This intended to run on a K8s Workload to scale the specific deployment with various way.
Constantly, watches the Prometheus API (http://host:port/api/v1/alerts) to
catch the firing alarms. We must have two different alarm rule for scaling out
and scaling in with the following labels.
For example:
prometheus.rules: |-
groups:
- name: Pod Memory
rules:
- alert: php-apache-scaling-out # This must be provided
expr: sum(container_memory_usage_bytes{container="php-apache", namespace="default"}) / (count(container_memory_usage_bytes{container="php-apache", namespace="default"})) > 11000000
for: 1m
labels:
severity: slack
scaling: out # Must include
annotations:
summary: High Memory Usage
- alert: php-apache-scaling-in # This must be provided
expr: sum(container_memory_usage_bytes{container="php-apache", namespace="default"}) / (count(container_memory_usage_bytes{container="php-apache", namespace="default"})) < 11000000
for: 1m
labels:
severity: slack
scaling: in # Must include
annotations:
summary: Low Memory UsageBefore deploying the pod, define the parameters
- name: workload
value: "Deployment"
- name: scale-name
value: "php-apache"
- name: namespace
value: "default"
- name: scaling-number
value: "1"
- name: max-pod-number
value: "10"
- name: min-pod-number
value: "2"
- name: time-interval
value: "60"
- name: managment-type
value: "prometheus_alert_api"
- name: prometheus-host
value: "prometheus-service"
- name: prometheus-port
value: "8080"
- name: scaling-out-name
value: "php-apache-scaling-out"
- name: scaling-in-name
value: "php-apache-scaling-in"If you set the alarms, you can simply run this deployment to scale out/in
kubectl apply -f https://raw.githubusercontent.com/eminaktas/k8s-workload-scaler/main/examples/prometheus_alert_api/php-apache-sample.yamlThen,
kubectl apply -f https://raw.githubusercontent.com/eminaktas/k8s-workload-scaler/main/examples/prometheus_alert_api/k8s-prometheus-sample.yamlIt will simply chekcs the Prometheus API and if receives a firing alarm it will trigger regarding the scaling type (we must define scaling: in and scaling: out labels)
SUPPORTED_WORKLOAD = [
'Deployment',
'StatefulSet',
'ReplicaSet',
'ReplicationController',
]