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 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
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 *
 *     http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.flink.training.solutions.hourlytips.scala

import org.apache.flink.api.java.utils.ParameterTool
import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.function.ProcessWindowFunction
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.windowing.windows.TimeWindow
import org.apache.flink.training.exercises.common.datatypes.TaxiFare
import org.apache.flink.training.exercises.common.sources.TaxiFareSource
import org.apache.flink.training.exercises.common.utils.ExerciseBase
import org.apache.flink.training.exercises.common.utils.ExerciseBase._
import org.apache.flink.util.Collector

/**
  * Scala reference implementation for the "Hourly Tips" exercise of the Flink training in the docs.
  *
  * The task of the exercise is to first calculate the total tips collected by each driver, hour by hour, and
  * then from that stream, find the highest tip total in each hour.
  *
  * Parameters:
  * -input path-to-input-file
  */
object HourlyTipsSolution {

  def main(args: Array[String]) {

    // read parameters
    val params = ParameterTool.fromArgs(args)
    val input = params.get("input", ExerciseBase.PATH_TO_FARE_DATA)

    val maxDelay = 60 // events are delayed by at most 60 seconds
    val speed = 600   // events of 10 minutes are served in 1 second

    // set up streaming execution environment
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
    env.setParallelism(ExerciseBase.parallelism)

    // start the data generator
    val fares = env.addSource(fareSourceOrTest(new TaxiFareSource(input, maxDelay, speed)))

    // total tips per hour by driver
    val hourlyTips = fares
      .map((f: TaxiFare) => (f.driverId, f.tip))
      .keyBy(_._1)
      .timeWindow(Time.hours(1))
      .reduce(
        (f1: (Long, Float), f2: (Long, Float)) => { (f1._1, f1._2 + f2._2) },
        new WrapWithWindowInfo())

    // max tip total in each hour
    val hourlyMax = hourlyTips
      .timeWindowAll(Time.hours(1))
      .maxBy(2)

    // print result on stdout
    printOrTest(hourlyMax)

    // execute the transformation pipeline
    env.execute("Hourly Tips (scala)")
  }

  class WrapWithWindowInfo() extends ProcessWindowFunction[(Long, Float), (Long, Long, Float), Long, TimeWindow] {
    override def process(key: Long, context: Context, elements: Iterable[(Long, Float)], out: Collector[(Long, Long, Float)]): Unit = {
      val sumOfTips = elements.iterator.next()._2
      out.collect((context.window.getEnd, key, sumOfTips))
    }
  }

}