Standalone
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Use pool: you can select this check box to leverage a
Databricks pool. If you do, you must indicate the pool ID instead of the
cluster ID in the Spark Configuration. You must also
select Job cluster from the Cluster
type drop-down list.
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In the
Endpoint field, enter the
URL address of the workspace of your Databricks on AWS. For example,
this URL could look like https://adb-$workspaceId.$random.azuredatabricks.net.
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In the
Cluster ID field,
enter the ID of the Databricks cluster to be used. This ID is
the value of the spark.databricks.clusterUsageTags.clusterId
property of your Spark cluster. You can find this property on
the properties list in the Environment tab in the Spark UI view of your cluster.
You can also
easily find this ID from the URL of your Databricks cluster. It
is present immediately after cluster/ in this URL.
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If you selected the Use pool option, in the Pool ID field, enter the ID of the
Databricks pool to be used. This ID is the value of the DatabricksInstancePoolId key of your
pool. You can find this key under Tags in the Configuration tab of your pool. It is also
available in the tags of the clusters that are using the pool.
You can also easily find this ID from the URL of
your Databricks pool. It is present immediately after cluster/instance-pools/view/ in this
URL.
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Click the
[...] button next to the
Token field to enter the
authentication token generated for your Databricks user account. You
can generate or find this token on the User settings page of your Databricks workspace.
For more information, see Personal access tokens from
the Databricks documentation.
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In the DBFS dependencies
folder field, enter the directory that is used to
store your Job related dependencies on Databricks Filesystem at
runtime, putting a slash (/) at the end of this directory. For
example, enter /jars/ to store
the dependencies in a folder named jars. This folder is created on the fly if it does
not exist then.
This directory stores your Job
dependencies on DBFS only. In your Job, use tS3Configuration, tDynamoDBConfiguration or, in a Spark Streaming Job,
the Kinesis components, to read or write your business data to the
related systems.
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Poll interval when retrieving Job
status (in ms): enter, without the quotation marks,
the time interval (in milliseconds) at the end of which you want the
Studio to ask Spark for the status of your Job. For example, this
status could be Pending or Running.
The
default value is 300000,
meaning 30 seconds. This interval is recommended by Databricks to
correctly retrieve the Job status.
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Cluster type: select the type of cluster to be used
between Job clusters and All-purpose
clusters.
The custom properties you defined in the Advanced
properties table are automatically taken into account by the
job clusters at runtime.
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Use policy: select this check box to enter the
name of the policy to be used by your job cluster. You can use a policy
to limit the ability to configure clusters based on a set of rules. For
more information about cluster policies, see Manage cluster policies from the official
Databricks documentation.
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Autoscale: select or clear this check box to
define the number of workers to be used by your job cluster.
- If you select this check box, autoscaling is enabled. Then define
the minimum number of workers in Min workers
and the maximum number of worders in Max
workers. Your job cluster is scaled up and down
within this scope based on its workload.
According to the
Databricks documentation, autoscaling works best with Databricks
runtime versions 3.0 or onwards.
- If you clear this check box, autoscaling is deactivated. Then
define the number of workers a job cluster is expected to have.
This number does not include the Spark driver node.
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Node type
and Driver node type:
select the node types for the workers and the Spark driver node.
These types determine the capacity of your nodes and their
pricing by Databricks.
For more information about
these node types and the Databricks Units they use, see
Supported Instance
Types from the Databricks documentation.
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Elastic disk: select this check box to enable your
job cluster to automatically scale up its disk space when its Spark
workers are running low on disk space.
For more details about this
elastic disk feature, search for the section about autoscaling local
storage from your Databricks documentation.
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SSH public key: if an SSH access has been set up
for your cluster, enter the public key of the generated SSH key pair.
This public key is automatically added to each node of your job cluster.
If no SSH access has been set up, ignore this field.
For more
information about SSH access to your cluster, see SSH access to clusters from the official
Databricks documentation.
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Configure cluster
log: select this check box to define where to
store your Spark logs for a long term. This storage system could
be S3 or DBFS.
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Do not restart the cluster when submitting: select this
check box to prevent the Studio restarting the cluster when the Studio is
submitting your Jobs. However, if you make changes in your Jobs, clear this
check box so that the Studio restarts your cluster to take these changes into
account.
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