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.spark.examples.sql.streaming;
  
  import org.apache.spark.api.java.function.FlatMapFunction;
  import org.apache.spark.sql.*;
  import org.apache.spark.sql.functions;
  import org.apache.spark.sql.streaming.StreamingQuery;
  import scala.Tuple2;
  
  import java.sql.Timestamp;
  import java.util.ArrayList;
  import java.util.Iterator;
  import java.util.List;
  
  /**
   * Counts words in UTF8 encoded, '\n' delimited text received from the network over a
   * sliding window of configurable duration. Each line from the network is tagged
   * with a timestamp that is used to determine the windows into which it falls.
   *
   * Usage: JavaStructuredNetworkWordCountWindowed <hostname> <port> <window duration>
   *   [<slide duration>]
   * <hostname> and <port> describe the TCP server that Structured Streaming
   * would connect to receive data.
   * <window duration> gives the size of window, specified as integer number of seconds
   * <slide duration> gives the amount of time successive windows are offset from one another,
   * given in the same units as above. <slide duration> should be less than or equal to
   * <window duration>. If the two are equal, successive windows have no overlap. If
   * <slide duration> is not provided, it defaults to <window duration>.
   *
   * To run this on your local machine, you need to first run a Netcat server
   *    `$ nc -lk 9999`
   * and then run the example
   *    `$ bin/run-example sql.streaming.JavaStructuredNetworkWordCountWindowed
   *    localhost 9999 <window duration in seconds> [<slide duration in seconds>]`
   *
   * One recommended <window duration>, <slide duration> pair is 10, 5
   */
  public final class JavaStructuredNetworkWordCountWindowed {
  
    public static void main(String[] args) throws Exception {
      if (args.length < 3) {
        System.err.println("Usage: JavaStructuredNetworkWordCountWindowed <hostname> <port>" +
          " <window duration in seconds> [<slide duration in seconds>]");
        System.exit(1);
      }
  
      String host = args[0];
      int port = Integer.parseInt(args[1]);
      int windowSize = Integer.parseInt(args[2]);
      int slideSize = (args.length == 3) ? windowSize : Integer.parseInt(args[3]);
      if (slideSize > windowSize) {
        System.err.println("<slide duration> must be less than or equal to <window duration>");
      }
      String windowDuration = windowSize + " seconds";
      String slideDuration = slideSize + " seconds";
  
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaStructuredNetworkWordCountWindowed")
        .getOrCreate();
  
      // Create DataFrame representing the stream of input lines from connection to host:port
      Dataset<Row> lines = spark
        .readStream()
        .format("socket")
        .option("host", host)
        .option("port", port)
        .option("includeTimestamp", true)
        .load();
  
      // Split the lines into words, retaining timestamps
      Dataset<Row> words = lines
        .as(Encoders.tuple(Encoders.STRING(), Encoders.TIMESTAMP()))
        .flatMap(
          new FlatMapFunction<Tuple2<String, Timestamp>, Tuple2<String, Timestamp>>() {
            @Override
            public Iterator<Tuple2<String, Timestamp>> call(Tuple2<String, Timestamp> t) {
              List<Tuple2<String, Timestamp>> result = new ArrayList<>();
              for (String word : t._1.split(" ")) {
                result.add(new Tuple2<>(word, t._2));
              }
              return result.iterator();
            }
          },
          Encoders.tuple(Encoders.STRING(), Encoders.TIMESTAMP())
        ).toDF("word", "timestamp");
  
      // Group the data by window and word and compute the count of each group
      Dataset<Row> windowedCounts = words.groupBy(
        functions.window(words.col("timestamp"), windowDuration, slideDuration),
        words.col("word")
      ).count().orderBy("window");
  
      // Start running the query that prints the windowed word counts to the console
      StreamingQuery query = windowedCounts.writeStream()
        .outputMode("complete")
        .format("console")
        .option("truncate", "false")
        .start();
  
      query.awaitTermination();
    }
  }
  