20171027 モニタリング勉強会
2. Self Introduction • Paul Traylor • LINE Fukuoka 開発室 • Currently responsible for updating monitoring environment at LINE Fukuoka • https://github.com/line/promgen • https://promcon.io/2017-munich/talks/prometheus-as-a- internal-service/ 3. Operating Prometheus at LINE Fukuoka • 4 HA Pairs • ~2000 targets per machine • ~800k samples per machine • ~3.5 million samples • ~7000 exporters https://git
Kubernetes with Prometheus
Kubernetes Meetup Tokyo #7 https://k8sjp.connpass.com/event/67092/
PromCon 2017: Prometheus as an (internal) Service
https://promcon.io/2017-munich/talks/prometheus-as-a-internal-service/ LINE is a large company with many different development teams. Momentum can be a powerful force within a company so it can take some time, training (including unlearning the old) and evangelizing to get a new system adopted. This talk will give a brief summary of our development team’s monitoring environment (Prometheus, Grafan
Prometheus Storage
This document discusses time series data storage and querying in Prometheus. It describes how Prometheus stores time series data as chunks on disk in a key-value store format, with compression to reduce storage needs. It also explains how Prometheus handles ingesting new time series data through appending to in-memory chunks before writing to disk, and how it handles querying time series data thro
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