gcloud beta dataproc batches submit pyspark

gcloud beta dataproc batches submit pyspark - submit a PySpark batch job

gcloud beta dataproc batches submit pyspark MAIN_PYTHON_FILE [--archives=[ARCHIVE,…]] [--async] [--batch=BATCH] [--container-image=CONTAINER_IMAGE] [--deps-bucket=DEPS_BUCKET] [--files=[FILE,…]] [--history-server-cluster=HISTORY_SERVER_CLUSTER] [--jars=[JAR,…]] [--kms-key=KMS_KEY] [--labels=[KEY=VALUE,…]] [--metastore-service=METASTORE_SERVICE] [--properties=[PROPERTY=VALUE,…]] [--py-files=[PY,…]] [--region=REGION] [--request-id=REQUEST_ID] [--service-account=SERVICE_ACCOUNT] [--staging-bucket=STAGING_BUCKET] [--tags=[TAGS,…]] [--ttl=TTL] [--version=VERSION] [--network=NETWORK     | --subnet=SUBNET] [GCLOUD_WIDE_FLAG …] [-- JOB_ARG …]

gcloud beta dataproc batches submit pyspark my-pyspark.py --batch=my-batch --deps-bucket=gs://my-bucket --region=us-central1 --py-files='path/to/my/python/script.py'

MAIN_PYTHON_FILE

URI of the main Python file to use as the Spark driver. Must be a .py file.

[-- JOB_ARG …]

Arguments to pass to the driver.

The '--' argument must be specified between gcloud specific args on the left and JOB_ARG on the right.

--archives=[ARCHIVE,…]

Archives to be extracted into the working directory. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.

--async

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

--batch=BATCH

The ID of the batch job to submit. The ID must contain only lowercase letters (a-z), numbers (0-9) and hyphens (-). The length of the name must be between 4 and 63 characters. If this argument is not provided, a random generated UUID will be used.

--container-image=CONTAINER_IMAGE

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

--deps-bucket=DEPS_BUCKET

A Cloud Storage bucket to upload workload dependencies.

--files=[FILE,…]

Files to be placed in the working directory.

--history-server-cluster=HISTORY_SERVER_CLUSTER

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

--jars=[JAR,…]

Comma-separated list of jar files to be provided to the classpaths.

--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, and numbers. Values must contain only hyphens (-), underscores (_), lowercase characters, and numbers.

--metastore-service=METASTORE_SERVICE

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

--properties=[PROPERTY=VALUE,…]

Specifies configuration properties for the workload. See Dataproc Serverless for Spark documentation for the list of supported properties.

--py-files=[PY,…]

Comma-separated list of Python scripts to be passed to the PySpark framework. Supported file types: .py, .egg and .zip.

Region resource - Dataproc region to use. Each Dataproc region constitutes an independent resource namespace constrained to deploying instances into Compute Engine zones inside the region. This represents a Cloud resource. (NOTE) Some attributes are not given arguments in this group but can be set in other ways.

To set the project attribute:

• provide the argument --region on the command line with a fully specified name;
• set the property dataproc/region with a fully specified name;
• provide the argument --project on the command line;
• set the property core/project.

--region=REGION

ID of the region or fully qualified identifier for the region.

To set the region attribute:

• provide the argument --region on the command line;
• set the property dataproc/region.

--request-id=REQUEST_ID

A unique ID that identifies the request. If the service receives two batch create requests with the same request_id, the second request is ignored and the operation that corresponds to the first batch created and stored in the backend is returned. Recommendation: Always set this value to a UUID. The value must 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.

--staging-bucket=STAGING_BUCKET

The Cloud Storage bucket to use to store job dependencies, config files, and job driver 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'. Run gcloud topic datetimes for information on duration formats.

--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 Access enabled.

Run $ gcloud help for details.

gcloud dataproc batches submit pyspark