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

Microsoft Security Blog
Microsoft Security Blog
Jina AI
Jina AI
量子位
博客园 - 叶小钗
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
IT之家
IT之家
S
SegmentFault 最新的问题
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
小众软件
小众软件
Hugging Face - Blog
Hugging Face - Blog
雷峰网
雷峰网
博客园 - 聂微东
美团技术团队
Last Week in AI
Last Week in AI
罗磊的独立博客
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 三生石上(FineUI控件)
WordPress大学
WordPress大学
宝玉的分享
宝玉的分享
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
博客园_首页
V
Visual Studio Blog
大猫的无限游戏
大猫的无限游戏
The Cloudflare 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 Azure Functions with the Datadog extension for Az...
2022-07-01 · via Datadog | The Monitor blog

Azure Functions is an on-demand serverless compute offering built on top of Azure App Service that enables you to deploy event-driven code without the need to provision and manage infrastructure. Because applications rely on Azure Functions to handle business-critical tasks such as processing orders or logging in users, it’s important to ensure that your functions respond quickly when they’re invoked.

Earlier this year, we released the Datadog extension for Azure App Service which collects traces and automatically correlates telemetry from resources running in Azure App Service. This extension includes support for Azure Functions, providing even deeper and granular insight into your serverless Azure infrastructure.

In this post, we’ll explore how our Azure App Service extension provides in-depth, function-level visibility so you can identify performance and resource bottlenecks to troubleshoot issues and optimize your Azure-hosted applications.

Collect Azure Functions traces with Datadog APM

Azure Functions run on function apps, which can host one or more functions. Our Azure App Service integration provides function app-level visibility into application performance, but to efficiently fine-tune your service you also need granular, function-level visibility. With Datadog APM and our App Service extension, you can get an end-to-end view of request traces from Azure Functions and other Azure-managed services as they propagate across your infrastructure.

When an Azure function is invoked, its lifecycle is visualized in a flame graph that’s broken down into spans. These spans let you see the duration of each step of a request so you can identify performance pain points such as high latency. Datadog also automatically tags traces by function name so you can see which specific function within a function app was executed, allowing you to focus your troubleshooting efforts. For example, in the screenshot below, we can see a .NET Azure function called SendEmailReminderAboutShoppingCart is throwing an exception.

azure-function-trace

Correlate Azure Functions data with the rest of your infrastructure

The Datadog App Service extension expands on our Azure App Service integration, enabling you to correlate Azure Functions trace data with metrics, traces, and logs from across your Azure-hosted resources. For instance, you can correlate Azure Functions traces with metrics collected from your underlying App Service plan at the time of the trace to determine whether a performance issue is related to an underlying capacity problem. If you see a correlation between a performance issue such as increased latency and high CPU or memory usage, you can take steps to reconfigure your functions and App Service to ensure they have the resources to scale as needed.

azure-function-correlation

Start monitoring Azure Functions

Together with our Azure App Service integration, the Datadog App Service extension lets you monitor Azure Functions in context with the rest of your serverless Azure infrastructure for even deeper visibility into the health and performance of your App Service plan. For more information, check out our documentation.

If you’re not already a Datadog customer, sign up today for a 14-day free trial.