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
C
Cybersecurity and Infrastructure Security Agency CISA
Microsoft Security Blog
Microsoft Security Blog
B
Blog RSS Feed
云风的 BLOG
云风的 BLOG
G
Google Developers Blog
Recent Announcements
Recent Announcements
A
About on SuperTechFans
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
Google Online Security Blog
Google Online Security Blog
Google DeepMind News
Google DeepMind News
S
Schneier on Security
S
Secure Thoughts
T
The Exploit Database - CXSecurity.com
Martin Fowler
Martin Fowler
P
Proofpoint News Feed
Security Latest
Security Latest
Jina AI
Jina AI
D
Darknet – Hacking Tools, Hacker News & Cyber Security
Recorded Future
Recorded Future
T
Tor Project blog
有赞技术团队
有赞技术团队
H
Hackread – Cybersecurity News, Data Breaches, AI and More
N
News | PayPal Newsroom
博客园 - 三生石上(FineUI控件)
MyScale Blog
MyScale Blog
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
Last Week in AI
Last Week in AI
F
Full Disclosure
Hacker News: Ask HN
Hacker News: Ask HN
Forbes - Security
Forbes - Security
D
DataBreaches.Net
人人都是产品经理
人人都是产品经理
NISL@THU
NISL@THU
C
Cisco Blogs
Recent Commits to openclaw:main
Recent Commits to openclaw:main
Google DeepMind News
Google DeepMind News
Project Zero
Project Zero
IT之家
IT之家
T
Threatpost
Cyberwarzone
Cyberwarzone
O
OpenAI News
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
J
Java Code Geeks
P
Proofpoint News Feed
The Last Watchdog
The Last Watchdog
月光博客
月光博客
Latest news
Latest news
MongoDB | Blog
MongoDB | Blog
Apple Machine Learning Research
Apple Machine Learning Research

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
Catch and remediate ECS issues faster with default monitors and the ECS Explorer
2025-11-06 · via Datadog | The Monitor blog

Organizations that run applications on Amazon Elastic Container Service (Amazon ECS) often juggle signals across container and task metrics, logs, and events while they hunt for the change or condition that broke a deployment. This work adds operational overhead and extends incident timelines as teams switch between tools and manually correlate symptoms.

To help solve these challenges, Datadog now provides default monitors and an integrated troubleshooting experience that helps you spot common ECS problems quickly and jump straight to the failing service or task to fix them. You can go directly from an alert to the ECS Explorer to understand service, task, and container health within your ECS clusters, without changing tools or sifting through logs.

In this post, you’ll learn how to:

Use the ECS Explorer to debug issues detected by monitors

Datadog’s default monitors for ECS and Fargate cover common failure points such as CPU, memory, network health, and ephemeral storage. You can enable these default monitors from the ECS Monitors page in Datadog and customize thresholds for your environment.

Default monitor options for ECS and Fargate, including CPU, memory, network, and storage thresholds.

From any triggered alert, you can pivot directly to the ECS Explorer. There, you’ll see the affected cluster, service, and task alongside key context: the running task definition, recent changes, logs, and service events. This workflow is designed to get you from “something is wrong” to a concrete hypothesis and resolution without switching tools.

Recommended ECS monitors for a task resource.

Identify cluster-level issues before they cascade

In ECS, a cluster is a grouping of resources where your services and tasks run. The cluster provides the underlying infrastructure, which can be in the form of container instances (Amazon EC2) or serverless compute (Fargate). When a cluster is constrained, symptoms appear everywhere: Tasks remain pending, services can’t scale, and latency climbs.

Datadog’s default monitors surface early signs of resource contention, such as high CPU and memory reservation. These findings indicate that most of the cluster’s capacity is already allocated to tasks. The resulting monitor alert links you to the ECS Explorer, where you can take the following steps:

  1. Compare CPU and memory utilization across all services in the cluster.
  2. Identify which services are being over-reserved relative to actual usage.
  3. Decide whether to adjust resource limits or scale the cluster.
Cluster-level ECS Explorer view with CPU and memory reservation charts and service comparison.

Cluster issues often lead to placement failures, where new tasks can’t start. The ECS Explorer service panel shows pending vs. running tasks, recent service events (for example, a task state change), and the current task definition.

You can open the task definition side panel to check for misconfigured quotas, missing permissions, and mismatched limits. If changes are necessary, you can roll out a corrected definition.

Zero in on Fargate task-level failures

Because Fargate abstracts the underlying compute, you need task-level visibility to resolve failures quickly. Datadog’s task monitors and the ECS Explorer help you mitigate common problems at the task level.

CPU and memory issues

When utilization exceeds provisioned resources, tasks might fail to start or throttle under load. From the triggered alert that you receive, you can jump to the affected task to check real-time CPU and memory. You can then open the associated task definition to check the requested CPU and memory values, and adjust the resources to better match the observed demand.

Task-level ECS Explorer view with CPU and memory utilization charts and resource configuration in the task definition.

Networking problems

Fargate tasks can encounter networking errors if AWS networking resources are misconfigured. For example, improperly configured VPC routes, subnet assignments, and security groups are common issues.

From the alert, you can pivot to the ECS Explorer to review the task’s network configuration and recent service events. Then you can correlate spikes in network errors with APM traces to identify code paths or deployments that introduced connectivity issues.

Ephemeral storage limits

Fargate tasks use ephemeral storage for logs, temporary files, and caches. When space runs out, tasks fail. Datadog includes a default monitor for this condition. From the alert, open the task definition to adjust the ephemeralStorage.sizeInGiB value as needed.

Task view showing ephemeral storage usage, with the JSON definition specifying the `sizeInGiB` value.

Regressions after deployments

When behavior changes after a deployment, you can compare recent task definition versions and container images directly in the ECS Explorer. The side-by-side diff highlights changes in configuration or image tags, making it clear whether a new release introduced the issue.

Side-by-side task definition diff showing version changes after deployment.

Improve your ECS monitoring with Datadog

Datadog’s default monitors for ECS and Fargate help you detect common issues—including resource saturation, placement failures, network errors, and ephemeral storage limits—and connect them to the task or service at fault. With the ability to pivot directly from alerts to information in the ECS Explorer, you can investigate and resolve problems faster. To learn more, check out our ECS documentation and ECS Explorer documentation.

If you don’t already have a Datadog account, you can sign up for a 14-day free trial to get started.