Introduction

I wrote the following article about Cloud Dataflow before. https://qiita.com/yuyu_hf/items/e8e738f542e1f30d7be4

In the article, I wasn't used to Apache Beam at the time, so I used Java instead of Scala, but when considering whether to use Java, Scala, or Scala + Scio, there is something that can be compared at a glance. I thought it would be good and tried each with simple processing

It's a very simple process that extracts a specific column in BigQuery and spits it out to GCS.

Template creation (Java edition)

Project creation

mvn archetype:generate \
    -DarchetypeGroupId=org.apache.beam \
    -DarchetypeArtifactId=beam-sdks-java-maven-archetypes-examples \
    -DgroupId=com.example \
    -DartifactId=bigquery-to-gcs \
    -Dversion="0.1" \
    -DinteractiveMode=false \
    -Dpackage=com.example

Library management

pom.xml

`pom.xml`


<?xml version="1.0" encoding="UTF-8"?>
<!--
    Licensed to the Apache Software Foundation (ASF) under one or more
    contributor license agreements.  See the NOTICE file distributed with
    this work for additional information regarding copyright ownership.
    The ASF licenses this file to You under the Apache License, Version 2.0
    (the "License"); you may not use this file except in compliance with
    the License.  You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
-->
<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>com.example</groupId>
  <artifactId>dataflow-bigquerytogcs</artifactId>
  <version>0.1</version>

  <packaging>jar</packaging>

  <properties>
    <beam.version>2.16.0</beam.version>

    <bigquery.version>v2-rev20181104-1.27.0</bigquery.version>
    <google-clients.version>1.27.0</google-clients.version>
    <hamcrest.version>2.1</hamcrest.version>
    <jackson.version>2.9.10</jackson.version>
    <joda.version>2.10.3</joda.version>
    <junit.version>4.13-beta-3</junit.version>
    <maven-compiler-plugin.version>3.7.0</maven-compiler-plugin.version>
    <maven-exec-plugin.version>1.6.0</maven-exec-plugin.version>
    <maven-jar-plugin.version>3.0.2</maven-jar-plugin.version>
    <maven-shade-plugin.version>3.1.0</maven-shade-plugin.version>
    <mockito.version>3.0.0</mockito.version>
    <pubsub.version>v1-rev20181105-1.27.0</pubsub.version>
    <slf4j.version>1.7.25</slf4j.version>
    <spark.version>2.4.4</spark.version>
    <hadoop.version>2.7.3</hadoop.version>
    <maven-surefire-plugin.version>2.21.0</maven-surefire-plugin.version>
    <nemo.version>0.1</nemo.version>
    <flink.artifact.name>beam-runners-flink-1.8</flink.artifact.name>
  </properties>

  <repositories>
    <repository>
      <id>apache.snapshots</id>
      <name>Apache Development Snapshot Repository</name>
      <url>https://repository.apache.org/content/repositories/snapshots/</url>
      <releases>
        <enabled>false</enabled>
      </releases>
      <snapshots>
        <enabled>true</enabled>
      </snapshots>
    </repository>
  </repositories>

  <build>
    <plugins>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-compiler-plugin</artifactId>
        <version>${maven-compiler-plugin.version}</version>
        <configuration>
          <source>1.8</source>
          <target>1.8</target>
        </configuration>
      </plugin>

      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-surefire-plugin</artifactId>
        <version>${maven-surefire-plugin.version}</version>
        <configuration>
          <parallel>all</parallel>
          <threadCount>4</threadCount>
          <redirectTestOutputToFile>true</redirectTestOutputToFile>
        </configuration>
        <dependencies>
          <dependency>
            <groupId>org.apache.maven.surefire</groupId>
            <artifactId>surefire-junit47</artifactId>
            <version>${maven-surefire-plugin.version}</version>
          </dependency>
        </dependencies>
      </plugin>

      <!-- Ensure that the Maven jar plugin runs before the Maven
        shade plugin by listing the plugin higher within the file. -->
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-jar-plugin</artifactId>
        <version>${maven-jar-plugin.version}</version>
      </plugin>

      <!--
        Configures `mvn package` to produce a bundled jar ("fat jar") for runners
        that require this for job submission to a cluster.
      -->
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-shade-plugin</artifactId>
        <version>${maven-shade-plugin.version}</version>
        <executions>
          <execution>
            <phase>package</phase>
            <goals>
              <goal>shade</goal>
            </goals>
            <configuration>
              <finalName>${project.artifactId}-bundled-${project.version}</finalName>
              <filters>
                <filter>
                  <artifact>*:*</artifact>
                  <excludes>
                    <exclude>META-INF/LICENSE</exclude>
                    <exclude>META-INF/*.SF</exclude>
                    <exclude>META-INF/*.DSA</exclude>
                    <exclude>META-INF/*.RSA</exclude>
                  </excludes>
                </filter>
              </filters>
              <transformers>
                <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
              </transformers>
            </configuration>
          </execution>
        </executions>
      </plugin>
    </plugins>

    <pluginManagement>
      <plugins>
        <plugin>
          <groupId>org.codehaus.mojo</groupId>
          <artifactId>exec-maven-plugin</artifactId>
          <version>${maven-exec-plugin.version}</version>
          <configuration>
            <cleanupDaemonThreads>false</cleanupDaemonThreads>
          </configuration>
        </plugin>
      </plugins>
    </pluginManagement>
  </build>

  <profiles>
    <profile>
      <id>direct-runner</id>
      <activation>
        <activeByDefault>true</activeByDefault>
      </activation>
      <!-- Makes the DirectRunner available when running a pipeline. -->
      <dependencies>
        <dependency>
          <groupId>org.apache.beam</groupId>
          <artifactId>beam-runners-direct-java</artifactId>
          <version>${beam.version}</version>
          <scope>runtime</scope>
        </dependency>
      </dependencies>
    </profile>

   <profile>
      <id>portable-runner</id>
      <activation>
        <activeByDefault>true</activeByDefault>
      </activation>
      <!-- Makes the PortableRunner available when running a pipeline. -->
      <dependencies>
        <dependency>
          <groupId>org.apache.beam</groupId>
          <artifactId>beam-runners-reference-java</artifactId>
          <version>${beam.version}</version>
          <scope>runtime</scope>
        </dependency>
      </dependencies>
    </profile>

    <profile>
      <id>apex-runner</id>
      <!-- Makes the ApexRunner available when running a pipeline. -->
      <dependencies>
        <dependency>
          <groupId>org.apache.beam</groupId>
          <artifactId>beam-runners-apex</artifactId>
          <version>${beam.version}</version>
          <scope>runtime</scope>
        </dependency>
        <!--
          Apex depends on httpclient version 4.3.6, project has a transitive dependency to httpclient 4.0.1 from
          google-http-client. Apex dependency version being specified explicitly so that it gets picked up. This
          can be removed when the project no longer has a dependency on a different httpclient version.
        -->
        <dependency>
          <groupId>org.apache.httpcomponents</groupId>
          <artifactId>httpclient</artifactId>
          <version>4.3.6</version>
          <scope>runtime</scope>
          <exclusions>
            <exclusion>
              <groupId>commons-codec</groupId>
              <artifactId>commons-codec</artifactId>
            </exclusion>
          </exclusions>
        </dependency>
        <!--
          Apex 3.6 is built against YARN 2.6. Version in the fat jar has to match
          what's on the cluster, hence we need to repeat the Apex Hadoop dependencies here.
        -->
        <dependency>
          <groupId>org.apache.hadoop</groupId>
          <artifactId>hadoop-yarn-client</artifactId>
          <version>${hadoop.version}</version>
          <scope>runtime</scope>
        </dependency>
        <dependency>
          <groupId>org.apache.hadoop</groupId>
          <artifactId>hadoop-common</artifactId>
          <version>${hadoop.version}</version>
          <scope>runtime</scope>
        </dependency>
      </dependencies>
    </profile>

    <profile>
      <id>dataflow-runner</id>
      <!-- Makes the DataflowRunner available when running a pipeline. -->
      <dependencies>
        <dependency>
          <groupId>org.apache.beam</groupId>
          <artifactId>beam-runners-google-cloud-dataflow-java</artifactId>
          <version>${beam.version}</version>
          <scope>runtime</scope>
        </dependency>
      </dependencies>
    </profile>

    <profile>
      <id>flink-runner</id>
      <!-- Makes the FlinkRunner available when running a pipeline. -->
      <dependencies>
        <dependency>
          <groupId>org.apache.beam</groupId>
          <!-- Please see the Flink Runner page for an up-to-date list
               of supported Flink versions and their artifact names:
               https://beam.apache.org/documentation/runners/flink/ -->
          <artifactId>${flink.artifact.name}</artifactId>
          <version>${beam.version}</version>
          <scope>runtime</scope>
        </dependency>
      </dependencies>
    </profile>

    <profile>
      <id>spark-runner</id>
      <!-- Makes the SparkRunner available when running a pipeline. Additionally,
           overrides some Spark dependencies to Beam-compatible versions. -->
      <properties>
        <netty.version>4.1.17.Final</netty.version>
      </properties>
      <dependencies>
        <dependency>
          <groupId>org.apache.beam</groupId>
          <artifactId>beam-runners-spark</artifactId>
          <version>${beam.version}</version>
          <scope>runtime</scope>
        </dependency>
        <dependency>
          <groupId>org.apache.beam</groupId>
          <artifactId>beam-sdks-java-io-hadoop-file-system</artifactId>
          <version>${beam.version}</version>
          <scope>runtime</scope>
        </dependency>
        <dependency>
          <groupId>org.apache.spark</groupId>
          <artifactId>spark-streaming_2.11</artifactId>
          <version>${spark.version}</version>
          <scope>runtime</scope>
          <exclusions>
            <exclusion>
              <groupId>org.slf4j</groupId>
              <artifactId>jul-to-slf4j</artifactId>
            </exclusion>
          </exclusions>
        </dependency>
        <dependency>
          <groupId>com.fasterxml.jackson.module</groupId>
          <artifactId>jackson-module-scala_2.11</artifactId>
          <version>${jackson.version}</version>
          <scope>runtime</scope>
        </dependency>
        <!-- [BEAM-3519] GCP IO exposes netty on its API surface, causing conflicts with runners -->
        <dependency>
          <groupId>org.apache.beam</groupId>
          <artifactId>beam-sdks-java-io-google-cloud-platform</artifactId>
          <version>${beam.version}</version>
          <exclusions>
            <exclusion>
              <groupId>io.grpc</groupId>
              <artifactId>grpc-netty</artifactId>
            </exclusion>
            <exclusion>
              <groupId>io.netty</groupId>
              <artifactId>netty-handler</artifactId>
            </exclusion>
          </exclusions>
        </dependency>
      </dependencies>
    </profile>
    <profile>
      <id>gearpump-runner</id>
      <dependencies>
        <dependency>
          <groupId>org.apache.beam</groupId>
          <artifactId>beam-runners-gearpump</artifactId>
          <version>${beam.version}</version>
          <scope>runtime</scope>
        </dependency>
      </dependencies>
    </profile>
    <profile>
      <id>samza-runner</id>
      <dependencies>
        <dependency>
          <groupId>org.apache.beam</groupId>
          <artifactId>beam-runners-samza</artifactId>
          <version>${beam.version}</version>
          <scope>runtime</scope>
        </dependency>
      </dependencies>
    </profile>
    <profile>
      <id>nemo-runner</id>
      <dependencies>
        <dependency>
          <groupId>org.apache.nemo</groupId>
          <artifactId>nemo-compiler-frontend-beam</artifactId>
          <version>${nemo.version}</version>
        </dependency>
        <dependency>
          <groupId>org.apache.hadoop</groupId>
          <artifactId>hadoop-common</artifactId>
          <version>${hadoop.version}</version>
          <exclusions>
            <exclusion>
              <groupId>org.slf4j</groupId>
              <artifactId>slf4j-api</artifactId>
            </exclusion>
            <exclusion>
              <groupId>org.slf4j</groupId>
              <artifactId>slf4j-log4j12</artifactId>
            </exclusion>
          </exclusions>
        </dependency>
      </dependencies>
    </profile>

    <profile>
      <id>jet-runner</id>
      <dependencies>
        <dependency>
          <groupId>org.apache.beam</groupId>
          <artifactId>beam-runners-jet-experimental</artifactId>
          <version>${beam.version}</version>
          <scope>runtime</scope>
        </dependency>
      </dependencies>
    </profile>

  </profiles>

  <dependencies>
    <!-- Adds a dependency on the Beam SDK. -->
    <dependency>
      <groupId>org.apache.beam</groupId>
      <artifactId>beam-sdks-java-core</artifactId>
      <version>${beam.version}</version>
    </dependency>

    <!-- Adds a dependency on the Beam Google Cloud Platform IO module. -->
    <dependency>
      <groupId>org.apache.beam</groupId>
      <artifactId>beam-sdks-java-io-google-cloud-platform</artifactId>
      <version>${beam.version}</version>
    </dependency>

    <!-- Dependencies below this line are specific dependencies needed by the examples code. -->
    <dependency>
      <groupId>com.google.api-client</groupId>
      <artifactId>google-api-client</artifactId>
      <version>${google-clients.version}</version>
      <exclusions>
        <!-- Exclude an old version of guava that is being pulled
             in by a transitive dependency of google-api-client -->
        <exclusion>
          <groupId>com.google.guava</groupId>
          <artifactId>guava-jdk5</artifactId>
        </exclusion>
      </exclusions>
    </dependency>

    <dependency>
      <groupId>com.google.apis</groupId>
      <artifactId>google-api-services-bigquery</artifactId>
      <version>${bigquery.version}</version>
      <exclusions>
        <!-- Exclude an old version of guava that is being pulled
             in by a transitive dependency of google-api-client -->
        <exclusion>
          <groupId>com.google.guava</groupId>
          <artifactId>guava-jdk5</artifactId>
        </exclusion>
      </exclusions>
    </dependency>

    <dependency>
      <groupId>com.google.http-client</groupId>
      <artifactId>google-http-client</artifactId>
      <version>${google-clients.version}</version>
      <exclusions>
        <!-- Exclude an old version of guava that is being pulled
             in by a transitive dependency of google-api-client -->
        <exclusion>
          <groupId>com.google.guava</groupId>
          <artifactId>guava-jdk5</artifactId>
        </exclusion>
      </exclusions>
    </dependency>

    <dependency>
      <groupId>com.google.apis</groupId>
      <artifactId>google-api-services-pubsub</artifactId>
      <version>${pubsub.version}</version>
      <exclusions>
        <!-- Exclude an old version of guava that is being pulled
             in by a transitive dependency of google-api-client -->
        <exclusion>
          <groupId>com.google.guava</groupId>
          <artifactId>guava-jdk5</artifactId>
        </exclusion>
      </exclusions>
    </dependency>

    <dependency>
      <groupId>joda-time</groupId>
      <artifactId>joda-time</artifactId>
      <version>${joda.version}</version>
    </dependency>

    <!-- Add slf4j API frontend binding with JUL backend -->
    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-api</artifactId>
      <version>${slf4j.version}</version>
    </dependency>

    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-jdk14</artifactId>
      <version>${slf4j.version}</version>
      <!-- When loaded at runtime this will wire up slf4j to the JUL backend -->
      <scope>runtime</scope>
    </dependency>

    <!-- Hamcrest and JUnit are required dependencies of PAssert,
         which is used in the main code of DebuggingWordCount example. -->
    <dependency>
      <groupId>org.hamcrest</groupId>
      <artifactId>hamcrest-core</artifactId>
      <version>${hamcrest.version}</version>
    </dependency>
    
    <dependency>
      <groupId>org.hamcrest</groupId>
      <artifactId>hamcrest-library</artifactId>
      <version>${hamcrest.version}</version>
    </dependency>

    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>${junit.version}</version>
    </dependency>

    <!-- The DirectRunner is needed for unit tests. -->
    <dependency>
      <groupId>org.apache.beam</groupId>
      <artifactId>beam-runners-direct-java</artifactId>
      <version>${beam.version}</version>
      <scope>test</scope>
    </dependency>
    
    <dependency>
      <groupId>org.mockito</groupId>
      <artifactId>mockito-core</artifactId>
      <version>${mockito.version}</version>
      <scope>test</scope>
    </dependency>
  </dependencies>
</project>

template

`BigqeuryToGCS.java`


package com.example;

import com.google.api.services.bigquery.model.TableRow;

import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.ParDo;


public class BigqeuryToGCS {

    public interface BigQueryToGCSOptions extends PipelineOptions {}

    public static void main(final String[] args) {
        final BigQueryToGCSOptions options = PipelineOptionsFactory.fromArgs(args)
                                                                   .withValidation()
                                                                   .as(BigQueryToGCSOptions.class);

        final Pipeline pipeline = Pipeline.create(options);

        pipeline.apply("Read from BigQuery", BigQueryIO.readTableRows()
                            .fromQuery("SELECT * FROM <DB>.<Table>"))
                .apply(ParDo.of(new DoFn<TableRow, String>() {
					@ProcessElement
					public void processElement(final ProcessContext c) {
						final TableRow row = c.element();
						final String col = String.valueOf(row.get("<Column Name>"));
						c.output(col);
						}
					}))
				.apply(TextIO.write().to("<GCS URL for output files>"));

    pipeline.run();
  }
}

Template execution

 mvn compile exec:java \
    ~/dataflow-intro
    -Dexec.mainClass=com.example.BigqueryToGCS \
    -Dexec.args="--project=<Project Name> \
    --tempLocation=<GCS URL> \
    --gcpTempLocation=<GCS URL> \
    --runner=DataflowRunner \
    --jobName=BigqueryToGCS" \
    -Pdataflow-runner

Template creation (Scala edition)

Next, I will rewrite the project created in Java to Scala.

Library management

Since Scala uses sbt, the library is managed by build.sbt. I want to put only the minimum library required for execution, so set as follows

`build.sbt`


name := "<Project Name>"
version := "0.0.1"
scalaVersion := "2.12.10"

val beamVersion = "2.5.0"

libraryDependencies ++= Seq(
  "com.google.cloud.dataflow" % "google-cloud-dataflow-java-sdk-all" % beamVersion
)

template

`BigqeuryToGCS.scala`


package com.hello

import com.google.api.services.bigquery.model.TableRow
import org.apache.beam.runners.dataflow.DataflowRunner
import org.apache.beam.runners.dataflow.options.DataflowPipelineOptions
import org.apache.beam.runners.dataflow.options.DataflowPipelineWorkerPoolOptions.AutoscalingAlgorithmType
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO
import org.apache.beam.sdk.Pipeline
import org.apache.beam.sdk.options.{ PipelineOptions, PipelineOptionsFactory }
import org.apache.beam.sdk.options.PipelineOptionsFactory
import org.apache.beam.sdk.util.Transport
import org.apache.beam.sdk.transforms.{ DoFn, ParDo }
import DoFn.ProcessElement
import org.apache.beam.sdk.io.TextIO
import scala.util.{ Failure, Success, Try}


class ColumnDoFn extends DoFn[TableRow, String] {
    @ProcessElement
    def processElement(c: ProcessContext) {
        val input = c.element()
        val col = String.valueOf(input.get("<Column>"))
        c.output(col)
    }
} 

object BigqueryToGCS {
    def main(args: Array[String]) :Unit = {
        trait BigQueryToGCSOptions extends PipelineOptions with DataflowPipelineOptions
        val options = PipelineOptionsFactory.create().as(classOf[BigQueryToGCSOptions])

        options.setProject("<Project>")
        options.setRunner(classOf[DataflowRunner])
        options.setRegion("asia-northeast1")
        options.setZone("asia-northeast1-a")
        options.setNumWorkers(1)
        options.setAutoscalingAlgorithm(AutoscalingAlgorithmType.NONE)
        options.setWorkerMachineType("n1-standard-1")
        options.setStagingLocation("<GCS URL for staging>")
        options.setGcpTempLocation("<GCS URL for tmp>")
        options.setTempLocation("<GCS URL for tmp>")

        val p = Pipeline.create(options)

        p.apply("", BigQueryIO.readTableRows().fromQuery("SELECT * FROM <DB>.<Table>"))
         .apply("", ParDo.of(new ColumnDoFn))
         .apply("", TextIO.write().to("<GCS URL for output files>"))

        p.run()
    }
}

Template execution

sbt "runMain com.hello.BigqueryToGCS --project=<Project Name> --runner=DataflowRunner -Pdataflow-runner"

Template creation (Scala + Scio edition)

Scio is a Scala API for Apache Beam and Cloud Dataflow developed by Spotify.

Project creation

sbt new spotify/scio.g8

Library management

Bigquery library is missing in scio template so add it

`build.sbt`


import sbt._
import Keys._

val scioVersion = "0.7.4"
val beamVersion = "2.11.0"
val scalaMacrosVersion = "2.1.1"

lazy val commonSettings = Defaults.coreDefaultSettings ++ Seq(
  organization := "example",
  // Semantic versioning http://semver.org/
  version := "0.1.0-SNAPSHOT",
  scalaVersion := "2.12.10",
  scalacOptions ++= Seq("-target:jvm-1.8",
                        "-deprecation",
                        "-feature",
                        "-unchecked"),
  javacOptions ++= Seq("-source", "1.8", "-target", "1.8")
)

lazy val paradiseDependency =
  "org.scalamacros" % "paradise" % scalaMacrosVersion cross CrossVersion.full
lazy val macroSettings = Seq(
  libraryDependencies += "org.scala-lang" % "scala-reflect" % scalaVersion.value,
  addCompilerPlugin(paradiseDependency)
)

lazy val root: Project = project
  .in(file("."))
  .settings(commonSettings)
  .settings(macroSettings)
  .settings(
    name := "yuyu_hf",
    description := "yuyu_hf",
    publish / skip := true,
    run / classLoaderLayeringStrategy := ClassLoaderLayeringStrategy.Flat,
    libraryDependencies ++= Seq(
      "com.spotify" %% "scio-core" % scioVersion,
      "com.spotify" %% "scio-test" % scioVersion % Test,
      "com.spotify" %% "scio-bigquery" % scioVersion,
      "org.apache.beam" % "beam-runners-direct-java" % beamVersion,
      // optional dataflow runner
      "org.apache.beam" % "beam-runners-google-cloud-dataflow-java" % beamVersion,
      "org.slf4j" % "slf4j-simple" % "1.7.25"
    )
  )
  .enablePlugins(PackPlugin)

lazy val repl: Project = project
  .in(file(".repl"))
  .settings(commonSettings)
  .settings(macroSettings)
  .settings(
    name := "repl",
    description := "Scio REPL for yuyu_hf",
    libraryDependencies ++= Seq(
      "com.spotify" %% "scio-repl" % scioVersion
    ),
    Compile / mainClass := Some("com.spotify.scio.repl.ScioShell"),
    publish / skip := true
  )
  .dependsOn(root)

template

`BigqeuryToGCS.scala`


package example

import com.spotify.scio.bigquery._
import com.spotify.scio.ContextAndArgs


object BigqueryToGCS {
  @BigQueryType.fromQuery("SELECT * FROM [<Project>:<DB>.<Table>]")
  class Row

  def main(cmdlineArgs: Array[String]): Unit = {
    val (sc, args) = ContextAndArgs(cmdlineArgs)

    sc.typedBigQuery[Row]()
      .map(r => r.sepal_length.getOrElse(""))
      .saveAsTextFile("<GCS URL for output files>")

    sc.close
  }
}

Template execution

sbt "runMain example.BigqueryToGCS --project=<Project Name> --runner=DataflowRunner"

Impressions

It took me a while to get used to Apache Beam personally, so at first I wrote it in Java, which has a lot of sample code in the world. I think it's a good idea to start by converting Java code to Scala first, and if you have Scala-like code, you may be able to process intermediate data more beautifully than Java. Whether or not to use Scio should be considered by looking at the library update frequency and team status.

References

http://syucream.hatenablog.jp/entry/2017/10/20/005648
https://qiita.com/kaz3284/items/8ba590f2bd176db6e86d

[JAVA] Cloud Dataflow template created with Scala

Introduction

Template creation (Java edition)

Project creation

Library management

pom.xml

template

BigqeuryToGCS.java

Template execution

Template creation (Scala edition)

Library management

build.sbt

template

BigqeuryToGCS.scala

Template execution

Template creation (Scala + Scio edition)

Project creation

Library management

build.sbt

template

BigqeuryToGCS.scala

Template execution

Impressions

References

`pom.xml`

`BigqeuryToGCS.java`

`build.sbt`

`BigqeuryToGCS.scala`

`build.sbt`

`BigqeuryToGCS.scala`