Why do I have so many duplicated processes? - docker

Question

I'm stressing my kubernetes API and I found out that every request is creating a process inside the Worker Node.
Deployment YAML:
apiVersion: apps/v1
kind: Deployment
metadata:
name: ${KUBE_APP_NAME}-deployment
namespace: ${KUBE_NAMESPACE}
labels:
app_version: ${KUBE_APP_VERSION}
spec:
replicas: 2
strategy:
type: RollingUpdate
rollingUpdate:
maxSurge: 1
maxUnavailable: 1
selector:
matchLabels:
app_name: ${KUBE_APP_NAME}
template:
metadata:
labels:
app_name: ${KUBE_APP_NAME}
spec:
containers:
- name: ${KUBE_APP_NAME}
image: XXX:${KUBE_APP_VERSION}
imagePullPolicy: Always
env:
- name: MONGODB_URI
valueFrom:
secretKeyRef:
name: mongodb
key: uri
- name: JWT_PASSWORD
valueFrom:
secretKeyRef:
name: jwt
key: password
ports:
- containerPort: 80
resources:
requests:
memory: "64Mi"
cpu: "250m"
limits:
memory: "128Mi"
cpu: "500m"
imagePullSecrets:
- name: regcred
Apache Bench used ab -p payload.json -T application/json -c 10 -n 2000
Why is this?

Answer 1

It's hard to answer your questions if this is normal that the requests are being kept open.
We don't know what exactly is your payload and how big it is. We also don't know if the image that you are using is handling those correctly.
You should use verbose=2 ab -v2 <host> and check what it taking so long.
You are using Apache Bench with -c 10 -n 2000 options which means there will be:
-c 10 concurrent connections at a time,
-n 2000 request total
You could use -k to enable HTTP KeepAlive
-k
Enable the HTTP KeepAlive feature, i.e., perform multiple requests within one HTTP session. Default is no KeepAlive.
It would be easier if you provided the output of using the ab.
As for the Kubernetes part.
We can read a definition of a pod available at Viewing Pods and Nodes:
A Pod is a Kubernetes abstraction that represents a group of one or more application containers (such as Docker or rkt), and some shared resources for those containers
...
The containers in a Pod share an IP Address and port space, are always co-located and co-scheduled, and run in a shared context on the same Node.