"Managed Service for Apache Spark" is the new name for the product formerly known as "Dataproc on Compute Engine" (cluster deployment) and "Google Cloud Serverless for Apache Spark" (serverless deployment).

Dataproc client libraries

This page shows how to get started with the Cloud Client Libraries for the Dataproc API. Client libraries make it easier to access Google Cloud APIs from a supported language. Although you can use Google Cloud APIs directly by making raw requests to the server, client libraries provide simplifications that significantly reduce the amount of code you need to write.

However, we recommend using the older Google API Client Libraries if running on App Engine standard environment. Read more about the Cloud Client Libraries and the older Google API Client Libraries in Client libraries explained.

Managed Service for Apache Spark Cloud Client Libraries may be in alpha or beta stage. See the library reference for details.

Install the client library

C++

See Setting up a C++ development environment for details about this client library's requirements and install dependencies.

C#

Also see Google Cloud Libraries for .NET.

For more information, see Setting Up a C# Development Environment.

Go

go get cloud.google.com/go/dataproc/apiv1

For more information, see Install the Cloud Client Libraries for Go.

For more information, see Setting Up a Go Development Environment.

Java

If you are using Maven, add this to your pom.xml file:

<dependency>
<groupId>com.google.cloud</groupId>
<artifactId>google-cloud-dataproc</artifactId>
<version>insertdataproc-library-versionhere</version>
</dependency>

If you are using Gradle, add this to your dependencies:

compilegroup:'com.google.cloud',name:'google-cloud-dataproc',version:'insertdataproc-library-versionhere'

For more information, see Setting Up a Java Development Environment.

Node.js

npm install --save @google-cloud/dataproc

For more information, see Setting Up a Node.js Development Environment.

PHP

composer require google/cloud

For more information, see Using PHP on Google Cloud.

Python

pip install --upgrade google-cloud-dataproc

For more information, see Setting Up a Python Development Environment.

Ruby

gem install google-cloud-dataproc

For more information, see Setting Up a Ruby Development Environment.

Set up authentication

To authenticate calls to Google Cloud APIs, client libraries support Application Default Credentials (ADC); the libraries look for credentials in a set of defined locations and use those credentials to authenticate requests to the API. With ADC, you can make credentials available to your application in a variety of environments, such as local development or production, without needing to modify your application code.

For production environments, the way you set up ADC depends on the service and context. For more information, see Set up Application Default Credentials.

For a local development environment, you can set up ADC with the credentials that are associated with your Google Account:

  1. Install the Google Cloud CLI. After installation, initialize the Google Cloud CLI by running the following command: 

    gcloudinit

    If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

  2. If you're using a local shell, then create local authentication credentials for your user account: 

    gcloudauthapplication-defaultlogin

    You don't need to do this if you're using Cloud Shell.

    If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity.

    A sign-in screen appears. After you sign in, your credentials are stored in the local credential file used by ADC.

Use the client library

The following example shows how to use the client library.

C++

+ 

#include"google/cloud/dataproc/v1/cluster_controller_client.h"
#include"google/cloud/common_options.h"
#include <iostream>
intmain(intargc,char*argv[])try{
if(argc!=3){
std::cerr << "Usage: " << argv[0] << " project-id region\n";
return1;
}
std::stringconstproject_id=argv[1];
std::stringconstregion=argv[2];
namespacedataproc=::google::cloud::dataproc_v1;
autoclient=dataproc::ClusterControllerClient(
dataproc::MakeClusterControllerConnection(region=="global"?""
:region));
for(autoc:client.ListClusters(project_id,region)){
if(!c)throwstd::move(c).status();
std::cout << c->cluster_name() << "\n";
}
return0;
}catch(google::cloud::Statusconst&status){
std::cerr << "google::cloud::Status thrown: " << status << "\n";
return1;
}

Go

import(
"context"
"fmt"
"io"
dataproc"cloud.google.com/go/dataproc/apiv1"
"cloud.google.com/go/dataproc/apiv1/dataprocpb"
"google.golang.org/api/option"
)
funccreateCluster(wio.Writer,projectID,region,clusterNamestring)error{
// projectID := "your-project-id"
// region := "us-central1"
// clusterName := "your-cluster"
ctx:=context.Background()
// Create the cluster client.
endpoint:=region+"-dataproc.googleapis.com:443"
clusterClient,err:=dataproc.NewClusterControllerClient(ctx,option.WithEndpoint(endpoint))
iferr!=nil{
returnfmt.Errorf("dataproc.NewClusterControllerClient: %w",err)
}
deferclusterClient.Close()
// Create the cluster config.
req:=&dataprocpb.CreateClusterRequest{
ProjectId:projectID,
Region:region,
Cluster:&dataprocpb.Cluster{
ProjectId:projectID,
ClusterName:clusterName,
Config:&dataprocpb.ClusterConfig{
MasterConfig:&dataprocpb.InstanceGroupConfig{
NumInstances:1,
MachineTypeUri:"n1-standard-2",
},
WorkerConfig:&dataprocpb.InstanceGroupConfig{
NumInstances:2,
MachineTypeUri:"n1-standard-2",
},
},
},
}
// Create the cluster.
op,err:=clusterClient.CreateCluster(ctx,req)
iferr!=nil{
returnfmt.Errorf("CreateCluster: %w",err)
}
resp,err:=op.Wait(ctx)
iferr!=nil{
returnfmt.Errorf("CreateCluster.Wait: %w",err)
}
// Output a success message.
fmt.Fprintf(w,"Cluster created successfully: %s",resp.ClusterName)
returnnil
}

Java

importcom.google.api.gax.longrunning.OperationFuture ;
importcom.google.cloud.dataproc.v1.Cluster ;
importcom.google.cloud.dataproc.v1.ClusterConfig ;
importcom.google.cloud.dataproc.v1.ClusterControllerClient ;
importcom.google.cloud.dataproc.v1.ClusterControllerSettings ;
importcom.google.cloud.dataproc.v1.ClusterOperationMetadata ;
importcom.google.cloud.dataproc.v1.InstanceGroupConfig ;
importjava.io.IOException;
importjava.util.concurrent.ExecutionException;
publicclass CreateCluster{
publicstaticvoidcreateCluster()throwsIOException,InterruptedException{
// TODO(developer): Replace these variables before running the sample.
StringprojectId="your-project-id";
Stringregion="your-project-region";
StringclusterName="your-cluster-name";
createCluster(projectId,region,clusterName);
}
publicstaticvoidcreateCluster(StringprojectId,Stringregion,StringclusterName)
throwsIOException,InterruptedException{
StringmyEndpoint=String.format("%s-dataproc.googleapis.com:443",region);
// Configure the settings for the cluster controller client.
ClusterControllerSettings clusterControllerSettings=
ClusterControllerSettings .newBuilder().setEndpoint(myEndpoint).build();
// Create a cluster controller client with the configured settings. The client only needs to be
// created once and can be reused for multiple requests. Using a try-with-resources
// closes the client, but this can also be done manually with the .close() method.
try(ClusterControllerClient clusterControllerClient=
ClusterControllerClient .create(clusterControllerSettings)){
// Configure the settings for our cluster.
InstanceGroupConfig masterConfig=
InstanceGroupConfig .newBuilder()
.setMachineTypeUri ("n1-standard-2")
.setNumInstances (1)
.build();
InstanceGroupConfig workerConfig=
InstanceGroupConfig .newBuilder()
.setMachineTypeUri ("n1-standard-2")
.setNumInstances (2)
.build();
ClusterConfig clusterConfig=
ClusterConfig .newBuilder()
.setMasterConfig (masterConfig)
.setWorkerConfig(workerConfig)
.build();
// Create the cluster object with the desired cluster config.
Cluster cluster=
Cluster .newBuilder().setClusterName(clusterName).setConfig(clusterConfig).build();
// Create the Cloud Dataproc cluster.
OperationFuture<Cluster,ClusterOperationMetadata>createClusterAsyncRequest=
clusterControllerClient.createClusterAsync (projectId,region,cluster);
Cluster response=createClusterAsyncRequest.get ();
// Print out a success message.
System.out.printf("Cluster created successfully: %s",response.getClusterName ());
}catch(ExecutionExceptione){
System.err.println(String.format("Error executing createCluster: %s ",e.getMessage()));
}
}
}

Node.js

. 
constdataproc=require('@google-cloud/dataproc');
// TODO(developer): Uncomment and set the following variables
// projectId = 'YOUR_PROJECT_ID'
// region = 'YOUR_CLUSTER_REGION'
// clusterName = 'YOUR_CLUSTER_NAME'
// Create a client with the endpoint set to the desired cluster region
constclient=newdataproc.v1.ClusterControllerClient ({
apiEndpoint:`${region}-dataproc.googleapis.com`,
projectId:projectId,
});
asyncfunctioncreateCluster(){
// Create the cluster config
constrequest={
projectId:projectId,
region:region,
cluster:{
clusterName:clusterName,
config:{
masterConfig:{
numInstances:1,
machineTypeUri:'n1-standard-2',
},
workerConfig:{
numInstances:2,
machineTypeUri:'n1-standard-2',
},
},
},
};
// Create the cluster
const[operation]=awaitclient.createCluster(request);
const[response]=awaitoperation.promise();
// Output a success message
console.log(`Cluster created successfully: ${response.clusterName}`);

Python

fromgoogle.cloudimport dataproc_v1 as dataproc
defcreate_cluster(project_id, region, cluster_name):
"""This sample walks a user through creating a Cloud Dataproc cluster
 using the Python client library.
 Args:
 project_id (string): Project to use for creating resources.
 region (string): Region where the resources should live.
 cluster_name (string): Name to use for creating a cluster.
 """
 # Create a client with the endpoint set to the desired cluster region.
 cluster_client = dataproc.ClusterControllerClient(
 client_options={"api_endpoint": f"{region}-dataproc.googleapis.com:443"}
 )
 # Create the cluster config.
 cluster = {
 "project_id": project_id,
 "cluster_name": cluster_name,
 "config": {
 "master_config": {"num_instances": 1, "machine_type_uri": "n1-standard-2"},
 "worker_config": {"num_instances": 2, "machine_type_uri": "n1-standard-2"},
 },
 }
 # Create the cluster.
 operation = cluster_client.create_cluster(
 request={"project_id": project_id, "region": region, "cluster": cluster}
 )
 result = operation.result()
 # Output a success message.
 print(f"Cluster created successfully: {result.cluster_name}")

Additional resources

C++

The following list contains links to more resources related to the client library for C++:

C#

The following list contains links to more resources related to the client library for C#:

Go

The following list contains links to more resources related to the client library for Go:

Java

The following list contains links to more resources related to the client library for Java:

Node.js

The following list contains links to more resources related to the client library for Node.js:

PHP

The following list contains links to more resources related to the client library for PHP:

Python

The following list contains links to more resources related to the client library for Python:

Ruby

The following list contains links to more resources related to the client library for Ruby:

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2026年08月26日 UTC.