This topic explains the process to deploy Dremio on the HPE Ezmeral Container Platform and connect to data lake storage.
values.yamlfile within the Helm Chart directory.
As part of setting up a Dremio cluster on Kubernetes, there are a number of important considerations that we recommend you review before deploying your cluster. Some of these values have an impact on the performance of your cluster and should be adjusted to your needs.
For a complete reference on all the options available in the
values.yaml, see the Values.yaml Reference documentation — this document covers all the available options and provides small code samples for each configuration option.
values.yamlfrom LoadBalancer to NodePort.
Tip: As a best practice, we recommend creating a
values.local.yaml (or equivalently named file) that stores the values that you wish to override as part of your setup of Dremio. This allows you to quickly update to the latest version of the chart by copying the
values.local.yaml across Helm chart updates.
$ helm install <release name> dremio_v2 -f <pathway>/values.yaml
$ helm install dremio dremio_v2 -f ./dremio_v2/values.yaml --namespace dremio
Note: If you created a
values.local.yaml(or equivalently named file), use that instead of
$ kubectl get pods <pod name>
$ kubectl get pods --namespace dremio
If this process takes more than a couple of minutes to complete, check the status of the pods to see where they are waiting. If they are stuck in the Pending state for an extended period of time, check on the status of the pod to check that there are sufficient resources for scheduling. To check, use the following command on the pending pod:
$ kubectl describe pods <pod name>
$ kubectl describe pods --namespace dremio
If the events at the bottom of the output mention insufficient CPU or memory, either adjust the values in your
values.local.yaml and restart the process or add more resources to your Kubernetes cluster.
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Connecting Dremio to Data Source(s): Dremio supports a variety of data sources, including NoSQL databases, relational databases, Hadoop, local filesystems, and cloud storage.
Connecting Dremio to Client Application: Dremio provides ODBC and JDBC interfaces, supporting a broad range of client applications.