Unified Monitoring of Containers and Microservices [I]
summary
Unified monitoring combines Zipkin + Kafka + Elasticsearch and ELK + Beats with an Apache Spark pipeline to detect anomalies and correlate traces, logs and metrics. The stack deploys via one-click Ansible, uses Zipkin emitters for distributed traces, pushes data through Kafka and Logstash into Elasticsearch, and runs Spark ML (k-means) to flag outliers. Demo shows an injected memory leak raising a service from ~0.78 ms to ~13 s and memory from ~800 MB to ~2.2 GB, with Sentinel-triggered alerts and Kabana links to traces.
AI-written summary · about
chapters (5)
- 0:00Introduction
- 1:12Context and motivation
- 9:55Solution conceptual view
- 20:25Solution demonstration
- 34:15Q&A and closing
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