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

Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
量子位
M
MIT News - Artificial intelligence
Y
Y Combinator Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Google DeepMind News
Google DeepMind News
Hugging Face - Blog
Hugging Face - Blog
博客园_首页
雷峰网
雷峰网
I
InfoQ
罗磊的独立博客
博客园 - 聂微东
酷 壳 – CoolShell
酷 壳 – CoolShell
大猫的无限游戏
大猫的无限游戏
D
Docker
H
Hackread – Cybersecurity News, Data Breaches, AI and More
腾讯CDC
博客园 - 三生石上(FineUI控件)
The GitHub Blog
The GitHub Blog
K
Kaspersky official blog
P
Privacy & Cybersecurity Law Blog
S
SegmentFault 最新的问题
T
Threat Research - Cisco Blogs
H
Help Net Security
小众软件
小众软件
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
C
CERT Recently Published Vulnerability Notes
WordPress大学
WordPress大学
T
Tenable Blog
T
The Blog of Author Tim Ferriss
C
Cisco Blogs
Simon Willison's Weblog
Simon Willison's Weblog
博客园 - Franky
A
Arctic Wolf
T
Threatpost
Scott Helme
Scott Helme
C
Cybersecurity and Infrastructure Security Agency CISA
D
Darknet – Hacking Tools, Hacker News & Cyber Security
T
The Exploit Database - CXSecurity.com
G
GRAHAM CLULEY
Security Latest
Security Latest
Spread Privacy
Spread Privacy
L
LINUX DO - 热门话题
V
Vulnerabilities – Threatpost
P
Privacy International News Feed
S
Schneier on Security
Latest news
Latest news
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
C
Cyber Attacks, Cyber Crime and Cyber Security
C
CXSECURITY Database RSS Feed - CXSecurity.com

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 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 - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
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.