Looking for an introduction to Google Cloud dataproc Autoscaling Policy? Our Google Cloud Support team is here to lend a hand with your queries and issues.

All About Google Cloud dataproc Autoscaling Policy

Did you know the Autoscaling policy defines when and how to autoscale the dataproc cluster? Furthermore, we can apply the autoscaling policy to multiple clusters.

According to our experts, we can opt to enable the autoscaling policy either while creating the cluster or via CLI.

gcloud dataproc clusters update cluster-name \
--autoscaling-policy=policy id or resource URI \
--region=region

Furthermore, the worker configuration is responsible for setting the number of workers spawned after passing the auto-scaling threshold. We can set this differently for Primary as well as Secondary workers.

Additionally, we can keep a rein on uncontrolled spawning on workers with the min and max instances.

The instance group’s weight determines the fraction of total workers in the cluster from this specific instance group.

Our experts would like to point out that one limitation of auto-scaling dataproc is that the scaling occurs based on memory requests rather than core requests.

In other words, YARN offers slots for containers according to memory requests, thereby ignoring core requests. Hence, by default, dataproc needs to autoscale only according to YARN pending or available memory.

Additionally, we may want to oversubscribe YARN cores in certain scenarios by running more containers.

Let us know in the comments if you would like to know more about Google Cloud dataproc autoscaling policy.

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Conclusion

To sum up, our Support Engineers offered us an introduction to Google Cloud dataproc Autoscaling Policy.