Fink| source| transform| sink 1. Flink 批处理Api

1.1 Source

Flink+kafka是如何实现exactly-once语义的

　　Flink通过checkpoint来保存数据是否处理完成的状态；

　　有JobManager协调各个TaskManager进行checkpoint存储，checkpoint保存在 StateBackend中，默认StateBackend是内存级的，也可以改为文件级的进行持久化保存。

执行过程实际上是一个两段式提交，每个算子执行完成，会进行“预提交”，直到执行完sink操作，会发起“确认提交”，如果执行失败，预提交会放弃掉。

如果宕机需要通过StateBackend进行恢复，只能恢复所有确认提交的操作。

Fink| source| transform| sink
1. Flink 批处理Api

Spark中要想实现有状态的，需要使用updateBykey或者借助redis；

而Fink是把它记录在State Bachend，只要是经过keyBy等处理之后结果会记录在State Bachend（已处理未提交；如果是处理完了就是已提交状态；），

它还会记录另外一种状态值：keyState，比如keyBy累积的结果；

StateBachend如果不想存储在内存中，也可以存储在fs文件中或者HDFS中； IDEA的工具只支持memory内存式存储，一旦重启就没了；部署到linux中就支持存储在文件中了；

Kakfa的自动提交：“enable.auto.commit”，比如从kafka出来后到sparkStreaming之后，一进来consumer会帮你自动提交，如果在处理过程中，到最后有一个没有写出去（比如写到redis、ES），虽然处理失败了但kafka的偏移量已经发生改变；所以移偏移量的时机很重要；

1.2 Transform 转换算子

map

Fink| source| transform| sink
1. Flink 批处理Api

val streamMap = stream.map { x => x * 2 }

object StartupApp {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val myKafkaConsumer: FlinkKafkaConsumer011[String] = MyKafkaUtil.getConsumer("GMALL_STARTUP")
    val dstream: DataStream[String] = env.addSource(myKafkaConsumer)

    //dstream.print().setParallelism(1) 测试从kafka中获得数据是否打通到了flink中

    //将json转换成json对象
    val startupLogDStream: DataStream[StartupLog] = dstream.map { jsonString =>
      JSON.parseObject(jsonString, classOf[StartupLog])
    }

    //需求一 相同渠道的值进行累加
    val sumDStream: DataStream[(String, Int)] = startupLogDStream.map { startuplog => (startuplog.ch, 1) }.keyBy(0)
　　　　.reduce { (startuplogCount1, startuplogCount2) =>
       val newCount: Int = startuplogCount1._2 + startuplogCount2._2
      (startuplogCount1._1, newCount)
    }
    //val sumDStream: DataStream[(String, Int)] = startupLogDStream.map{startuplog => (startuplog.ch,1)}.keyBy(0).sum(1)
    //sumDStream.print()
    env.execute()

  }
}

flatMap

flatMap的函数签名：def flatMap[A,B](as: List[A])(f: A ⇒ List[B]): List[B]
例如: flatMap(List(1,2,3))(i ⇒ List(i,i))
结果是List(1,1,2,2,3,3), 
而List("a b", "c d").flatMap(line ⇒ line.split(" "))
结果是List(a, b, c, d)

val streamFlatMap = stream.flatMap{
    x => x.split(" ")
}

Filter

Fink| source| transform| sink
1. Flink 批处理Api

val streamFilter = stream.filter{
    x => x == 1
}

KeyBy

Fink| source| transform| sink
1. Flink 批处理Api

DataStream → KeyedStream：输入必须是Tuple类型，逻辑地将一个流拆分成不相交的分区，每个分区包含具有相同key的元素，在内部以hash的形式实现的。

以HashCode来进行分区，可能有些key值不相同也会分到相同区。

滚动聚合算子（Rolling Aggregation）

这些算子可以针对KeyedStream的每一个支流做聚合,必须KeyBy分组之后再sum聚合等。
  sum()
  min()
  max()
  minBy()
  maxBy()

Reduce

KeyedStream → DataStream：一个分组数据流的聚合操作，合并当前的元素和上次聚合的结果，产生一个新的值，返回的流中包含每一次聚合的结果，而不是只返回最后一次聚合的最终结果。

val stream2 = env.readTextFile("YOUR_PATH\sensor.txt")
  .map( data => {
    val dataArray = data.split(",")
    SensorReading(dataArray(0).trim, dataArray(1).trim.toLong, dataArray(2).trim.toDouble)
  })
  .keyBy("id")
  .reduce( (x, y) => SensorReading(x.id, x.timestamp + 1, y.temperature) )

Split 和 Select

Fink| source| transform| sink
1. Flink 批处理Api

DataStream 通过split得到→ SplitStream --->通过select得到DataStream：根据某些特征把一个DataStream拆分成两个或者多个DataStream。

SplitStream→DataStream：从一个SplitStream中获取一个或者多个DataStream。

    //需求二   把 appstore   和其他的渠道的数据 分成两个流
    val splitableStream: SplitStream[StartupLog] = startupLogDStream.split { startuplog =>
      var flagList: List[String] = List()
      if (startuplog.ch.equals("appstore")) {
        flagList = List("apple")
      } else {
        flagList = List("other")
      }
      flagList
    }
    val appleStream: DataStream[StartupLog] = splitableStream.select("apple")
    //appleStream.print("this is apple").setParallelism(1)
    val otherdStream: DataStream[StartupLog] = splitableStream.select("other")
    //otherdStream.print("this is other").setParallelism(1)

Connect和 CoMap

Fink| source| transform| sink
1. Flink 批处理Api

connecte的两条流数据类型可以不同，但一次操作只能合并2条流；

DataStream,DataStream → ConnectedStreams：连接两个保持他们类型的数据流，两个数据流被Connect之后，只是被放在了一个同一个流中，内部依然保持各自的数据和形式不发生任何变化，两个流相互独立。

ConnectedStreams → DataStream：作用于ConnectedStreams上，功能与map和flatMap一样，对ConnectedStreams中的每一个Stream分别进行map和flatMap处理。

 //需求三 把上面两个流合并为一个
    val connStream: ConnectedStreams[StartupLog, StartupLog] = appleStream.connect(otherdStream)
    val allDataStream: DataStream[String] = connStream.map((startuplog1: StartupLog) => startuplog1.ch, (startuplog2: StartupLog) => startuplog2.ch)
    allDataStream.print("all").setParallelism(1)

CoMap,CoFlatMap

ConnectedStreams → DataStream：作用于ConnectedStreams上，功能与map和flatMap一样，对ConnectedStreams中的每一个Stream分别进行map和flatMap处理。

val warning = high.map( sensorData => (sensorData.id, sensorData.temperature) )
val connected = warning.connect(low)

val coMap = connected.map(
    warningData => (warningData._1, warningData._2, "warning"),
    lowData => (lowData.id, "healthy")
)

Union

Fink| source| transform| sink
1. Flink 批处理Api

DataStream → DataStream：对两个或者两个以上的DataStream进行union操作，产生一个包含所有DataStream元素的新DataStream。注意:如果你将一个DataStream跟它自己做union操作，在新的DataStream中，你将看到每一个元素都出现两次。

可以合并多条流，但是数据结构必须一样；

    //合并流union
    val unionDStream: DataStream[StartupLog] = appleStream.union(otherdStream)
    unionDStream.print("union").setParallelism(1)

Connect与 Union 区别：

1 、 Union之前两个流的类型必须是一样，Connect可以不一样，在之后的coMap中再去调整成为一样的。

2 Connect只能操作两个流，Union可以操作多个

1.2 Sink

Flink没有类似于spark中foreach方法，让用户进行迭代的操作。虽有对外的输出操作都要利用Sink完成。最后通过类似如下方式完成整个任务最终输出操作。

   myDstream.addSink(new MySink(xxxx))

官方提供了一部分的框架的sink。除此以外，需要用户自定义实现sink。

Fink| source| transform| sink
1. Flink 批处理Api

Kafka

object MyKafkaUtil {
  val prop = new Properties()
  prop.setProperty("bootstrap.servers","hadoop101:9092")
  prop.setProperty("group.id","gmall")

  def getConsumer(topic:String ):FlinkKafkaConsumer011[String]= {
    val myKafkaConsumer:FlinkKafkaConsumer011[String] = new FlinkKafkaConsumer011[String](topic, new SimpleStringSchema(), prop)
    myKafkaConsumer
  }

  def getProducer(topic:String):FlinkKafkaProducer011[String]={
    new FlinkKafkaProducer011[String]("hadoop101:9092",topic,new SimpleStringSchema())
  }

}


//sink到kafka
    unionDStream.map(_.toString).addSink(MyKafkaUtil.getProducer("gmall_union"))
    ///opt/module/kafka/bin/kafka-console-consumer.sh --zookeeper hadoop101:2181 --topic gmall_union

从kafka到kafka

启动kafka

kafka生产者：[kris@hadoop101 kafka]$ bin/kafka-console-producer.sh --broker-list hadoop101:9092 --topic sensor

kafka消费者：

[kris@hadoop101 kafka]$ bin/kafka-console-consumer.sh --bootstrap-server hadoop101:9092 --topic sinkTest --from-beginning
SensorReading(sensor_1,1547718199,35.80018327300259)
SensorReading(sensor_6,1547718201,15.402984393403084)
SensorReading(sensor_7,1547718202,6.720945201171228)
SensorReading(sensor_10,1547718205,38.101067604893444)
SensorReading(sensor_1,1547718206,35.1)
SensorReading(sensor_1,1547718207,35.6)

Redis

import org.apache.flink.streaming.connectors.redis.RedisSink
import org.apache.flink.streaming.connectors.redis.common.config.FlinkJedisPoolConfig
import org.apache.flink.streaming.connectors.redis.common.mapper.{RedisCommand, RedisCommandDescription, RedisMapper}

object MyRedisUtil {
  private val config: FlinkJedisPoolConfig = new FlinkJedisPoolConfig.Builder().setHost("hadoop101").setPort(6379).build()

  def getRedisSink(): RedisSink[(String, String)] = {
    new RedisSink[(String, String)](config, new MyRedisMapper)
  }

}
class MyRedisMapper extends RedisMapper[(String, String)]{
  //用何种命令进行保存
  override def getCommandDescription: RedisCommandDescription = {
    new RedisCommandDescription(RedisCommand.HSET, "channel_sum") //hset类型， apple, 111
  }
  //流中的元素哪部分是value
  override def getKeyFromData(channel_sum: (String, String)): String = channel_sum._2
  //流中的元素哪部分是key
  override def getValueFromData(channel_sum: (String, String)): String = channel_sum._1

}


object StartupApp {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val myKafkaConsumer: FlinkKafkaConsumer011[String] = MyKafkaUtil.getConsumer("GMALL_STARTUP")
    val dstream: DataStream[String] = env.addSource(myKafkaConsumer)

    //dstream.print().setParallelism(1) 测试从kafka中获得数据是否打通到了flink中

    //将json转换成json对象
    val startupLogDStream: DataStream[StartupLog] = dstream.map { jsonString =>
      JSON.parseObject(jsonString, classOf[StartupLog])
    }

//sink到redis
    //把按渠道的统计值保存到redis中  hash   key: channel_sum  field ch  value: count
    //按照不同渠道进行累加
    val chCountDStream: DataStream[(String, Int)] = startupLogDStream.map(startuplog => (startuplog.ch, 1)).keyBy(0).sum(1)
    //把上述结果String, Int转换成String, String类型
    val channelDStream: DataStream[(String, String)] = chCountDStream.map(chCount => (chCount._1, chCount._2.toString))
    channelDStream.addSink(MyRedisUtil.getRedisSink())

object MyEsUtil {
  val hostList: util.List[HttpHost] = new util.ArrayList[HttpHost]()
  hostList.add(new HttpHost("hadoop101", 9200, "http"))
  hostList.add(new HttpHost("hadoop102", 9200, "http"))
  hostList.add(new HttpHost("hadoop103", 9200, "http"))

  def getEsSink(indexName: String): ElasticsearchSink[String] = {
    //new接口---> 要实现一个方法
    val esSinkFunc: ElasticsearchSinkFunction[String] = new ElasticsearchSinkFunction[String] {
      override def process(element: String, ctx: RuntimeContext, indexer: RequestIndexer): Unit = {
        val jSONObject: JSONObject = JSON.parseObject(element)
        val indexRequest: IndexRequest = Requests.indexRequest().index(indexName).`type`("_doc").source(jSONObject)
        indexer.add(indexRequest)
      }
    }
    val esSinkBuilder = new ElasticsearchSink.Builder[String](hostList, esSinkFunc)
    esSinkBuilder.setBulkFlushMaxActions(10)
    val esSink: ElasticsearchSink[String] = esSinkBuilder.build()
    esSink
  }
}



object StartupApp {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val myKafkaConsumer: FlinkKafkaConsumer011[String] = MyKafkaUtil.getConsumer("GMALL_STARTUP")
    val dstream: DataStream[String] = env.addSource(myKafkaConsumer)
//sink之三  保存到ES
    val esSink: ElasticsearchSink[String] = MyEsUtil.getEsSink("gmall_startup")
    dstream.addSink(esSink) //dstream来自kafka的数据源
GET gmall_startup/_search

Mysql

class MyjdbcSink(sql: String) extends RichSinkFunction[Array[Any]] {
  val driver = "com.mysql.jdbc.Driver"
  val url = "jdbc:mysql://hadoop101:3306/gmall?useSSL=false"
  val username = "root"
  val password = "123456"
  val maxActive = "20"
  var connection: Connection = null

  // 创建连接
  override def open(parameters: Configuration) {
    val properties = new Properties()
    properties.put("driverClassName",driver)
    properties.put("url",url)
    properties.put("username",username)
    properties.put("password",password)
    properties.put("maxActive",maxActive)

    val dataSource: DataSource = DruidDataSourceFactory.createDataSource(properties)
    connection = dataSource.getConnection()
  }

  // 把每个Array[Any] 作为数据库表的一行记录进行保存

  override def invoke(values: Array[Any]): Unit = {
    val ps: PreparedStatement = connection.prepareStatement(sql)
    for (i <- 0 to values.length-1) {
      ps.setObject(i+1, values(i))
    }
    ps.executeUpdate()
  }


  override def close(): Unit = {
    if (connection != null){
      connection.close()
    }
  }
}


object StartupApp {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val myKafkaConsumer: FlinkKafkaConsumer011[String] = MyKafkaUtil.getConsumer("GMALL_STARTUP")
    val dstream: DataStream[String] = env.addSource(myKafkaConsumer)

    //dstream.print().setParallelism(1) 测试从kafka中获得数据是否打通到了flink中

    //将json转换成json对象
    val startupLogDStream: DataStream[StartupLog] = dstream.map { jsonString =>
      JSON.parseObject(jsonString, classOf[StartupLog])
    }

   //sink之四 保存到Mysql中
    startupLogDStream.map(startuplog => Array(startuplog.mid, startuplog.uid, startuplog.ch, startuplog.area,startuplog.ts))
      .addSink(new MyjdbcSink("insert into fink_startup values(?,?,?,?,?)"))

    env.execute()

  }
}

Fink| source| transform| sink 1. Flink 批处理Api

1.1 Source

1.2 Transform 转换算子

map

flatMap

Filter

KeyBy

滚动聚合算子（Rolling Aggregation）

Reduce

Split 和 Select

Connect和 CoMap

Union

1.2 Sink

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