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Make beam-examples run with spark runner #533

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14 changes: 14 additions & 0 deletions examples/java/pom.xml
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Original file line number Diff line number Diff line change
Expand Up @@ -270,12 +270,26 @@
<scope>runtime</scope>
</dependency>

<dependency>
<groupId>org.apache.beam</groupId>
<artifactId>beam-runners-flink_2.10</artifactId>
<version>${project.version}</version>
<scope>runtime</scope>
</dependency>

<dependency>
<groupId>org.apache.beam</groupId>
<artifactId>beam-runners-google-cloud-dataflow-java</artifactId>
<version>${project.version}</version>
</dependency>

<dependency>
<groupId>org.apache.beam</groupId>
<artifactId>beam-runners-spark</artifactId>
<version>${project.version}</version>
<scope>runtime</scope>
</dependency>

<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-jdk14</artifactId>
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22 changes: 14 additions & 8 deletions .../apache/beam/runners/spark/TfIdfTest.java → ...ava/org/apache/beam/examples/TfIdfIT.java
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Expand Up @@ -16,34 +16,41 @@
* limitations under the License.
*/

package org.apache.beam.runners.spark;
package org.apache.beam.examples;

import org.apache.beam.examples.complete.TfIdf;
import org.apache.beam.runners.spark.EvaluationResult;
import org.apache.beam.runners.spark.SparkPipelineOptions;
import org.apache.beam.runners.spark.SparkRunner;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.coders.StringDelegateCoder;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.testing.PAssert;
import org.apache.beam.sdk.testing.TestPipeline;
import org.apache.beam.sdk.transforms.Create;
import org.apache.beam.sdk.transforms.Keys;
import org.apache.beam.sdk.transforms.RemoveDuplicates;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;

import org.junit.Test;
import org.junit.runner.RunWith;
import org.junit.runners.JUnit4;

import java.net.URI;
import java.util.Arrays;

/**
* A test based on {@code TfIdf} from the SDK.
* End-to-end tests of {@link TfIdf}.
*/
public class TfIdfTest {
@RunWith(JUnit4.class)
public class TfIdfIT {

@Test
public void testTfIdf() throws Exception {
SparkPipelineOptions opts = PipelineOptionsFactory.as(SparkPipelineOptions.class);
opts.setRunner(SparkRunner.class);
Pipeline pipeline = Pipeline.create(opts);
public void testE2ETfIdfSpark() throws Exception {
SparkPipelineOptions options = PipelineOptionsFactory.as(SparkPipelineOptions.class);
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@amitsela amitsela Jun 28, 2016

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Could you use PipelineOptionsFactory.fromArgs to be runner agnostic ? Is there a benefit in that ? maybe apply the same to all runners ?

options.setRunner(SparkRunner.class);
Pipeline pipeline = TestPipeline.create(options);

pipeline.getCoderRegistry().registerCoder(URI.class, StringDelegateCoder.of(URI.class));

Expand All @@ -63,5 +70,4 @@ public void testTfIdf() throws Exception {
EvaluationResult res = SparkRunner.create().run(pipeline);
res.close();
}

}
11 changes: 0 additions & 11 deletions runners/spark/pom.xml
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Expand Up @@ -221,17 +221,6 @@
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.beam</groupId>
<artifactId>beam-examples-java</artifactId>
<exclusions>
<!-- Use Hadoop/Spark's backend logger instead of jdk14 for tests -->
<exclusion>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-jdk14</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.avro</groupId>
<artifactId>avro-mapred</artifactId>
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20 changes: 14 additions & 6 deletions runners/spark/src/test/java/org/apache/beam/runners/spark/io/NumShardsTest.java
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Expand Up @@ -21,16 +21,18 @@
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;

import org.apache.beam.examples.WordCount;
import org.apache.beam.runners.spark.EvaluationResult;
import org.apache.beam.runners.spark.SparkPipelineOptions;
import org.apache.beam.runners.spark.SparkRunner;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.coders.StringUtf8Coder;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.transforms.Count;
import org.apache.beam.sdk.transforms.Create;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.transforms.SimpleFunction;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;

import com.google.common.base.Charsets;
Expand All @@ -55,8 +57,7 @@
public class NumShardsTest {

private static final String[] WORDS_ARRAY = {
"hi there", "hi", "hi sue bob",
"hi sue", "", "bob hi"};
"hi", "there", "hi", "hi", "sue", "bob", "hi", "sue", "", "bob", "hi"};
private static final List<String> WORDS = Arrays.asList(WORDS_ARRAY);

private File outputDir;
Expand All @@ -70,14 +71,22 @@ public void setUp() throws IOException {
outputDir.delete();
}

/** A SimpleFunction that converts a Word and Count into a printable string. */
private static class FormatAsTextFn extends SimpleFunction<KV<String, Long>, String> {
@Override
public String apply(KV<String, Long> input) {
return input.getKey() + ": " + input.getValue();
}
}

@Test
public void testText() throws Exception {
SparkPipelineOptions options = PipelineOptionsFactory.as(SparkPipelineOptions.class);
options.setRunner(SparkRunner.class);
Pipeline p = Pipeline.create(options);
PCollection<String> inputWords = p.apply(Create.of(WORDS).withCoder(StringUtf8Coder.of()));
PCollection<String> output = inputWords.apply(new WordCount.CountWords())
.apply(MapElements.via(new WordCount.FormatAsTextFn()));
PCollection<String> output = inputWords.apply(Count.<String>perElement())
.apply(MapElements.via(new FormatAsTextFn()));
output.apply(TextIO.Write.to(outputDir.getAbsolutePath()).withNumShards(3).withSuffix(".txt"));
EvaluationResult res = SparkRunner.create().run(p);
res.close();
Expand All @@ -97,5 +106,4 @@ public void testText() throws Exception {
assertEquals(3, count);
assertTrue(expected.isEmpty());
}

}