Cloud Spanner to Cloud Storage template

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

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.
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 gsutil ls gs://dataproc-templates-binaries to list available template versions).
INSTANCE: Required. Spanner instance ID.
DATABASE: Required. Spanner database ID.
TABLE: Required. Spanner input table name or a SQL query on the Spanner input table.
Example (the SQL query should be within parentheses): (select * from TABLE)
CLOUD_STORAGE_OUTPUT_PATH: Required. Cloud Storage path where output will be stored.
Example: gs://example-bucket/example-folder/
FORMAT: Required. Output data format. Options: avro, parquet, csv, or json. Note: If avro, you must add "file:///usr/lib/spark/external/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/external/spark-avro.jar, [ ... other jars]
MODE: Required. Write mode for Cloud Storage output. Options: append, overwrite, ignore, or errorifexists.
NUM_PARTITIONS: Optional. The maximum number of partitions that can be used for parallelism of table reads and writes.
INPUT_PARTITION_COLUMN, LOWERBOUND, UPPERBOUND: Optional. If used, all of the following parameters must be specified:
- INPUT_PARTITION_COLUMN: Spanner input table partition column name.
- LOWERBOUND: Spanner input table partition column lower bound used to determine the partition stride.
- UPPERBOUND: Spanner input table partition column upper bound used to decide the partition stride.
TEMP_VIEW and TEMP_QUERY: Optional. You can use these two optional parameters to apply a Spark SQL transformation while loading data into Cloud Storage. TEMP_VIEW must be the same as table name used in query, and TEMP_QUERY is the query statement.
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-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="1.1" \
    --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=SPANNERTOGCS \
    --templateProperty log.level="LOG_LEVEL" \
    --templateProperty project.id="PROJECT_ID" \
    --templateProperty spanner.gcs.input.spanner.id="INSTANCE" \
    --templateProperty spanner.gcs.input.database.id="DATABASE" \
    --templateProperty spanner.gcs.input.table.id="TABLE" \
    --templateProperty spanner.gcs.output.gcs.path="CLOUD_STORAGE_OUTPUT_PATH" \
    --templateProperty spanner.gcs.output.gcs.saveMode="MODE" \
    --templateProperty spanner.gcs.output.gcs.format="FORMAT" \
    --templateProperty spanner.gcs.input.sql.partitionColumn="INPUT_PARTITION_COLUMN" \
    --templateProperty spanner.gcs.input.sql.lowerBound="LOWERBOUND" \
    --templateProperty spanner.gcs.input.sql.upperBound="UPPERBOUND" \
    --templateProperty spanner.spanner.gcs.input.sql.numPartitions="NUM_PARTITIONS" \
    --templateProperty spanner.gcs.temp.table="TEMP_VIEW" \
    --templateProperty spanner.gcs.temp.query="TEMP_QUERY"

Windows (PowerShell)

gcloud dataproc batches submit spark `
    --class=com.google.cloud.dataproc.templates.main.DataProcTemplate `
    --version="1.1" `
    --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=SPANNERTOGCS `
    --templateProperty log.level="LOG_LEVEL" `
    --templateProperty project.id="PROJECT_ID" `
    --templateProperty spanner.gcs.input.spanner.id="INSTANCE" `
    --templateProperty spanner.gcs.input.database.id="DATABASE" `
    --templateProperty spanner.gcs.input.table.id="TABLE" `
    --templateProperty spanner.gcs.output.gcs.path="CLOUD_STORAGE_OUTPUT_PATH" `
    --templateProperty spanner.gcs.output.gcs.saveMode="MODE" `
    --templateProperty spanner.gcs.output.gcs.format="FORMAT" `
    --templateProperty spanner.gcs.input.sql.partitionColumn="INPUT_PARTITION_COLUMN" `
    --templateProperty spanner.gcs.input.sql.lowerBound="LOWERBOUND" `
    --templateProperty spanner.gcs.input.sql.upperBound="UPPERBOUND" `
    --templateProperty spanner.spanner.gcs.input.sql.numPartitions="NUM_PARTITIONS" `
    --templateProperty spanner.gcs.temp.table="TEMP_VIEW" `
    --templateProperty spanner.gcs.temp.query="TEMP_QUERY"

Windows (cmd.exe)

gcloud dataproc batches submit spark ^
    --class=com.google.cloud.dataproc.templates.main.DataProcTemplate ^
    --version="1.1" ^
    --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=SPANNERTOGCS ^
    --templateProperty log.level="LOG_LEVEL" ^
    --templateProperty project.id="PROJECT_ID" ^
    --templateProperty spanner.gcs.input.spanner.id="INSTANCE" ^
    --templateProperty spanner.gcs.input.database.id="DATABASE" ^
    --templateProperty spanner.gcs.input.table.id="TABLE" ^
    --templateProperty spanner.gcs.output.gcs.path="CLOUD_STORAGE_OUTPUT_PATH" ^
    --templateProperty spanner.gcs.output.gcs.saveMode="MODE" ^
    --templateProperty spanner.gcs.output.gcs.format="FORMAT" ^
    --templateProperty spanner.gcs.input.sql.partitionColumn="INPUT_PARTITION_COLUMN" ^
    --templateProperty spanner.gcs.input.sql.lowerBound="LOWERBOUND" ^
    --templateProperty spanner.gcs.input.sql.upperBound="UPPERBOUND" ^
    --templateProperty spanner.spanner.gcs.input.sql.numPartitions="NUM_PARTITIONS" ^
    --templateProperty spanner.gcs.temp.table="TEMP_VIEW" ^
    --templateProperty spanner.gcs.temp.query="TEMP_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.
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 gsutil ls gs://dataproc-templates-binaries to list available template versions).
INSTANCE: Required. Spanner instance ID.
DATABASE: Required. Spanner database ID.
TABLE: Required. Spanner input table name or a SQL query on the Spanner input table.
Example (the SQL query should be within parentheses): (select * from TABLE)
CLOUD_STORAGE_OUTPUT_PATH: Required. Cloud Storage path where output will be stored.
Example: gs://example-bucket/example-folder/
FORMAT: Required. Output data format. Options: avro, parquet, csv, or json. Note: If avro, you must add "file:///usr/lib/spark/external/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/external/spark-avro.jar, [ ... other jars]
MODE: Required. Write mode for Cloud Storage output. Options: append, overwrite, ignore, or errorifexists.
NUM_PARTITIONS: Optional. The maximum number of partitions that can be used for parallelism of table reads and writes.
INPUT_PARTITION_COLUMN, LOWERBOUND, UPPERBOUND: Optional. If used, all of the following parameters must be specified:
- INPUT_PARTITION_COLUMN: Spanner input table partition column name.
- LOWERBOUND: Spanner input table partition column lower bound used to determine the partition stride.
- UPPERBOUND: Spanner input table partition column upper bound used to decide the partition stride.
TEMP_VIEW and TEMP_QUERY: Optional. You can use these two optional parameters to apply a Spark SQL transformation while loading data into Cloud Storage. TEMP_VIEW must be the same as table name used in query, and TEMP_QUERY is the query statement.
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-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": "1.1",
    "properties": {
      "PROPERTY": "PROPERTY_VALUE"
    }
  },
  "sparkBatch":{
    "mainClass":"com.google.cloud.dataproc.templates.main.DataProcTemplate",
    "args":[
      "--template","SPANNERTOGCS",
      "--templateProperty","log.level=LOG_LEVEL",
      "--templateProperty","project.id=PROJECT_ID",
      "--templateProperty","spanner.gcs.input.spanner.id=INSTANCE",
      "--templateProperty","spanner.gcs.input.database.id=DATABASE",
      "--templateProperty","spanner.gcs.input.table.id=TABLE",
      "--templateProperty","spanner.gcs.output.gcs.path=CLOUD_STORAGE_OUTPUT_PATH",
      "--templateProperty","spanner.gcs.output.gcs.saveMode=MODE",
      "--templateProperty","spanner.gcs.output.gcs.format=FORMAT",
      "--templateProperty","spanner.gcs.input.sql.partitionColumn=INPUT_PARTITION_COLUMN",
      "--templateProperty","spanner.gcs.input.sql.lowerBound=LOWERBOUND",
      "--templateProperty","spanner.gcs.input.sql.upperBound=UPPERBOUND",
      "--templateProperty","spanner.gcs.input.sql.numPartitions=NUM_PARTITIONS",
      "--templateProperty","spanner.gcs.temp.table=TEMP_VIEW",
      "--templateProperty","spanner.gcs.temp.query=TEMP_QUERY"
    ],
    "jarFileUris":[
      "file:///usr/lib/spark/external/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"
  }
}