content: /*
   * 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 under the License.
   */
  
  package org.apache.beam.learning.katas.windowing.fixedwindow;
  
  // beam-playground:
  //   name: FixedTimeWindow
  //   description: Task from katas to count the number of events that happened based on fixed window with 1-day duration.
  //   multifile: false
  //   context_line: 52
  //   categories:
  //     - Combiners
  //     - Streaming
  //   complexity: MEDIUM
  //   tags:
  //     - count
  //     - windowing
  //     - timestamp
  //     - event
  
  import org.apache.beam.learning.katas.util.Log;
  import org.apache.beam.sdk.Pipeline;
  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.Create;
  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.apache.beam.sdk.values.TimestampedValue;
  import org.joda.time.Duration;
  import org.joda.time.Instant;
  
  public class Task {
  
    public static void main(String[] args) {
      PipelineOptions options = PipelineOptionsFactory.fromArgs(args).create();
      Pipeline pipeline = Pipeline.create(options);
  
      PCollection<String> events =
          pipeline.apply(
              Create.timestamped(
                  TimestampedValue.of("event", Instant.parse("2019-06-01T00:00:00+00:00")),
                  TimestampedValue.of("event", Instant.parse("2019-06-01T00:00:00+00:00")),
                  TimestampedValue.of("event", Instant.parse("2019-06-01T00:00:00+00:00")),
                  TimestampedValue.of("event", Instant.parse("2019-06-01T00:00:00+00:00")),
                  TimestampedValue.of("event", Instant.parse("2019-06-05T00:00:00+00:00")),
                  TimestampedValue.of("event", Instant.parse("2019-06-05T00:00:00+00:00")),
                  TimestampedValue.of("event", Instant.parse("2019-06-08T00:00:00+00:00")),
                  TimestampedValue.of("event", Instant.parse("2019-06-08T00:00:00+00:00")),
                  TimestampedValue.of("event", Instant.parse("2019-06-08T00:00:00+00:00")),
                  TimestampedValue.of("event", Instant.parse("2019-06-10T00:00:00+00:00"))
              )
          );
  
      PCollection<KV<String, Long>> output = applyTransform(events);
  
      output.apply(Log.ofElements());
  
      pipeline.run();
    }
  
    static PCollection<KV<String, Long>> applyTransform(PCollection<String> events) {
      return events
          .apply(Window.into(FixedWindows.of(Duration.standardDays(1))))
          .apply(Count.perElement());
    }
  
  }