Google Cloud SQL

Google Cloud SQL is a fully managed service that allows you to manage and administer your relational databases in GCP

By - Manish Kumar Barnwal
Updated on
August 21, 2023


What is Cloud SQL in GCP?

It manages your databases by abstracting the underlying infrastructure, which makes it easier to provision and maintain databases without worrying about hardware or software management.

It integrates seamlessly with other Google Cloud services such as BigQuery, App Engine, Compute Engine, and Kubernetes Engine. Cloud SQL also supports popular third-party integrations like MongoDB and Confluent, providing you with extensive support.

When to use Cloud SQL?

Cloud SQL is ideal for businesses that require a managed solution for their relational databases, regardless of their size and structure. It is particularly useful when you need to migrate existing databases to the cloud, maintain high availability and durability, and ensure secure storage and access to your data. Cloud SQL caters to various workloads and use cases, including web applications, mobile applications, analytics, machine learning, and more.

How does Cloud SQL work?

Google Cloud SQL is a fully managed relational database service offered by Google Cloud Platform. It enables you to set up, maintain, and administer your relational databases on Google Cloud with ease.

Cloud SQL supports MySQL, PostgreSQL, and SQL Server, allowing you to choose the database engine that best fits your application requirements. By handling administrative tasks such as patches, updates, backups, and replicas, Cloud SQL allows you to focus on building and scaling your applications.

Features & Advantages

Google Cloud SQL Features & Services

The comprehensive set of features offered by Cloud SQL makes it a popular choice for users looking to leverage the benefits of managed relational databases. In this section, we will explore the key features of Cloud SQL:

  • Managed Database Service: Cloud SQL abstracts the underlying infrastructure and handles administrative tasks such as patches, updates, backups, and replicas, allowing you to focus on your applications and services.
  • Database Engine Support: It supports popular database engines, including MySQL, PostgreSQL, and SQL Server, enabling seamless migration and compatibility with existing applications.
  • Scalability & Performance: Cloud SQL offers the ability to scale storage and compute resources easily, ensuring optimal performance for your databases as they grow.
  • High Availability & Durability: It provides built-in replication, automatic failover, and data redundancy across multiple zones and regions, ensuring high availability and durability of your data.
  • Security: Google Cloud SQL offers data encryption at rest and in transit, along with integration with Identity and Access Management (IAM) for granular access control and protection.
  • Integration: It integrates seamlessly with other Google Cloud services, such as BigQuery, App Engine, Compute Engine, and Kubernetes Engine, as well as popular third-party integrations like MongoDB and Confluent.

What is the benefit of Google Cloud SQL?

Users can enjoy the several benefits & advantages that come with Cloud SQL, including the following:

  1. Efficient Management: Fully managed service frees you from the burden of database administration tasks, allowing you to focus on building and scaling your applications.
  2. Cost Optimization: Pay-as-you-go pricing model enables users to optimize their database costs based on their specific needs and usage patterns.
  3. Seamless Migration: Support for popular database engines facilitates smooth migration of existing databases to the cloud, ensuring compatibility with your applications and minimizing disruption.
  4. High Performance: Delivers high performance and low-latency access to databases, catering to use cases that require fast data retrieval and processing.
  5. Robust Security: Features strong security features, including data encryption and IAM integration, ensure the protection of your data in the cloud.


How much does Cloud SQL cost?

The pricing for Cloud SQL is determined by various factors, including the database engine, instance type, storage, CPUs, memory, location, network usage, and IP addresses used.

  • Storage: Costs are based on the provisioned storage for Cloud SQL in GiB per month.
  • CPUs: Charges depend on the number of CPUs selected for the Cloud SQL instance.
  • Memory: Costs are associated with the amount of memory selected for the Cloud SQL instance.
  • Location: The location where data is hosted affects the pricing.
  • Network Usage: Costs depend on the amount of network traffic leaving the instance.
  • IP Addresses: Charges are based on the number of assigned and used IP addresses.

MySQL and PostgreSQL pricing have similar structures, while SQL Server pricing is slightly different.

MySQL vs PostgreSQL vs SQL Serve Pricing

MySQL & PostgreSQL Pricing

Cloud SQL pricing for MySQL and PostgreSQL consists of the following charges:

  • CPU & Memory Pricing: Depends on the region where your instance is located and the commitment term.
  • Storage & Networking Pricing: Depends on the region where your instance is located.
  • Instance Pricing: Charges are based on the region where your instance is located and are billed for every second the instance is running. Each second of usage counts toward a chargeable minute.

SQL Server Pricing

Cloud SQL for SQL Server consists of the following charges:

  • CPU & Memory Pricing: Same as the others
  • Storage & Networking Pricing: Same as the others
  • Licensing: Instead of instance pricing, SQL Server instances are charged a 10-minute minimum for licenses. After 10 minutes, SQL Server licenses are charged in 1-minute increments.

Cloud SQL Pricing Sheet

These examples show MySQL pricing with different configurations. You can create and configure your Cloud SQL instance according to your requirements.

Use the Google Cloud Pricing Calculator to estimate your pricing before creating your instance for accurate projections.

Cost Optimization

Cloud SQL Cost Optimization

Cloud SQL is an essential component for managing and scaling databases in Google Cloud, and cost optimization is crucial when using it efficiently.

  1. Long-term Commitments: Leverage commit use discounts for predictable workloads on a one-year or three-year basis to achieve significant cost savings. You can save 25% on on-demand pricing for a one-year commitment and 52% for a three-year commitment.
  2. Logs Expiration and Retention: Control the retention period of logs by setting the expiration time. If you need to maintain logs for an extended period due to data governance or regulatory requirements, set a longer retention time or export logs to cheaper long-term storage options such as Google Cloud Storage.
  3. Use Labels & Tags to Group Instances: Assigning labels & Tags to your instances can help you group them based on specific attributes, such as project, environment, or application. This allows you to filter instances by labels & tags, reducing the number of instances processed and ultimately reducing costs.
  4. Disable Log Exports for Non-Essential Logs: Ensure that you only export logs essential for your use case if you have enabled log exports to destinations such as BigQuery or Pub/Sub. Exporting non-essential logs increases costs without providing significant value.
  5. Use Log Views: Log Views allow you to create a customized view of your logs, displaying only the log entries that meet specific criteria. This can help reduce logging costs by filtering out unnecessary logs.

Cloud SQL Recommendations & Best Practices

Here are some best practices to help you reduce Cloud SQL costs and optimize resource usage:

  1. Idle Instances: Idle resources are one of the main reasons for wasted cloud spending. Cloud SQL's Active Assist feature detects idle instances and provides recommendations and insights on how to reduce costs. You can use Cloud Scheduler jobs to trigger Cloud Functions that will start and stop instances automatically, saving up to 75% of the cost to run an instance per week compared to continuous usage.
  2. Overprovisioned Instances: Developers sometimes provision unnecessarily large instances, leading to unnecessary spending. Active Assist detects instances that are unnecessarily large for a given workload and provides recommendations for resizing. It uses machine learning and Google's Cloud SQL telemetry data to identify instances with low peak utilization, ensuring they can still handle peak workloads after being resized.
  3. Network Egress: Charges for network egress can occur even when both the function and the Cloud SQL instance are in the same region. To avoid these charges, add a private IP address for the SQL instance, a Serverless VPC Connector, and change the database connection code to use the private IP address explicitly.

Remember that cost optimization is an ongoing process, and you should continually monitor and adjust your usage to ensure that you are getting the most value for your investment in Cloud SQL.

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