examples/java/src/main/java/org/apache/beam/examples/WordCount.java

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 *     http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.beam.examples;

import org.apache.beam.examples.common.ExampleUtils;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.metrics.Counter;
import org.apache.beam.sdk.metrics.Metrics;
import org.apache.beam.sdk.options.Default;
import org.apache.beam.sdk.options.Description;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.Validation.Required;
import org.apache.beam.sdk.transforms.Count;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.transforms.PTransform;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.SimpleFunction;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;

/**
 * An example that counts words in Shakespeare and includes Beam best practices.
 *
 * <p>This class, {@link WordCount}, is the second in a series of four successively more detailed
 * 'word count' examples. You may first want to take a look at {@link MinimalWordCount}.
 * After you've looked at this example, then see the {@link DebuggingWordCount}
 * pipeline, for introduction of additional concepts.
 *
 * <p>For a detailed walkthrough of this example, see
 *   <a href="https://beam.apache.org/get-started/wordcount-example/">
 *   https://beam.apache.org/get-started/wordcount-example/
 *   </a>
 *
 * <p>Basic concepts, also in the MinimalWordCount example:
 * Reading text files; counting a PCollection; writing to text files
 *
 * <p>New Concepts:
 * <pre>
 *   1. Executing a Pipeline both locally and using the selected runner
 *   2. Using ParDo with static DoFns defined out-of-line
 *   3. Building a composite transform
 *   4. Defining your own pipeline options
 * </pre>
 *
 * <p>Concept #1: you can execute this pipeline either locally or using by selecting another runner.
 * These are now command-line options and not hard-coded as they were in the MinimalWordCount
 * example.
 *
 * <p>To change the runner, specify:
 * <pre>{@code
 *   --runner=YOUR_SELECTED_RUNNER
 * }
 * </pre>
 *
 * <p>To execute this pipeline, specify a local output file (if using the
 * {@code DirectRunner}) or output prefix on a supported distributed file system.
 * <pre>{@code
 *   --output=[YOUR_LOCAL_FILE | YOUR_OUTPUT_PREFIX]
 * }</pre>
 *
 * <p>The input file defaults to a public data set containing the text of of King Lear,
 * by William Shakespeare. You can override it and choose your own input with {@code --inputFile}.
 */
public class WordCount {

  /**
   * Concept #2: You can make your pipeline assembly code less verbose by defining your DoFns
   * statically out-of-line. This DoFn tokenizes lines of text into individual words; we pass it
   * to a ParDo in the pipeline.
   */
  static class ExtractWordsFn extends DoFn<String, String> {
    private final Counter emptyLines = Metrics.counter(ExtractWordsFn.class, "emptyLines");

    @ProcessElement
    public void processElement(ProcessContext c) {
      if (c.element().trim().isEmpty()) {
        emptyLines.inc();
      }

      // Split the line into words.
      String[] words = c.element().split(ExampleUtils.TOKENIZER_PATTERN);

      // Output each word encountered into the output PCollection.
      for (String word : words) {
        if (!word.isEmpty()) {
          c.output(word);
        }
      }
    }
  }

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

  /**
   * A PTransform that converts a PCollection containing lines of text into a PCollection of
   * formatted word counts.
   *
   * <p>Concept #3: This is a custom composite transform that bundles two transforms (ParDo and
   * Count) as a reusable PTransform subclass. Using composite transforms allows for easy reuse,
   * modular testing, and an improved monitoring experience.
   */
  public static class CountWords extends PTransform<PCollection<String>,
      PCollection<KV<String, Long>>> {
    @Override
    public PCollection<KV<String, Long>> expand(PCollection<String> lines) {

      // Convert lines of text into individual words.
      PCollection<String> words = lines.apply(
          ParDo.of(new ExtractWordsFn()));

      // Count the number of times each word occurs.
      PCollection<KV<String, Long>> wordCounts =
          words.apply(Count.<String>perElement());

      return wordCounts;
    }
  }

  /**
   * Options supported by {@link WordCount}.
   *
   * <p>Concept #4: Defining your own configuration options. Here, you can add your own arguments
   * to be processed by the command-line parser, and specify default values for them. You can then
   * access the options values in your pipeline code.
   *
   * <p>Inherits standard configuration options.
   */
  public interface WordCountOptions extends PipelineOptions {

    /**
     * By default, this example reads from a public dataset containing the text of
     * King Lear. Set this option to choose a different input file or glob.
     */
    @Description("Path of the file to read from")
    @Default.String("gs://apache-beam-samples/shakespeare/kinglear.txt")
    String getInputFile();
    void setInputFile(String value);

    /**
     * Set this required option to specify where to write the output.
     */
    @Description("Path of the file to write to")
    @Required
    String getOutput();
    void setOutput(String value);
  }

  public static void main(String[] args) {
    WordCountOptions options = PipelineOptionsFactory.fromArgs(args).withValidation()
      .as(WordCountOptions.class);
    Pipeline p = Pipeline.create(options);

    // Concepts #2 and #3: Our pipeline applies the composite CountWords transform, and passes the
    // static FormatAsTextFn() to the ParDo transform.
    p.apply("ReadLines", TextIO.read().from(options.getInputFile()))
     .apply(new CountWords())
     .apply(MapElements.via(new FormatAsTextFn()))
     .apply("WriteCounts", TextIO.write().to(options.getOutput()));

    p.run().waitUntilFinish();
  }
}