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
Run Synthetic tests in your CI/CD pipelines with the Datadog CircleCI orb
2022-05-05 · via Datadog | The Monitor blog

CircleCI is a CI/CD service that allows organizations to rapidly build, test, and deploy within their pipelines on a single platform. If you are using CircleCI for your CI/CD pipelines, you can now leverage the Datadog Synthetics CircleCI orb to implement Synthetic tests as part of shift-left testing. CI/CD testing is a widely adopted DevOps standard that helps teams mitigate any potential issues that could arise as a result of faulty code deployments.

Datadog Synthetic Monitoring enables you to implement a suite of end-to-end tests in your CI/CD pipelines, so you can automatically detect and block the deployment of code changes that would break key workflows or endpoints. The Datadog CircleCI orb enables you to implement these Synthetic tests in your CircleCI pipelines, surfacing issues that you can then diagnose and debug with Datadog. Adding Synthetic tests to your CircleCI pipelines will help you ensure that new code deployments are reliable, preempting any potential problems for your end users.

In this post, we’ll show you how to set up the CircleCI orb to run Synthetic tests, debug failing Synthetic tests, and monitor CircleCI pipelines together with Datadog’s existing CircleCI integration.

Set up the Datadog CircleCI orb to run Synthetic tests

A CircleCI orb is an open source, reusable config file for automating processes, expediting project startup, and easing integration between your CircleCI pipelines and services across the stack. To get started, simply add your Datadog API and application keys as environment variables to your CircleCI project. You can then start configuring the Datadog CircleCI orb to execute Datadog Synthetic tests in your pipelines.

To make sure that code deployments don’t introduce unexpected issues for your end users, you can implement a suite of Synthetic end-to-end tests to confirm that your application is functioning as expected. For example, the CircleCI pipeline configuration below utilizes the Synthetics CircleCI orb.

version: 2.1

orbs:

synthetics-ci: datadog/synthetics-ci-orb@1.0.1

jobs:

e2e-tests:

docker:

- image: cimg/base:stable

steps:

- synthetics-ci/run-tests:

public_ids: 'abc-d3f-ghi, jkl-mn0-pqr'

workflows:

run-tests:

jobs:

- e2e-tests

The above configuration will automatically trigger the Synthetic tests associated with the specified public_ids every time you deploy code changes. You can configure the orb to block the deployment if the tests fail using a config override or through the Synthetics UI. The results of your Synthetic tests will appear in your CircleCI pipeline (as shown in the example below).

View your Synthetic test executions with CircleCI

If you want to run tests on applications that are not publicly available (e.g., internal applications), you can also configure local testing by starting a local server and triggering Synthetic tests using the datadog-ci tunnel (as shown in the example below).

version: 2.1

orbs:

synthetics-ci: datadog/synthetics-ci-orb@1.0.1

jobs:

e2e-tests:

docker:

- image: your-image

steps:

- checkout

- run:

name: Running server in background

command: npm start

background: true

- synthetics-ci/run-tests:

config_path: tests/tunnel-config.json

files: tests/*.synthetics.json

test_search_query: 'tag:e2e-tests'

tunnel: true

workflows:

test-server:

jobs:

- build-image

- integration-tests:

requires:

- build-image

By virtue of the orb’s reusability, you can leverage the same unique test suite in development, staging, and production to simplify and standardize your testing across different environments. For example, you can configure your tests to automatically run in your dev environment after they’ve passed locally to ensure that new features work properly before deployment. Simply override the startURL in your global config file so that it points to your development environment.

Debug failing Synthetic tests

Datadog Synthetic Monitoring integrates with APM to offer invaluable context for troubleshooting failing tests (e.g., duration, device, and browser) alongside metrics, logs, and other data. For example, you can inspect a test’s correlated trace to learn more about why it failed. In this case, the trace shows you that the request to your API returned a 404 error. Upon further investigation, you find that this request was being transmitted to a recently deprecated client library, so you may be able to resolve this issue by switching to a different provider.

Troubleshoot failing tests with Datadog Synthetic Monitoring

Improve the reliability and efficiency of your CircleCI pipelines with Datadog

Using the Datadog CircleCI orb in conjunction with Datadog’s CircleCI integration allows you to monitor your CircleCI pipeline performance. In addition to running Synthetic tests in CircleCI, you can also use Datadog CI Visibility to optimize your pipelines. Once you’ve instrumented your CircleCI pipelines with CI Visibility, health and performance metrics will automatically stream into Datadog. You can view the results of your jobs in the CI Results Explorer and investigate problematic jobs to see where errors and bottlenecks might be occurring.

Each job contains a batch of test IDs specified by the orb. Once Datadog has run the tests, you can click on a batch of results—tagged with useful metadata, including status, branch, and duration—to pinpoint the specific test(s) that failed. As in the image below, Datadog itemizes the test results from the orb’s execution, allowing you to compare the results of individual test runs across different browsers, devices, and locations.

View job results in the CI Results Explorer

CI Visibility shows you which pipelines are the slowest or most error-prone so you can take action to improve the reliability of your tests and builds. Additionally, CI Visibility automatically detects flaky tests—another common source of failing jobs. As in the example below, you can inspect the erroneous span to get more helpful context for troubleshooting the failure.

Leverage CI visibility to troubleshoot problematic tests

Test with the CircleCI orb and monitor your pipelines with Datadog

The Datadog CircleCI orb enables you to easily incorporate Synthetic tests into your CircleCI pipelines, allowing all of your development teams to quickly detect issues before they degrade your user experience. This integration extends our existing support for Synthetic Monitoring in your CI pipelines, complementing the Datadog GitHub Action and Datadog plugin for Jenkins. CI Visibility also provides rich context around the health of your CircleCI pipelines, enabling you to optimize your CI/CD workflows. If you’re new to Datadog, sign up for a 14-day free trial.

-a