I have a very simple Kubernetes Cluster that uses GKE (Google Cloud Platform) with 1 node (4 vCPU / 16 Go RAM)
This morning I was load testing an API (written in Python) on this cluster with Locust. I only have one endpoint on my API, so I prepared a locust file to run differents configurations on that endpoint (with random parameters etc).
I first ran my Locust test with 10 users, 1 user generated every seconds (until limit reached), and the request wait time is between 1 and 2.5 seconds. I tested it first on just 1 pod and then I ran the exact same test on 3 pods. I noticed almost no improvement with 3 pods and the response times were just more spreaded.
Here are the results for 1 Pod :
And the charts:
And here are the results for 3 Pods
And the charts:
I am fairly new to K8s and I don't really understand everything, but my initial though was that increasing the number of running pods should improve the preformance right ? I should be able to handle 3 times more requests in the same time, but the results are far from that excepted conclusion.
And here is the yaml file defining the deployment on the K8s cluster (note: I set myself the replicas to 1 or 3 because for some reasons the autoscaler don't work) :
apiVersion: apps/v1
kind: Deployment
metadata:
labels:
app: app_name
name: app_name
namespace: default
spec:
replicas: 3 or 1
selector:
matchLabels:
app: app_name
strategy:
rollingUpdate:
maxSurge: 25%
maxUnavailable: 25%
type: RollingUpdate
template:
metadata:
labels:
app: app_name
spec:
containers:
- image: >-
gcr.io/path_to_image
imagePullPolicy: IfNotPresent
name: app_name-v2-sha256-1
---
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
labels:
app: app_name
name: app_name-hpa-cbrt
namespace: default
spec:
maxReplicas: 3
metrics:
- resource:
name: cpu
targetAverageUtilization: 80
type: Resource
minReplicas: 1
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: app_name
Additional info : The API calls a MySQL database and perform a request that get approx. 70k rows and do some stuff on those results. It only returns a json with few rows at the end.
Thanks in advance for any help you could provide !
Edit : Here is the definition of the load balancer, it's GCP generated and untouched
apiVersion: v1
kind: Service
metadata:
annotations:
cloud.google.com/neg: '{"ingress":true}'
creationTimestamp: "2021-05-11T10:03:16Z"
finalizers:
- service.kubernetes.io/load-balancer-cleanup
labels:
app: app_name
app.kubernetes.io/managed-by: gcp-cloud-build-deploy
app.kubernetes.io/name: app_name
app.kubernetes.io/version: b8a19c9rth54erthe4rth8459633451fe8e73e038
name: app_service
namespace: default
resourceVersion: "87954231"
selfLink: /api/v1/namespaces/default/services/app_service
uid: service_uid
spec:
clusterIP: 661.661.661.661
externalTrafficPolicy: Cluster
ports:
- nodePort: 30129
port: 8080
protocol: TCP
targetPort: 8080
selector:
app: app_name
sessionAffinity: None
type: LoadBalancer
status:
loadBalancer:
ingress:
- ip: 666.666.666.666
Related
Hi I'm planning to upgrade my Airflow version from 1.11 to 1.15 which is deployed in OpenShift. As there are very large numbers of DAG's so I planned to upgrade in the bride release rather than going to Airflow 2.2
The error which I'm getting is most probably due to the fernet key:
ERROR: The `secret_key` setting under the webserver config has an insecure value - Airflow has
failed safe and refuses to start. Please change this value to a new, per-environment,
randomly generated string, for example using this command `openssl rand -hex 30`
Earlier I was using static Fernet Key and the YAML file is as follows:
apiVersion:v1
kind:Secret
metadata:
name : airflow-secret
namespace : CUSTOM_NAMESPACE
labels:
app:airflow
type: Opaque
stringData:
fernet-key: my_fernet_key
My Python Version : 3.8
My Airflow Webserver Config :
apiVersion: v1
kind: DeploymentConfig
metadata:
name: airflow-webserver
namespace: CUSTOM_NAMESPACE
labels:
app: airflow
spec:
strategy:
type: Rolling
trigger:
- type : ConfigChange
- type : ImageChange
ImageChangeParams:
automatic: true
containerNames:
- airflow-webserver
from:
kind: ImageStreamTag
namespace: CUSTOM_NAMESPACE
replicas: 1
revisionHistoryLimit : 10
paused: false
selector :
app : airflow
deploymentconfig : airflow-webserver
template:
metadata:
labels:
name: airflow-webserver
app: airflow
deploymentconfig : airflow-webserver
spec:
volumes:
- name: airflow-dags
persistentVolumeClaims:
claimName: airflow-dags
containers:
- name: airflow-webserver
image: airflow:latest
resources:
limits:
memory: 4Gi
env:
- name : FERNET_KEY
valueFrom:
secretKeyRef:
name: airflow-secrets
key : fernet-key
- name : SERVICE_ACCOUNT_NAME
valueFrom:
secretKeyRef:
name: airflow-service-account
key : service-account-name
ports:
- containerPort: 8080
protocol: TCP
volumeMounts:
- name: airflow-dags
mountPath: /opt/airflow/dags
- name: airflow-logs
mountPath: /opt/airflow/logs
My understanding is we need to somehow provide dynamic value in fernet key but for my case its static, Any Possible way to resolve the error.
Thank!
The main issue there was default value stored in airflow.cfg i.e.
secret_key = temporary_value
We can generate the secret_key by seeing the error message:
openssl rand -hex 30
suppose the value is --> 94b9d6124ff2e9a5783d94dc7aa3641ebb8929bdbbf2f3989402f9e400ac
We need to put the value into the secret_key in airflow.cfg
secret_key = 94b9d6124ff2e9a5783d94dc7aa3641ebb8929bdbbf2f3989402f9e400ac
I'm trying to patch a deployment and remove its volumes using patch_namespaced_deployment (https://github.com/kubernetes-client/python) with the following arguments, but it's not working.
patch_namespaced_deployment(
name=deployment_name,
namespace='default',
body={"spec": {"template": {
"spec": {"volumes": None,
"containers": [{'name': container_name, 'volumeMounts': None}]
}
}
}
},
pretty='true'
)
How to reproduce it:
Create this file app.yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: myclaim
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 1Gi
---
apiVersion: v1
kind: PersistentVolume
metadata:
annotations:
pv.kubernetes.io/bound-by-controller: "yes"
finalizers:
- kubernetes.io/pv-protection
labels:
volume: pv0001
name: pv0001
resourceVersion: "227035"
selfLink: /api/v1/persistentvolumes/pv0001
spec:
accessModes:
- ReadWriteOnce
capacity:
storage: 5Gi
claimRef:
apiVersion: v1
kind: PersistentVolumeClaim
name: myclaim
namespace: default
resourceVersion: "227033"
hostPath:
path: /mnt/pv-data/pv0001
type: ""
persistentVolumeReclaimPolicy: Recycle
volumeMode: Filesystem
status:
phase: Bound
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: pv-deploy
spec:
replicas: 1
selector:
matchLabels:
app: mypv
template:
metadata:
labels:
app: mypv
spec:
containers:
- name: shell
image: centos:7
command:
- "bin/bash"
- "-c"
- "sleep 10000"
volumeMounts:
- name: mypd
mountPath: "/tmp/persistent"
volumes:
- name: mypd
persistentVolumeClaim:
claimName: myclaim
- kubectl apply -f app.yaml
- kubectl describe deployment.apps/pv-deploy (to check the volumeMounts and Volumes)
- kubectl patch deployment.apps/pv-deploy --patch '{"spec": {"template": {"spec": {"volumes": null, "containers": [{"name": "shell", "volumeMounts": null}]}}}}'
- kubectl describe deployment.apps/pv-deploy (to check the volumeMounts and Volumes)
- Delete the application now: kubectl delete -f app.yaml
- kubectl create -f app.yaml
- Patch the deployment using the python library function as stated above. The *VolumeMounts* section is removed but the Volumes still exist.
** EDIT **
Running the kubectl patch command works as expected. But
after executing the Python script and running a describe deployment command, the persistentVolumeClaim is replaced with an emptyDir like this
Volumes:
mypd:
Type: EmptyDir (a temporary directory that shares a pod's lifetime)
Medium:
SizeLimit: <unset>
What you're trying to do is called a strategic merge patch (https://kubernetes.io/docs/tasks/manage-kubernetes-objects/update-api-object-kubectl-patch/). As you can see in the documentation, With a strategic merge patch, a list is either replaced or merged depending on its patch strategy so this may be why you're seeing this behavior.
I think you should go with replace https://jamesdefabia.github.io/docs/user-guide/kubectl/kubectl_replace/ and instead of trying to manage a part of your deployment object, replace it with a new one.
Is there any method to process messages with high priority first?
I tried creating three topics 'high', 'medium', and 'low', and subscribed to all three topics with one consumer and if there is an unprocessed message in the 'high' topic it will pause the other two. Is there any better way for implementing message priority?
I tried using the logic given below.
topics = ['high', 'medium', 'low']
consumer.subscribe(topics)
high_topic_partition = TopicPartition(priority['high'], 0)
medium_topic_partition = TopicPartition(priority['medium'], 0)
low_topic_partition = TopicPartition(priority['low'], 0)
while True:
messages = consumer.poll()
high_priotity_unprocessed_msg = consumer.end_offsets([high_topic_partition])[high_topic_partition] - consumer.position(high_topic_partition)
medium_priotity_unprocessed_msg = consumer.end_offsets([medium_topic_partition])[medium_topic_partition] - consumer.position(medium_topic_partition)
low_priotity_unprocessed_msg = consumer.end_offsets([low_topic_partition])[low_topic_partition] - consumer.position(low_topic_partition)
if high_priotity_unprocessed_msg >0:
consumer.pause(medium_topic_partition)
consumer.pause(low_topic_partition)
else:
consumer.resume(medium_topic_partition)
if medium_priotity_unprocessed_msg >0:
consumer.pause(low_topic_partition)
else:
consumer.resume(low_topic_partition)
if messages:
process(messages)
One option that you may evaluate is basically just having more parallelism on higher priority messages...
For example:
Topic1 (Priority Low): 1 partitions
Topic2 (Priority medium): 5 partitions
Topic3 (Priority High): 20 partitions
And then basically have:
1 consumers to get the data from topic1
5 consumers from topic2
20 consumers from topic3
👆Now, I would suggest you the easiest way to do this is basically write the code once... but externalize the configuration of the "topic name"... and then just scale it up (of course using containers)... Please refer to this reading:
https://12factor.net/config
For example, the code could be as simple as:
SuperAwesomeAppBinaryCode:
topic = %MY_TOPIC_NAME_INJECTED_BY_ENV_VAR%
consumer.subscribe(topic)
while True:
messages = consumer.poll()
if messages:
process(messages)
Now, if we have that code deployed on, let's say K8s, you could have 3 different deployments, running the same code, but injecting the right topic for each case, for example:
Low Priority Messages
apiVersion: apps/v1
kind: Deployment
metadata:
name: LowPriorityProcessor
labels:
app: LowPriorityProcessor
spec:
replicas: 1
selector:
matchLabels:
app: LowPriorityProcessor
template:
metadata:
labels:
app: LowPriorityProcessor
spec:
containers:
- name: LowPriorityProcessor
image: SuperAwesomeAppBinaryCode:1.0.0
env:
- name: MY_TOPIC_NAME_INJECTED_BY_ENV_VAR
value: topic1
ports:
- containerPort: 80
Medium Priority Messages
apiVersion: apps/v1
kind: Deployment
metadata:
name: MediumPriorityProcessor
labels:
app: MediumPriorityProcessor
spec:
replicas: 5
selector:
matchLabels:
app: MediumPriorityProcessor
template:
metadata:
labels:
app: MediumPriorityProcessor
spec:
containers:
- name: MediumPriorityProcessor
image: SuperAwesomeAppBinaryCode:1.0.0
env:
- name: MY_TOPIC_NAME_INJECTED_BY_ENV_VAR
value: topic2
ports:
- containerPort: 80
High Priority Messages
apiVersion: apps/v1
kind: Deployment
metadata:
name: HighPriorityProcessor
labels:
app: HighPriorityProcessor
spec:
replicas: 20
selector:
matchLabels:
app: HighPriorityProcessor
template:
metadata:
labels:
app: HighPriorityProcessor
spec:
containers:
- name: HighPriorityProcessor
image: SuperAwesomeAppBinaryCode:1.0.0
env:
- name: MY_TOPIC_NAME_INJECTED_BY_ENV_VAR
value: topic3
ports:
- containerPort: 80
And then just let the parallelism do its magic 😉
If you check carefully the only thing that changes from one "k8s deployment" to another is the topic and the number of replicas.
Notes:
You can achieve this without K8s.... using Docker Swarm or even just docker-compose or running manually the instances 🤷♂️, but why would you like to reinvent the wheel, but for sure in some edge cases, there is no much option...
A nice reading about this topic can be found here
This question might seem like a duplicate of this.
I am trying to run Apache Beam python pipeline using flink on an offline instance of Kubernetes. However, since I have user code with external dependencies, I am using the Python SDK harness as an External Service - which is causing errors (described below).
The kubernetes manifest I use to launch the beam python SDK:
apiVersion: apps/v1
kind: Deployment
metadata:
name: beam-sdk
spec:
replicas: 1
selector:
matchLabels:
app: beam
component: python-beam-sdk
template:
metadata:
labels:
app: beam
component: python-beam-sdk
spec:
hostNetwork: True
containers:
- name: python-beam-sdk
image: apachebeam/python3.7_sdk:latest
imagePullPolicy: "Never"
command: ["/opt/apache/beam/boot", "--worker_pool"]
ports:
- containerPort: 50000
name: yay
apiVersion: v1
kind: Service
metadata:
name: beam-python-service
spec:
type: NodePort
ports:
- name: yay
port: 50000
targetPort: 50000
selector:
app: beam
component: python-beam-sdk
When I launch my pipeline with the following options:
beam_options = PipelineOptions([
"--runner=FlinkRunner",
"--flink_version=1.9",
"--flink_master=10.101.28.28:8081",
"--environment_type=EXTERNAL",
"--environment_config=10.97.176.105:50000",
"--setup_file=./setup.py"
])
I get the following error message (within the python sdk service):
NAME READY STATUS RESTARTS AGE
beam-sdk-666779599c-w65g5 1/1 Running 1 4d20h
flink-jobmanager-74d444cccf-m4g8k 1/1 Running 1 4d20h
flink-taskmanager-5487cc9bc9-fsbts 1/1 Running 2 4d20h
flink-taskmanager-5487cc9bc9-zmnv7 1/1 Running 2 4d20h
(base) [~]$ sudo kubectl logs -f beam-sdk-666779599c-w65g5
2020/02/26 07:56:44 Starting worker pool 1: python -m apache_beam.runners.worker.worker_pool_main --service_port=50000 --container_executable=/opt/apache/beam/boot
Starting worker with command ['/opt/apache/beam/boot', '--id=1-1', '--logging_endpoint=localhost:39283', '--artifact_endpoint=localhost:41533', '--provision_endpoint=localhost:42233', '--control_endpoint=localhost:44977']
2020/02/26 09:09:07 Initializing python harness: /opt/apache/beam/boot --id=1-1 --logging_endpoint=localhost:39283 --artifact_endpoint=localhost:41533 --provision_endpoint=localhost:42233 --control_endpoint=localhost:44977
2020/02/26 09:11:07 Failed to obtain provisioning information: failed to dial server at localhost:42233
caused by:
context deadline exceeded
I have no idea what the logging- or artifact endpoint (etc.) is. And by inspecting the source code it seems like that the endpoints has been hard-coded to be located at localhost.
(You said in a comment that the answer to the referenced post is valid, so I'll just address the specific error you ran into in case someone else hits it.)
Your understanding is correct; the logging, artifact, etc. endpoints are essentially hardcoded to use localhost. These endpoints are meant to be only used internally by Beam and are not configurable. So the Beam worker is implicitly assumed to be on the same host as the Flink task manager. Typically, this is accomplished by making the Beam worker pool a sidecar of the Flink task manager pod, rather than a separate service.
I am running my backend using Python and Django with uWSGI. We recently migrated it to Kubernetes (GKE) and our pods are consuming a LOT of memory and the rest of the cluster is starving for resources. We think that this might be related to the uWSGI configuration.
This is our yaml for the pods:
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: my-pod
namespace: my-namespace
spec:
strategy:
type: RollingUpdate
rollingUpdate:
maxSurge: 10
maxUnavailable: 10
selector:
matchLabels:
app: my-pod
template:
metadata:
labels:
app: my-pod
spec:
containers:
- name: web
image: my-img:{{VERSION}}
imagePullPolicy: IfNotPresent
ports:
- containerPort: 8000
protocol: TCP
command: ["uwsgi", "--http", ":8000", "--wsgi-file", "onyo/wsgi.py", "--workers", "5", "--max-requests", "10", "--master", "--vacuum", "--enable-threads"]
resources:
requests:
memory: "300Mi"
cpu: 150m
limits:
memory: "2Gi"
cpu: 1
livenessProbe:
httpGet:
httpHeaders:
- name: Accept
value: application/json
path: "/healthcheck"
port: 8000
initialDelaySeconds: 15
timeoutSeconds: 5
periodSeconds: 30
readinessProbe:
httpGet:
httpHeaders:
- name: Accept
value: application/json
path: "/healthcheck"
port: 8000
initialDelaySeconds: 15
timeoutSeconds: 5
periodSeconds: 30
envFrom:
- configMapRef:
name: configmap
- secretRef:
name: secrets
volumeMounts:
- name: service-account-storage-credentials-volume
mountPath: /credentials
readOnly: true
- name: csql-proxy
image: gcr.io/cloudsql-docker/gce-proxy:1.11
command: ["/cloud_sql_proxy",
"-instances=my-project:region:backend=tcp:1234",
"-credential_file=/secrets/credentials.json"]
ports:
- containerPort: 1234
name: sql
securityContext:
runAsUser: 2 # non-root user
allowPrivilegeEscalation: false
volumeMounts:
- name: credentials
mountPath: /secrets/sql
readOnly: true
volumes:
- name: credentials
secret:
secretName: credentials
- name: volume
secret:
secretName: production
items:
- key: APPLICATION_CREDENTIALS_CONTENT
path: key.json
We are using the same uWSGI configuration that we had before the migration (when the backend was being executed in a VM).
Is there a best practice config for running uWSGI in K8s? Or maybe something that I am doing wrong in this particular config?
You activated 5 workers in uwsgi, that could mean 5 times the need of memory if your application is using lazy-loading techniques (my advice: load everything at startup and trust pre-fork check this). However, you could try reducing number of workers and instead raising number of threads.
Also, you should drop max-requests, this makes your app reload every 10 requests, that's non-sense in a production environment (doc). If you have troubles with memory leaks, use reload-on-rss instead.
I would do something like this, maybe less or more threads per worker depending on how your app uses it (adjust according to cpu usage/availability per pod in production):
command: ["uwsgi", "--http", ":8000", "--wsgi-file", "onyo/wsgi.py", "--workers", "2", "--threads", "10", "--master", "--vacuum", "--enable-threads"]
ps: as zerg said in comment you should of course ensure your app is not running DEBUG mode, together with low logging output.