I am trying to setup solr 8.0 on GKE. I can successfully run it on my local instance. But when I configured it on GKE, it keeps giving 502 error.
Here's my deployment file:
apiVersion: apps/v1
kind: Deployment
metadata:
name: solr
namespace: api
labels:
app: solr
spec:
replicas: 1
revisionHistoryLimit: 10
strategy:
rollingUpdate:
maxSurge: 1
maxUnavailable: 0
type: RollingUpdate
selector:
matchLabels:
app: solr
template:
metadata:
labels:
app: solr
spec:
containers:
- name: app
image: solr:8
imagePullPolicy: Always
ports:
- name: http
containerPort: 8983
resources:
limits:
cpu: 250m
ephemeral-storage: 1Gi
memory: 512Mi
requests:
cpu: 250m
ephemeral-storage: 1Gi
memory: 512Mi
livenessProbe:
initialDelaySeconds: 20
httpGet:
path: /
port: http
Service:
apiVersion: v1
kind: Service
metadata:
name: solr
namespace: api
labels:
app: solr
spec:
type: ClusterIP
ports:
- name: solr
port: 8080
targetPort: 8983
selector:
app: solr
and ingress:
- host: solr.*****.***
http:
paths:
- pathType: ImplementationSpecific
backend:
service:
name: solr
port:
name: http
Things I have tried so far:
I have tried running the service on different ports and default ports.
I can exec into pod and access solr through command line. It is working fine.
Using port-forwarding kubectl port-forward --namespace api my-pod-name 8080:8983 I can access solr admin dashboard using the temporary url that google provides. But when i use the subdomain created for solr, it keeps giving me 502 Server error
Error: Server Error
The server encountered a temporary error and could not complete your request.
Please try again in 30 seconds.
Logs display error failed_to_pick_backend when I open subdomain that I added.
Related
My application uses apache2 web server. Due to restrictions in the kubernetes cluster, I do not have root previliges inside pod. So I have changed default port of apache2 from 80 to 8080 to be able to run as non-root user.
My problem is that once I build the docker image and run it in local it runs fine, but when I deploy using kubernetes in the cluster it keeps failing with:
Action '-D FOREGROUND' failed.
resulting in CrashLoopBackOff.
So, basically the apache2 server is not able to run in the pod with non-root user, but runs fine in local with docker run.
Any help is appreciated.
I am attaching my deployment and service files for reference:
apiVersion: apps/v1
kind: Deployment
metadata:
name: &DeploymentName app
spec:
replicas: 1
selector:
matchLabels: &appName
app: *DeploymentName
template:
metadata:
name: main
labels:
<<: *appName
spec:
securityContext:
fsGroup: 2000
runAsUser: 1000
runAsGroup: 3000
volumes:
- name: var-lock
emptyDir: {}
containers:
- name: *DeploymentName
image: image:id
ports:
- containerPort: 8080
volumeMounts:
- mountPath: /etc/apache2/conf-available
name: var-lock
- mountPath: /var/lock/apache2
name: var-lock
- mountPath: /var/log/apache2
name: var-lock
- mountPath: /mnt/log/apache2
name: var-lock
readinessProbe:
tcpSocket:
port: 8080
initialDelaySeconds: 180
periodSeconds: 60
livenessProbe:
tcpSocket:
port: 8080
initialDelaySeconds: 300
periodSeconds: 180
imagePullPolicy: Always
tty: true
env:
- name: POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
envFrom:
- configMapRef:
name: *DeploymentName
resources:
limits:
cpu: 1
memory: 2Gi
requests:
cpu: 1
memory: 2Gi
---
apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
name: &hpaName app
spec:
maxReplicas: 1
minReplicas: 1
scaleTargetRef:
apiVersion: extensions/v1beta1
kind: Deployment
name: *hpaName
targetCPUUtilizationPercentage: 60
---
apiVersion: v1
kind: Service
metadata:
labels:
app: app
name: app
spec:
selector:
app: app
ports:
- protocol: TCP
name: http-web-port
port: 80
targetPort: 8080
- protocol: TCP
name: https-web-port
port: 443
targetPort: 443
CrashLoopBackOff is a common error in Kubernetes, indicating a pod constantly crashing in an endless loop.
The CrashLoopBackOff error can be caused by a variety of issues, including:
Insufficient resources-lack of resources prevents the container from loading Locked file—a file was already locked by another container
Locked database-the database is being used and locked by other pods
Failed reference—reference to scripts or binaries that are not present on the container
Setup error- an issue with the init-container setup in Kubernetes
Config loading error—a server cannot load the configuration file.
Misconfigurations- a general file system misconfiguration
Connection issues—DNS or kube-DNS is not able to connect to a third-party service
Deploying failed services—an attempt to deploy services/applications that have already failed (e.g. due to a lack of access to other services)
To fix kubernetes CrashLoopbackoff error refer to this link and also check out stackpost for more information.
I'm pretty new to Kubernetes and trying to deploy what I think is a pretty common use case onto a GKE cluster we have created with Terraform, microservices all hosted on one cluster, but cannot for the life of me get the routing to serve traffic to the correct services. The setup I'm trying to create is as follows:
Deployment & Service for each microservice (canvas-service and video-service)
Single Ingress (class GCE) on the cluster, hosted at a static IP, that routes traffic to each service based on path
The current config looks like this:
canvas-service.yaml
# Deployment (Service Manager)
apiVersion: apps/v1
kind: Deployment
metadata:
name: canvas-service-deployment
labels:
name: canvas-service
spec:
replicas: 2
selector:
matchLabels:
app: canvas-service
template:
metadata:
labels:
app: canvas-service
spec:
restartPolicy: Always
containers:
- name: canvas-service
image: gcr.io/emile-learning-dev/canvas-service-image:latest
readinessProbe:
httpGet:
path: /health
port: 8080
ports:
- name: root
containerPort: 8080
resources:
requests:
memory: "4096Mi"
cpu: "1000m"
limits:
memory: "8192Mi"
cpu: "2000m"
---
# Service
apiVersion: v1
kind: Service
metadata:
name: canvas-service
spec:
type: NodePort
ports:
- port: 80
protocol: TCP
targetPort: 8080
selector:
app: canvas-service
video-service.yaml
# Deployment (Service Manager)
apiVersion: apps/v1
kind: Deployment
metadata:
name: video-service-deployment
labels:
name: video-service
spec:
replicas: 2
selector:
matchLabels:
app: video-service
template:
metadata:
labels:
app: video-service
spec:
restartPolicy: Always
containers:
- name: video-service
image: gcr.io/emile-learning-dev/video-service-image:latest
readinessProbe:
httpGet:
path: /health
port: 8080
ports:
- name: root
containerPort: 8080
resources:
requests:
memory: "4096Mi"
cpu: "1000m"
limits:
memory: "8192Mi"
cpu: "2000m"
---
# Service
apiVersion: v1
kind: Service
metadata:
name: video-service
spec:
type: NodePort
ports:
- port: 80
protocol: TCP
targetPort: 8080
selector:
app: video-service
services-ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: services-ingress
annotations:
kubernetes.io/ingress.global-static-ip-name: "services-ip"
spec:
defaultBackend:
service:
name: canvas-service
port:
number: 80
rules:
- http:
paths:
- path: /canvas/*
pathType: ImplementationSpecific
backend:
service:
name: canvas-service
port:
number: 80
- path: /video/*
pathType: ImplementationSpecific
backend:
service:
name: video-service
port:
number: 80
The output of kubectl describe ingress services-ingress looks like this:
Name: services-ingress
Namespace: default
Address: 34.107.136.153
Default backend: canvas-service:80 (10.244.2.17:8080,10.244.5.15:8080)
Rules:
Host Path Backends
---- ---- --------
*
/canvas/* canvas-service:80 (10.244.2.17:8080,10.244.5.15:8080)
/video/* video-service:80 (10.244.1.15:8080,10.244.8.51:8080)
Annotations: ingress.kubernetes.io/backends: {"k8s-be-30551--8ba41a687ec15071":"HEALTHY","k8s-be-32145--8ba41a687ec15071":"HEALTHY"}
ingress.kubernetes.io/forwarding-rule: k8s2-fr-xlhz0sas-default-services-ingress-hqyvwyy1
ingress.kubernetes.io/target-proxy: k8s2-tp-xlhz0sas-default-services-ingress-hqyvwyy1
ingress.kubernetes.io/url-map: k8s2-um-xlhz0sas-default-services-ingress-hqyvwyy1
kubernetes.io/ingress.global-static-ip-name: services-ip
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Sync 9m36s loadbalancer-controller UrlMap "k8s2-um-xlhz0sas-default-services-ingress-hqyvwyy1" created
Normal Sync 9m34s loadbalancer-controller TargetProxy "k8s2-tp-xlhz0sas-default-services-ingress-hqyvwyy1" created
Normal Sync 9m26s loadbalancer-controller ForwardingRule "k8s2-fr-xlhz0sas-default-services-ingress-hqyvwyy1" created
Normal IPChanged 9m26s loadbalancer-controller IP is now 34.107.136.153
Normal Sync 6m56s (x5 over 11m) loadbalancer-controller Scheduled for sync
For testing, I have a healthcheck route at /health for each service. What I'm running into is that when I hit {public_ip}/health (using the default backend) I get the expected response. But when I hit {public_ip}/canvas/health or {public_ip}/video/health, I get a 404 Not Found.
I know it has something to do with the fact that the entire service route structure is on the /canvas or /video route, but thought that the /* was supposed to address exactly that. I'd like to basically make the root route for each service exist on the corresponding subpaths /canvas and /video. Would love to hear any thoughts you guys have as to what I'm doing wrong that's leading to traffic not being routed correctly.
If it's an issue with the GCP default Ingress resource or this isn't within its functionality, I'm totally open to using an nginx Ingress. But, I haven't been able to get an nginx Ingress to expose an IP at all so figured the GCP Ingress would probably be a shorter path to getting this cluster working. If I'm wrong about this also please let me know.
It's due to the paths defined in Ingress. Changing the path type to /video and /canvas will make this work.
To understand the reason behind this, you need to read nginx ingress controller documentation about path and pathType. You can also use the regex pattern in the path according to the documentation here https://kubernetes.github.io/ingress-nginx/user-guide/ingress-path-matching/
Tip: When in doubt, you can always go to the nginx ingress controller pods and check the nginx.conf file about the location determined by nginx.
Implementation Goal
Expose Zookeeper instance, running on kubernetes, to the internet.
(configuration & version information provided at the bottom)
Implementation Attempt
I currently have a minikube cluster running on ubuntu 14.04, backed by docker containers.
I'm running a bare metal k8s cluster, and I'm trrying to expose a zookeeper service to the internet. Seeing as my cluster is not running on a cloud provider, I set up metallb, in order to provide a network-loadbalancer implementation for my zookeeper service.
On startup everything looks good, an external IP is assigned and I can access it from the same host via a curl command.
$ kubectl get pods -n metallb-system
NAME READY STATUS RESTARTS AGE
controller-5c9894b5cd-9gh8m 1/1 Running 0 5h59m
speaker-j2z8q 1/1 Running 0 5h59m
$ kubectl get svc
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
kubernetes ClusterIP 10.xxx.xxx.xxx <none> 443/TCP 6d19h
zk-cs LoadBalancer 10.xxx.xxx.xxx 172.1.1.x 2181:30035/TCP 56m
zk-hs LoadBalancer 10.xxx.xxx.xxx 172.1.1.x 2888:30664/TCP,3888:31113/TCP 6m15s
When I curl the above mentioned external IP's, I get a valid response
$ curl -D- "http://172.1.1.x:2181"
curl: (52) Empty reply from server
So far it all looks good, I can access the LB from outside the cluster with no issues, but this is where my lack of Kubernetes/Networking knowledge gets me.I'm finding it impossible to expose this LB to the internet. I've tried running minikube tunnel which I had high hopes for, only to be deeply disappointed.
Running a curl command from another node, whilst minikube tunnel is running will just see the request time out.
$ curl -D- "http://172.1.1.x:2181"
curl: (28) Failed to connect to 172.1.1.x port 2181: Timed out
At this point, as I mentioned before, I'm stuck.
Is there any way that I can get this service exposed to the internet without giving my soul to AWS or GCP?
Any help will be greatly appreciated.
Service Configuration
apiVersion: v1
kind: Service
metadata:
name: zk-hs
labels:
app: zk
spec:
selector:
app: zk
ports:
- port: 2888
targetPort: 2888
name: server
protocol: TCP
- port: 3888
targetPort: 3888
name: leader-election
protocol: TCP
clusterIP: ""
type: LoadBalancer
---
apiVersion: v1
kind: Service
metadata:
name: zk-cs
labels:
app: zk
spec:
selector:
app: zk
ports:
- name: client
protocol: TCP
port: 2181
targetPort: 2181
type: LoadBalancer
---
apiVersion: policy/v1beta1
kind: PodDisruptionBudget
metadata:
name: zk-pdb
spec:
selector:
matchLabels:
app: zk
maxUnavailable: 1
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: zk
spec:
selector:
matchLabels:
app: zk
serviceName: zk-hs
replicas: 1
updateStrategy:
type: RollingUpdate
podManagementPolicy: OrderedReady
template:
metadata:
labels:
app: zk
spec:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: "app"
operator: In
values:
- zk
topologyKey: "kubernetes.io/hostname"
containers:
- name: zookeeper
imagePullPolicy: Always
image: "library/zookeeper:3.6"
resources:
requests:
memory: "1Gi"
cpu: "0.5"
ports:
- containerPort: 2181
name: client
- containerPort: 2888
name: server
- containerPort: 3888
name: leader-election
volumeMounts:
- name: datadir
mountPath: /var/lib/zookeeper
- name: zoo-config
mountPath: /conf
volumes:
- name: zoo-config
configMap:
name: zoo-config
securityContext:
fsGroup: 2000
runAsUser: 1000
runAsNonRoot: true
volumeClaimTemplates:
- metadata:
name: datadir
spec:
accessModes: [ "ReadWriteOnce" ]
resources:
requests:
storage: 10Gi
---
apiVersion: v1
kind: ConfigMap
metadata:
name: zoo-config
namespace: default
data:
zoo.cfg: |
tickTime=10000
dataDir=/var/lib/zookeeper
clientPort=2181
initLimit=10
syncLimit=4
apiVersion: v1
kind: ConfigMap
metadata:
namespace: metallb-system
name: config
data:
config: |
address-pools:
- name: default
protocol: layer2
addresses:
- 172.1.1.1-172.1.1.10
minikube: v1.13.1
docker: 18.06.3-ce
You can do it with minikube, but the idea of minikube is just to test stuff on your local environment. So, by default, it does not have the correct IPTable permissions, and yes you can adjust that, but if your goal is only to use without any loud provider, I'll higly recommend you to use kubeadm (https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/create-cluster-kubeadm/).
This tool will provide you a very customizable cluster configuration and you will be able to set your network problems without headaches.
it is possible to fix the ip and port somewhere in my yaml.
My application has 3 parts: a fronted with its respective balancer, a backend with its respective balancer and the database with statefulset and to persevere the volume, these 3 applications have their respective hpa rules.
I put the yaml of the backend if it is possible to set the ip and the port since I am working local and every so often I have to change the port or the ip.
backend.yml
apiVersion: apps/v1
kind: Deployment
metadata:
name: backend
spec:
replicas: 2
selector:
matchLabels:
app: backend
template:
metadata:
labels:
app: backend
spec:
containers:
- name: backend
image: XXXXXXX
command: ["/bin/sh"]
args: ["-c", "node index.js"]
ports:
- containerPort: 4000
imagePullPolicy: IfNotPresent
env:
- name: HOST_DB
value: "172.17.0.3"
- name: PORT_DB
value: "31109"
resources:
requests:
memory: "128Mi"
cpu: "200m"
limits:
memory: "256Mi"
cpu: "1000m"
readinessProbe:
httpGet:
path: /
port: 4000
initialDelaySeconds: 5
periodSeconds: 10
livenessProbe:
tcpSocket:
port: 4000
initialDelaySeconds: 15
periodSeconds: 20
---
apiVersion: v1
kind: Service
metadata:
name: backend
spec:
selector:
app: backend
ports:
- protocol: TCP
port: 5000
targetPort: 4000
name: https
type: LoadBalancer
---
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
name: backend
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: backend
minReplicas: 2
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 50
the result image is this
The IP is anyway fixed to the single node's IP in case of minikube.You can hardcode the NodePort by specifying nodePort in the service. Without nodePort specified in the service kubernetes will assign a port from the range 30000-32767
apiVersion: v1
kind: Service
metadata:
name: backend
spec:
type: NodePort
selector:
app: backend
ports:
- protocol: TCP
port: 5000
targetPort: 4000
name: https
nodePort: 30007
Follow this guide to expose applications via NodePort type service in minikube.
I'm trying to deploy two services on Google container engine, I have created a cluster with 3 Nodes.
My docker images are in private docker hub repo that's why I have created a secret and used in Deployments, The ingress is creating a load balancer in the Google cloud console but it shows that backend services are not healthy and inside the kubernetes section under workloads it says Does not have minimum availability.
I'm new to kubernetes, what can be a problem?
Here are my yamls:
Deployment.yaml:
kind: Deployment
apiVersion: apps/v1
metadata:
name: pythonaryapp
labels:
app: pythonaryapp
spec:
replicas: 1 #We always want more than 1 replica for HA
selector:
matchLabels:
app: pythonaryapp
template:
metadata:
labels:
app: pythonaryapp
spec:
containers:
- name: pythonaryapp #1st container
image: docker.io/arycloud/docker_web_app:pythonaryapp #Dockerhub image
ports:
- containerPort: 8080 #Exposes the port 8080 of the container
env:
- name: PORT #Env variable key passed to container that is read by app
value: "8080" # Value of the env port.
readinessProbe:
httpGet:
path: /healthz
port: 8080
periodSeconds: 2
timeoutSeconds: 2
successThreshold: 2
failureThreshold: 10
imagePullSecrets:
- name: docksecret
---
kind: Deployment
apiVersion: apps/v1
metadata:
name: pythonaryapp1
labels:
app: pythonaryapp1
spec:
replicas: 1 #We always want more than 1 replica for HA
selector:
matchLabels:
app: pythonaryapp1
template:
metadata:
labels:
app: pythonaryapp1
spec:
containers:
- name: pythonaryapp1 #1st container
image: docker.io/arycloud/docker_web_app:pythonaryapp1 #Dockerhub image
ports:
- containerPort: 8080 #Exposes the port 8080 of the container
env:
- name: PORT #Env variable key passed to container that is read by app
value: "8080" # Value of the env port.
readinessProbe:
httpGet:
path: /healthz
port: 8080
periodSeconds: 2
timeoutSeconds: 2
successThreshold: 2
failureThreshold: 10
imagePullSecrets:
- name: docksecret
---
And here's services.yaml:
kind: Service
apiVersion: v1
metadata:
name: pythonaryapp
spec:
type: NodePort
selector:
app: pythonaryapp
ports:
- protocol: TCP
port: 8080
---
---
kind: Service
apiVersion: v1
metadata:
name: pythonaryapp1
spec:
type: NodePort
selector:
app: pythonaryapp1
ports:
- protocol: TCP
port: 8080
---
And Here's my ingress.yaml:
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
name: mysvcs
spec:
rules:
- http:
paths:
- path: /
backend:
serviceName: pythonaryapp
servicePort: 8080
- path: /<name>
backend:
serviceName: pythonaryapp1
servicePort: 8080
Update:
Here's flask service code:
from flask import Flask
app = Flask(__name__)
#app.route('/')
def hello_world():
return 'Hello World, from Python Service.', 200
if __name__ == '__main__':
app.run()
And, on running the container of it's docker image it retunrs 200 sttaus code at the root path /.
Thanks in advance!
Have a look at this post. It might contain helpful tips for your issue.
For example I do see a readiness probe but not a liveness probe in your config files.
This post suggests that “Does not have minimum availability” in k8s could be a result of a CrashloopBackoff caused by a failing liveness probe.
In GKE, the ingress is implemented by GCP LoadBalancer. The GCP LB is checking the health of the service by calling it in the service address with the root path '/'. Make sure that your container can respond with 200 on the root, or alternatively change the LB backend service health check route (you can do it in the GCP console)