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 \
$KUBE_METRICS_ADAPTER_SRC/deploy/charts/kube-metrics-adapter \
--set prometheus.url=http://prometheus.istio-system.svc:9090
执行如下命令验证部署情况：
#验证POD
kubectl --kubeconfig "$USER_CONFIG" get po -n kube-system | grep metrics-adapter
asm-custom-metrics-kube-metrics-adapter-6fb4949988-ht8pv 1/1 Running 0 30s
#验证CRD
kubectl --kubeconfig "$USER_CONFIG" api-versions | grep "autoscaling/v2beta"
autoscaling/v2beta1
autoscaling/v2beta2
#验证CRD
kubectl --kubeconfig "$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 $FLAAGER_SRC/kustomize/tester/service.yaml -n test
3 部署HPA
3.1 根据应用请求数量扩缩容
首先我们创建一个感知应用请求数量(istio_requests_total)的HorizontalPodAutoscaler配置：
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
name: podinfo-total
namespace: test
annotations:
metric-config.external.prometheus-query.prometheus/processed-requests-per-second: |
sum(rate(istio_requests_total{destination_workload_namespace="test",reporter="destination"}[1m]))
spec:
maxReplicas: 5
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
metrics:
- type: External
external:
metric:
name: prometheus-query
selector:
matchLabels:
query-name: processed-requests-per-second
target:
type: AverageValue
averageValue: "10"
执行如下命令部署这个HPA配置：
kubectl --kubeconfig "$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-avg
namespace: test
annotations:
metric-config.external.prometheus-query.prometheus/latency-average: |
sum(rate(istio_request_duration_milliseconds_sum{destination_workload_namespace="test",reporter="destination"}[1m]))
/sum(rate(istio_request_duration_milliseconds_count{destination_workload_namespace="test",reporter="destination"}[1m]))
spec:
maxReplicas: 5
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
metrics:
- type: External
external:
metric:
name: prometheus-query
selector:
matchLabels:
query-name: latency-average
target:
type: AverageValue
averageValue: "0.005"
3.3 根据P95分布扩缩容
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
name: podinfo-p95
namespace: test
annotations:
metric-config.external.prometheus-query.prometheus/p95-latency: |
histogram_quantile(0.95,sum(irate(istio_request_duration_milliseconds_bucket{destination_workload_namespace="test",destination_canonical_service="podinfo"}[5m]))by (le))
spec:
maxReplicas: 5
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: podinfo
metrics:
- type: External
external:
metric:
name: prometheus-query
selector:
matchLabels:
query-name: p95-latency
target:
type: AverageValue
averageValue: "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 k="kubectl --kubeconfig $USER_CONFIG"
loadtester=$(k -n test get pod -l "app=flagger-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、QPS=10、并发数为2的请求。
hey命令详细参考如下：
Usage: hey [options...] <url>
Options:
-n Number of requests to run. Default is 200.
-c Number of workers to run concurrently. Total number of requests cannot
be 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 response
metrics 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 TCP
connections 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 2021
NAME 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 hpa
watch 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：
