Is database engine specific built-in replication supported?

    • If yes, we recommend using built-in replication.
    • If no, we recommend exploring other migration options.

The following sections describe the tools that can be used for continuous replication migrations, along with their limitations and best practices.

Database Migration Service for continuous replication migration

Database Migration Service supports homogeneous migrations to Cloud SQL for SQL Server, when the source is Amazon RDS.

Database Migration Service is a cost-effective and straightforward tool. We recommend Database Migration Service for situations with the following circumstances:

  • You can afford minimal downtime.
  • You don't need real-time synchronization.
  • You don't need to perform data transformations during the migration.

If you choose this tool, consider the following restrictions and best practices:

  • The amount of downtime depends on the frequency of your transaction log backups.
  • Backups don't include the SQL Server logins, permissions, or server roles, because they're at the instance level. Script them out from the source instance, and then transfer them to the target instance by using PowerShell scripts or tools like DBAtools.

For a full list of limitations, see Known limitations.

Database engine built-in replication

Cloud SQL supports replication for SQL Server. However, Standard Amazon RDS for SQL Server can only be a Subscriber. Built-in replication from Amazon RDS Standard is not available. Only Amazon RDS Custom for SQL Server can be set up as a built-in Publisher.

For a list of supported and unsupported features on Amazon RDS, see Amazon RDS for Microsoft SQL Server.

Other approaches for continuous replication migration

Other continuous replication migration approaches include the following:

  • Refactor your applications to perform Y (writing and reading) or use a data-access microservice.

    • Continuous replication is performed by your applications.
    • The migration effort is focused on the refactoring or development of tools that connect to your database instances.
    • Reader applications are then gradually refactored and deployed to use the target instance.

  • Implement functions that periodically query data on your source instance, filter for only new data, and write data to CSV, JSON, or Parquet files.

    • These files are stored in a Google Cloud Storage bucket.
    • The files can be written immediately to your target database instance by using Cloud Run functions.
    • Change data capture (CDC) capabilities can help you achieve a near real-time replication migration.
    • You can stream CDC into an Amazon S3 data lake in Parquet format, by using AWS Database Migration Service (AWS DMS).
    • You can have a custom implementation to read the files and write their content into Cloud SQL.

Third-party tools for continuous replication migrations

In some cases, it might be better to use one third-party tool for most database engines. Such cases might be if you prefer to use a managed migration service and you need to ensure that the target database is always in near-real-time sync with the source, or if you need more complex transformations like data cleaning, restructuring, and adaptation during the migration process.

If you decide to use a third-party tool, choose one of the following recommendations, which you can use for most database engines.

Striim is an end-to-end, in-memory platform for collecting, filtering, transforming, enriching, aggregating, analyzing, and delivering data in real time:

  • Advantages:

    • Handles large data volumes and complex migrations.
    • Built-in change data capture for SQL Server.
    • Preconfigured connection templates and no-code pipelines.
    • Able to handle mission-critical, large databases that operate under heavy transactional load.
    • Exactly-once delivery.

  • Disadvantages:

For more information about Striim, see Running Striim in the Google Cloud.

Debezium is an open source distributed platform for CDC, and can stream data changes to external subscribers:

  • Advantages:

    • Open source.
    • Strong community support.
    • Cost effective.
    • Fine-grained control on rows, tables, or databases.
    • Specialized for change capture in real time from database transaction logs.

  • Disadvantages:

    • Requires specific experience with Kafka and ZooKeeper.
    • At-least-once delivery of data changes, which means that you need duplicates handling.
    • Manual monitoring setup using Grafana and Prometheus.
    • No support for incremental batch replication.

For more information about Debezium migrations, see Near Real Time Data Replication using Debezium.

Fivetran is an automated data movement platform for moving data out of and across cloud data platforms.

  • Advantages:

    • Preconfigured connection templates and no-code pipelines.
    • Propagates any schema changes from your source to the target database.
    • Exactly-once delivery of your data changes, which means that you don't need duplicates handling.

  • Disadvantages:

    • Not open source.
    • Support for complex data transformation is limited.

Define the migration plan and timeline

For a successful database migration and production cut-over, we recommend that you prepare a well-defined, comprehensive migration plan. To help reduce the impact on your business, we recommend that you create a list of all the necessary work items.

Defining the migration scope reveals the work tasks that you must do before, during, and after the database migration process. For example, if you decide not to migrate certain tables from a database, you might need pre-migration or post-migration tasks to implement this filtering. You also ensure that your database migration doesn't affect your existing service-level agreement (SLA) and business continuity plan.

We recommend that your migration planning documentation include the following documents:

  • Technical design document (TDD)
  • RACI matrix
  • Timeline (such as a T-Minus plan)

Database migrations are an iterative process, and first migrations are often slower than the later ones. Usually, well-planned migrations run without issues, but unplanned issues can still arise. We recommend that you always have a rollback plan. As a best practice, follow the guidance from Migrate to Google Cloud: Best practices for validating a migration plan.

TDD

The TDD documents all technical decisions to be made for the project. Include the following in the TDD:

  • Business requirements and criticality
  • Recovery time objective (RTO)
  • Recovery point objective (RPO)
  • Database migration details
  • Migration effort estimates
  • Migration validation recommendations

RACI matrix

Some migrations projects require a RACI matrix, which is a common project management document that defines which individuals or groups are responsible for tasks and deliverables within the migration project.

Timeline

Prepare a timeline for each database that needs to be migrated. Include all work tasks that must be performed, and defined start dates and estimated end dates.

For each migration environment, we recommend that you create a T-minus plan. A T-minus plan is structured as a countdown schedule, and lists all the tasks required to complete the migration project, along with the responsible groups and estimated duration.

The timeline should account for not only pre-migration preparation tasks execution, but also validating, auditing, or testing tasks that happen after the data transfer takes place.

The duration of migration tasks typically depends on database size, but there are also other aspects to consider, like business logic complexity, application usage, and team availability.

A T-Minus plan might look like the following:

Date Phase Category Tasks Role T-minus Status
11/1/2023 Pre-migration Assessment Create assessment report Discovery team -21 Complete
11/7/2023 Pre-migration Target preparation Design target environment as described by the design document Migration team -14 Complete
11/15/2023 Pre-migration Company governance Migration date and T-Minus approval Leadership -6 Complete
11/18/2023 Migration Set up DMS Build connection profiles Cloud migration engineer -3 Complete
11/19/2023 Migration Set up DMS Build and start migration jobs Cloud migration engineer -2 Not started
11/19/2023 Migration Monitor DMS Monitor DMS Jobs and DDL changes in the source instance Cloud migration engineer -2 Not started
11/21/2023 Migration Cutover DMS Promote DMS replica Cloud migration engineer 0 Not started
11/21/2023 Migration Migration validation Database migration validation Migration team 0 Not started
11/21/2023 Migration Application test Run capabilities and performance tests Migration team 0 Not started
11/22/2023 Migration Company governance Migration validation GO or NO GO Migration team 1 Not started
11/23/2023 Post-migration Validate monitoring Configure monitoring Infrastructure team 2 Not started
11/25/2023 Post-migration Security Remove DMS user account Security team 4 Not started

Multiple database migrations

If you have multiple databases to migrate, your migration plan should contain tasks for all of the migrations.

We recommend that you start the process by migrating a smaller, ideally non-mission-critical database. This approach can help you to build your knowledge and confidence in the migration process and tooling. You can also detect any flaws in the process in the early stages of the overall migration schedule.

If you have multiple databases to migrate, the timelines can be parallelized. For example, to speed up the migration process, you might choose to migrate a group of small, static, or less mission-critical databases at the same time, as shown in the following diagram.

Parallel database migration tasks.

In the example shown in the diagram, databases 1-4 are a group of small databases that are migrated at the same time.

Define the preparation tasks

The preparation tasks are all the activities that you need to complete to fulfill the migration prerequisites. If you don't complete the preparation tasks, the migration can't take place or the migrated database might be unusable as a result.

Preparation tasks can be categorized as follows:

  • Amazon RDS instance preparations and prerequisites
  • Source database preparation and prerequisites
  • Cloud SQL setup
  • Migration specific setup

Amazon RDS instance preparation and prerequisites

Consider the following common setup and prerequisite tasks:

  • Depending on your migration path, you might need to allow remote connections on your RDS instances. If your RDS instance is configured to be private in your VPC, private RFC 1918 connectivity must exist between Amazon and Google Cloud.

  • You might need to configure a new security group to allow remote connections on required ports.

    • By default, in AWS, network access is turned off for database instances.
    • You can specify rules in a security group that allow access from an IP address range, port, or security group. The same rules apply to all database instances that are associated with that security group.

  • For ongoing replication (streaming changes through CDC), you must use a full RDS instance and not a read replica, with CDC enabled. For details, see Using change data capture with SQL Server.

  • If you're using third-party tools, upfront settings and configurations are usually required before using the tool. Check the documentation from the third-party tool.

Source database preparation and prerequisites

  • Ensure that the source database has buffer storage and memory available during the migration operations. For example, if you are using transaction log backup files, monitor the storage use and be sure to have additional buffer storage space.
  • Document the settings of your database parameters, and apply them on your target instance before doing migration testing and validation.

  • Take baseline measurements on your source environment in production use. Consider the following:

    • Measure the size of your data, as well as your workload's performance. How long do important queries or transactions take, on average? How long during peak times?
    • Document the baseline measurements for later comparison, to help you decide if the fidelity of your database migration is satisfactory. Decide if you can switch your production workloads and decommission your source environment, or if you still need it for fallback purposes.

Cloud SQL setup

Carefully choose the size and specifications of your target Cloud SQL database instance to match the source for similar performance needs. Pay special attention to disk size and throughput, IOPS, and number of vCPUs. Incorrect sizing can lead to long migration times, database performance problems, database errors, and application performance problems.

Ensure that the destination is the correct fit. It's important to note that Amazon RDS configuration options might vary from Cloud SQL. In the event that Cloud SQL doesn't meet your requirements, consider options that include databases on Compute Engine.

You must confirm the following properties and requirements before you create your Cloud SQL instances, because they can't be changed later without recreating them.

  • Choose the project and region of your target Cloud SQL instances carefully. Cloud SQL instances can't be migrated between Google Cloud projects and regions without data transfer.

  • Migrate to a matching major version on Cloud SQL. For example, if your source uses SQL Server 15.0, migrate to Cloud SQL for SQL Server 15.0. If the versions are different, the compatibility level setting should be the same to ensure the same engine capabilities.

  • Replicate user information separately, if you are using built-in database engine backups or Database Migration Service. For details, review the limitations in the Database engine specific backups section.

  • Review the database engine specific configuration flags and compare their source and target instance values. Make sure you understand their impact and whether they need to be the same or not. For example, we recommend comparing the values in the sys.configurations view on your source database with the values on the Cloud SQL instance. Note that not all flags must be the same and not all flag changes are allowed on a Cloud SQL instance.

For more information about Cloud SQL setup, see the following:

Migration-specific setup

If you use file export and import to migrate, or use the Database Migration Service migration tool, you need to create a Cloud Storage bucket. The bucket stores the database and transaction log backup files. For more information about using Database Migration Service, see Store backup files in a Cloud Storage bucket.

If you use replication, you must ensure that the Cloud SQL replica has access to your primary database. This can be accomplished through the documented connectivity options.

Depending on your scenario and criticality, you might need to implement a fallback scenario, which usually includes reversing the direction of the replication. In this case, you might need an additional replication mechanism from Cloud SQL back to your source Amazon instance.

For most third-party tools, you need to provision migration specific resources. For example, for Striim, you need to use the Google Cloud Marketplace to begin. Then, to set up your migration environment in Striim, you can use the Flow Designer to create and change applications, or you can select a pre-existing template. Applications can also be coded using the Tungsten Query Language (TQL) programming language. Using a data validation dashboard, you can get a visual representation of data handled by your Striim application.

You can decommission the resources that connect your Amazon and Google Cloud environment after the migration is completed and validated.

Define the execution tasks

Execution tasks implement the migration work itself. The tasks depend on your chosen migration tool, as described in the following subsections.

Built-in database engine backups

For more information and instructions for database specific backups, see Import data from a BAK file to Cloud SQL for SQL Server and Exporting data from RDS for SQL Server. For more information about how to automate transaction log file uploads, see Schedule transaction log file uploads for Amazon RDS.

Database Migration Service migration jobs

Define and configure migration jobs in Database Migration Service to migrate data from a source instance to the destination database. Migration jobs connect to the source database instance through user-defined connection profiles.

Test all the prerequisites to ensure the job can run successfully. Choose a time when your workloads can afford a small downtime for the migration and production cut-over.

The migration process usually involves the following tasks:

  • Export a full backup of the source database, and then upload it to a Cloud Storage bucket.
  • Take a backup of the transaction log files, and then upload it to the same Cloud Storage bucket. For more information about how to automate this process, see Schedule transaction log file uploads for Amazon RDS.
  • In Database Migration Service, you monitor the processing of the transaction log backups.
  • Stop writing to the source database.
  • Wait for the source and target to be synchronized, which is when the final transaction log backup is processed.
  • Stop the ongoing replication and promote the migration job.By promoting a migration job, the destination Cloud SQL instance is disconnected from the source database instance, and then it is promoted to a primary instance.
  • Deploy the applications that point to the new target database.

For a detailed migration setup process, see Migrate your SQL Server databases to Cloud SQL for SQL Server.

Database engine built-in replication

If you are using Amazon RDS Standard, you might need to first migrate to the Amazon RDS Custom version and then replicate to Cloud SQL.

Cloud SQL supports replication for SQL Server. For more information about replication from an external server, see Migrating data from SQL Server 2017 to Cloud SQL for SQL Server using snapshot replication.

Third-party tools

Define any execution tasks for the third-party tool you've chosen. For example, if you decide to use Striim, you need to create apps in your namespace, and configure the CDC reader to connect to the Amazon instance. For details, see SQL Server setup in the Striim documentation.

Define fallback scenarios

Define fallback action items for each migration execution task, to safeguard against unforeseen issues that might occur during the migration process. The fallback tasks usually depend on the migration strategy and tools used.

Fallback might require significant effort. As a best practice, don't perform production cut-over until your test results are satisfactory. Both the database migration and the fallback scenario should be properly tested to avoid a severe outage.

Define success criteria and timebox all your migration execution tasks. Doing a migration dry run helps collect information about the expected times for each task. For example, for a scheduled maintenance migration, you can afford the downtime represented by the cut-over window. However, it's important to plan your next action in case the one-time migration job or the restore of the backup fails midway. Depending on how much time of your planned downtime has elapsed, you might have to postpone the migration if the migration task doesn't finish in the expected amount of time.

A fallback plan usually refers to rolling back the migration after you perform the production cut-over, if issues on the target instance appear. If you implement a fallback plan, remember that it must be treated as a full database migration, including planning and testing.

If you choose not to have a fallback plan, make sure you understand the possible consequences. Having no fallback plan can add unforeseen effort and cause avoidable disruptions in your migration process.

Although a fallback is a last resort, and most database migrations don't end up using it, we recommend that you always have a fallback strategy.

Simple fallback

In this fallback strategy, you switch your applications back to the original source database instance. Adopt this strategy if you can afford downtime when you fall back or if you don't need the transactions committed on the new target system.

If you do need all the written data on your target database, and you can afford some downtime, you can consider stopping writes to your target database instance, taking built-in backups and restoring them on your source instance, and then re-connecting your applications to the initial source database instance. Depending on the nature of your workload and amount of data written on the target database instance, you could bring it into your initial source database system at a later time, especially if your workloads aren't dependent on any specific record creation time or any time ordering constraints.

Reverse replication

In this strategy, you replicate the writes that happen on your new target database after production cut-over back to your initial source database. In this way, you keep the original source in sync with the new target database and have the writes happening on the new target database instance. Its main disadvantage is that you can't test the replication stream until after you cut-over to the target database instance, therefore it doesn't allow end-to-end testing and it has a small period of no fallback.

Choose this approach when you can still keep your source instance for some time and you migrate using the continuous replication migration.

Forward replication

This strategy is a variation of reverse replication. You replicate the writes on your new target database to a third database instance of your choice. You can point your applications to this third database, which connects to the server and runs read-only queries while the server is unavailable. You can use any replication mechanism, depending on your needs. The advantage of this approach is that it can be fully end-to-end tested.

Take this approach when you want to be covered by a fallback at all times or when you must discard your initial source database shortly after the production cut-over.

Duplicate writes

If you choose a Y (writing and reading) or data-access microservice migration strategy, this fallback plan is already set. This strategy is more complicated, because you need to refactor applications or develop tools that connect to your database instances.

Your applications write to both initial source and target database instances, which lets you perform a gradual production cut-over until you are using only your target database instances. If there are any issues, you connect your applications back to the initial source with no downtime. You can discard the initial source and the duplicate writing mechanism when you consider the migration performed with no issues observed.

We recommend this approach when it's critical to have no migration downtime, have a reliable fallback in place, and when you have time and resources to perform application refactoring.

Perform testing and validation

The goals of this step are to test and validate the following:

  • Successful migration of the data in the database.
  • Integration with existing applications after they are switched to use the new target instance.

Define the key success factors, which are subjective to your migration. The following are examples of subjective factors:

  • Which data to migrate. For some workloads, it might not be necessary to migrate all of the data. You might not want to migrate data that is already aggregated, redundant, archived, or old. You might archive that data in a Cloud Storage bucket, as a backup.
  • An acceptable percentage of data loss. This particularly applies to data used for analytics workloads, where losing part of the data does not affect general trends or performance of your workloads.
  • Data quality and quantity criteria, which you can apply to your source environment and compare to the target environment after the migration.
  • Performance criteria. Some business transactions might be slower in the target environment, but the processing time is still within defined expectations.

The storage configurations in your source environment might not map directly to Google Cloud environment targets. For example, configurations from the General Purpose SSD (gp2 and gp3) volumes with IOPS burst performance or Provisioned IOPS SSD. To compare and properly size the target instances, benchmark your source instances, in both the assessment and validation phases.

In the benchmarking process, you apply production-like sequences of operations to the database instances. During this time, you capture and process metrics to measure and compare the relative performance of both source and target systems.

For conventional, server based configurations, use relevant measurements observed during peak loads. For flexible resource capacity models like Aurora Serverless, consider looking at historical metric data to observe your scaling needs.

The following tools can be used for testing, validation, and database benchmarking:

  • HammerDB: an open source database benchmarking and load testing tool. It supports complex transactional and analytic workloads, based on industry standards, on multiple database engines (both TPROC-C and TPROC-H). HammerDB has detailed documentation and a wide community of users. You can share and compare results across several database engines and storage configurations. For more information, see Load testing SQL Server using HammerDB and Benchmark Amazon RDS SQL Server performance using HammerDB.
  • DBT2 Benchmark Tool: benchmarking specialized for MySQL. A set of database workload kits mimics an application for a company that owns warehouses and involves a mix of read and write transactions. Use this tool if you want to use a ready-made online transaction processing (OLTP) load test.
  • DbUnit: an open source unit testing tool used to test relational database interactions in Java. The setup and use is straightforward, and it supports multiple database engines (MySQL, PostgreSQL, SQL Server, and others). However, the test execution can be slow sometimes, depending on the size and complexity of the database. We recommend this tool when simplicity is important.
  • DbFit: an open source database testing framework that supports test-driven code development and automated testing. It uses a basic syntax for creating test cases and features data-driven testing, version control, and test result reporting. However, support for complex queries and transactions is limited and it doesn't have large community support or extensive documentation, compared to other tools. We recommend this tool if your queries are not complex and you want to perform automated tests and integrate them with your continuous integration and delivery process.

To run an end-to-end test, including testing of the migration plan, always perform a migration dry run exercise. A dry run performs the full-scope database migration without switching any production workloads, and it offers the following advantages:

  • Lets you ensure that all objects and configurations are properly migrated.
  • Helps you define and execute your migration test cases.
  • Offers insights into the time needed for the actual migration, so you can calibrate your timeline.
  • Represents an occasion to test, validate, and adapt the migration plan. Sometimes you can't plan for everything in advance, so this helps you to spot any gaps.

Data testing can be performed on a small set of the databases to be migrated or the entire set. Depending on the total number of databases and the tools used for implementing their migration, you can decide to adopt a risk based approach. With this approach, you perform data validation on a subset of databases migrated through the same tool, especially if this tool is a managed migration service.

For testing, you should have access to both source and target databases and do the following tasks:

  • Compare source and target schemas. Check if all tables and executables exist. Check row counts and compare data at the database level.
  • Run custom data validation scripts.
  • Test that the migrated data is also visible in the applications that switched to use the target database (migrated data is read through the application).
  • Perform integration testing between the switched applications and the target database by testing various use cases. This testing includes both reading and writing data to the target databases through the applications so that the workloads fully support migrated data together with newly created data.
  • Test the performance of the most used database queries to observe if there's any degradation due to misconfigurations or wrong sizing.

Ideally, all these migration test scenarios are automated and repeatable on any source system. The automated test cases suite is adapted to perform against the switched applications.

If you're using Database Migration Service as your migration tool, see Verify a migration.

Data Validation Tool

For performing data validation, we recommend that you use the Data Validation Tool (DVT). The DVT is an open sourced Python CLI tool, backed by Google, that provides an automated and repeatable solution for validation across different environments.

The DVT can help streamline the data validation process by offering customized, multi-level validation functions to compare source and target tables on the table, column, and row level. You can also add validation rules.

The DVT covers many Google Cloud data sources, including AlloyDB for PostgreSQL, BigQuery, Cloud SQL, Spanner, JSON, and CSV files on Cloud Storage. It can also be integrated with Cloud Run functions and Cloud Run for event based triggering and orchestration.

The DVT supports the following types of validations:

  • Schema level comparisons
  • Column (including 'AVG', 'COUNT', 'SUM', 'MIN', 'MAX', 'GROUP BY', and 'STRING_AGG')
  • Row (including hash and exact match in field comparisons)
  • Custom query results comparison

For more information about the DVT, see the Git repository and Data validation made easy with Google Cloud's Data Validation Tool.

Perform the migration

The migration tasks include the activities to support the transfer from one system to another.

Consider the following best practices for your data migration:

  • Inform the involved teams whenever a plan step begins and finishes.
  • If any of the steps take longer than expected, compare the time elapsed with the maximum amount of time allotted for that step. Issue regular intermediary updates to involved teams when this happens.
  • If the time span is greater than the maximal amount of time reserved for each step in the plan, consider rolling back.
  • Make "go or no-go" decisions for every step of the migration and cut-over plan.
  • Consider rollback actions or alternative scenarios for each of the steps.

Perform the migration by following your defined execution tasks, and refer to the documentation for your selected migration tool.

Perform the production cut-over

The high-level production cut-over process can differ depending on your chosen migration strategy. If you can have downtime on your workloads, then your migration cut-over begins by stopping writes to your source database.

For continuous replication migrations, you typically do the following high-level steps in the cut-over process:

  • Stop writing to the source database.
  • Drain the source.
  • Stop the replication process.
  • Deploy the applications that point to the new target database.

After the data has been migrated by using the chosen migration tool, you validate the data in the target database. You confirm that the source database and the target databases are in sync and the data in the target instance adheres to your chosen migration success standards.

Once the data validation passes your criteria, you can perform the application level cut-over. Deploy the workloads that have been refactored to use the new target instance. You deploy the versions of your applications that point to the new target database instance. The deployments can be performed either through rolling updates, staged releases, or by using a blue-green deployment pattern. Some application downtime might be incurred.

Follow the best practices for your production cut-over:

  • Monitor your applications that work with the target database after the cut-over.
  • Define a time period of monitoring to consider whether or not you need to implement your fallback plan.
  • Note that your Cloud SQL or AlloyDB for PostgreSQL instance might need a restart if you change some database flags.
  • Consider that the effort of rolling back the migration might be greater than fixing issues that appear on the target environment.

Cleanup the source environment and configure the Cloud SQL instance

After the cut-over is completed, you can delete the source databases. We recommend performing the following important actions before the cleanup of your source instance:

  • Create a final backup of each source database. These backups provide you with an end state of the source databases. The backups might also be required in some cases for compliance with some data regulations.

  • Collect the database parameter settings of your source instance. Alternatively, check that they match the ones you've gathered in the inventory building phase. Adjust the target instance parameters to match the ones from the source instance.

  • Collect database statistics from the source instance and compare them to the ones in the target instance. If the statistics are disparate, it's hard to compare the performance of the source instance and target instance.

In a fallback scenario, you might want to implement the replication of your writes on the Cloud SQL instance back to your original source database. The setup resembles the migration process but would run in reverse: the initial source database would become the new target.

As a best practice to keep the source instances up to date after the cut-over, replicate the writes performed on the target Cloud SQL instances back to the source database. If you need to roll back, you can fall back to your old source instances with minimal data loss.

Besides the source environment cleanup, the following critical configurations for your Cloud SQL instances must be done:

  • Configure a maintenance window for your primary instance to control when disruptive updates can occur.
  • Configure the storage on the instance so that you have at least 20% available space to accommodate any critical database maintenance operations that Cloud SQL might perform. To receive an alert if available disk space gets lower than 20%, create a metrics-based alerting policy for the disk utilization metric.

Don't start an administrative operation before the previous operation has completed.

For more information, see the following:

Optimize your environment after migration

Optimization is the last phase of your migration. In this phase, you iterate on optimization tasks until your target environment meets your optimization requirements. The steps of each iteration are as follows:

  1. Assess your current environment, teams, and optimization loop.
  2. Establish your optimization requirements and goals.
  3. Optimize your environment and your teams.
  4. Tune the optimization loop.

You repeat this sequence until you've achieved your optimization goals.

For more information about optimizing your Google Cloud environment, see Migrate to Google Cloud: Optimize your environment and Performance optimization process.

Establish your optimization requirements

Review the following optimization requirements for your Google Cloud environment and choose the ones that best fit your workloads.

Increase the reliability and availability of your database

With Cloud SQL, you can implement a high availability and disaster recovery strategy that aligns with your recovery time objective (RTO) and recovery point objective (RPO). To increase reliability and availability, consider the following:

  • In cases of read-heavy workloads, add read replicas to offload traffic from the primary instance.
  • For mission critical workloads, use the high-availability configuration, replicas for regional failover, and a robust disaster recovery configuration.
  • For less critical workloads, automated and on-demand backups can be sufficient.
  • To prevent accidental removal of instances, use instance deletion protection.
  • Enable point-in-time recovery (PITR) for your Cloud SQL for SQL Server instance.
  • Automate SQL database backups by using maintenance plans.

Increase the cost effectiveness of your database infrastructure

To have a positive economic impact, your workloads must use the available resources and services efficiently. Consider the following options:

  • Provide the database with the minimum required storage capacity by doing the following:

    • To scale storage capacity automatically as your data grows, enable automatic storage increases. However, ensure that you configure your instances to have some buffer in peak workloads. Remember that database workloads tend to increase over time.

  • Identify possible overestimated resources:

    • Rightsizing your Cloud SQL instances can reduce the infrastructure cost without adding additional risks to the capacity management strategy.
    • Cloud Monitoring provides predefined dashboards that help identify the health and capacity utilization of many Google Cloud components, including Cloud SQL. For details, see Create and manage custom dashboards.

  • Identify instances that don't require high availability or disaster recovery configurations, and remove them from your infrastructure.

  • Remove tables and objects that are no longer needed. You can store them in a full backup or an archival Cloud Storage bucket.

  • Evaluate the most cost-effective storage type (SSD or HDD) for your use case.

    • For most use cases, SSD is the most efficient and cost-effective choice.
    • If your datasets are large (10 TB or more), latency-insensitive, or infrequently accessed, HDD might be more appropriate.
    • For details, see Choose between SSD and HDD storage.

  • Purchase committed use discounts for workloads with predictable resource needs.

  • Use Active Assist to get cost insights and recommendations.

    For more information and options, see Do more with less: Introducing Cloud SQL cost optimization recommendations with Active Assist.

To reduce licencing costs, specifically for Cloud SQL for SQL Server, consider the following:

  • Migrate to SQL Server Standard Edition, if the SLAs match your requirements.
  • Turn off simultaneous multithreading (SMT) and add 25% more cores. The additional cores might compensate for any performance impact from turning off SMT. This strategy can help reduce licensing costs, but it might impact your instance's performance. We recommend that you perform load testing on your instance to ensure your SLAs are not affected.
  • Consider a heterogeneous migration from Cloud SQL for SQL Server to Cloud SQL for PostgreSQL or AlloyDB for PostgreSQL, depending on your workload.

Increase the performance of your database infrastructure

Minor database-related performance issues frequently have the potential to impact the entire operation. To maintain and increase your Cloud SQL instance performance, consider the following guidelines:

  • If you have a large number of database tables, they can affect instance performance and availability, and cause the instance to lose its SLA coverage.

  • Ensure that your instance isn't constrained on memory or CPU.

    • For performance-intensive workloads, ensure that your instance has at least 60 GB of memory.
    • For slow database inserts, updates, or deletes, check the locations of the writer and database; sending data over long distances introduces latency.

  • Improve query performance by using the predefined Query Insights dashboard in Cloud Monitoring (or similar database engine built-in features). Identify the most expensive commands and try to optimize them.

  • Prevent database files from becoming unnecessarily large. Set autogrow in MBs rather than as a percentage, using increments appropriate to the requirement.

  • Check reader and database location. Latency affects read performance more than write performance.

  • Prevent data and index fragmentation. Set a schedule to rebuild your indexes in SQL Server, depending on how often your data changes.

  • Change the SQL Server engine specific settings so that they work optimally for Cloud SQL.

  • For index and statistics maintenance, see SQL Server Maintenance Solution.

  • Update statistics regularly on Cloud SQL for SQL Server.

  • Consider performing ETL operations on a read replica, because they might affect your instance's cache.

For more information about increasing performance, see Performance in "Diagnose issues", and Cloud SQL - SQL Server Performance Analysis and Query Tuning.

Increase database observability capabilities

Diagnosing and troubleshooting issues in applications that connect to database instances can be challenging and time-consuming. For this reason, a centralized place where all team members can see what's happening at the database and instance level is essential. You can monitor Cloud SQL instances in the following ways:

  • Cloud SQL uses built-in memory custom agents to collect query telemetry.

    • Use Cloud Monitoring to collect measurements of your service and the Google Cloud resources that you use. Cloud Monitoring includes predefined dashboards for several Google Cloud products, including a Cloud SQL monitoring dashboard.
    • You can create custom dashboards that help you monitor metrics and set up alert policies so that you can receive timely notifications.
    • Alternatively, you can consider using third-party monitoring solutions that are integrated with Google Cloud, such as Datadog and Splunk.

  • Cloud Logging collects logging data from common application components.

  • Cloud Trace collects latency data and executed query plans from applications to help you track how requests propagate through your application.

  • Database Center provides an AI-assisted, centralized database fleet overview. You can monitor the health of your databases, availability configuration, data protection, security, and industry compliance.

  • View and query logs for your Cloud SQL instance.

  • Observe the status of your databases to help you improve the performance of your environment, and to troubleshoot eventual issues.

General Cloud SQL best practices and operational guidelines

Apply the best practices for Cloud SQL to configure and tune the database.

Some important Cloud SQL general recommendations are as follows:

  • If you have large instances, we recommend that you split them into smaller instances, when possible.
  • Configure storage to accommodate critical database maintenance. Ensure you have at least 20% available space to accommodate any critical database maintenance operations that Cloud SQL might perform.
  • Having too many database tables can affect database upgrade time. Ideally, aim to have under 10,000 tables per instance.
  • Choose the appropriate size for your instances to account for transaction (binary) log retention, especially for high write activity instances.

To be able to efficiently handle any database performance issues that you might encounter, use the following guidelines until your issue is resolved:

Scale up infrastructure: Increase resources (such as disk throughput, vCPU, and RAM). Depending on the urgency and your team's availability and experience, vertically scaling your instance can resolve most performance issues. Later, you can further investigate the root cause of the issue in a test environment and consider options to eliminate it.

Perform and schedule database maintenance operations: Index defragmentation, statistics updates, vacuum analyze, and reindex heavily updated tables. Check if and when these maintenance operations were last performed, especially on the affected objects (tables, indexes). Find out if there was a change from normal database activities. For example, recently adding a new column or having lots of updates on a table.

Perform database tuning and optimization: Are the tables in your database properly structured? Do the columns have the correct data types? Is your data model right for the type of workload? Investigate your slow queries and their execution plans. Are they using the available indexes? Check for index scans, locks, and waits on other resources. Consider adding indexes to support your critical queries. Eliminate non-critical indexes and foreign keys. Consider rewriting complex queries and joins. The time it takes to resolve your issue depends on the experience and availability of your team and can range from hours to days.

Scale out your reads: Consider having read replicas. When scaling vertically isn't sufficient for your needs, and database tuning and optimization measures aren't helping, consider scaling horizontally. Routing read queries from your applications to a read replica improves the overall performance of your database workload. However, it might require additional effort to change your applications to connect to the read replica.

Database re-architecture: Consider partitioning and indexing the database. This operation requires significantly more effort than database tuning and optimization, and it might involve a data migration, but it can be a long-term fix. Sometimes, poor data model design can lead to performance issues, which can be partially compensated by vertical scale-up. However, a proper data model is a long-term fix. Consider partitioning your tables. Archive data that isn't needed anymore, if possible. Normalize your database structure, but remember that denormalizing can also improve performance.

Database sharding: You can scale out your writes by sharding your database. Sharding is a complicated operation and involves re-architecting your database and applications in a specific way and performing data migration. You split your database instance in multiple smaller instances by using a specific partitioning criteria. The criteria can be based on customer or subject. This option lets you horizontally scale both your writes and reads. However, it increases the complexity of your database and application workloads. It might also lead to unbalanced shards called hotspots, which would outweigh the benefit of sharding.

Specifically for Cloud SQL for SQL Server, consider the following best practices:

  • To update the SQL Server settings for optimal performance with Cloud SQL, see SQL Server settings.
  • Determine the capacity of the I/O subsystem before you deploy SQL Server.

  • If you have large instances, split them into smaller instances, where possible:

    • A disk size of 4 TB or greater provides more throughput and IOPS.
    • Higher vCPU provides more IOPS and throughput. When using higher vCPU, monitor the database waits for parallelism, which might also increase.

  • Turn off SMT, if performance is diminished in some situations. For example, if an application executes threads that become a bottleneck and the architecture of the CPU doesn't handle this effectively.

  • Set a schedule to reorganize or rebuild your indexes, depending on how often your data changes.

  • Set an appropriate fill factor to reduce fragmentation. Monitor SQL Server for missing indexes that might offer improved performance.

  • Prevent database files from becoming unnecessarily large. Set autogrow in MBs rather than as a percentage, using increments appropriate to the requirement. Also, proactively manage the database growth before the autogrow threshold is reached.

  • To scale storage capacity automatically, enable automatic storage increases. Cloud SQL can add storage space if the database and the instance run out of space.

  • Ensure you understand the locale requirements, sorting order, and case and accent sensitivity of the data that you're working with. When you select collation for your server, database, column, or expression, you're assigning certain characteristics to your data.

  • Recursive hash joins or hash bailouts cause reduced performance in a server. If you see many hash warning events in a trace, update the statistics on the columns that are being joined. For more information, see Hash Warning Event Class.

For more details, see General best practices and Operational guidelines for Cloud SQL for SQL Server.

What's next

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