基本流程可以总结为一下
1. 创建env
2. 定义Time类型
3. 设置并行度
4. 获取流，可以是使用readTextFile("path"),也可以是用
.addSource( new FlinkKafkaConsumer[String]("hotitems", new SimpleStringSchema(), properties) )消费kafka的数据，properties需要前面定义好
new Properity()
5. 通过map将每一行的数据切分并转换为对应的类型
6. 指定时间戳和水印
7. .filter(_.behavior == "pv")
8. .keyBy("itemId")
9. .timeWindow(Time.hours(1), Time.minutes(5))
10. .aggregate( new CountAgg(), new WindowResultFunction() )//此处需要实现这两个方法，分别继承对应的类
11. .keyBy("windowEnd")
12. .process( new TopNHotItems(3))//此处也需要完成该方法
13. .print()
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import java.sql.Timestamp
import java.util.Properties
import org.apache.flink.api.common.functions.AggregateFunction
import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.api.common.state.{ListState, ListStateDescriptor}
import org.apache.flink.api.java.tuple.{Tuple, Tuple1}
import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.functions.KeyedProcessFunction
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.function.WindowFunction
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.windowing.windows.TimeWindow
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
import org.apache.flink.util.Collector
import scala.collection.mutable.ListBuffer
// 输入数据样例类
case class UserBehavior( userId: Long, itemId: Long, categoryId: Int, behavior:String, timestamp: Long )
// 输出数据样例类
case class ItemViewCount( itemId: Long, windowEnd: Long, count: Long )
object HotItems {
def main(args: Array[String]): Unit = {
val properties =new Properties()
properties.setProperty("bootstrap.servers","localhost:9092")
properties.setProperty("group.id","consumer-group")
properties.setProperty("key.deserializer","org.apache.kafka.common.serialization.StringDeserializer")
properties.setProperty("value.deserializer","org.apache.kafka.common.serialization.StringDeserializer")
properties.setProperty("auto.offset.reset","latest")
// 创建一个env
val env = StreamExecutionEnvironment.getExecutionEnvironment
// 显式地定义Time类型
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
env.setParallelism(1)
val stream = env
// .readTextFile("D:\\Projects\\BigData\\UserBehaviorAnalysis\\HotItemsAnalysis\\src\\main\\resources\\UserBehavior.csv")
.addSource(new FlinkKafkaConsumer[String]("hotitems",new SimpleStringSchema(), properties) )
.map(line => {
val linearray = line.split(",")
UserBehavior( linearray(0).toLong, linearray(1).toLong, linearray(2).toInt, linearray(3), linearray(4).toLong )
})
// 指定时间戳和watermark
.assignAscendingTimestamps(_.timestamp *1000)
.filter(_.behavior =="pv")
.keyBy("itemId")
.timeWindow(Time.hours(1), Time.minutes(5))
.aggregate(new CountAgg(),new WindowResultFunction() )
.keyBy("windowEnd")
.process(new TopNHotItems(3))
.print()
// 调用execute执行任务
env.execute("Hot Items Job")
}
// 自定义实现聚合函数
class CountAggextends AggregateFunction[UserBehavior, Long, Long]{
override def add(value: UserBehavior, accumulator: Long): Long = accumulator +1
override def createAccumulator(): Long =0L
override def getResult(accumulator: Long): Long = accumulator
override def merge(a: Long, b: Long): Long = a + b
}
// 自定义实现Window Function，输出ItemViewCount格式
class WindowResultFunctionextends WindowFunction[Long, ItemViewCount, Tuple, TimeWindow]{
override def apply(key: Tuple, window: TimeWindow, input:Iterable[Long], out: Collector[ItemViewCount]): Unit = {
val itemId: Long = key.asInstanceOf[Tuple1[Long]].f0
val count = input.iterator.next()
out.collect(ItemViewCount(itemId, window.getEnd, count))
}
}
// 自定义实现process function
class TopNHotItems(topSize: Int)extends KeyedProcessFunction[Tuple, ItemViewCount,String]{
// 定义状态ListState
private var itemState: ListState[ItemViewCount] = _
override def open(parameters: Configuration): Unit = {
super.open(parameters)
// 命名状态变量的名字和类型
val itemStateDesc =new ListStateDescriptor[ItemViewCount]("itemState",classOf[ItemViewCount])
itemState = getRuntimeContext.getListState(itemStateDesc)
}
override def processElement(i: ItemViewCount, context: KeyedProcessFunction[Tuple, ItemViewCount,String]#Context, collector: Collector[String]): Unit = {
itemState.add(i)
// 注册定时器，触发时间定为 windowEnd + 1，触发时说明window已经收集完成所有数据
context.timerService.registerEventTimeTimer( i.windowEnd +1 )
}
// 定时器触发操作，从state里取出所有数据，排序取TopN，输出
override def onTimer(timestamp: Long, ctx: KeyedProcessFunction[Tuple, ItemViewCount,String]#OnTimerContext, out: Collector[String]): Unit = {
// 获取所有的商品点击信息
val allItems: ListBuffer[ItemViewCount] = ListBuffer()
import scala.collection.JavaConversions._
for(item <-itemState.get){
allItems += item
}
// 清除状态中的数据，释放空间
itemState.clear()
// 按照点击量从大到小排序，选取TopN
val sortedItems = allItems.sortBy(_.count)(Ordering.Long.reverse).take(topSize)
// 将排名数据格式化，便于打印输出
val result:StringBuilder =new StringBuilder
result.append("====================================\n")
result.append("时间：").append(new Timestamp(timestamp -1)).append("\n")
for( i <- sortedItems.indices ){
val currentItem: ItemViewCount = sortedItems(i)
// 输出打印的格式 e.g. No1： 商品ID=12224 浏览量=2413
result.append("No").append(i+1).append(":")
.append(" 商品ID=").append(currentItem.itemId)
.append(" 浏览量=").append(currentItem.count).append("\n")
}
result.append("====================================\n\n")
// 控制输出频率
Thread.sleep(100)
out.collect(result.toString)
}
}
}