Run an Apache Spark batch workload

Setup

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project. Learn how to check if billing is enabled on a project.

4. Enable the Dataproc API.

   Enable the API

5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

6. Make sure that billing is enabled for your Google Cloud project. Learn how to check if billing is enabled on a project.

7. Enable the Dataproc API.

   Enable the API

Submit a Spark batch workload

Console

Go to Dataproc Batches in the Google Cloud console. Click CREATE to open the Create batch page.

Select and fill in the following fields on the page to submit a Spark batch workload that computes the approximate value of pi:

• Batch Info
  • Batch ID: Specify an ID for your batch workload. This value must be 4-63 lowercase characters. Valid characters are /[a-z][0-9]-/.
  • Region: Select a region where your workload will run.
• Container
  • Batch type: Spark
  • Runtime version: The default runtime version is selected. You can optionally specify a non-default Dataproc Serverless runtime version.
  • Main class:

    org.apache.spark.examples.SparkPi

  • Jar files:

    file:///usr/lib/spark/examples/jars/spark-examples.jar

  • Arguments: 1000
• Execution Configuration You can specify a service account to use to run your workload. If you do not specify a service account, the workload will run under the Compute Engine default service account.
• Network configuration The VPC subnetwork that executes Serverless Spark workloads must meet the requirements listed in Dataproc Serverless for Spark network configuration. The subnetwork list display subnets in your selected network that are enabled for Private Google Access.
• Properties: Enter the Key (property name) and Value of any supported Spark properties you want your Spark batch workload to use. Note: Unlike Dataproc on Compute Engine cluster properties, Dataproc Serverless for Spark workload properties do not include a "spark:" prefix.
• Other options
  • Configure the batch workload to use an external self-managed Hive Metastore.
  • Use a Spark History Server.

How the workload calculates Pi: The Spark workload estimates a value of Pi using the Monte Carlo method. It generates x,y points on a coordinate plane that models a circle enclosed by a unit square. The input argument (1000) determines the number of x,y pairs to generate; the more pairs generated, the greater the accuracy of the estimation. This estimation leverages Cloud Dataproc worker nodes to parallelize the computation. For more information, see Estimating Pi using the Monte Carlo Method and see JavaSparkPi.java on GitHub.

Click SUBMIT to run the Spark batch workload.

gcloud

To submit a Spark batch workload to compute the approximate value of pi, run the following gcloud CLI gcloud dataproc batches submit spark command locally in a terminal window or in Cloud Shell.

How the workload calculates Pi: The Spark workload estimates a value of Pi using the Monte Carlo method. It generates x,y points on a coordinate plane that models a circle enclosed by a unit square. The input argument (1000) determines the number of x,y pairs to generate; the more pairs generated, the greater the accuracy of the estimation. For more information, see Estimating Pi using the Monte Carlo Method and see JavaSparkPi.java on GitHub.

gcloud dataproc batches submit spark \
    --region=REGION \
    --jars=file:///usr/lib/spark/examples/jars/spark-examples.jar \
    --class=org.apache.spark.examples.SparkPi \
    -- 1000

Notes:

• Subnetwork: The VPC subnetwork that executes Serverless Spark workloads must meet the requirements listed in Dataproc Serverless for Spark network configuration. If the default network's subnet for the region specified in the gcloud dataproc batches submit command is not enabled for Private Google Access, you must do one of the following:
  1. Enable the default network's subnet for the region for Private Google Access, or
  2. Use the --subnet=[SUBNET_URI] flag in the command to specify a subnet that has Private Google Access enabled. You can run the gcloud compute networks describe [NETWORK_NAME] command to list the URIs of subnets in a network.
• --jars: The example jar file is pre-installed, and the 1000 command argument passed to the SparkPi workload specifies 1000 iterations of the pi estimation logic (workload input arguments are included after the "-- ").
• --properties: You can add the --properties flag to enter any supported Spark properties you want your Spark batch workload to use.
• --deps-bucket: You can add this flag to specify a Cloud Storage bucket where Dataproc Serverless for Spark will upload workload dependencies. The gs:// URI prefix of the bucket is not required; you can specify the bucket path/name only, for example, "mybucketname". Exception: If your batch workload references files on your local machine, the --deps-bucket flag is required; Dataproc Serverless for Spark will upload the local file(s) to a /dependencies folder in the bucket before running the batch workload.
• Other options:
  • You can add other optional command flags. For example, the following command configures the batch workload to use an external self-managed Hive Metastore using a standard Spark configuration:

    gcloud dataproc batches submit \
        --properties=spark.sql.catalogImplementation=hive,spark.hive.metastore.uris=METASTORE_URI,spark.hive.metastore.warehouse.dir=WAREHOUSE_DIR> \
        other args ...
               

    See gcloud dataproc batches submit for supported command flags.
  • Use a Persistent History Server.
    1. Create a Persistent History Server (PHS) on a single-node Dataproc cluster. Note: the Cloud Storage bucket-name must exist.

       gcloud dataproc clusters create PHS_CLUSTER_NAME \
           --region=REGION \
           --single-node \
           --enable-component-gateway \
           --properties=spark:spark.history.fs.logDirectory=gs://bucket-name/phs/*/spark-job-history
                    

    2. Submit a batch workload, specifying your running Persistent History Server.

       gcloud dataproc batches submit spark \
           --region=REGION \
           --jars=file:///usr/lib/spark/examples/jars/spark-examples.jar \
           --class=org.apache.spark.examples.SparkPi \
           --history-server-cluster=projects/project-id/regions/region/clusters/PHS-cluster-name
           -- 1000
                     

  • Specify a non-default Dataproc Serverless runtime version.

    gcloud dataproc batches submit spark \
        --region=REGION \
        --jars=file:///usr/lib/spark/examples/jars/spark-examples.jar \
        --class=org.apache.spark.examples.SparkPi \
        --version=VERSION
        -- 1000
                  

API

This section shows how to create a batch workload to compute the approximate value of pi using the Dataproc Serverless for Spark batches.create API.

How the workload calculates Pi: The Spark workload estimates a value of Pi using the Monte Carlo method. It generates x,y points on a coordinate plane that models a circle enclosed by a unit square. The input argument (1000) determines the number of x,y pairs to generate; the more pairs generated, the greater the accuracy of the estimation. For more information, see Estimating Pi using the Monte Carlo Method and see JavaSparkPi.java on GitHub.

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

• project-id: Google Cloud project ID
• region: region
Notes:
• Custom-container-image: Specify the custom container image using the Docker image naming format: {hostname}/{project-id}/{image}:{tag}, for example, "gcr.io/my-project-id/my-image:1.0.1". Note: You must host your custom container on Container Registry.
• Subnetwork: If the default network's subnet for the specified region is not enabled for Private Google Access, you must do one of the following:
  1. Enable the default network's subnet for the region for Private Google Access, or
  2. Use the ExecutionConfig.subnetworkUri field to specify a subnet that has Private Google Access enabled. You can run the gcloud compute networks describe [NETWORK_NAME] command to list the URIs of subnets in a network.
• sparkBatch.jarFileUris: The example jar file is pre-installed in the Spark execution environment. The "1000" sparkBatch.args is passed to the SparkPi workload, and specifies 1000 iterations of the pi estimation logic.
• Spark properties: You can use the RuntimeConfig.properties field to enter any supported Spark properties you want your Spark batch workload to use.
• Other options:
  • Configure the batch workload to use an external self-managed Hive Metastore.
  • Use a Spark History Server.
  • Use the RuntimeConfig.version field as part of the batches.create request to specify a non-default Dataproc Serverless runtime version
  .

HTTP method and URL:

POST https://dataproc.googleapis.com/v1/projects/project-id/locations/region/batches

Request JSON body:

{
  "sparkBatch":{
    "args":[
      "1000"
    ],
    "jarFileUris":[
      "file:///usr/lib/spark/examples/jars/spark-examples.jar"
    ],
    "mainClass":"org.apache.spark.examples.SparkPi"
  }
}

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, 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:

$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/locations/region/batches/batch-id",
  "uuid":",uuid",
  "createTime":"2021-07-22T17:03:46.393957Z",
  "sparkBatch":{
    "mainClass":"org.apache.spark.examples.SparkPi",
    "args":[
      "1000"
    ],
    "jarFileUris":[
      "file:///usr/lib/spark/examples/jars/spark-examples.jar"
    ]
  },
  "runtimeInfo":{
    "outputUri":"gs://dataproc-.../driveroutput"
  },
  "state":"SUCCEEDED",
  "stateTime":"2021-07-22T17:06:30.301789Z",
  "creator":"account-email-address",
  "runtimeConfig":{
    "properties":{
      "spark:spark.executor.instances":"2",
      "spark:spark.driver.cores":"2",
      "spark:spark.executor.cores":"2",
      "spark:spark.app.name":"projects/project-id/locations/region/batches/batch-id"
    }
  },
  "environmentConfig":{
    "peripheralsConfig":{
      "sparkHistoryServerConfig":{
      }
    }
  },
  "operation":"projects/project-id/regions/region/operation-id"
}

Estimate workload costs.

Dataproc Serverless for Spark workloads consume Data Compute Unit (DCU) and shuffle storage resources. See Dataproc Serverless pricing for an example that outputs Dataproc UsageMetrics to estimate workload resource consumption and costs.

What's next

Learn about:

• Dataproc Serverless quotas
• Spark properties