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

K
Kaspersky official blog
罗磊的独立博客
F
Fortinet All Blogs
人人都是产品经理
人人都是产品经理
量子位
V
Visual Studio Blog
Blog — PlanetScale
Blog — PlanetScale
M
MIT News - Artificial intelligence
B
Blog RSS Feed
腾讯CDC
博客园_首页
aimingoo的专栏
aimingoo的专栏
博客园 - 三生石上(FineUI控件)
博客园 - Franky
S
SegmentFault 最新的问题
N
Netflix TechBlog - Medium
小众软件
小众软件
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
L
LINUX DO - 热门话题
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Martin Fowler
Martin Fowler
D
Docker
P
Privacy & Cybersecurity Law Blog
S
Securelist
V
V2EX
Jina AI
Jina AI
阮一峰的网络日志
阮一峰的网络日志
T
Tor Project blog
The Hacker News
The Hacker News
Microsoft Azure Blog
Microsoft Azure Blog
AWS News Blog
AWS News Blog
The GitHub Blog
The GitHub Blog
有赞技术团队
有赞技术团队
T
The Exploit Database - CXSecurity.com
Help Net Security
Help Net Security
酷 壳 – CoolShell
酷 壳 – CoolShell
Application and Cybersecurity Blog
Application and Cybersecurity Blog
博客园 - 叶小钗
Recent Announcements
Recent Announcements
Cloudbric
Cloudbric
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
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Latest news
Latest news
MongoDB | Blog
MongoDB | Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Recorded Future
Recorded Future
V2EX - 技术
V2EX - 技术

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
Monitor and diagnose network performance issues with SNMP Traps
2022-06-14 · via Datadog | The Monitor blog

Monitoring your on-premise or hybrid infrastructure means keeping track of potentially thousands of devices, any one of which could be a point of failure. Additionally, silos between application and network teams can create visibility gaps that complicate troubleshooting. For network engineers investigating bottlenecks, being able to view real-time infrastructure health and performance data alongside application metrics is essential for ensuring their organizations meet key SLOs.

To help with this, Datadog Network Device Monitoring (NDM) collects telemetry data from your on-premise equipment by polling devices with Simple Network Management Protocol (SNMP). This provides valuable insights into your entire fleet of devices, including routers, switches, and firewalls. However, polling by itself can miss network issues that occur outside of polling periods, and some information about your devices—such as hardware failures—may not be available via SNMP polling at all.

For complete visibility into your network equipment, Datadog NDM now collects SNMP Traps, enabling you to catch critical network issues right when they happen. Support for SNMP Traps expands on our existing NDM suite, helping you consolidate troubleshooting efforts within a single pane of glass. You can easily view, sort, and filter SNMP Traps side-by-side with your other network infrastructure metrics. You can also set up monitors for SNMP Traps, allowing you to receive notifications for issues before they impact the rest of the network.

Details for an SNMP Trap event, including the event attributes and tags.

Identify device issues as soon as they occur

SNMP Trap events are triggered by network devices when they encounter unusual activity, such as a sudden state change on a piece of equipment. Because of this, you can use Traps to capture issues that might otherwise go unnoticed due to device instability. For example, if an interface is flapping between an available and a broken state every 15 seconds, relying on polls that run every 60 seconds could lead you to misjudge the degree of network instability. Traps can also fill visibility gaps for certain hardware components, such as device battery or chassis health.

To make sure you receive alerts every time a critical SNMP Trap triggers, you can set up Datadog monitors on specific Trap events. This enables you to receive alerts via email, ticketing tools like ServiceNow, or mobile device notifications. You can use these monitors to quickly identify and troubleshoot network latency, as well as spot hardware health problems that could indicate larger performance issues such as packet loss and latency.

A triggered SNMP monitor displaying a warning about a high error rate on a host.

Let’s say a fan on one of your network devices breaks, causing the equipment to overheat. The event triggers an SNMP Trap, which Datadog catches and sends you a notification about. Looking at the Trap details helps you judge the severity of the issue and determine the appropriate next steps. In this case, you notice that a critical router is affected and decide to investigate further.

Troubleshoot network equipment incidents with detailed device metrics

As soon as you’re alerted about a device issue via SNMP Traps, you can use Datadog to begin troubleshooting. For instance, you can use Log Patterns to spot related Traps coming from other devices, or you can analyze the health of your entire network using the Network Devices page. This allows you to visualize key metrics from every device in your network, across every layer.

In the scenario of the overheating device described earlier, you could pivot to the Network Devices page to investigate the impact on your overall network health. There, you can visualize detailed network metrics—such as the number of packet drops—in order to determine whether the issue is affecting other devices. For example, you might discover that the rest of your network is experiencing an increased workload to compensate for the unavailable host.

The Network Devices page for a device, with graphs for the inbound/outbound throughput, bandwidth utilization, and interface errors.

You can also drill down into a list of interfaces on each device for fine-grained analysis. If you have a device with an overly saturated network interface, it could be hogging the available bandwidth and causing latency on the rest of the network. You can go straight from a Trap notifying you about high bandwidth on a device to pinpointing the problematic interface and evaluating the overall effect on network performance. You can also view additional metrics via dashboards to correlate network issues with the rest of your stack. Here, you could look at frontend performance data to determine the impact on user experience.

Streamline device monitoring with SNMP Traps

With SNMP Trap support from NDM, you receive full visibility into potential device issues—no matter where or when they happen in your network. You can leverage alerts on SNMP Trap data alongside a variety of network metrics to diagnose issues, assess their severity, and immediately start troubleshooting.

SNMP Traps is available in Datadog Agent versions 7.37 and up. If you’re an existing customer, you can get started with Network Device Monitoring using our documentation. Or, if you’re new to Datadog, you can sign up for a 14-day free trial.