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 * 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
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package org.apache.nemo.examples.beam;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.transforms.Count;
import org.apache.beam.sdk.transforms.Filter;
import org.apache.beam.sdk.transforms.FlatMapElements;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.TypeDescriptors;

import java.util.Arrays;
/**
 * MinimalWordCount program from BEAM.
 */
public final class MinimalWordCount {
  /**
   * Private Constructor.
   */
  private MinimalWordCount() {
  }
  /**
   * Main function for the MinimalWordCount Beam program.
   * @param args arguments.
   */
  public static void main(final String[] args) {
    final String inputFilePath = args[0];
    final String outputFilePath = args[1];
    final PipelineOptions options = NemoPipelineOptionsFactory.create();
    options.setJobName("MinimalWordCount");
    // Create the Pipeline object with the options we defined above
    final Pipeline p = Pipeline.create(options);
    // Concept #1: Apply a root transform to the pipeline; in this case, TextIO.Read to read a set
    // of input text files. TextIO.Read returns a PCollection where each element is one line from
    // the input text (a set of Shakespeare's texts).
    // This example reads a public data set consisting of the complete works of Shakespeare.
    p.apply(TextIO.read().from(inputFilePath))
      // Concept #2: Apply a FlatMapElements transform the PCollection of text lines.
      // This transform splits the lines in PCollection<String>, where each element is an
      // individual word in Shakespeare's collected texts.
      .apply(
        FlatMapElements.into(TypeDescriptors.strings())
          .via((String word) -> Arrays.asList(word.split("[^\\p{L}]+"))))
      // We use a Filter transform to avoid empty word
      .apply(Filter.by((String word) -> !word.isEmpty()))
      // Concept #3: Apply the Count transform to our PCollection of individual words. The Count
      // transform returns a new PCollection of key/value pairs, where each key represents a
      // unique word in the text. The associated value is the occurrence count for that word.
      .apply(Count.perElement())
      // Apply a MapElements transform that formats our PCollection of word counts into a
      // printable string, suitable for writing to an output file.
      .apply(
        MapElements.into(TypeDescriptors.strings())
          .via(
            (KV<String, Long> wordCount) ->
              wordCount.getKey() + ": " + wordCount.getValue()))
      // Concept #4: Apply a write transform, TextIO.Write, at the end of the pipeline.
      // TextIO.Write writes the contents of a PCollection (in this case, our PCollection of
      // formatted strings) to a series of text files.
      //
      // By default, it will write to a set of files with names like wordcounts-00001-of-00005
      .apply(TextIO.write().to(outputFilePath));
    p.run();
  }
}