惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

B
Blog
Microsoft Security Blog
Microsoft Security Blog
Jina AI
Jina AI
博客园 - 叶小钗
J
Java Code Geeks
博客园 - 聂微东
博客园 - 司徒正美
大猫的无限游戏
大猫的无限游戏
阮一峰的网络日志
阮一峰的网络日志
V
V2EX
美团技术团队
WordPress大学
WordPress大学
M
MIT News - Artificial intelligence
雷峰网
雷峰网
酷 壳 – CoolShell
酷 壳 – CoolShell
GbyAI
GbyAI
罗磊的独立博客
T
The Blog of Author Tim Ferriss
aimingoo的专栏
aimingoo的专栏
T
Tailwind CSS Blog
The Cloudflare Blog
Stack Overflow Blog
Stack Overflow Blog
N
Netflix TechBlog - Medium
小众软件
小众软件

Datadog | The Monitor blog

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
How to monitor Google Compute Engine with Datadog
2017-03-07 · via Datadog | The Monitor blog

This post is the final part of a 3-part series on how to monitor Google Compute Engine. Part 1 explores the key metrics available from GCE, and part 2 is about collecting those metrics using Google-native tools.

To have a clear picture of GCE’s operations, you need a system dedicated to storing, visualizing, and correlating your Google Compute Engine metrics with metrics from the rest of your infrastructure. If you’ve read our post on collecting GCE metrics, you’ve seen how you can quickly and easily pull metrics using the Stackdriver Monitoring API and gcloud, and had a chance to see Google’s monitoring service, Stackdriver, in action.

Though these solutions are excellent starting points, they have their limitations, especially when it comes to integration with varied infrastructure components and platforms, as well as data retention for long-term monitoring and trend analysis.

Datadog's out-of-the-box, customizable Google Compute Engine dashboard

Datadog enables you to collect metrics from many Google Cloud platform services, including GCE, for visualization, alerting, and full-infrastructure correlation. Datadog will automatically collect the key performance metrics discussed in parts one and two of this series, and make them available in a customizable dashboard, as seen above. Datadog retains your data for 15 months at full granularity, so you can easily compare real-time metrics against values from last month, last quarter, or last year. And if you install the Datadog Agent, you gain additional system resource metrics (including memory usage, disk I/O, and more) and benefit from integrations with more than 1,000 technologies and services.

You can integrate Datadog with GCE in two ways:

Enable the Google Cloud Platform integration

Enabling the Google Cloud Platform integration is the quickest way to start monitoring your GCE instances and the rest of your GCP resources, including Google App Engine applications and Google Container Engine (GKE) containers. And since Datadog supports OAuth login with your GCP account, you can start seeing your GCE metrics in just a few clicks.

Integrating GCP with Datadog is as easy as signing into your Google account.

Once signed in, add the id of the project you want to monitor, optionally restrict the set of hosts to monitor, and click Update Configuration.

After a couple of minutes you should see metrics streaming into the customizable Google Compute Engine dashboard. And if you’re using other Google services, like Google App Engine or Google Pub/Sub, you’ll automatically have access to built-in dashboards for those services, too.

Install the Agent

The Datadog Agent is open source software that collects and reports metrics from your hosts so that you can view and monitor them in Datadog. Installing the Agent usually takes just a single command.

Installation instructions for a variety of platforms are available here.

As soon as the Agent is up and running, you should see your host reporting metrics in your Datadog account.

Hosts reporting in.

No additional configuration is necessary, but if you want to collect more than just host metrics, head over to the integrations page to enable monitoring for over 1,000 applications and services.

Monitoring GCE with Datadog dashboards

The template GCE dashboard in Datadog is a great resource, but you can easily create a more comprehensive dashboard to monitor your entire application stack by adding graphs and metrics from your other systems. For example, you might want to graph GCE metrics alongside metrics from Kubernetes or Docker, performance metrics from your applications, or host-level metrics such as memory usage on application servers. To start extending the template dashboard, clone the default GCE dashboard by clicking on the gear on the upper right of the dashboard and selecting Clone Dashboard.

Customize the out-of-the-box dashboard by making a clone.

Drilling down with tags

All Google Compute Engine metrics are tagged with the following information:

  • availability-zone
  • cloud_provider
  • instance-type
  • instance-id
  • automatic-restart
  • on-host-maintenace
  • numeric_project_id
  • name
  • project
  • zone
  • any additional labels and tags you added in GCP
Use template variables to slice and dice with tags.

You can easily slice your metrics to isolate a particular subset of hosts using tags. In the out-of-the-box GCE screenboard, you can use the template variable selectors in the upper left to drill down to a specific host or set of hosts. And you can similarly use tags in any Datadog graph or alert definition to filter or aggregate your metrics.

Alerts

Once Datadog is capturing and visualizing your metrics, you will likely want to set up some alerts to be automatically notified of potential issues. With powerful algorithmic alerting features like outlier detection and anomaly detection, you can be automatically alerted to unexpected instance behavior.

Observability awaits

We’ve now walked through how to use Datadog to collect, visualize, and alert on your Google Compute Engine metrics. If you’ve followed along with your Datadog account, you should now have greater visibility into the state of your instances.

If you don’t yet have a Datadog account, you can start monitoring Google Compute Engine right away with a free trial.

Source Markdown for this post is available on GitHub. Questions, corrections, additions, etc.? Please let us know.