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Metrics APIs provided by Kubernetes can be gotten by:
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~]$ kubectl api-versions |
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custom.metrics.k8s.io/v1beta1 |
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metrics.k8s.io/v1beta1 |
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cAdvisor automatically scrapes metrics from every active pod in the system regarding to CPU and memory usage. The state of core metrics can be requested by the followings:
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~]$ kubectl top |
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node NAME CPU(cores) |
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CPU% MEMORY(bytes) |
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MEMORY% 172.24.16. |
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104 1248m 62% 5710Mi 74% 172.24.16. |
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105 1268m 63% 5423Mi 71% 172.24.16. |
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107 1215m 60% 5191Mi 68%
172.24.16.112 253m 6% 846Mi 11% |
With this command you can
172.24.16.112 253m 6% 846Mi 11%
With this command you can get core metrics from nodes respectively to CPU and memory usage. The printout shows the names of nodes actually the IP addresses of nodes, the usage of CPUs in percentage and milli standard for 2 CPUs in the example furthermore memory usage in percentage and Mi (MiB) standard.
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~]$ kubectl top pod --namespace=kube-system | grep elasticsearch |
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NAME CPU(cores) MEMORY(bytes) |
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elasticsearch-data-0 71m 1106Mi |
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elasticsearch-data-1 65m 1114Mi |
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elasticsearch-data-2 75m 1104Mi |
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elasticsearch-master-0 4m 1068Mi |
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elasticsearch-master-1 7m 1076Mi |
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elasticsearch-master-2 3m 1075Mi |
Console output shows pod names and their CPU and memory consumptions in the same format.
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Basic example application in Python:
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from prometheus_client import start_http_server, Histogram |
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import random |
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import time |
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function_exec = Histogram('function_exec_time', |
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'Time spent processing a function',
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'Time spent processing a function', ['func_name']) |
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def func(): |
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if (random.random() < 0.02): |
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time.sleep(2) |
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return
return time.sleep(0.2) |
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start_http_server(9100) |
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while True: |
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start_time = time.time() |
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func() |
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function_exec.labels(func_name="func").observe(time.time() - start_time) |
This application imports http_server and Histogram metrics from Prometheus client library and exposes metrics from the func() function. Prometheus can scrape these metrics from port 9100.
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~]$ kubectl |
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get --raw "/apis/custom.metrics.k8s.io/v1beta1" | jq . |
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{
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{ "kind": "APIResourceList", |
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"apiVersion": "v1", |
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"groupVersion": "custom.metrics.k8s.io/v1beta1", |
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"resources": |
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{
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[ { "name": "pods/go_memstats_heap_released_bytes", |
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"singularName": "", |
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"namespaced": true, |
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"kind": "MetricValueList", |
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"verbs": |
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[ "get" |
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]
},
{
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] }, { "name": "jobs.batch/http_requests", |
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"singularName": "", |
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"namespaced": true, |
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"kind": "MetricValueList", |
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"verbs": |
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[ "get" |
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]
}
]
] } ] } |
The command result lists the custom metrics in the system, each metrics can be requested one by one for more details:
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kubectl get –raw "/apis/custom.metrics.k8s.io/v1beta1/namespaces/kube-system/pods/*/http_requests" | jq . |
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{
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{ "kind": "MetricValueList", |
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"apiVersion": "custom.metrics.k8s.io/v1beta1", |
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"metadata": |
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{ "selfLink": "/apis/custom.metrics.k8s.io/v1beta1/namespaces/kube-system/pods/%2A/http_requests" |
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}, |
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"items": |
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{
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[ { "describedObject": |
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{ "kind": "Pod", |
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"namespace": "kube-system", |
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"name": "podinfo-bd494c88d-lmt2j", |
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"apiVersion": "/v1 |
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},
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" }, "metricName": "http_requests", |
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"timestamp": "2019-02-14T10:21:19Z", |
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"value": "898m" |
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},
{
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}, { "describedObject": |
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{ "kind": "Pod", |
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"namespace": "kube-system", |
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"name": "podinfo-bd494c88d-lxng7 |
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", "apiVersion": "/v1" |
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},
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}, "metricName": "http_requests", |
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"timestamp": "2019-02-14T10:21:19Z", |
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"value": "898m" |
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}
]
}
} ] } ~]$ curl http://$(kubectl get service podinfo --namespace=kube-system -o jsonpath='{ .spec.clusterIP }'):9898/metrics |
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… |
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http_request_duration_seconds_bucket{method="GET",path="healthz",status="200",le="0.005"} 2040 |
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http_request_duration_seconds_bucket{method="GET",path="healthz",status="200",le="0.01"} 2040 |
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http_request_duration_seconds_bucket{method="GET",path="healthz",status="200",le="0.025"} 2040 |
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http_request_duration_seconds_bucket{method="GET",path="healthz",status="200",le="0.05"} 2072 |
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http_request_duration_seconds_bucket{method="GET",path="healthz",status="200",le="0.1"} 2072 |
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http_request_duration_seconds_bucket{method="GET",path="healthz",status="200",le="0.25"} 2072 |
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… |
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# HELP http_requests_total The total number of HTTP requests. |
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# TYPE http_requests_total counter |
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http_requests_total{status="200"} 5593 |
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This is a HTTP request with cURL, it shows the custom metrics exposed by an HTTP server of an application running in a Kubernetes pod.
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HPA manifest with core metrics scraping:
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php-apache-hpa.yml |
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apiVersion: autoscaling/v1 |
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kind: HorizontalPodAutoscaler |
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metadata: |
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name: php-apache-hpa |
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spec: |
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scaleTargetRef: |
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apiVersion: extensions/v1beta1 |
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kind: |
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Deployment name: php-apache-deployment |
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minReplicas: |
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1 maxReplicas: |
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5 targetCPUUtilizationPercentage: 50 |
In this example HPA scrapes metrics from CPU consumption of php-apache-deployment. The initial pod number is one and the maximum replica counts are five. HPA initiates pod scaling when the CPU utilization is higher than 50%. If the utilization is less than 50% HPA starts scaling down the number of pods by one.
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HPA manifest with custom metrics scraping:
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podinfo-hpa-custom.yaml |
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apiVersion: autoscaling/v2beta1 |
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kind: HorizontalPodAutoscaler |
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metadata: |
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name: |
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podinfo namespace: kube-system |
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spec: |
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scaleTargetRef: |
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apiVersion: extensions/v1beta1 |
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kind: |
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Deployment name: |
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podinfo minReplicas: |
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2 maxReplicas: |
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metrics:
- type: Pods
pods:
metricName: http_requests
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10
metrics:
- type: Pods
pods:
metricName: http_requests
targetAverageValue: 10
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In the second example HPA uses custom metrics to manage the performance. The podinfo application contains the implementation of an HTTP server which exposes the metrics in Prometheus format. The initial number of pods are two and the maximum are ten. The custom metric is the cardinality of the http requests on the HTTP server regarding to the metrics exposed.
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The starter command for custom metric based HPA manifest is:
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~]$ kubectl create -f podinfo-hpa-custom.yaml --namespace=kube-system |
In case of starting core metrics HPA the command is the same.
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After HPA start you can get information about the state of actual performance management of the system with:
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~]$ kubectl describe hpa podinfo --namespace=kube-system |
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Name: podinfo
Namespace: kube-system
Labels: <none>
Annotations: <none>
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Name: podinfo Namespace: kube-system Labels: <none> Annotations: <none> CreationTimestamp: Tue, 19 Feb 2019 10:08:21 +0100 |
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Reference: Deployment/podinfo |
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Metrics: ( current / target )
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Metrics: ( current / target ) "http_requests" on pods: |
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901m / 10 |
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Min replicas: |
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Max replicas: 10
Deployment pods: 2 current / 2 desired
Conditions:
Type Status Reason Message
---- ------ ------ -------
AbleToScale True ReadyForNewScale recommended size matches current size
ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from pods metric http_requests
ScalingLimited True TooFewReplicas the desired replica count is increasing faster than the maximum scale rate
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2 Max replicas: 10 Deployment pods: 2 current / 2 desired Conditions: Type Status Reason Message ---- ------ ------ ------- AbleToScale True ReadyForNewScale recommended size matches current size ScalingActive True ValidMetricFound the HPA was able to successfully calculate a replica count from pods metric http_requests ScalingLimited True TooFewReplicas the desired replica count is increasing faster than the maximum scale rate Events: <none> |
Note that: HPA API supports scaling based on both core and custom metrics within the same HPA object.
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