gcloud beta dataproc sessions create - create a Dataproc session

gcloud beta dataproc sessions createCOMMAND[--async][--container-image=CONTAINER_IMAGE][--history-server-cluster=HISTORY_SERVER_CLUSTER][--kernel=KERNEL][--kms-key=KMS_KEY][--labels=[KEY=VALUE,…]][--max-idle=MAX_IDLE][--metastore-service=METASTORE_SERVICE][--property=[PROPERTY=VALUE,…]][--request-id=REQUEST_ID][--service-account=SERVICE_ACCOUNT][--session_template=SESSION_TEMPLATE][--staging-bucket=STAGING_BUCKET][--tags=[TAGS,…]][--ttl=TTL][--user-workload-authentication-type=USER_WORKLOAD_AUTHENTICATION_TYPE][--version=VERSION][--network=NETWORK    |--subnet=SUBNET][GCLOUD_WIDE_FLAG …]

--async

Return immediately without waiting for the operation in progress to complete.

--container-image=CONTAINER_IMAGE

Optional custom container image to use for the batch/session runtimeenvironment. If not specified, a default container image will be used. The valueshould follow the container image naming format:{registry}/{repository}/{name}:{tag}, for example,gcr.io/my-project/my-image:1.2.3

--history-server-cluster=HISTORY_SERVER_CLUSTER

Spark History Server configuration for the batch/session job. Resource name ofan existing Dataproc cluster to act as a Spark History Server for the workloadin the format: "projects/{project_id}/regions/{region}/clusters/{cluster_name}".

--kernel=KERNEL

Jupyter kernel type. The value could be "python" or "scala".KERNEL must be one of:python,scala.

--kms-key=KMS_KEY

Cloud KMS key to use for encryption.

--labels=[KEY=VALUE,…]

List of label KEY=VALUE pairs to add.

Keys must start with a lowercase character and contain only hyphens(-), underscores (_), lowercase characters, andnumbers. Values must contain only hyphens (-), underscores(_), lowercase characters, and numbers.

--max-idle=MAX_IDLE

The duration after which an idle session will be automatically terminated, forexample, "20m" or "2h". A session is considered idle if it has no active Sparkapplications and no active Jupyter kernels. Rungcloudtopic datetimes for information on duration formats.

--metastore-service=METASTORE_SERVICE

Name of a Dataproc Metastore service to be used as an external metastore in theformat: "projects/{project-id}/locations/{region}/services/{service-name}".

--property=[PROPERTY=VALUE,…]

Specifies configuration properties.

--request-id=REQUEST_ID

A unique ID that identifies the request. If the service receives two sessioncreate requests with the same request_id, the second request is ignored and theoperation that corresponds to the first session created and stored in thebackend is returned. Recommendation: Always set this value to a UUID. The valuemust contain only letters (a-z, A-Z), numbers (0-9), underscores (),and hyphens (-). The maximum length is 40 characters.

--service-account=SERVICE_ACCOUNT

The IAM service account to be used for a batch/session job.

--session_template=SESSION_TEMPLATE

The session template to use for creating the session.

--staging-bucket=STAGING_BUCKET

The Cloud Storage bucket to use to store job dependencies, config files, and jobdriver console output. If not specified, the default [staging bucket](https://cloud.google.com/dataproc-serverless/docs/concepts/buckets) is used.

--tags=[TAGS,…]

Network tags for traffic control.

--ttl=TTL

The duration after the workload will be unconditionally terminated, for example,'20m' or '1h'. Rungcloudtopic datetimes for information on duration formats.

--user-workload-authentication-type=USER_WORKLOAD_AUTHENTICATION_TYPE

Whether to use END_USER_CREDENTIALS or SERVICE_ACCOUNT to run the workload.

--version=VERSION

Optional runtime version. If not specified, a default version will be used.

At most one of these can be specified:

--network=NETWORK

Network URI to connect network to.

--subnet=SUBNET

Subnetwork URI to connect network to. Subnet must have Private Google Accessenabled.

Run$gcloud help for details.

spark

(BETA) Create a Spark session.