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

腾讯CDC
博客园 - Franky
MyScale Blog
MyScale Blog
L
LangChain Blog
Martin Fowler
Martin Fowler
Recent Announcements
Recent Announcements
Stack Overflow Blog
Stack Overflow Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 司徒正美
量子位
A
About on SuperTechFans
C
Check Point Blog
大猫的无限游戏
大猫的无限游戏
Last Week in AI
Last Week in AI
小众软件
小众软件
Apple Machine Learning Research
Apple Machine Learning Research
I
InfoQ
V
Visual Studio Blog
Vercel News
Vercel News
B
Blog
爱范儿
爱范儿
aimingoo的专栏
aimingoo的专栏
U
Unit 42

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 your TeamCity builds with Datadog CI Visibility
Nicholas Thomson, Kassen Qian · 2023-04-27 · via Datadog | The Monitor blog
Nicholas Thomson

Nicholas Thomson

Technical Content Writer

Kassen Qian

Kassen Qian

As the complexity of modern software development lifecycles increase, it’s important to have a comprehensive monitoring solution for your continuous integration (CI) pipelines so that you can quickly pinpoint and triage issues, especially when you have a large number of pipelines running.

Datadog now offers deep, end-to-end visibility into your TeamCity builds with our new TeamCity integration for CI Pipeline Visibility, helping you identify bottlenecks in your CI system, track and address performance regressions, and proactively improve the efficiency of your CI system. Making data-driven decisions to increase the performance and reliability of your pipelines will help you improve end-user experience by allowing your team to push code releases faster and with fewer errors.

In this post, we’ll show you how to:

  • Integrate TeamCity with CI Visibility

  • Investigate pipeline failures to fix erroneous builds

Integrate TeamCity with CI Visibility

To configure the TeamCity integration with Datadog CI Visibility, first download the Datadog CI plugin on the TeamCity server. Then, ensure that the last build of your build chains is a composite build. Build chains in TeamCity map to pipelines in Datadog, and individual builds map to pipeline executions.

Add the following parameters to your project:

  • datadog.ci.api.key: your Datadog API key

  • datadog.ci.site: datadoghq.com

  • datadog.ci.enabled: true

Once you’ve enabled the integration, data from your TeamCity pipelines will automatically flow into Datadog. If you navigate to the Pipelines page, you can see TeamCity pipelines alongside any other providers you may have instrumented with CI Visibility.

The Pipelines page shows you TeamCity pipelines alongside any other providers instrumented with CI Visibility

Investigate pipeline failures to fix erroneous builds

After you enable the TeamCity integration in CI Visibility, you can use the Pipeline overview page to get a high-level view of the health and performance of your TeamCity build chains, with key metrics such as executions, failure rate, build duration, and more.

Say you’re an engineer at an e-commerce company where one of the checkout services for your primary application is undergoing a major revamp under a tight deadline. After pushing new code, you notice that your builds are extremely slow—much slower than normal. You can go to the Pipelines page in CI Visibility to confirm if your particular pipeline is experiencing high build durations. Then, you can click on the build chain from the Pipeline overview page to investigate the pipeline in more detail.

The Pipeline details page shows you the status of the last build

At the top of this Pipeline Detail view, you can see the status of the last build, with a link to the build chain in TeamCity. Below that are timeseries widgets illustrating the total number of builds, the error rate, build duration, and other key metrics that can help you determine when the build chain began to experience errors. In this case, you see the error rate spiking repeatedly over the past several days. The Job Summary gives you more granular information about your build chain, such as which specific jobs in this pipeline failed the most, which ones took the longest, and which jobs have experienced performance regressions compared to the previous week. Information like this can help you identify the areas in your CI system where optimization will result in the greatest performance gains.

To investigate further, you can scroll down to see the individual builds for this pipeline. If you click on an execution, you can see a flame graph view that visually breaks down the pipeline execution into the individual jobs that ran sequentially and in parallel.

If you click on an execution you can see a flame graph showing you each build’s respective duration broken down by job

The flame graph shows you each build’s respective duration broken down by job and, if the build was erroneous, the exact parts of the build that failed. This can help you pinpoint problematic jobs that may be at the root of a failed build.

The Info tab shows you repository and commit information along with other git metadata, so you can easily see the source of each build. To investigate further, you reach out to the team member who pushed the commit for this build and discover that the issue is caused by a typo. (We strongly recommend that customers use a TeamCity username style that contains author email, so that Datadog can automatically detect git author email addresses and correlate commit information to pipeline data.) Once resolved, the build chain functions without error so you can build and test successfully, and release your updated checkout service to customers on time.

Understand and optimize TeamCity build chain performance

CI Visibility support for TeamCity is now generally available, giving you deep visibility into your build chains so you can troubleshoot failed builds, identify performance regressions faster, and increase your release velocity.

For more information, see our documentation and blog post on the TeamCity Agent integration.

If you’re new to Datadog, sign up for 14-day free trial.