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

AI
AI
GbyAI
GbyAI
Blog — PlanetScale
Blog — PlanetScale
F
Fortinet All Blogs
Microsoft Azure Blog
Microsoft Azure Blog
L
LangChain Blog
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
U
Unit 42
aimingoo的专栏
aimingoo的专栏
N
Netflix TechBlog - Medium
H
Hackread – Cybersecurity News, Data Breaches, AI and More
A
About on SuperTechFans
B
Blog
I
InfoQ
T
The Exploit Database - CXSecurity.com
H
Heimdal Security Blog
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
L
LINUX DO - 最新话题
Google Online Security Blog
Google Online Security Blog
D
Darknet – Hacking Tools, Hacker News & Cyber Security
Help Net Security
Help Net Security
Stack Overflow Blog
Stack Overflow Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
T
Threat Research - Cisco Blogs
量子位
P
Privacy & Cybersecurity Law Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
SecWiki News
SecWiki News
S
Security @ Cisco Blogs
Cisco Talos Blog
Cisco Talos Blog
博客园_首页
The Last Watchdog
The Last Watchdog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
小众软件
小众软件
有赞技术团队
有赞技术团队
NISL@THU
NISL@THU
WordPress大学
WordPress大学
K
Kaspersky official blog
D
DataBreaches.Net
Hugging Face - Blog
Hugging Face - Blog
Vercel News
Vercel News
雷峰网
雷峰网
Webroot Blog
Webroot Blog
B
Blog RSS Feed
W
WeLiveSecurity
Scott Helme
Scott Helme
A
Arctic Wolf
阮一峰的网络日志
阮一峰的网络日志
G
Google Developers Blog
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO

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
Autodiscovery: Tracking services across ephemeral containers
2016-06-20 · via Datadog | The Monitor blog

Docker is being adopted rapidly, and for good reason: it simplifies many aspects of running a service in production. But Docker-powered services typically run many more containers than traditional services run hosts, so monitoring is much more complex. And with orchestrators like Kubernetes, Mesos, and ECS managing your containers, you may not even know where your containers are running at any given moment. When your containers can shift from host to host without your knowledge or intervention, manual monitoring of your services becomes nearly impossible.

How can you monitor a service which is running on a shifting set of hosts?

Datadog’s Autodiscovery feature automatically keeps track of what is running where, and gathers detailed metrics from your containers and services—wherever they may be running. With Autodiscovery, you can now continuously monitor all your services, no matter how dynamic or ephemeral the underlying infrastructure may be.

What sorcery is this?

With Autodiscovery enabled, the Datadog Agent continuously listens to Docker events. Whenever a container is created or started, the Agent identifies which service is running in the container, looks for the appropriate monitoring configuration for that service, and starts collecting and reporting metrics. Whenever a container is stopped or destroyed, the Agent understands that too.

I want it! What do I need to do?

If you followed our standard instructions for deploying Datadog on Kubernetes or DC/OS, you have Autodiscovery enabled by default. Without any further customization, the Datadog Agent will attempt auto-configuration for roughly a dozen services, including Redis and Elasticsearch, which do not require custom credentials or configuration.

To use Autodiscovery on a different container platform, or to enable additional checks, you only need to do two things. First, define the configuration templates for the containers you want to monitor. Then, enable Autodiscovery for the Datadog Agent.

Configuration templates

The config templates for Autodiscovery have a very similar structure to the standard YAML configuration files used by the Datadog Agent, with two exceptions. First, Autodiscovery templates specify Docker image names or labels, telling the Agent which containers the monitoring configuration should apply to. And second, Autodiscovery makes use of variables like %%host%% and %%port%% to apply the configuration to dynamic infrastructure.

Our documentation provides an example config template for Apache (httpd) monitoring.

You can store these templates as local config files, or in a shared configuration store like etcd or Consul. You can also add your Autodiscovery configuration templates using pod annotations in Kubernetes (since Agent version 5.12) or label annotations for Docker containers (since version 6.20).

Configuring the Agent

To configure your Datadog Agents to use Autodiscovery when installed on a host, edit your datadog.yaml configuration file to include the listeners and config_providers keys, as we show in our documentation. For Agents installed as Docker containers, Autodiscovery is enabled automatically.

Use variables to account for dynamic infrastructure

If you’re using a container orchestrator for scheduling, you probably don’t know which host IP address or port number the Agent needs to access to connect to a given service. That’s why our configuration templates support variables like %%host%% and %%port%%, which the Agent will fill in with actual values retrieved from the Docker API. You can also use indices for variables that return a list of values, for example, %%port_4%% if your service exposes multiple ports and you need to select one in particular.

Try it out

It is now easier than ever to monitor highly dynamic, ephemeral container infrastructure!

If you are already a Datadog user, you can start using Autodiscovery right now. Otherwise you can try it out by signing up for a free trial.

Datadog Agent image on the Docker Store

You can get the Docker-certified Datadog Agent image—which includes the new Autodiscovery feature—directly from the Docker Store. In one step you’ll be able to pull the image and immediately begin monitoring your hosts and containerized services, however dynamic or high-scale your infrastructure may be. Docker is a great partner to work with and we’re thrilled to be featured on the store.