AI BriefingHow Does Oliveyoung QA Use Datadog
Key point
The Oliveyoung QA team shares how they use Datadog's log and RUM features to manage service quality.
Details
The Oliveyoung QA team is actively using Datadog to quickly identify issues that occur during operations and to prevent potential problems before deployment.
Their main usage can be divided into two areas. The first is Log utilization. Through APM Log, they monitor the frequency of errors by service and quickly identify the responsible person when an error occurs. They also parse custom logs made in Python using Log Pipelines and regular expressions to build performance dashboards for real-time monitoring.
The second is checking real-user errors through RUM (Real User Monitoring). Through the Error Tracking feature, they detect new or spiking errors on the QA server and production server, fixing problems before deployment. They also use Sessions Explorer to trace the prior actions and page navigation paths of users who encountered errors, precisely analyzing the causes of hard-to-reproduce issues.
This summary was generated automatically by AI. Check the original for the author's claims and context. Copyright belongs to the original author.
Our guide explains how the AI works. Report summary errors, attribution issues, or removal requests via Contact.
Related
KT Cloud Proposes Three Architectural Boundaries for Heterogeneous Data Integration
Yeogi-eottae Builds APM Scatter Chart with OpenTelemetry and ClickHouse