I changed my initialization script after creating a cluster with 2 worker nodes for spark. Then I changed the script a bit and tried to update the cluster with 2 more worker nodes. The script failed because I simply forgot to apt-get update before apt-get install, so dataproc reports error and the cluster's status changed to ERROR. When I try to reduce the size back to 2 nodes again, it doesn't work anymore with the following message
ERROR: (gcloud.dataproc.clusters.update) Cluster 'cluster-1' must be running before it can be updated, current cluster state is 'ERROR'.
The two worker nodes are still added, but they don't seem to be detected by a running spark application at first because no more executors are added. I manually reset the two instances on the Google Compute Engine page, and then 4 executors are added. So it seems everything is working fine again except that the cluster's status is still ERROR, and I cannot increase or decrease the number of worker nodes anymore.
How can I update the cluster status back to normal (RUNNING)?
In your case ERROR indicates that workflow to re-configure the cluster has failed, and Dataproc is not sure of its health. At this point Dataproc cannot guarantee that another reconfigure attempt will succeed so further updates are disallowed. You can however submit jobs.
Your best bet is to delete it and start over.
Related
Redis in our production environment is in cluster mode, with 6 nodes, 3 master nodes and 3 slave nodes. When nodes are switched due to network and other reasons, Redis-client cannot automatically refresh these nodes, and will report exception
MOVED 5649 192.168.1.1:6379
The vertx-redis-client version I use is 4.2.1
My configuration looks like this:
RedisOptions options = new RedisOptions();
options.setType(RedisClientType.CLUSTER)
.setRole(RedisRole.MASTER)
.setUseReplicas(RedisReplicas.NEVER)
Every time I encounter this problem, I need to restart my application service to restore it. I want to ask if there is any way to make vertx-redis-client automatically refresh the node?
Thank you ~
We sometimes use Python scripts to spin up and monitor Kubernetes Pods running on Google Kubernetes Engine using the Official Python client library for kubernetes. We also enable auto-scaling on several of our node pools.
According to this, "Master VM is automatically scaled, upgraded, backed up and secured". The post also seems to indicate that some automatic scaling of the control plane / Master VM occurs when the node count increases from 0-5 to 6+ and potentially at other times when more nodes are added.
It seems like the control plane can go down at times like this, when many nodes have been brought up. In and around when this happens, our Python scripts that monitor pods via the control plane often crash, seemingly unable to find the KubeApi/Control Plane endpoint triggering some of the following exceptions:
ApiException, urllib3.exceptions.NewConnectionError, urllib3.exceptions.MaxRetryError.
What's the best way to handle this situation? Are there any properties of the autoscaling events that might be helpful?
To clarify what we're doing with the Python client is that we are in a loop reading the status of the pod of interest via read_namespaced_pod every few minutes, and catching exceptions similar to the provided example (in addition we've tried also catching exceptions for the underlying urllib calls). We have also added retrying with exponential back-off, but things are unable to recover and fail after a specified max number of retries, even if that number is high (e.g. keep retrying for >5 minutes).
One thing we haven't tried is recreating the kubernetes.client.CoreV1Api object on each retry. Would that make much of a difference?
When a nodepool size changes, depending on the size, this can initiate a change in the size of the master. Here are the nodepool sizes mapped with the master sizes. In the case where the nodepool size requires a larger master, automatic scaling of the master is initiated on GCP. During this process, the master will be unavailable for approximately 1-5 minutes. Please note that these events are not available in Stackdriver Logging.
At this point all API calls to the master will fail, including the ones from the Python API client and kubectl. However after 1-5 minutes the master should be available and calls from both the client and kubectl should work. I was able to test this by scaling my cluster from 3 node to 20 nodes and for 1-5 minutes the master wasn't available .
I obtained the following errors from the Python API client:
Max retries exceeded with url: /api/v1/pods?watch=False (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at>: Failed to establish a new connection: [Errno 111] Connection refused',))
With kubectl I had :
“Unable to connect to the server: dial tcp”
After 1-5 minutes the master was available and the calls were successful. There was no need to recreate kubernetes.client.CoreV1Api object as this is just an API endpoint.
According to your description, your master wasn't accessible after 5 minutes which signals a potential issue with your master or setup of the Python script. To troubleshoot this further on side while your Python script runs, you can check for availability of master by running any kubectl command.
I have a cluster in Azure and it failed to update automatically so I'm trying a manual update. I tried via the portal, it failed so I kicked off an update using PS, it failed also. The update starts then just sits at "UpdatingUserConfiguration" then after an hour or so fails with a time out. I have removed all application types and check my certs for "NETWORK SERVCIE". The cluster is 5 VM single node type, Windows.
Error
Set-AzureRmServiceFabricUpgradeType : Code: ClusterUpgradeFailed,
Message: Cluster upgrade failed. Reason Code: 'UpgradeDomainTimeout',
Upgrade Progress:
'{"upgradeDescription":{"targetCodeVersion":"6.0.219.9494","
targetConfigVersion":"1","upgradePolicyDescription":{"upgradeMode":"UnmonitoredAuto","forceRestart":false,"u
pgradeReplicaSetCheckTimeout":"37201.09:59:01","kind":"Rolling"}},"targetCodeVersion":"6.0.219.9494","target
ConfigVersion":"1","upgradeState":"RollingBackCompleted","upgradeDomains":[{"name":"1","state":"Completed"},
{"name":"2","state":"Completed"},{"name":"3","state":"Completed"},{"name":"4","state":"Completed"}],"rolling
UpgradeMode":"UnmonitoredAuto","upgradeDuration":"02:02:07","currentUpgradeDomainDuration":"00:00:00","unhea
lthyEvaluations":[],"currentUpgradeDomainProgress":{"upgradeDomainName":"","nodeProgressList":[]},"startTime
stampUtc":"2018-05-17T03:13:16.4152077Z","failureTimestampUtc":"2018-05-17T05:13:23.574452Z","failureReason"
:"UpgradeDomainTimeout","upgradeDomainProgressAtFailure":{"upgradeDomainName":"1","nodeProgressList":[{"node
Name":"_mstarsf10_1","upgradePhase":"PreUpgradeSafetyCheck","pendingSafetyChecks":[{"kind":"EnsureSeedNodeQu
orum"}]}]}}'.
Any ideas on what I can do about a "EnsureSeedNodeQuorum" error ?
The root cause was only 3 seed nodes in the cluster as a result of the cluster being build with a VM scale set that had "overprovision" set to true. Lesson learned, remember to set "overprovision" to false.
I ended up deleting the cluster and scale set and recreated using my stored ARM template.
We have a dataproc cluster we dynamically resize for large jobs. I submitted a cluster resize request to reduce our cluster to its original size (1m,2workers) from 10-workers, 3-preemptive workers but this still hasn't completed an hour later.
Is this normal? is there a way to re-issue the request? at the moment I get cluster update in progress style messages.
If you downscale Dataproc 1.2+ cluster using Graceful Decommissioning this is expected that it could take a long time if there are running jobs on cluster - downscale operation will wait until YARN containers will finish on decommissioned nodes.
Also, if you are intensively using HDFS, nodes decommissioning could take a long time for data to be replicated to prevent data loss.
You can not issue another resize operation until current operation is finished.
I have a working kubernetes cluster (v1.4.6) with an active job that has a single failing pod (e.g. it is constantly restarted) - this is a test, the job should never reach completion.
If I restart the same cluster (e.g. reboot the node), the job is properly re-scheduled and continues to be restarted
If I upgrade the cluster to v1.5.3, then the job is marked as completed once the cluster is up. The upgrade is basically the same as restart - both use the same etcd cluster.
Is this the expected behavior when going to v1.5.x? If not, what can be done to have the job continue running?
I should provide a little background on my problem - the job is to ultimately become a driver in the update process and it is important to have it running (even in face of cluster restarts) until it achieves a certain goal. Is this possible using a job?
In v1.5.0 extensions/v1beta1.Jobs was deprecated in favor of batch/v1.Job, so simply upgrading the cluster without updating the job definition is expected to cause side effects.
See the Kubernetes CHANGELOG for a complete list of changes and deprecations in v1.5.0.