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An idiomatic C++ client library for the Cloud Dataproc API, a managed Apache Spark and Apache Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. This library allows you to manage Cloud Dataproc resources, but it does not provide APIs to run C++ applications in Cloud Dataproc.
While this library is GA, please note Google Cloud C++ client libraries do not follow Semantic Versioning.
QuickstartThe following shows the code that you'll run in the google/cloud/dataproc/quickstart/
directory, which should give you a taste of the Cloud Dataproc API C++ client library API.
#include "google/cloud/dataproc/v1/cluster_controller_client.h"
#include "google/cloud/common_options.h"
#include <iostream>
int main(int argc, char* argv[]) try {
if (argc != 3) {
std::cerr << "Usage: " << argv[0] << " project-id region\n";
return 1;
}
std::string const project_id = argv[1];
std::string const region = argv[2];
namespace dataproc = ::google::cloud::dataproc_v1;
auto client = dataproc::ClusterControllerClient(
dataproc::MakeClusterControllerConnection(region == "global" ? ""
: region));
for (auto c : client.ListClusters(project_id, region)) {
if (!c) throw std::move(c).status();
std::cout << c->cluster_name() << "\n";
}
return 0;
} catch (google::cloud::Status const& status) {
std::cerr << "google::cloud::Status thrown: " << status << "\n";
return 1;
}
Main classes
This library offers multiple *Client
classes, which are listed below. Each one of these classes exposes all the RPCs for a gRPC service
as member functions of the class. This library groups multiple gRPC services because they are part of the same product or are often used together. A typical example may be the administrative and data plane operations for a single product.
The library also has other classes that provide helpers, configuration parameters, and infrastructure to mock the *Client
classes when testing your application.
dataproc_v1::AutoscalingPolicyServiceClient
dataproc_v1::BatchControllerClient
dataproc_v1::ClusterControllerClient
dataproc_v1::JobControllerClient
dataproc_v1::WorkflowTemplateServiceClient
The library automatically retries requests that fail with transient errors, and uses exponential backoff to backoff between retries. Application developers can override the default policies.
More InformationExcept 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 2025-08-14 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-14 UTC."],[[["This page details the C++ client library for the Cloud Dataproc API, a service for managing Apache Spark and Hadoop clusters for data processing, querying, and machine learning tasks."],["The library supports the management of Cloud Dataproc resources through multiple `*Client` classes, like `AutoscalingPolicyServiceClient`, `BatchControllerClient`, `ClusterControllerClient`, `JobControllerClient`, and `WorkflowTemplateServiceClient`."],["The C++ client library does not follow Semantic Versioning, even though it is considered generally available (GA)."],["The library includes features like automatic retries for transient errors with exponential backoff and allows for custom configuration of endpoints and authentication credentials."],["The latest available release is `2.37.0-rc` and lists a history of version releases going back to `2.11.0` in the documents."]]],[]]
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