Cloud Storage to BigQuery template

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

Use the template

Run the template using the gcloud CLI or Dataproc API.

gcloud

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.
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_PATH: Required. Source Cloud Storage path.
Example: gs://dataproc-templates/hive_to_cloud_storage_output"
FORMAT: Required. Input data format. Options: avro, parquet, csv, or json. 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]
DATASET: Required. Destination BigQuery dataset.
TABLE: Required. Destination BigQuery table.
TEMP_BUCKET: Required. Temporary Cloud Storage bucket used for staging data before loading to BigQuery.
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
TEMPVIEW and SQL_QUERY: Optional. You can use these two optional parameters to apply a Spark SQL transformation while loading data into BigQuery. TEMPVIEW is the temporary view name, and SQL_QUERY is the query statement. TEMPVIEW and the table name in SQL_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 key.
Example: projects/PROJECT_ID/regions/REGION/keyRings/KEY_RING_NAME/cryptoKeys/KEY_NAME

Execute the following command:

Linux, macOS, or Cloud Shell

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" \
    --subnet="SUBNET" \
    --kms-key="KMS_KEY" \
    --service-account="SERVICE_ACCOUNT" \
    --properties="PROPERTY=PROPERTY_VALUE" \
    --labels="LABEL=LABEL_VALUE" \
    -- --template=GCSTOBIGQUERY \
    --templateProperty log.level="LOG_LEVEL" \
    --templateProperty project.id="PROJECT_ID" \
    --templateProperty gcs.bigquery.input.location="CLOUD_STORAGE_PATH" \
    --templateProperty gcs.bigquery.input.format="FORMAT" \
    --templateProperty gcs.bigquery.output.dataset="DATASET" \
    --templateProperty gcs.bigquery.output.table="TABLE" \
    --templateProperty gcs.bigquery.temp.bucket.name="TEMP_BUCKET" \
    --templateProperty gcs.bigquery.temp.table="TEMPVIEW" \
    --templateProperty gcs.bigquery.temp.query="SQL_QUERY"

Windows (PowerShell)

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" `
    --subnet="SUBNET" `
    --kms-key="KMS_KEY" `
    --service-account="SERVICE_ACCOUNT" `
    --properties="PROPERTY=PROPERTY_VALUE" `
    --labels="LABEL=LABEL_VALUE" `
    -- --template=GCSTOBIGQUERY `
    --templateProperty log.level="LOG_LEVEL" `
    --templateProperty project.id="PROJECT_ID" `
    --templateProperty gcs.bigquery.input.location="CLOUD_STORAGE_PATH" `
    --templateProperty gcs.bigquery.input.format="FORMAT" `
    --templateProperty gcs.bigquery.output.dataset="DATASET" `
    --templateProperty gcs.bigquery.output.table="TABLE" `
    --templateProperty gcs.bigquery.temp.bucket.name="TEMP_BUCKET" `
    --templateProperty gcs.bigquery.temp.table="TEMPVIEW" `
    --templateProperty gcs.bigquery.temp.query="SQL_QUERY"

Windows (cmd.exe)

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" ^
    --subnet="SUBNET" ^
    --kms-key="KMS_KEY" ^
    --service-account="SERVICE_ACCOUNT" ^
    --properties="PROPERTY=PROPERTY_VALUE" ^
    --labels="LABEL=LABEL_VALUE" ^
    -- --template=GCSTOBIGQUERY ^
    --templateProperty log.level="LOG_LEVEL" ^
    --templateProperty project.id="PROJECT_ID" ^
    --templateProperty gcs.bigquery.input.location="CLOUD_STORAGE_PATH" ^
    --templateProperty gcs.bigquery.input.format="FORMAT" ^
    --templateProperty gcs.bigquery.output.dataset="DATASET" ^
    --templateProperty gcs.bigquery.output.table="TABLE" ^
    --templateProperty gcs.bigquery.temp.bucket.name="TEMP_BUCKET" ^
    --templateProperty gcs.bigquery.temp.table="TEMPVIEW" ^
    --templateProperty gcs.bigquery.temp.query="SQL_QUERY"

REST

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.
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_PATH: Required. Source Cloud Storage path.
Example: gs://dataproc-templates/hive_to_cloud_storage_output"
FORMAT: Required. Input data format. Options: avro, parquet, csv, or json. 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]
DATASET: Required. Destination BigQuery dataset.
TABLE: Required. Destination BigQuery table.
TEMP_BUCKET: Required. Temporary Cloud Storage bucket used for staging data before loading to BigQuery.
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
TEMPVIEW and SQL_QUERY: Optional. You can use these two optional parameters to apply a Spark SQL transformation while loading data into BigQuery. TEMPVIEW is the temporary view name, and SQL_QUERY is the query statement. TEMPVIEW and the table name in SQL_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 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", "GCSTOBIGQUERY",
      "--templateProperty","log.level=LOG_LEVEL",
      "--templateProperty","project.id=PROJECT_ID",
      "--templateProperty","gcs.bigquery.input.location=CLOUD_STORAGE_PATH",
      "--templateProperty","gcs.bigquery.input.format=FORMAT",
      "--templateProperty","gcs.bigquery.output.dataset=DATASET",
      "--templateProperty","gcs.bigquery.output.table=TABLE",
      "--templateProperty","gcs.bigquery.temp.bucket.name=TEMP_BUCKET",
      "--templateProperty","gcs.bigquery.temp.table=TEMPVIEW",
      "--templateProperty","gcs.bigquery.temp.query=SQL_QUERY"
    ],
    "jarFileUris":[
      "file:///usr/lib/spark/connector/spark-avro.jar", "gs://dataproc-templates-binaries/TEMPLATE_VERSION/java/dataproc-templates.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"
  }
}