led设计网站建设,wordpress netease,做网站好看的背景图片,怎么制作表格excel初学者视频简介#xff1a; 应用级扩缩容是相对于运维级而言的。像监控CPU/内存的利用率就属于应用无关的纯运维指标#xff0c;针对这种指标进行扩缩容的HPA配置就是运维级扩缩容。而像请求数量、请求延迟、P99分布等指标就属于应用相关的#xff0c;或者叫业务感知的监控指标。 本篇…简介 应用级扩缩容是相对于运维级而言的。像监控CPU/内存的利用率就属于应用无关的纯运维指标针对这种指标进行扩缩容的HPA配置就是运维级扩缩容。而像请求数量、请求延迟、P99分布等指标就属于应用相关的或者叫业务感知的监控指标。 本篇将介绍3种应用级监控指标在HPA中的配置以实现应用级自动扩缩容。
应用级扩缩容是相对于运维级而言的。像监控CPU/内存的利用率就属于应用无关的纯运维指标针对这种指标进行扩缩容的HPA配置就是运维级扩缩容。而像请求数量、请求延迟、P99分布等指标就属于应用相关的或者叫业务感知的监控指标。
本篇将介绍3种应用级监控指标在HPA中的配置以实现应用级自动扩缩容。
Setup HPA
1 部署metrics-adapter
执行如下命令部署kube-metrics-adapter(完整脚本参见demo_hpa.sh)。
helm --kubeconfig $USER_CONFIG -n kube-system install asm-custom-metrics \$KUBE_METRICS_ADAPTER_SRC/deploy/charts/kube-metrics-adapter \--set prometheus.urlhttp://prometheus.istio-system.svc:9090
执行如下命令验证部署情况
#验证POD
kubectl --kubeconfig $USER_CONFIG get po -n kube-system | grep metrics-adapterasm-custom-metrics-kube-metrics-adapter-6fb4949988-ht8pv 1/1 Running 0 30s#验证CRD
kubectl --kubeconfig $USER_CONFIG api-versions | grep autoscaling/v2betaautoscaling/v2beta1
autoscaling/v2beta2#验证CRD
kubectl --kubeconfig $USER_CONFIG get --raw /apis/external.metrics.k8s.io/v1beta1 | jq .{kind: APIResourceList,apiVersion: v1,groupVersion: external.metrics.k8s.io/v1beta1,resources: []
}
2 部署loadtester
执行如下命令部署flagger loadtester
kubectl --kubeconfig $USER_CONFIG apply -f $FLAAGER_SRC/kustomize/tester/deployment.yaml -n test
kubectl --kubeconfig $USER_CONFIG apply -f $FLAAGER_SRC/kustomize/tester/service.yaml -n test
3 部署HPA
3.1 根据应用请求数量扩缩容
首先我们创建一个感知应用请求数量(istio_requests_total)的HorizontalPodAutoscaler配置
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:name: podinfo-totalnamespace: testannotations:metric-config.external.prometheus-query.prometheus/processed-requests-per-second: |sum(rate(istio_requests_total{destination_workload_namespacetest,reporterdestination}[1m]))
spec:maxReplicas: 5minReplicas: 1scaleTargetRef:apiVersion: apps/v1kind: Deploymentname: podinfometrics:- type: Externalexternal:metric:name: prometheus-queryselector:matchLabels:query-name: processed-requests-per-secondtarget:type: AverageValueaverageValue: 10
执行如下命令部署这个HPA配置
kubectl --kubeconfig $USER_CONFIG apply -f resources_hpa/requests_total_hpa.yaml
执行如下命令校验
kubectl --kubeconfig $USER_CONFIG get --raw /apis/external.metrics.k8s.io/v1beta1 | jq .
结果如下
{kind: APIResourceList,apiVersion: v1,groupVersion: external.metrics.k8s.io/v1beta1,resources: [{name: prometheus-query,singularName: ,namespaced: true,kind: ExternalMetricValueList,verbs: [get]}]
}
类似地我们可以使用其他维度的应用级监控指标配置HPA。举例如下不再冗述。
3.2 根据平均延迟扩缩容
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:name: podinfo-latency-avgnamespace: testannotations:metric-config.external.prometheus-query.prometheus/latency-average: |sum(rate(istio_request_duration_milliseconds_sum{destination_workload_namespacetest,reporterdestination}[1m]))/sum(rate(istio_request_duration_milliseconds_count{destination_workload_namespacetest,reporterdestination}[1m]))
spec:maxReplicas: 5minReplicas: 1scaleTargetRef:apiVersion: apps/v1kind: Deploymentname: podinfometrics:- type: Externalexternal:metric:name: prometheus-queryselector:matchLabels:query-name: latency-averagetarget:type: AverageValueaverageValue: 0.005
3.3 根据P95分布扩缩容
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:name: podinfo-p95namespace: testannotations:metric-config.external.prometheus-query.prometheus/p95-latency: |histogram_quantile(0.95,sum(irate(istio_request_duration_milliseconds_bucket{destination_workload_namespacetest,destination_canonical_servicepodinfo}[5m]))by (le))
spec:maxReplicas: 5minReplicas: 1scaleTargetRef:apiVersion: apps/v1kind: Deploymentname: podinfometrics:- type: Externalexternal:metric:name: prometheus-queryselector:matchLabels:query-name: p95-latencytarget:type: AverageValueaverageValue: 4
验证HPA
1 生成负载
执行如下命令产生实验流量以验证HPA配置自动扩容生效。
alias kkubectl --kubeconfig $USER_CONFIG
loadtester$(k -n test get pod -l appflagger-loadtester -o jsonpath{.items..metadata.name})
k -n test exec -it ${loadtester} -c loadtester -- hey -z 5m -c 2 -q 10 http://podinfo:9898
这里运行了一个持续5分钟、QPS10、并发数为2的请求。
hey命令详细参考如下
Usage: hey [options...] urlOptions:-n Number of requests to run. Default is 200.-c Number of workers to run concurrently. Total number of requests cannotbe smaller than the concurrency level. Default is 50.-q Rate limit, in queries per second (QPS) per worker. Default is no rate limit.-z Duration of application to send requests. When duration is reached,application stops and exits. If duration is specified, n is ignored.Examples: -z 10s -z 3m.-o Output type. If none provided, a summary is printed.csv is the only supported alternative. Dumps the responsemetrics in comma-separated values format.-m HTTP method, one of GET, POST, PUT, DELETE, HEAD, OPTIONS.-H Custom HTTP header. You can specify as many as needed by repeating the flag.For example, -H Accept: text/html -H Content-Type: application/xml .-t Timeout for each request in seconds. Default is 20, use 0 for infinite.-A HTTP Accept header.-d HTTP request body.-D HTTP request body from file. For example, /home/user/file.txt or ./file.txt.-T Content-type, defaults to text/html.-a Basic authentication, username:password.-x HTTP Proxy address as host:port.-h2 Enable HTTP/2.-host HTTP Host header.-disable-compression Disable compression.-disable-keepalive Disable keep-alive, prevents re-use of TCPconnections between different HTTP requests.-disable-redirects Disable following of HTTP redirects-cpus Number of used cpu cores.(default for current machine is 4 cores)
2 自动扩容
执行如下命令观察扩容情况
watch kubectl --kubeconfig $USER_CONFIG -n test get hpa/podinfo-total
结果如下
Every 2.0s: kubectl --kubeconfig /Users/han/shop_config/ack_zjk -n test get hpa/podinfo East6C16G: Tue Jan 26 18:01:30 2021NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
podinfo Deployment/podinfo 10056m/10 (avg) 1 5 2 4m45s
另外两个HPA类似命令如下
kubectl --kubeconfig $USER_CONFIG -n test get hpawatch kubectl --kubeconfig $USER_CONFIG -n test get hpa/podinfo-latency-avg
watch kubectl --kubeconfig $USER_CONFIG -n test get hpa/podinfo-p95
3 监控指标
同时我们可以实时在Prometheus中查看相关的应用级监控指标的实时数据。示意如下 原文链接
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