Faster Python apps with open source APM
summary
Open source Elastic APM claims to trace Python apps end to end by adding a library and shipping samples from pip install elastic-apm into an APM Server → Elasticsearch → Kibana stack. The demo is a coffee shop Django demo on GitHub runnable with Docker that shows request timelines, response time percentiles, stack traces down to individual lines and distributed tracing across services. Agents sample to limit data, capture app metadata not user payloads, and do not replace infrastructure or full log monitoring.
AI-written summary · about
chapters (12)
- 0:00Intro
- 3:57What is APM
- 6:23Why APM
- 9:33How it works
- 12:28Demonstration
- 16:07Framework Limitations
- 17:35URL
- 18:17Index
- 19:37Agent
- 20:31Scaling
- 21:53Data collection
- 23:12Distributed tracing
related talks
38mApplication Performances Monitoring with Elastic APM and the ELK Stack
27mOpenAPM – Application Performance Management tailored to your needs
38mGaining Insights of your Microservices with Elastic APM
46mApplication Performance Monitoring for Your Java Projects
24m"Introduction to OpenTelemetry with Python"
17mTrace your applications with OpenTelemetry
01h 04mTracing distributed services: experiences with implementing APM for the JVM
42mUnified observability, AIOps, and incident response for AWS