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
The Cloudflare Blog
IT之家
IT之家
V
V2EX
雷峰网
雷峰网
MyScale Blog
MyScale Blog
P
Proofpoint News Feed
Stack Overflow Blog
Stack Overflow Blog
博客园 - Franky
Engineering at Meta
Engineering at Meta
S
SegmentFault 最新的问题
GbyAI
GbyAI
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - 司徒正美
云风的 BLOG
云风的 BLOG
小众软件
小众软件
博客园 - 叶小钗
Blog — PlanetScale
Blog — PlanetScale
C
Check Point Blog
A
About on SuperTechFans
B
Blog
月光博客
月光博客
宝玉的分享
宝玉的分享
Last Week in AI
Last Week in AI

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
Automatically detect error and latency patterns with Watc...
2020-03-31 · via Datadog | The Monitor blog

When monitoring highly distributed applications—which might rely on hundreds of services and infrastructure components across multiple cloud-based and on-premise environments—it can be challenging to identify errors, detect causes of high latency, and determine the root cause of issues. Even if you already have robust monitoring and alerts, your infrastructure and applications will likely change over time, which may make it difficult to reliably detect irregular behavior. To meet this challenge, we developed Watchdog Insights for Datadog APM.

Watchdog Insights watches the indexed spans returned by your Trace Analytics queries in real time, and scans all available tags in the results. It automatically detects which tagged objects from your environment including users, hosts, services, and any other reserved or custom tags are associated with higher-than-usual error rates or latency. This feature enables devops teams to quickly identify issues and dig deeper into the relevant traces and associated services, infrastructure components, and code profiles to discover possible root causes.

watchdog-insights-updated-screenshot01

Dig deeper into indexed spans in Trace Analytics

Watchdog Insights is embedded in our Trace Analytics UI, so you can investigate indexed spans and outlier data in a single view. Simply navigate to the Analytics view under the Traces tab, and use tags like service and env to filter your spans. If a notification appears in the Watchdog Insights widget (highlighted above), click on it to see any outlier behavior and a list of tags that appear in indexed spans exhibiting that behavior.

Clicking on any of the tags listed will let you drill down into their traces and view other associated tags for wider context. Once you’ve identified where errors or latency spikes originated, you can start taking steps to resolve the issue. For example, you can check the health metrics of the service throwing the errors to see if it’s the result of a bottleneck and determine whether to provision more resources. You might also discover a database shard associated with high latency that needs to be scaled up after it’s exceeded its transaction throughput capabilities.

Find the outlier in the haystack

Even with comprehensive monitoring and a robust set of alerts, you can still encounter challenges spotting issues, understanding their impact, and identifying their root cause. Watchdog Insights helps discover unexpected and erroneous behavior in your applications and infrastructure.

Quickly pinpoint issues in multi-tenant architecture

One common hurdle you’ll encounter while managing a multi-tenant application is knowing which infrastructure components are underperforming and what customers might be affected. If a customer says they’re experiencing issues with an application, identifying the exact resources the customer is using, which might be provisioned across thousands of hosts, partitions, and shards, can be difficult and time consuming. In Trace Analytics, indexed spans can include tags so that they are associated with both the underlying infrastructure components and the users. This way, you can easily see the user whose request generated the indexed span and the resources behind it.

Watchdog scans indexed spans for error patterns and abnormal latency, surfacing any tags attached to a disproportionate number of spans from traces ending in errors or executing slowly. For further convenience, Trace Outliers groups together the tags that often appear together on the same erroring or slow traces. This means that if Trace Outliers detects that a specific tagged infrastructure component is correlated with unusually high error rates, it will quickly identify tagged users with similar error rates that are also associated with those components, so you can immediately know where to focus your efforts. This also means that when a specific tagged resource is correlated with an unusually high latency, Watchdog will quickly flag it along with other tags that are associated with that resource so you can identify the root cause faster.

watchdog-insights-updated-screenshot02

Whether identifying the resources behind a customer’s issue, or finding customers affected by underperforming infrastructure or code, Watchdog Insights will save you time and effort by quickly identifying which elements have similar error rate or latency patterns.

Spot feature flag performance degradation

Feature flags allow engineering teams to safely test new features and deliver additional functionality to their users. When you enable a feature, you’ll want to know quickly if it’s behaving as expected or if it’s affecting application performance. Watchdog can immediately identify whether indexed spans tagged with a new feature flag are showing performance degradation. This lets you quickly course correct and roll-back the feature or troubleshoot as needed.

AIOps and beyond

Watchdog is powered by artificial intelligence for IT operations (AIOps), which makes it easy for teams to spot unusual behavior and pinpoint the origin of an issue in their applications and more than 1,000 integrations. Our many AIOps-driven features include Watchdog Insights for Log Management, Metric Correlations, and Log Patterns. Monitoring your system with Datadog’s AIOps-driven features can save you time detecting potential issues by quickly identifying abnormal behavior so you can start troubleshooting and reduce MTTR.

If you are not already using Datadog, sign up today for a 14-day free trial.