environments layer a. Events, logs, & metrics b. Common service to centralize, rotate, and delete 2. Event router responsible for storing our events and metrics a. Enables visualization, trending, alerting good things b. Enable threshold-based alerting & health checks iii. Transform logs/events into metrics to enable statistical analysis iv. Adrian Cockcraft – “Monitoring is so important that our monitoring prevent them in the future ii. Use for fact-based problem-solving e. ENABLE CREATION OF PRODUCTION METRICS AS PART OF DAILY WORK i. Create infrastructure to make it easy for Dev or Ops to create telemetry

先提前下载以下2个镜像（具体版本根据实际使用的配置yaml文件来决定） docker.io/kubernetesui/dashboard:v2.6.0 docker.io/kubernetesui/metrics-scraper:v1.0.8 # wget h�ps://raw.githubusercontent.com/kubernetes/dashboard/v2.6.0/aio/deploy/recom grafana/grafana:8.3.3 kubeStateMetrics-deployment.yaml: image: k8s.gcr.io/kube-state-metrics/kube- state-metrics:v2.3.0 kubeStateMetrics-deployment.yaml: image: quay.io/brancz/kube-rbac- proxy:v0 k8s.gcr.io/kube-state-metrics/kube-state-metrics:v2.3.0 docker pull k8s.gcr.io/prometheus-adapter/prometheus-adapter:v0.9.1 #替换为： docker pull docker.io/lbbi/kube-state-metrics:v2.3.0 docker pull

browser Metrics, metrics everywhere (but where the heck do you start?) by Tammy Everts & Cliff Crocker (SOASTA) @tameverts @cliffcrocker http://www.slideshare.net/CloudTest/metrics-metrics-everywher

Guidance 1. Pull all factual evidence (chat logs, etc.) to help build the timeline; any specific metrics observed, investigative paths taken, results, and other resolutions considered 2. Don’t allow fear deployment pipeline demonstrates that changes can be low risk and pre-approved. ii. Use previous metrics to demonstrate high success rates and low MTTR iii. Link and provide traceability from planning

processes, or review processes 2. Places where significant rework is generated or received. iii. Key metrics in value stream 1. Lead Time 2. Process time 3. Percent Complete & Accurate (%C/A) c. CREATE

Expands upon Canary. Links production monitoring with release process to automatically roll back when metrics deviations, outside of expected ranges, are detected i. APPLICATION-BASED PATTERNS TO ENABLE SAFER