I am using HPA(HorizontalPodAutoscaling) along with custom metrics in kubernetes. I can scale my pod count according to my custom metrics value.
kind: HorizontalPodAutoscaler
apiVersion: autoscaling/v2alpha1
metadata:
name: sample-metrics-app-hpa
spec:
scaleTargetRef:
kind: Deployment
name: sample-metrics-app
minReplicas: 2
maxReplicas: 10
metrics:
- type: Object
object:
target:
kind: Service
name: sample-metrics-app
metricName: http_requests
targetValue: 100
Is it possible to define multiple target value, for example if the http request hits 100 then the pod should scale by 10(min replica should be 2 for this), if it hits 1000, then the pod should scale by 20(min replica needs to be 10).
As I know it is not possible to achieve such result with HPA. Moreover, your configuration says that every 100 http requests hpa will add 1 more pod. Not 2 --> 10 at once, but again, 1-->2-->3 every 100 requests.
For more details please reach Horizontal Pod Autoscaler Walkthrough
Related
I am running a Kubernetes horizontal pod autoscaler to scale kafka consumers based on the consumer group lag. The HPA yaml file is shown below.
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
name: kafka-consumer-application
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: kafka-consumer-application
minReplicas: 1
maxReplicas: 10
metrics:
- type: External
external:
metric:
name: kafka_consumergroup_lag
target:
type: AverageValue
averageValue:5
I observed that the HPA is scaling replicas not strictly according to the formula ceil(currentReplicas * currentMetricValue/desiredMetricValue ).
For instance, when the metric (consumer lag) was 108 with only one replica, Kubernetes scaled up only 4 replicas (as shown in the screen shot below), while theoretically it should scale to 10 (maximum replicas allowed)....
Any idea on the reason? am I missing something such as the maximim number of replicas that can be scaled/replicated per single iteration of the HPA reconciliation loop?
Please notice the message in the screenshot 'ScalingLimited True ScaleUpLimit the desired replica count is increasing faster than the maximum scale rate' what does it mean?
Thanks.
I referred this stackoverflow question to set up my HPA(Horizontal Pod Autoscaler) for google kubernetes engine(gke) workload. According to the details of that question and the details specified here I mentioned my targetAverageValue to be 50 which should be considered 50% but when I run the command kubectl describe hpa this is the line I notice in the logs
Metrics: ( current / target ) "kubernetes.io|container|accelerator|duty_cycle" (target average value): 33500m / 50
This is my hpa yaml
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: gpu-metric
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: parabole-dj-u1
minReplicas: 1
maxReplicas: 5
metrics:
- type: External
external:
metricName: kubernetes.io|container|accelerator|duty_cycle
targetAverageValue: 50
It seems to be measuring using some other unit. What then should be my targetAverageValue if I want it to autoscale at 50% duty_cycle?
Adding the screenshot of the duty cycle metric from the portal like #Alberto Pau asked duty_cycle image
Your configuration is correct, HPA always shows in the mili units.
The current utilization is probably 33.5%, just divide the number with the "m" by 1000 and you get the percentages.
Is it possible to have the HPA scale based on the number of available running pods?
I have set up a readiness probe that cuts out a pod based it's internal state (idle, working, busy). When a pod is 'busy', it no longer receives new requests. But the cpu, and memory demands are low.
I don't want to scale based on cpu, mem, or other metrics.
Seeing as the readiness probe removes it from active service, can I scale based on the average number of active (not busy) pods? When that number drops below a certain point more pods are scaled.
TIA for any suggestions.
You can create custom metrics, a number of busy-pods for HPA.
That is, the application should emit a metric value when it is busy. And use that metric to create HorizontalPodAutoscaler.
Something like this:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: custom-metric-sd
namespace: default
spec:
scaleTargetRef:
apiVersion: apps/v1beta1
kind: Deployment
name: custom-metric-sd
minReplicas: 1
maxReplicas: 20
metrics:
- type: Pods
pods:
metricName: busy-pods
targetAverageValue: 4
Here is another reference for HPA with custom metrics.
I have the below Horizontal Pod Autoscaller configuration on Google Kubernetes Engine to scale a deployment by a custom metric - RabbitMQ messages ready count for a specific queue: foo-queue.
It picks up the metric value correctly.
When inserting 2 messages it scales the deployment to the maximum 10 replicas.
I expect it to scale to 2 replicas since the targetValue is 1 and there are 2 messages ready.
Why does it scale so aggressively?
HPA configuration:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: foo-hpa
namespace: development
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: foo
minReplicas: 1
maxReplicas: 10
metrics:
- type: External
external:
metricName: "custom.googleapis.com|rabbitmq_queue_messages_ready"
metricSelector:
matchLabels:
metric.labels.queue: foo-queue
targetValue: 1
I think you did a great job explaining how targetValue works with HorizontalPodAutoscalers. However, based on your question, I think you're looking for targetAverageValue instead of targetValue.
In the Kubernetes docs on HPAs, it mentions that using targetAverageValue instructs Kubernetes to scale pods based on the average metric exposed by all Pods under the autoscaler. While the docs aren't explicit about it, an external metric (like the number of jobs waiting in a message queue) counts as a single data point. By scaling on an external metric with targetAverageValue, you can create an autoscaler that scales the number of Pods to match a ratio of Pods to jobs.
Back to your example:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: foo-hpa
namespace: development
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: foo
minReplicas: 1
maxReplicas: 10
metrics:
- type: External
external:
metricName: "custom.googleapis.com|rabbitmq_queue_messages_ready"
metricSelector:
matchLabels:
metric.labels.queue: foo-queue
# Aim for one Pod per message in the queue
targetAverageValue: 1
will cause the HPA to try keeping one Pod around for every message in your queue (with a max of 10 pods).
As an aside, targeting one Pod per message is probably going to cause you to start and stop Pods constantly. If you end up starting a ton of Pods and process all of the messages in the queue, Kubernetes will scale your Pods down to 1. Depending on how long it takes to start your Pods and how long it takes to process your messages, you may have lower average message latency by specifying a higher targetAverageValue. Ideally, given a constant amount of traffic, you should aim to have a constant number of Pods processing messages (which requires you to process messages at about the same rate that they are enqueued).
According to https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
From the most basic perspective, the Horizontal Pod Autoscaler controller operates on the ratio between desired metric value and current metric value:
desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricValue )]
From the above I understand that as long as the queue has messages the k8 HPA will continue to scale up since currentReplicas is part of the desiredReplicas calculation.
For example if:
currentReplicas = 1
currentMetricValue / desiredMetricValue = 2/1
then:
desiredReplicas = 2
If the metric stay the same in the next hpa cycle currentReplicas will become 2 and desiredReplicas will be raised to 4
Try to follow this instruction that describes horizontal autoscale settings for RabbitMQ in k8s
Kubernetes Workers Autoscaling based on RabbitMQ queue size
In particular, targetValue: 20 of metric rabbitmq_queue_messages_ready is recommended instead of targetValue: 1:
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
name: workers-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1beta1
kind: Deployment
name: my-workers
minReplicas: 1
maxReplicas: 10
metrics:
- type: External
external:
metricName: "custom.googleapis.com|rabbitmq_queue_messages_ready"
metricSelector:
matchLabels:
metric.labels.queue: myqueue
**targetValue: 20
Now our deployment my-workers will grow if RabbitMQ queue myqueue has more than 20 non-processed jobs in total
I'm using the same Prometheus metrics from RabbitMQ (I'm using Celery with RabbitMQ as broker).
Did anyone here considered using rabbitmq_queue_messages_unacked metric rather than rabbitmq_queue_messages_ready?
The thing is, that rabbitmq_queue_messages_ready is decreasing as soon the message pulled by a worker and I'm afraid that long-running task might be killed by HPA, while rabbitmq_queue_messages_unacked stays until the task completed.
For example, I have a message that will trigger a new pod (celery-worker) to run a task that will take 30 minutes. The rabbitmq_queue_messages_ready will decrease as the pod is running and the HPA cooldown/delay will terminate pod.
EDIT: seems like a third one rabbitmq_queue_messages is the right one - which is the sum of both unacked and ready:
sum of ready and unacknowledged messages - total queue depth
documentation
I am trying to setup HPA for Ingress Controller based on custom metric nginx_ingress_controller_nginx_process_connections_total.
But while fetching the metrics from localhost:10254/metrics, I could see three such metrics with filter as follows:
# HELP nginx_ingress_controller_nginx_process_connections_total total number of connections with state {active, accepted, handled}
# TYPE nginx_ingress_controller_nginx_process_connections_total counter
nginx_ingress_controller_nginx_process_connections_total{controller_class="nginx",controller_namespace="ingress-nginx",controller_pod="nginx-ingress-controller-7dddd-mssssf",state="accepted"} 479707
nginx_ingress_controller_nginx_process_connections_total{controller_class="nginx",controller_namespace="ingress-nginx",controller_pod="nginx-ingress-controller-7dddd-mssssf",state="active"} 3
nginx_ingress_controller_nginx_process_connections_total{controller_class="nginx",controller_namespace="ingress-nginx",controller_pod="nginx-ingress-controller-7dddd-mssssf",state="handled"} 479707
Out of these metrics, I want to use the below metric for HPA.
nginx_ingress_controller_nginx_process_connections_total{controller_class="nginx",controller_namespace="ingress-nginx",controller_pod="nginx-ingress-controller-7dddd-mssssf",state="active"}
How can I use the specified metric from these different values. My yaml file for HPA is given below.
kind: HorizontalPodAutoscaler
apiVersion: autoscaling/v2beta1
metadata:
name: ingress-hpa
spec:
scaleTargetRef:
kind: Deployment
name: nginx-ingress-controller
minReplicas: 3
maxReplicas: 10
metrics:
- type: Pods
pods:
metricName: <I need to set the custom metric here>
targetAverageValue: 10000
You can use HPA custom metrics. You need to expose endpoint in POD to fetch the metrics also setup Prometheus and custom metric api server.