Use the Cloud Storage connector with Apache Spark

This tutorial show you how to run example code that uses the Cloud Storage connector with Apache Spark.

Objectives

Write a simple wordcount Spark job in Java, Scala, or Python, then run the job on a Cloud Dataproc cluster.

Costs

This tutorial uses billable components of Google Cloud Platform, including:

  • Compute Engine
  • Cloud Dataproc
  • Cloud Storage

Use the Pricing Calculator to generate a cost estimate based on your projected usage. New Cloud Platform users might be eligible for a free trial.

Before you begin

Run the steps below to prepare to run the code in this tutorial.

  1. Set up your project. If necessary, set up a project with the Cloud Dataproc, Compute Engine, and Cloud Storage APIs enabled and the Cloud SDK installed on your local machine.

    1. Google アカウントにログインします。

      Google アカウントをまだお持ちでない場合は、新しいアカウントを登録します。

    2. GCP プロジェクトを選択または作成します。

      プロジェクト セレクタのページに移動

    3. Google Cloud Platform プロジェクトに対して課金が有効になっていることを確認します。 詳しくは、課金を有効にする方法をご覧ください。

    4. Cloud Dataproc, Compute Engine, and Cloud Storage API を有効にします。

      APIを有効にする

    5. Cloud SDK をインストールして初期化します。

  2. Create a Cloud Storage bucket. You need a Cloud Storage to hold tutorial data. If you do not have one ready to use, create a new bucket in your project.

    1. GCP Console で、Cloud Storage ブラウザページに移動します。

      Cloud Storage ブラウザページに移動

    2. [バケットを作成] をクリックします。
    3. [バケットを作成] ダイアログ内で、以下の属性を指定します。
    4. [作成] をクリックします。

  3. Set local environment variables. Set environment variables on your local machine. Set your GCP project-id and the name of the Cloud Storage bucket you will use for this tutorial. Also provide the name and zone of an existing or new Cloud Dataproc cluster. You can create a cluster to use in this tutorial in the next step.

    PROJECT=project-id
    
    BUCKET_NAME=bucket-name
    
    CLUSTER=cluster-name
    
    ZONE=cluster-region Example: "us-west1-a"
    

  4. Create a Cloud Dataproc cluster. Run the command, below, to create a single-node Cloud Dataproc cluster in the specified Compute Engine zone.

    gcloud dataproc clusters create $CLUSTER \
        --project=${PROJECT} \
        --zone=${ZONE} \
        --single-node
    

  5. Copy public data to your Cloud Storage bucket. Copy a public data Shakespeare text snippet into the input folder of your Cloud Storage bucket:

    gsutil cp gs://pub/shakespeare/rose.txt \
        gs://${BUCKET_NAME}/input/rose.txt
    

  6. Set up a Java (Apache Maven), Scala (SBT), or Python development environment.

Prepare the Spark wordcount job

Select a tab, below, to follow the steps to prepare a job package or file to submit to your cluster. You can prepare one of the following job types;

Java

  1. Copy pom.xml file to your local machine. The following pom.xml file specifies Scala and Spark library dependencies, which are given a provided scope to indicate that the Cloud Dataproc cluster will provide these libraries at runtime. The pom.xml file does not specify a Cloud Storage dependency because the connector implements the standard HDFS interface. When a Spark job accesses Cloud Storage cluster files (files with URIs that start with gs://), the system automatically uses the Cloud Storage connector to access the files in Cloud Storage
    <?xml version="1.0" encoding="UTF-8"?>
    <project xmlns="http://maven.apache.org/POM/4.0.0"
        xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
        xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
      <modelVersion>4.0.0</modelVersion>
    
      <groupId>dataproc.codelab</groupId>
      <artifactId>word-count</artifactId>
      <version>1.0</version>
    
      <properties>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
      </properties>
    
      <dependencies>
        <dependency>
          <groupId>org.scala-lang</groupId>
          <artifactId>scala-library</artifactId>
          <version>Scala version, for example, 2.11.8</version>
          <scope>provided</scope>
        </dependency>
        <dependency>
          <groupId>org.apache.spark</groupId>
          <artifactId>spark-core_Scala major.minor.version, for example, 2.11</artifactId>
          <version>Spark version, for example, 2.3.1</version>
          <scope>provided</scope>
        </dependency>
      </dependencies>
    </project>
    
  2. Copy word-count.java to your local machine. This is a simple Spark job in Java that reads text files from Cloud Storage, performs a word count, then writes the text file results to Cloud Storage.
    package dataproc.codelab;
    
    import java.util.Arrays;
    import org.apache.spark.SparkConf;
    import org.apache.spark.api.java.JavaPairRDD;
    import org.apache.spark.api.java.JavaRDD;
    import org.apache.spark.api.java.JavaSparkContext;
    import scala.Tuple2;
    
    public class WordCount {
      public static void main(String[] args) {
        if (args.length != 2) {
          throw new IllegalArgumentException("Exactly 2 arguments are required: <inputUri> <outputUri>");
        }
        String inputPath = args[0];
        String outputPath = args[1];
        JavaSparkContext sparkContext = new JavaSparkContext(new SparkConf().setAppName("Word Count"));
        JavaRDD<String> lines = sparkContext.textFile(inputPath);
        JavaRDD<String> words = lines.flatMap(
            (String line) -> Arrays.asList(line.split(" ")).iterator()
        );
        JavaPairRDD<String, Integer> wordCounts = words.mapToPair(
            (String word) -> new Tuple2<>(word, 1)
        ).reduceByKey(
            (Integer count1, Integer count2) -> count1 + count2
        );
        wordCounts.saveAsTextFile(outputPath);
      }
    }
    
  3. Build the package.
    mvn clean package
    
    If the build is successful, a target/spark-with-gcs-1.0-SNAPSHOT.jar is created.
  4. Stage the package to Cloud Storage.
    gsutil cp target/word-count-1.0.jar \
        gs://${BUCKET_NAME}/java/word-count-1.0.jar
    

Scala

  1. Copy build.sbt file to your local machine. The following build.sbt file specifies Scala and Spark library dependencies, which are given a provided scope to indicate that the Cloud Dataproc cluster will provide these libraries at runtime. The build.sbt file does not specify a Cloud Storage dependency because the connector implements the standard HDFS interface. When a Spark job accesses Cloud Storage cluster files (files with URIs that start with gs://), the system automatically uses the Cloud Storage connector to access the files in Cloud Storage
    scalaVersion := "Scala version, for example, 2.11.8"
    
    name := "word-count"
    organization := "dataproc.codelab"
    version := "1.0"
    
    libraryDependencies ++= Seq(
      "org.scala-lang" % "scala-library" % scalaVersion.value % "provided",
      "org.apache.spark" %% "spark-core" % "Spark version, for example, 2.3.1" % "provided"
    )
    
    
  2. Copy word-count.scala to your local machine. This is a simple Spark job in Java that reads text files from Cloud Storage, performs a word count, then writes the text file results to Cloud Storage.
    package dataproc.codelab
    
    import org.apache.spark.SparkContext
    import org.apache.spark.SparkConf
    
    object WordCount {
      def main(args: Array[String]) {
        if (args.length != 2) {
          throw new IllegalArgumentException(
              "Exactly 2 arguments are required: <inputPath> <outputPath>")
        }
    
        val inputPath = args(0)
        val outputPath = args(1)
    
        val sc = new SparkContext(new SparkConf().setAppName("Word Count"))
        val lines = sc.textFile(inputPath)
        val words = lines.flatMap(line => line.split(" "))
        val wordCounts = words.map(word => (word, 1)).reduceByKey(_ + _)
        wordCounts.saveAsTextFile(outputPath)
      }
    }
    
    
  3. Build the package.
    sbt clean package
    
    If the build is successful, a target/scala-2.11/word-count_2.11-1.0.jar is created.
  4. Stage the package to Cloud Storage.
    gsutil cp target/scala-2.11/word-count_2.11-1.0.jar \
        gs://${BUCKET_NAME}/scala/word-count_2.11-1.0.jar
    

Python

  1. Copy word-count.py to your local machine. This is a simple Spark job in Python using PySpark that reads text files from Cloud Storage, performs a word count, then writes the text file results to Cloud Storage.
    #!/usr/bin/env python
    
    import pyspark
    import sys
    
    if len(sys.argv) != 3:
      raise Exception("Exactly 2 arguments are required: <inputUri> <outputUri>")
    
    inputUri=sys.argv[1]
    outputUri=sys.argv[2]
    
    sc = pyspark.SparkContext()
    lines = sc.textFile(sys.argv[1])
    words = lines.flatMap(lambda line: line.split())
    wordCounts = words.map(lambda word: (word, 1)).reduceByKey(lambda count1, count2: count1 + count2)
    wordCounts.saveAsTextFile(sys.argv[2])
    

Submit the job

Run the following gcloud command to submit the wordcount job to your Cloud Dataproc cluster.

Java

gcloud dataproc jobs submit spark \
    --cluster=${CLUSTER} \
    --class dataproc.codelab.WordCount \
    --jars gs://${BUCKET_NAME}/java/word-count-1.0.jar \
    -- gs://${BUCKET_NAME}/input/ gs://${BUCKET_NAME}/output/

Scala

gcloud dataproc jobs submit spark \
    --cluster=${CLUSTER} \
    --class dataproc.codelab.WordCount \
    --jars gs://${BUCKET_NAME}/scala/word-count_2.11-1.0.jar \
    -- gs://${BUCKET_NAME}/input/ gs://${BUCKET_NAME}/output/

Python

gcloud dataproc jobs submit pyspark word-count.py \
    --cluster=${CLUSTER} \
    -- gs://${BUCKET_NAME}/input/ gs://${BUCKET_NAME}/output/

View the output

After the job finishes, run the following Cloud SDK gsutil command to view the wordcount output.

gsutil cat gs://${BUCKET_NAME}/output/*

The wordcount output should be similar to the following:

(a,2)
(call,1)
(What's,1)
(sweet.,1)
(we,1)
(as,1)
(name?,1)
(any,1)
(other,1)
(rose,1)
(smell,1)
(name,1)
(would,1)
(in,1)
(which,1)
(That,1)
(By,1)

Cleaning up

After you've finished the Use Cloud Dataproc tutorial, you can clean up the resources that you created on GCP so they won't take up quota and you won't be billed for them in the future. The following sections describe how to delete or turn off these resources.

Deleting the project

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To delete the project:

  1. GCP Console で [プロジェクト] ページに移動します。

    プロジェクト ページに移動

  2. プロジェクト リストで、削除するプロジェクトを選択し、[削除] をクリックします。
  3. ダイアログでプロジェクト ID を入力し、[シャットダウン] をクリックしてプロジェクトを削除します。

Deleting the Cloud Dataproc cluster

Instead of deleting your project, you may wish to only delete your cluster within the project.

Deleting the Cloud Storage bucket

GCP Console

  1. GCP Console で、Cloud Storage ブラウザページに移動します。

    Cloud Storage ブラウザページに移動

  2. 削除するバケットのチェックボックスをクリックします。
  3. バケット削除するには、[削除]()をクリックします。

コマンドライン

    バケットを削除します。
    gsutil rb [BUCKET_NAME]
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