Monitoring Microservices The Right Way
summary
Microservice monitoring shifts from Graphite+StatsD push metrics to Prometheus scraping and service discovery because systems produce thousands of ephemeral entities and high‑cardinality time series (big companies report thousands of services). Requirements are explicit: use a label-based query model for high‑dimensional metrics, auto‑discovery/scraping (OpenMetrics exposition), and scalable long‑term storage (Thanos, Cortex, M3, InfluxDB or remote write) so queries across months work. Logs.io is cited as a SaaS stack built on Elasticsearch, Kibana, Prometheus and Jaeger.
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chapters (8)
- 0:00Intro
- 1:06Cloud-Native Observability
- 4:15Microservices instead of a Monolith
- 5:39Cloud Native Architecture and Kubernetes
- 6:56Many Open Source & Cloud Services to Monitor
- 9:40Flexible Querying over High Cardinality
- 14:02Metric Scraping and Autodiscovery
- 17:53Clustered Scalable Solution
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