Flink 多流转换算子

1.拆分流

代码片段：

val env = StreamExecutionEnvironment.getExecutionEnvironment
env.setParallelism(1)

val streamFromFile = env.readTextFile("C:\Users\Mi\Documents\project\idea\FlinkTitorial\src\main\resources\sensor.txt")

val stream1: DataStream[SensorReading] = streamFromFile.map(d => {
  val arr = d.split(",")
  SensorReading(arr(0).trim, arr(1).trim.toLong, arr(2).toDouble)
})

//拆分流
val splitStream: SplitStream[SensorReading] = stream1.split(data => if (data.temperature > 30) Seq("high") else Seq("low"))
val highStream = splitStream.select("high")
val lowStream = splitStream.select("low")
val allStream = splitStream.select("high", "low")

2.collect合并流

collect合并的两个流，数据类型可以不一样。

代码片段：

//合并流
val warning: DataStream[(String, Double)] = highStream.map(data => (data.id, data.temperature))
//connect()方法合并流
val connectedStream: ConnectedStreams[(String, Double), SensorReading] = warning.connect(lowStream)
//合并流的map操作，为每个流分别传入一个映射函数
val coMapStream: DataStream[Product] = connectedStream.map(
  data1 => (data1._1, data1._2, "high temperature warning"),
  data2 => (data2.id, "healthy")
)

3.union合并流

union合并操作可以传多个流参数，并且因为返回类型为DataStream，支持链式操作。

代码片段：

val unionStream: DataStream[SensorReading] = highStream.union(lowStream,highStream).union(lowStream)