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 * 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
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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.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
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.ParDo;
import org.apache.beam.sdk.transforms.SimpleFunction;
import org.apache.beam.sdk.values.KV;


/**
 * An example that counts words in Shakespeare.
 *
 * <p>This class, {@link MinimalWordCount}, is the first in a series of four successively more
 * detailed 'word count' examples. Here, for simplicity, we don't show any error-checking or
 * argument processing, and focus on construction of the pipeline, which chains together the
 * application of core transforms.
 *
 * <p>Next, see the {@link WordCount} pipeline, then the {@link DebuggingWordCount}, and finally the
 * {@link WindowedWordCount} pipeline, for more detailed examples that introduce additional
 * concepts.
 *
 * <p>Concepts:
 *
 * <pre>
 *   1. Reading data from text files
 *   2. Specifying 'inline' transforms
 *   3. Counting items in a PCollection
 *   4. Writing data to text files
 * </pre>
 *
 * <p>No arguments are required to run this pipeline. It will be executed with the DirectRunner. You
 * can see the results in the output files in your current working directory, with names like
 * "wordcounts-00001-of-00005. When running on a distributed service, you would use an appropriate
 * file service.
 */
public class MinimalWordCount {

  public static void main(String[] args) {
    // Create a PipelineOptions object. This object lets us set various execution
    // options for our pipeline, such as the runner you wish to use. This example
    // will run with the DirectRunner by default, based on the class path configured
    // in its dependencies.
    PipelineOptions options = PipelineOptionsFactory.create();

    // Create the Pipeline object with the options we defined above.
    Pipeline p = Pipeline.create(options);

    // Apply the pipeline's transforms.

    // 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("gs://apache-beam-samples/shakespeare/*"))

     // Concept #2: Apply a ParDo transform to our PCollection of text lines. This ParDo invokes a
     // DoFn (defined in-line) on each element that tokenizes the text line into individual words.
     // The ParDo returns a PCollection<String>, where each element is an individual word in
     // Shakespeare's collected texts.
     .apply("ExtractWords", ParDo.of(new DoFn<String, String>() {
                       @ProcessElement
                       public void processElement(ProcessContext c) {
                         for (String word : c.element().split(ExampleUtils.TOKENIZER_PATTERN)) {
                           if (!word.isEmpty()) {
                             c.output(word);
                           }
                         }
                       }
                     }))

     // 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.<String>perElement())

     // Apply a MapElements transform that formats our PCollection of word counts into a printable
     // string, suitable for writing to an output file.
     .apply("FormatResults", MapElements.via(new SimpleFunction<KV<String, Long>, String>() {
                       @Override
                       public String apply(KV<String, Long> input) {
                         return input.getKey() + ": " + input.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 wordcount-00001-of-00005
     .apply(TextIO.write().to("wordcounts"));

    // Run the pipeline.
    p.run().waitUntilFinish();
  }
}