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Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Monitor Google Cloud SQL performance with Datadog
2017-07-11 · via Datadog | The Monitor blog

Google Cloud SQL is a fully managed service that enables users to easily set up, maintain, and scale MySQL databases (with PostgreSQL support currently in beta). Hosted on Google Cloud Platform, Cloud SQL automatically handles software updates and enables users to configure automated backups and failover to ensure high availability.

Without full visibility into your Google Cloud SQL databases, it can be difficult to track availability, resource usage, and other metrics that are critical to your applications. Thanks to our Google Cloud SQL integration, you can monitor all of these metrics and more in the same place as the rest of your Google Cloud infrastructure and applications.

Monitor Google Cloud SQL with Datadog's out-of-the-box dashboard.
Monitor Google Cloud SQL with Datadog's out-of-the-box dashboard
Monitor Google Cloud SQL with Datadog's out-of-the-box dashboard.

Key Cloud SQL metrics to monitor

Once you enable the integration, you can start monitoring your Google Cloud SQL databases in a customizable, out-of-the-box dashboard like the one shown above. This high-level overview provides visibility into the health and performance of your Cloud SQL databases, including key metrics like:

  • Disk utilization: Google Cloud SQL enables users to configure second-generation instances to automatically scale up storage as needed. However, if you have not configured this option, or if you are running first-generation instances, then you need to keep a close eye on disk utilization and add more storage if possible, before writes start to fail.
  • CPU: Monitoring CPU utilization can help provision your Cloud SQL instances efficiently. Unexpected periods of high CPU utilization may indicate that you need to provision additional resources for your instances. In other cases, if you notice that CPU and other resources are being underutilized, you can probably scale down.
  • Connections: Keep an eye on this metric to ensure that you don’t exceed your instances’ connection limits. The Cloud SQL documentation also includes helpful tips on how to effectively manage your database connections.
  • Questions: If you’re using MySQL as your database engine, the questions metric tracking queries sent by clients is a valuable high-level metric to monitor. You can set up an alert to notify you to sudden drops in question throughput, which may point to throughput issues in your database or client applications.

Monitor Google Cloud SQL in context

Datadog’s Google Cloud Platform integration enables you to correlate Cloud SQL performance with the rest of your Google Cloud services (plus more than 1,000 technologies), and analyze them all in one place. You can immediately start visualizing Google Cloud SQL, Google Compute Engine, and Google App Engine metrics in an out-of-the-box screenboard like the one shown below.

Monitor Google Cloud SQL

Handle any weather the cloud may bring

Datadog’s robust alerting features allow you to set up automatic alerts to detect unexpected behavior across your services. So you can trigger a notification if any individual Google Cloud SQL database goes down, or set up anomaly detection to alert you to unexpected drops in query throughput.

You can configure Datadog alerts to track any of the Cloud SQL metrics mentioned above (or see the full list of available metrics here), and trigger notifications through Slack, PagerDuty, HipChat, and other issue resolution tools. Each Cloud SQL metric is automatically tagged with identifying metadata (database_id, project, and region), so you can slice and dice your metrics to easily create custom alerts and dashboards.

Get started

If you’re already using Datadog, you can start monitoring Cloud SQL—and the rest of your Google Cloud environment—in just a few minutes with our GCP integration. If you’re new to Datadog, here’s a free 14-day full-featured trial.