Cloud Storage to Cloud Storage template

Use the Dataproc Serverless Cloud Storage to Cloud Storage template to extract data from Cloud Storage to Cloud Storage.

Use the template

Run the template using the gcloud CLI or Dataproc API.

gcloudREST

Before using any of the command data below, make the following replacements:

• PROJECT_ID: Required. Your Google Cloud project ID listed in the IAM Settings.
• REGION: Required. Compute Engine region.
• SUBNET: Optional. If a subnet is not specified, the subnet in the specified REGION in the default network is selected.

  Example: projects/PROJECT_ID/regions/REGION/subnetworks/SUBNET_NAME

• TEMPLATE_VERSION: Required. Specify latest for the latest template version, or the date of a specific version, for example, 2023-03-17_v0.1.0-beta (visit gs://dataproc-templates-binaries or run gcloud storage ls gs://dataproc-templates-binaries to list available template versions).
• CLOUD_STORAGE_INPUT_PATH: Required. Cloud Storage path from where input data will be read.

  Example: gs://example-bucket/example-folder/

• FORMAT: Required. Input data format. Options: avro, parquet, or orc. Note: If avro, you must add "file:///usr/lib/spark/connector/spark-avro.jar" to the jars gcloud CLI flag or API field.

  Example (the file:// prefix references a Dataproc Serverless jar file):

  --jars=file:///usr/lib/spark/connector/spark-avro.jar, [ ... other jars]
• CLOUD_STORAGE_OUTPUT_PATH: Required. Cloud Storage path where output will be stored.

  Example: gs://example-bucket/example-folder/

• OUTPUT_FILE_FORMAT: Required. Output data format. Options: avro, csv parquet, json or orc. Note: If avro, you must add "file:///usr/lib/spark/connector/spark-avro.jar" to the jars gcloud CLI flag or API field.

  Example (the file:// prefix references a Dataproc Serverless jar file):

  --jars=file:///usr/lib/spark/connector/spark-avro.jar, [ ... other jars]
• MODE: Required. Write mode for Cloud Storage output. Options: Append, Overwrite, Ignore, or ErrorIfExists.
• TEMP_TABLE and TEMP_QUERY: Optional. You can use these two optional parameters to apply a Spark SQL transformation while loading data into Cloud Storage. TEMP_TABLE is the temporary view name, and TEMP_QUERY is the query statement. TEMP_TABLE and the table name in TEMP_QUERY must match.
• SERVICE_ACCOUNT: Optional. If not provided, the default Compute Engine service account is used.
• PROPERTY and PROPERTY_VALUE: Optional. Comma-separated list of Spark property=value pairs.
• LABEL and LABEL_VALUE: Optional. Comma-separated list of label=value pairs.
• LOG_LEVEL: Optional. Level of logging. Can be one of ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, or WARN. Default: INFO.
• KMS_KEY: Optional. The Cloud Key Management Service key to use for encryption. If a key is not specified, data is encrypted at rest using a Google-owned and Google-managed encryption key.

  Example: projects/PROJECT_ID/regions/REGION/keyRings/KEY_RING_NAME/cryptoKeys/KEY_NAME

Execute the following command:

Linux, macOS, or Cloud Shell

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

gcloud dataproc batches submit spark \
    --class=com.google.cloud.dataproc.templates.main.DataProcTemplate \
    --version="1.2" \
    --project="PROJECT_ID" \
    --region="REGION" \
    --jars="gs://dataproc-templates-binaries/TEMPLATE_VERSION/java/dataproc-templates.jar,file:///usr/lib/spark/connector/spark-avro.jar" \
    --subnet="SUBNET" \
    --kms-key="KMS_KEY" \
    --service-account="SERVICE_ACCOUNT" \
    --properties="PROPERTY=PROPERTY_VALUE" \
    --labels="LABEL=LABEL_VALUE" \
    -- --template=GCSTOGCS \
    --templateProperty log.level="LOG_LEVEL" \
    --templateProperty project.id="PROJECT_ID" \
    --templateProperty gcs.gcs.input.location="CLOUD_STORAGE_INPUT_PATH" \
    --templateProperty gcs.gcs.input.format="INPUT_FILE_FORMAT" \
    --templateProperty gcs.gcs.output.location="CLOUD_STORAGE_OUTPUT_PATH" \
    --templateProperty gcs.gcs.output.format="OUTPUT_FILE_FORMAT" \
    --templateProperty gcs.gcs.write.mode="MODE" \
    --templateProperty gcs.gcs.temp.table="TEMP_TABLE" \
    --templateProperty gcs.gcs.temp.query="TEMP_QUERY"

Windows (PowerShell)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

gcloud dataproc batches submit spark `
    --class=com.google.cloud.dataproc.templates.main.DataProcTemplate `
    --version="1.2" `
    --project="PROJECT_ID" `
    --region="REGION" `
    --jars="gs://dataproc-templates-binaries/TEMPLATE_VERSION/java/dataproc-templates.jar,file:///usr/lib/spark/connector/spark-avro.jar" `
    --subnet="SUBNET" `
    --kms-key="KMS_KEY" `
    --service-account="SERVICE_ACCOUNT" `
    --properties="PROPERTY=PROPERTY_VALUE" `
    --labels="LABEL=LABEL_VALUE" `
    -- --template=GCSTOGCS `
    --templateProperty log.level="LOG_LEVEL" `
    --templateProperty project.id="PROJECT_ID" `
    --templateProperty gcs.gcs.input.location="CLOUD_STORAGE_INPUT_PATH" `
    --templateProperty gcs.gcs.input.format="INPUT_FILE_FORMAT" `
    --templateProperty gcs.gcs.output.location="CLOUD_STORAGE_OUTPUT_PATH" `
    --templateProperty gcs.gcs.output.format="OUTPUT_FILE_FORMAT" `
    --templateProperty gcs.gcs.write.mode="MODE" `
    --templateProperty gcs.gcs.temp.table="TEMP_TABLE" `
    --templateProperty gcs.gcs.temp.query="TEMP_QUERY"

Windows (cmd.exe)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

gcloud dataproc batches submit spark ^
    --class=com.google.cloud.dataproc.templates.main.DataProcTemplate ^
    --version="1.2" ^
    --project="PROJECT_ID" ^
    --region="REGION" ^
    --jars="gs://dataproc-templates-binaries/TEMPLATE_VERSION/java/dataproc-templates.jar,file:///usr/lib/spark/connector/spark-avro.jar" ^
    --subnet="SUBNET" ^
    --kms-key="KMS_KEY" ^
    --service-account="SERVICE_ACCOUNT" ^
    --properties="PROPERTY=PROPERTY_VALUE" ^
    --labels="LABEL=LABEL_VALUE" ^
    -- --template=GCSTOGCS ^
    --templateProperty log.level="LOG_LEVEL" ^
    --templateProperty project.id="PROJECT_ID" ^
    --templateProperty gcs.gcs.input.location="CLOUD_STORAGE_INPUT_PATH" ^
    --templateProperty gcs.gcs.input.format="INPUT_FILE_FORMAT" ^
    --templateProperty gcs.gcs.output.location="CLOUD_STORAGE_OUTPUT_PATH" ^
    --templateProperty gcs.gcs.output.format="OUTPUT_FILE_FORMAT" ^
    --templateProperty gcs.gcs.write.mode="MODE" ^
    --templateProperty gcs.gcs.temp.table="TEMP_TABLE" ^
    --templateProperty gcs.gcs.temp.query="TEMP_QUERY"

Before using any of the request data, make the following replacements:

• PROJECT_ID: Required. Your Google Cloud project ID listed in the IAM Settings.
• REGION: Required. Compute Engine region.
• SUBNET: Optional. If a subnet is not specified, the subnet in the specified REGION in the default network is selected.

  Example: projects/PROJECT_ID/regions/REGION/subnetworks/SUBNET_NAME

• TEMPLATE_VERSION: Required. Specify latest for the latest template version, or the date of a specific version, for example, 2023-03-17_v0.1.0-beta (visit gs://dataproc-templates-binaries or run gcloud storage ls gs://dataproc-templates-binaries to list available template versions).
• CLOUD_STORAGE_INPUT_PATH: Required. Cloud Storage path from where input data will be read.

  Example: gs://example-bucket/example-folder/

• FORMAT: Required. Input data format. Options: avro, parquet, or orc. Note: If avro, you must add "file:///usr/lib/spark/connector/spark-avro.jar" to the jars gcloud CLI flag or API field.

  Example (the file:// prefix references a Dataproc Serverless jar file):

  --jars=file:///usr/lib/spark/connector/spark-avro.jar, [ ... other jars]
• CLOUD_STORAGE_OUTPUT_PATH: Required. Cloud Storage path where output will be stored.

  Example: gs://example-bucket/example-folder/

• OUTPUT_FILE_FORMAT: Required. Output data format. Options: avro, csv parquet, json or orc. Note: If avro, you must add "file:///usr/lib/spark/connector/spark-avro.jar" to the jars gcloud CLI flag or API field.

  Example (the file:// prefix references a Dataproc Serverless jar file):

  --jars=file:///usr/lib/spark/connector/spark-avro.jar, [ ... other jars]
• MODE: Required. Write mode for Cloud Storage output. Options: Append, Overwrite, Ignore, or ErrorIfExists.
• TEMP_TABLE and TEMP_QUERY: Optional. You can use these two optional parameters to apply a Spark SQL transformation while loading data into Cloud Storage. TEMP_TABLE is the temporary view name, and TEMP_QUERY is the query statement. TEMP_TABLE and the table name in TEMP_QUERY must match.
• SERVICE_ACCOUNT: Optional. If not provided, the default Compute Engine service account is used.
• PROPERTY and PROPERTY_VALUE: Optional. Comma-separated list of Spark property=value pairs.
• LABEL and LABEL_VALUE: Optional. Comma-separated list of label=value pairs.
• LOG_LEVEL: Optional. Level of logging. Can be one of ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, or WARN. Default: INFO.
• KMS_KEY: Optional. The Cloud Key Management Service key to use for encryption. If a key is not specified, data is encrypted at rest using a Google-owned and Google-managed encryption key.

  Example: projects/PROJECT_ID/regions/REGION/keyRings/KEY_RING_NAME/cryptoKeys/KEY_NAME

HTTP method and URL:

POST https://dataproc.googleapis.com/v1/projects/PROJECT_ID/locations/REGION/batches

Request JSON body:

{
  "environmentConfig":{
    "executionConfig":{
      "subnetworkUri":"SUBNET",
      "kmsKey": "KMS_KEY",
      "serviceAccount": "SERVICE_ACCOUNT"
    }
  },
  "labels": {
    "LABEL": "LABEL_VALUE"
  },
  "runtimeConfig": {
    "version": "1.2",
    "properties": {
      "PROPERTY": "PROPERTY_VALUE"
    }
  },
  "sparkBatch": {
    "mainClass": "com.google.cloud.dataproc.templates.main.DataProcTemplate",
    "args": [
      "--template","GCSTOGCS",
      "--templateProperty","project.id=PROJECT_ID",
      "--templateProperty","log.level=LOG_LEVEL",
      "--templateProperty","gcs.gcs.input.location=CLOUD_STORAGE_INPUT_PATH",
      "--templateProperty","gcs.gcs.input.format=INPUT_FILE_FORMAT",
      "--templateProperty","gcs.gcs.output.location=CLOUD_STORAGE_OUTPUT_PATH",
      "--templateProperty","gcs.gcs.output.format=OUTPUT_FILE_FORMAT",
      "--templateProperty","gcs.gcs.write.mode=MODE",
      "--templateProperty","gcs.gcs.temp.table=TEMP_TABLE",
      "--templateProperty","gcs.gcs.temp.query=TEMP_QUERY"
    ],
    "jarFileUris":[
      "gs://dataproc-templates-binaries/TEMPLATE_VERSION/java/dataproc-templates.jar",
      "file:///usr/lib/spark/connector/spark-avro.jar"
    ]
  }
}

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://dataproc.googleapis.com/v1/projects/PROJECT_ID/locations/REGION/batches"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://dataproc.googleapis.com/v1/projects/PROJECT_ID/locations/REGION/batches" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_ID/regions/REGION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.dataproc.v1.BatchOperationMetadata",
    "batch": "projects/PROJECT_ID/locations/REGION/batches/BATCH_ID",
    "batchUuid": "de8af8d4-3599-4a7c-915c-798201ed1583",
    "createTime": "2023-02-24T03:31:03.440329Z",
    "operationType": "BATCH",
    "description": "Batch"
  }
}