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

T
The Blog of Author Tim Ferriss
WordPress大学
WordPress大学
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
小众软件
小众软件
博客园_首页
Blog — PlanetScale
Blog — PlanetScale
B
Blog RSS Feed
Martin Fowler
Martin Fowler
M
MIT News - Artificial intelligence
博客园 - 三生石上(FineUI控件)
博客园 - 【当耐特】
N
News | PayPal Newsroom
K
Kaspersky official blog
大猫的无限游戏
大猫的无限游戏
人人都是产品经理
人人都是产品经理
N
Netflix TechBlog - Medium
B
Blog
Recorded Future
Recorded Future
U
Unit 42
J
Java Code Geeks
Security Latest
Security Latest
H
Hackread – Cybersecurity News, Data Breaches, AI and More
V
Vulnerabilities – Threatpost
Cisco Talos Blog
Cisco Talos 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
Scott Helme
Scott Helme
Apple Machine Learning Research
Apple Machine Learning Research
aimingoo的专栏
aimingoo的专栏
T
Threatpost
Last Week in AI
Last Week in AI
Know Your Adversary
Know Your Adversary
Project Zero
Project Zero
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Cloudbric
Cloudbric
AWS News Blog
AWS News Blog
NISL@THU
NISL@THU
有赞技术团队
有赞技术团队
博客园 - 叶小钗
N
News and Events Feed by Topic
V
V2EX
T
Troy Hunt's Blog
月光博客
月光博客
博客园 - Franky
P
Proofpoint News Feed
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
Visual Studio Blog
C
Cisco Blogs
The Cloudflare Blog
T
Tor Project blog
Google Online Security Blog
Google Online Security Blog

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
.NET monitoring with Datadog APM and distributed tracing
2019-04-05 · via Datadog | The Monitor blog

Since it was first introduced in 2002, Microsoft’s .NET Framework has garnered a robust user base that includes organizations like UPS, Stack Overflow, and Jet.com. And now, thanks to the rise of the .NET Core runtime, this high-performance framework also supports cross-platform development. To provide deeper visibility into all of these environments, Datadog APM and distributed tracing are generally available for .NET Framework and .NET Core applications. With this addition, .NET joins a growing list of supported frameworks and languages in APM.

.NET monitoring in Datadog APM flame graph

Unleash Datadog’s .NET monitoring on your applications

Datadog APM gives you deep visibility into your .NET applications within minutes. In most cases, you’ll see data flowing into your account without changing a single line of code. Our open source .NET tracing library automatically instruments web frameworks (ASP.NET MVC, ASP.NET Core MVC, ASP.NET Web API 2), ADO.NET-compatible databases like SQL Server and PostgreSQL, and other data stores (e.g., Redis, Elasticsearch, and MongoDB), with more integrations in the works. Datadog’s .NET client includes support for OpenTracing, the vendor-neutral standard for distributed tracing, so you can easily port your applications without making major updates to your code.

Datadog APM tracks requests as they travel across distributed caches, data stores, and cloud services in your environment. These are just some of the features that can help you analyze and understand all of this data in real time:

  • The Service Map automatically maps out your microservices architecture based on data collected from APM
  • Service overview dashboards provide a summary of each service’s health, error rates, and anomalous performance trends
  • App Analytics helps you find “needle in the haystack” request traces for investigating errors and bottlenecks
  • Watchdog uses machine learning to alert you to automatically detected anomalies in your .NET apps

Using data you’re already collecting from APM, the Service Map automatically maps out your application architecture so that you can understand how requests flow across the services in your environment and identify dependencies between services. This provides a great starting point for troubleshooting issues across your microservices.

.NET monitoring in Datadog APM service map

Explore an overview of each .NET service

From the Service Map, you can drill down to inspect the performance of any individual service. APM automatically generates an overview dashboard with key metrics (request throughput, latency, and errors) for each service in your environment. You’ll also have the option to enable suggested alerts that will notify you of elevated error rates, high 90th-percentile latency, and other issues.

.NET monitoring in Datadog APM dashboard

Optimize application performance with the Span Summary

As Datadog APM traces requests across services in your environment, it breaks down each trace into spans. Each span represents a specific unit of work completed within a trace (such as an API call or a database query). From the service overview dashboard, you can click on any endpoint to see a Span Summary—a breakdown of the frequency and latency of function calls or operations completed by your services. You can use this summary to quickly identify the areas that you can focus on optimizing first, to get the maximum return on investment. Below, it’s clear that we can improve latency the most by optimizing our todoes controller, which is accounting for almost all the latency in our requests.

.NET monitoring in Datadog APM looking at Span Summary to see more frequently occurring spans

Investigate issues using flame graphs

Although high-level overview dashboards are useful for keeping tabs on the health of your applications, you can also get finer-grained insights by inspecting each trace in a flame graph. If you click on a span, you’ll see detailed metadata about that operation (e.g., the exact SQL query executed).

In the example below, an ASP.NET MVC web application returned a 500 error after trying to execute a SQL Server query. The flame graph allows us to visually identify the bottlenecks in processing this request. We can also look at the full stack trace to help diagnose and debug the error.

.NET monitoring in Datadog APM and distributed tracing inspecting error for ASP.NET

Correlate traces with host metrics and logs

Each flame graph is deeply integrated with other types of monitoring data you’re collecting from your applications and infrastructure. As you’re inspecting a flame graph, you can navigate to the “Host” tab to see more context about the host. This allows you to quickly get a sense of host-level resource constraints (CPU, memory, network bandwidth) at the time of the request. Datadog automatically picks up metadata from Azure and other technologies, and applies it as tags to your APM metrics and request traces, so you can connect the dots across related components of your environment. As shown in the example below, when you click on a span you can see the Azure VM’s availability zone and resource group, among other information.

Inspect .NET application request trace from Datadog APM and correlate with host-level metrics by selecting the Host Info tab

Furthermore, if you’re using Datadog log management to automatically collect and process logs, you can navigate to the “Logs” tab to view relevant logs from your services, which can help you get additional context around the trace. Visualizing all of this information in one pane of glass can help you identify the source of any issue, whether it’s a bug in your application code, a slow SQL query, or an overloaded server.

Inspect .NET application request trace from Datadog APM and correlate with host-level logs by selecting the Logs tab

Get customer-level visibility with App Analytics

App Analytics makes it easy to investigate issues in your .NET applications and understand how they impact your business. If a user reports an issue, App Analytics can help you investigate by pinpointing the relevant analyzed spans collected from that specific user. You can use tags and facets to quickly slice and dice your distributed request traces across any dimension—whether it’s an endpoint, a single SQL query, a specific checkout ID, or a combination thereof—and drill down to inspect the exact analyzed spans you need.

Pinpointing errors in .NET apps with Datadog App Analytics

Watchdog auto-alerts you to performance issues

While alerts provide critical visibility into known issues, Watchdog goes a step further by helping you track potentially unknown problems in your application, where you haven’t set any alerts. Watchdog autonomously tracks your services and keeps you posted about application performance anomalies, such as elevated error rates on specific endpoints, anomalous spikes in latency, or network issues with your cloud provider. When it finds something potentially noteworthy, it highlights the timeframe in question and provides a plain-language summary of what happened, so that you can understand the scope of the issue and how it could impact your users.

.NET monitoring in Datadog Watchdog APM and distributed tracing

Connecting the dots across .NET apps and infrastructure

Datadog integrates with cloud services like Azure and AWS so you can monitor your .NET applications and your infrastructure in one place. For example, you can add a “Service Summary” widget to a dashboard to see a high-level overview of your .NET application alongside health and performance metrics from the application’s underlying Azure VMs and other related cloud services.

With .NET monitoring you can visualize .NET application metrics alongside data from your Azure infrastructure and cloud services

Dive into .NET monitoring with Datadog

Support for .NET is now generally available in Datadog APM—check out our documentation to start getting deeper visibility into your applications. If you don’t have a Datadog account, you can sign up for a free 14-day trial to monitor your .NET applications and the rest of your environment in one place.