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
J
Java Code Geeks
GbyAI
GbyAI
aimingoo的专栏
aimingoo的专栏
L
LangChain Blog
I
InfoQ
D
Docker
F
Fortinet All Blogs
Y
Y Combinator Blog
Martin Fowler
Martin Fowler
月光博客
月光博客
B
Blog
Engineering at Meta
Engineering at Meta
T
Tailwind CSS Blog
罗磊的独立博客
博客园_首页
G
Google Developers Blog
Stack Overflow Blog
Stack Overflow Blog
Recent Announcements
Recent Announcements
D
DataBreaches.Net
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
B
Blog RSS Feed
IT之家
IT之家
V
V2EX

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
Troubleshoot streaming data pipelines directly from APM w...
2024-01-25 · via Datadog | The Monitor blog

When monitoring applications with streaming data pipelines, there are additional complexities to consider that are not present in traditional batch-processing systems. Whether you’re using streaming data pipelines to power a digital trading platform, capture sensor data from an IoT device, or recommend news articles to users, it can be challenging to identify the root cause of delays when you’re dealing with distributed systems, real-time data, and the dynamic nature of events. As a result, monitoring tools must detect issues quickly, scale flexibly, analyze and correlate complex events, and provide end-to-end visibility from data ingestion to processing and output.

Datadog Data Streams Monitoring (DSM) offers these robust capabilities, enabling you to optimize your event-driven applications that use streaming data pipelines such as Kafka or RabbitMQ. DSM allows you to track and improve application performance by providing visibility into all of the services and queues across your pipeline in one place. And, to make it even more convenient to monitor these applications, we’re excited to announce that we’ve embedded the DSM topology visualization directly into APM on the Service Page. The DSM integration in APM displays a high-level overview of your streaming data architecture, allowing you to identify performance issues such as blocked messages, offline consumers, and high-latency queues so you can troubleshoot and resolve issues even faster.

In this post, we’ll discuss how the DSM integration in APM helps you:

See a high-level overview of your streaming data dependencies in one place

Viewing your entire application architecture with the DSM integration in APM enables you to analyze application metrics alongside the topology of your streaming data pipelines so you can remediate bottlenecks. Within the APM Service Page, you can toggle between the DSM map and APM dependency map in the Dependencies view to see your monitors, lag, application requests, error rate, and more.

View  of the DSM map on the Service Page in APM

The DSM integration in APM displays your service’s queues, upstream producers, and downstream consumers. With all of this information in one convenient location, you can gain insight into where delays and inefficiencies occur through the queues in your pipeline and formulate targeted optimization strategies. Understanding your application’s dependencies with services and queues makes it easier to track the flow of data and events and measure how your resources are affected when issues arise.

To catch issues proactively, you can set a monitor that alerts on consumer lag, throughput, end-to-end latency for your pipelines, or the time messages spend in the queue. You can also view and interact with active DSM monitors in the APM side panel.

Conduct investigations without workflow disruption

The DSM integration in APM helps you avoid context switching as you troubleshoot and resolve pipeline inefficiencies. When an issue occurs, you can correlate APM metrics, such as application traces and faulty deployments, with pipeline information and investigate without interrupting your workflow. This insight enables you to quickly pinpoint the issue’s root cause with precise accuracy.

For example, let’s say APM alerts you that a service is experiencing consumer lag. You can start the investigation by reviewing the DSM map on the same Service Page. Selecting the service node on the DSM map lets you view the side panel that contains all the telemetry you need to troubleshoot, like traces, logs, throughput, and infrastructure metrics of your service. You can also see which individuals or teams are assigned on-call duty for affected service dependencies, so you can reach out and collaborate with them for fast resolution.

Side panel telemetry of an example authenticator service that displays latency metrics

The DSM integration in APM also visualizes alerts for pipeline components that experience high consumer lag and routes you to the DSM page if you need more details for your investigation.

Monitor your streaming data pipelines in APM today

The Data Streams Monitoring integration in APM allows you to view your streaming data pipelines alongside your entire application architecture to understand how your application interacts through services and queues. With the DSM integration in APM, you can conduct investigations without disruption to remediate bottlenecks quickly, pinpoint root causes, and improve application performance.

You can quickly identify which services are eligible to use DSM by navigating to the Service Page of any services with upstream producers or downstream consumers. To set up your service with DSM, visit our setup guide. To learn more about DSM and APM, visit our documentation.

If you’re new to Datadog, get started now with a free trial.