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 * 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 java.io.IOException;
import java.util.concurrent.ThreadLocalRandom;
import org.apache.beam.examples.common.ExampleBigQueryTableOptions;
import org.apache.beam.examples.common.ExampleOptions;
import org.apache.beam.examples.common.WriteOneFilePerWindow;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.options.Default;
import org.apache.beam.sdk.options.DefaultValueFactory;
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.transforms.DoFn;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.windowing.FixedWindows;
import org.apache.beam.sdk.transforms.windowing.Window;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;
import org.joda.time.Duration;
import org.joda.time.Instant;


/**
 * An example that counts words in text, and can run over either unbounded or bounded input
 * collections.
 *
 * <p>This class, {@link WindowedWordCount}, is the last in a series of four successively more
 * detailed 'word count' examples. First take a look at {@link MinimalWordCount},
 * {@link WordCount}, and {@link DebuggingWordCount}.
 *
 * <p>Basic concepts, also in the MinimalWordCount, WordCount, and DebuggingWordCount examples:
 * Reading text files; counting a PCollection; writing to GCS; executing a Pipeline both locally
 * and using a selected runner; defining DoFns;
 * user-defined PTransforms; defining PipelineOptions.
 *
 * <p>New Concepts:
 * <pre>
 *   1. Unbounded and bounded pipeline input modes
 *   2. Adding timestamps to data
 *   3. Windowing
 *   4. Re-using PTransforms over windowed PCollections
 *   5. Accessing the window of an element
 *   6. Writing data to per-window text files
 * </pre>
 *
 * <p>By default, the examples will run with the {@code DirectRunner}.
 * To change the runner, specify:
 * <pre>{@code
 *   --runner=YOUR_SELECTED_RUNNER
 * }
 * </pre>
 * See examples/java/README.md for instructions about how to configure different runners.
 *
 * <p>To execute this pipeline locally, 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}.
 *
 * <p>By default, the pipeline will do fixed windowing, on 1-minute windows.  You can
 * change this interval by setting the {@code --windowSize} parameter, e.g. {@code --windowSize=10}
 * for 10-minute windows.
 *
 * <p>The example will try to cancel the pipeline on the signal to terminate the process (CTRL-C).
 */
public class WindowedWordCount {
    static final int WINDOW_SIZE = 10;  // Default window duration in minutes
  /**
   * Concept #2: A DoFn that sets the data element timestamp. This is a silly method, just for
   * this example, for the bounded data case.
   *
   * <p>Imagine that many ghosts of Shakespeare are all typing madly at the same time to recreate
   * his masterworks. Each line of the corpus will get a random associated timestamp somewhere in a
   * 2-hour period.
   */
  static class AddTimestampFn extends DoFn<String, String> {
    private final Instant minTimestamp;
    private final Instant maxTimestamp;

    AddTimestampFn(Instant minTimestamp, Instant maxTimestamp) {
      this.minTimestamp = minTimestamp;
      this.maxTimestamp = maxTimestamp;
    }

    @ProcessElement
    public void processElement(ProcessContext c) {
      Instant randomTimestamp =
          new Instant(
              ThreadLocalRandom.current()
                  .nextLong(minTimestamp.getMillis(), maxTimestamp.getMillis()));

      /**
       * Concept #2: Set the data element with that timestamp.
       */
      c.outputWithTimestamp(c.element(), new Instant(randomTimestamp));
    }
  }

  /** A {@link DefaultValueFactory} that returns the current system time. */
  public static class DefaultToCurrentSystemTime implements DefaultValueFactory<Long> {
    @Override
    public Long create(PipelineOptions options) {
      return System.currentTimeMillis();
    }
  }

  /** A {@link DefaultValueFactory} that returns the minimum timestamp plus one hour. */
  public static class DefaultToMinTimestampPlusOneHour implements DefaultValueFactory<Long> {
    @Override
    public Long create(PipelineOptions options) {
      return options.as(Options.class).getMinTimestampMillis()
          + Duration.standardHours(1).getMillis();
    }
  }

  /**
   * Options for {@link WindowedWordCount}.
   *
   * <p>Inherits standard example configuration options, which allow specification of the
   * runner, as well as the {@link WordCount.WordCountOptions} support for
   * specification of the input and output files.
   */
  public interface Options extends WordCount.WordCountOptions,
      ExampleOptions, ExampleBigQueryTableOptions {
    @Description("Fixed window duration, in minutes")
    @Default.Integer(WINDOW_SIZE)
    Integer getWindowSize();
    void setWindowSize(Integer value);

    @Description("Minimum randomly assigned timestamp, in milliseconds-since-epoch")
    @Default.InstanceFactory(DefaultToCurrentSystemTime.class)
    Long getMinTimestampMillis();
    void setMinTimestampMillis(Long value);

    @Description("Maximum randomly assigned timestamp, in milliseconds-since-epoch")
    @Default.InstanceFactory(DefaultToMinTimestampPlusOneHour.class)
    Long getMaxTimestampMillis();
    void setMaxTimestampMillis(Long value);

    @Description("Fixed number of shards to produce per window, or null for runner-chosen sharding")
    Integer getNumShards();
    void setNumShards(Integer numShards);
  }

  public static void main(String[] args) throws IOException {
    Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
    final String output = options.getOutput();
    final Instant minTimestamp = new Instant(options.getMinTimestampMillis());
    final Instant maxTimestamp = new Instant(options.getMaxTimestampMillis());

    Pipeline pipeline = Pipeline.create(options);

    /**
     * Concept #1: the Beam SDK lets us run the same pipeline with either a bounded or
     * unbounded input source.
     */
    PCollection<String> input = pipeline
      /** Read from the GCS file. */
      .apply(TextIO.read().from(options.getInputFile()))
      // Concept #2: Add an element timestamp, using an artificial time just to show windowing.
      // See AddTimestampFn for more detail on this.
      .apply(ParDo.of(new AddTimestampFn(minTimestamp, maxTimestamp)));

    /**
     * Concept #3: Window into fixed windows. The fixed window size for this example defaults to 1
     * minute (you can change this with a command-line option). See the documentation for more
     * information on how fixed windows work, and for information on the other types of windowing
     * available (e.g., sliding windows).
     */
    PCollection<String> windowedWords =
        input.apply(
            Window.<String>into(
                FixedWindows.of(Duration.standardMinutes(options.getWindowSize()))));

    /**
     * Concept #4: Re-use our existing CountWords transform that does not have knowledge of
     * windows over a PCollection containing windowed values.
     */
    PCollection<KV<String, Long>> wordCounts = windowedWords.apply(new WordCount.CountWords());

    /**
     * Concept #5: Format the results and write to a sharded file partitioned by window, using a
     * simple ParDo operation. Because there may be failures followed by retries, the
     * writes must be idempotent, but the details of writing to files is elided here.
     */
    wordCounts
        .apply(MapElements.via(new WordCount.FormatAsTextFn()))
        .apply(new WriteOneFilePerWindow(output, options.getNumShards()));

    PipelineResult result = pipeline.run();
    try {
      result.waitUntilFinish();
    } catch (Exception exc) {
      result.cancel();
    }
  }

}