Flink学习笔记

Apache Flink

概述

Flink是构建在Data Stream之上一款有状态计算框架。由于该款框架出现的较晚2014.12月发布，通常被人们认为是第3代流计算框架。

第一代：MapReduce 2006年 批 磁盘 M->R 矢量 | 2014.9 Storm诞生 流 延迟低/吞吐小

第二代：Spark RDD 2014.2 批 内存 DAG （若干Stage） | 使用micro-batch 模拟 流处理 DStream 延迟高/吞吐大

第三代Flink Datastream 2014.12 流计算 内存 Datafollow Graph（若干个Task） | Flink Dataset在流计算构建批处理

流处理应用领域：风险控制/智能交通/疾病预测/互联网金融/…

Flink 架构

宏观战略

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Flink VS Spark

Spark计算核心是构建在RDD的批处理之上，通过批模拟流计算。而Flink构建流处理之上，通过流模拟批。

Flink计算架构

JobManagers- 所谓Master ，负责协调分布式任务执行。 负责调度任务，协调checkpoint，协调故障恢复等。

There is always at least one Job Manager. A high-availability setup will have multiple JobManagers, one of which one is always the leader, and the others are standby.

TaskManagers- 所谓slaves（工作节点/Worker）,负责真正任务执行，执行一些Task（等价Spark Stage）下的subtask。负责流计算当中数据缓存或者数据shuffle.计算机节点连接JobManager汇报自身状态信息，并且告知主节点自己分配到任务的计算状态。

There must always be at least one TaskManager.

client - 主要是在任务计算之前将任务翻译成Dataflow Graph，将该Dataflow Graph提交给JobManagers。

Task - Flink会将任务通过Operator Chain的方式将一个任务划分为若干个Task，每个Task都有自己的并行度，根据设置并行度创建相应的subtask（线程）。通过Operator Chain可以减少线程-线程间通信成本和系统开销。

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**Task Slots ** - 每个Task Slot代表TaskManager 计算资源子集。Task Slot可以均分TaskManager 的内存。比如说一个TaskManager 有3个Task Slot.则每个Task slot就代表1/3的内存空间。不同job的subtask之间可以通过Task Slot进行隔离。同一个Job的不同task的subtask可以共享Task slots。默认所有的subtask因为共享的是同一个资源组default,因此一个Job所需的Task Slots的数量就等于该Job下Task的最大并行度。

解决“背压”问题：

​ 背压：数据涌入速度大于数据处理速度
​ 本地交换(local exchange)：task1和task2在同一个工作节点，那么buffer pool可以直接交给下一个任务，当task2消费 buffer pool中的信息速度减慢时，当前任务task1填充buffer pool的速度也会减慢。
​ 远程交换(remote exchange):TaskManager保证每个task至少有一个incoming(传入)和一个outgoing(传出)
​ 缓冲区。当下游的receiver(接收者)的处理速度低于上游的sender(发送者)发送速度，那么接收者接收到缓冲区
​ 的数据就会开始累积，当挤满后就不在接收数据。上游的发送者利用水位机制，当网络中的缓冲数据过多时暂停发 送。

Flink架构执行流程：

​ 当Flink集群启动后，首先会启动一个JobManager和一个或多个TaskManager，代码经过Dataflow graph(类似DAG)交给 客户端。由Client提交任务给JobManager，JobManager
​ 再调度任务给各个TaskManager去执行，然后TaskManager将心跳和统计信息汇报给JobManager。TaskManager之间会以流的形式进行数据传输。以上三者都属于JVM进程。

Flink环境搭建

• 设置CentOS进程数和文件数(重启生效) -可选

[root@Spark ~]# vi /etc/security/limits.conf

* soft nofile 204800
* hard nofile 204800
* soft nproc 204800
* hard nproc 204800

• 配置主机名(重启生效)

[root@Spark ~]# cat /etc/hostname
Spark

• 设置IP映射

[root@Spark ~]# cat /etc/hosts
127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
::1         localhost localhost.localdomain localhost6 localhost6.localdomain6
192.168.11.100  Spark

• 防火墙服务

[root@Spark ~]# systemctl stop firewalld
[root@Spark ~]# systemctl disable firewalld
[root@Spark ~]# firewall-cmd --state
not running

• 安装JDK1.8+

[root@Spark ~]# rpm -ivh jdk-8u171-linux-x64.rpm 
[root@Spark ~]# ls -l /usr/java/
total 4
lrwxrwxrwx. 1 root root   16 Mar 26 00:56 default -> /usr/java/latest
drwxr-xr-x. 9 root root 4096 Mar 26 00:56 jdk1.8.0_171-amd64
lrwxrwxrwx. 1 root root   28 Mar 26 00:56 latest -> /usr/java/jdk1.8.0_171-amd64
[root@Spark ~]# vi .bashrc 
JAVA_HOME=/usr/java/latest
PATH=$PATH:$JAVA_HOME/bin
CLASSPATH=.
export JAVA_HOME
export PATH
export CLASSPATH
[root@Spark ~]# source ~/.bashrc

• SSH配置免密

[root@Spark ~]# ssh-keygen -t rsa
Generating public/private rsa key pair.
Enter file in which to save the key (/root/.ssh/id_rsa): 
Created directory '/root/.ssh'.
Enter passphrase (empty for no passphrase): 
Enter same passphrase again: 
Your identification has been saved in /root/.ssh/id_rsa.
Your public key has been saved in /root/.ssh/id_rsa.pub.
The key fingerprint is:
4b:29:93:1c:7f:06:93:67:fc:c5:ed:27:9b:83:26:c0 root@CentOS
The key's randomart image is:
+--[ RSA 2048]----+
|                 |
|         o   . . |
|      . + +   o .|
|     . = * . . . |
|      = E o . . o|
|       + =   . +.|
|        . . o +  |
|           o   . |
|                 |
+-----------------+
[root@Spark ~]# ssh-copy-id CentOS
The authenticity of host 'centos (192.168.40.128)' can't be established.
RSA key fingerprint is 3f:86:41:46:f2:05:33:31:5d:b6:11:45:9c:64:12:8e.
Are you sure you want to continue connecting (yes/no)? yes
Warning: Permanently added 'centos,192.168.40.128' (RSA) to the list of known hosts.
root@centos's password: 
Now try logging into the machine, with "ssh 'CentOS'", and check in:

  .ssh/authorized_keys

to make sure we haven't added extra keys that you weren't expecting.
[root@Spark ~]# ssh root@CentOS
Last login: Tue Mar 26 01:03:52 2019 from 192.168.40.1
[root@Spark ~]# exit
logout
Connection to CentOS closed.

• 安装配置Flink

[root@Spark ~]# tar -zxf flink-1.8.1-bin-scala_2.11.tgz -C /usr/
[root@Spark ~]# cd /usr/flink-1.8.1/
[root@Spark ~]# vi conf/flink-conf.yaml
jobmanager.rpc.address: Spark
taskmanager.numberOfTaskSlots: 3

[root@Spark ~]#  vi conf/slaves
Spark
[root@Spark ~]# ./bin/start-cluster.sh

Quick Start

• 引入依赖

<dependency>
    <groupId>org.apache.flinkgroupId>
    <artifactId>flink-scala_2.11artifactId>
    <version>1.8.1version>
dependency>
<dependency>
    <groupId>org.apache.flinkgroupId>
    <artifactId>flink-streaming-scala_2.11artifactId>
    <version>1.8.1version>
dependency>

• Quick start

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)

//3.对数据做转换 - operator
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.sum(1)
.print()

//执行计算
fsEnv.execute("FlinkWordCountsQuickStart")

将程序打包 ，通过UI页面或者./bin/flink run执行

[root@Spark flink-1.8.1]# ./bin/flink run 
							-c com.baizhi.quickstart.FlinkWordCountsQuickStart 
							-p 3 
							/root/flink-1.0-SNAPSHOT-jar-with-dependencies.jar

[root@Spark flink-1.8.1]# ./bin/flink list -m Spark:8081
Waiting for response...
------------------ Running/Restarting Jobs -------------------
13.11.2019 16:49:36 : 36e8f1ec3173ccc2c5e1296d1564da87 : FlinkWordCountsQuickStart (RUNNING)
--------------------------------------------------------------
No scheduled jobs.
[root@Spark flink-1.8.1]# ./bin/flink cancel -m Spark:8081 36e8f1ec3173ccc2c5e1296d1564da87
Cancelling job 36e8f1ec3173ccc2c5e1296d1564da87.
Cancelled job 36e8f1ec3173ccc2c5e1296d1564da87.

DataSource

DataSource指定了流计算的输入，用户可以通过StreamExecutionEnvironment.addSource(sourceFunction),Flink已经预先实现了一些DataSource的实现，如果用户需要自定义自己的实现可以通过实现SourceFunction接口（非并行Source）或者ParallelSourceFunction 接口（实现并行Source）或者继承RichParallelSourceFunction .

File Based(了解)

readTextFile(path) - 读取文本文件，底层通过TextInputFormat 一行行读取文件数据，返回是一个DataStream[String] - 仅仅处理一次

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

//2.创建DataStream -细化
val filePath="file:///D:\\data"
val dataStream: DataStream[String] = fsEnv.readTextFile(filePath)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.sum(1)
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

readFile(fileInputFormat, path) - 读取文本文件，底层通过指定输入格式 - 仅仅处理一次

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

//2.创建DataStream -细化
val filePath="file:///D:\\data"
val inputFormat = new TextInputFormat(null)
val dataStream: DataStream[String] = fsEnv.readFile(inputFormat,filePath)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.sum(1)
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

readFile(fileInputFormat, path, watchType, interval, pathFilter) - 以上两个方法底层调用都是该方法。

 //1.创建StreamExecutionEnvironment
    val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

    //2.创建DataStream -细化
    val filePath="file:///D:\\data"
    val inputFormat = new TextInputFormat(null)

    inputFormat.setFilesFilter(new FilePathFilter {
      override def filterPath(path: Path): Boolean = {
        if(path.getName().startsWith("1")){ //过滤不符合的文件
          return true
        }
        false
      }
    })
    val dataStream: DataStream[String] = fsEnv.readFile(inputFormat,filePath,
      FileProcessingMode.PROCESS_CONTINUOUSLY,1000)
    //3.对数据做转换
    dataStream.flatMap(_.split("\\s+"))
      .map((_,1))
      .keyBy(0)
      .sum(1)
      .print()

    fsEnv.execute("FlinkWordCountsQuickStart")

定期的扫描文件，如果文件内容被修改了，该文件会被完整的重新读取。因此可能会产生重复计算。

Collection(测试)

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.fromCollection(List("this is a demo","hello world"))
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.sum(1)
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

自定义SourceFunction（掌握）

class UserDefineParallelSourceFunction extends ParallelSourceFunction[String]{

  val lines=Array("this is a demo","hello world","hello flink")
  @volatile
  var isRunning=true
  //运行
  override def run(sourceContext: SourceFunction.SourceContext[String]): Unit = {
    while (isRunning){
      Thread.sleep(1000)
      sourceContext.collect(lines(new Random().nextInt(lines.length)))
    }
  }
  //取消
  override def cancel(): Unit = {
    isRunning=false
  }
}         

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.addSource(new UserDefineParallelSourceFunction)
dataStream.setParallelism(10)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.sum(1)
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

Flink和Kafka Source（重点）

<dependency>
    <groupId>org.apache.flinkgroupId>
    <artifactId>flink-connector-kafka_2.11artifactId>
    <version>1.8.1version>
dependency>

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

//2.创建DataStream -细化
val props = new Properties()
props.setProperty(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,
                  "Spark:9092")
props.setProperty(ConsumerConfig.GROUP_ID_CONFIG, "g1")

val flinkKafkaConsumer = new FlinkKafkaConsumer[String]("topic01",
                                                        new SimpleStringSchema(),props)

val dataStream: DataStream[String] = fsEnv.addSource(flinkKafkaConsumer)
dataStream.setParallelism(10)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.sum(1)
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

只能获取value信息，如果用户需要获取key/offset/partition信息用户需要定制KafkaDeserializationSchema

获取Record元数据信息

class UserDefineKafkaDeserializationSchema
extends KafkaDeserializationSchema[(Int,Long,String,String,String)]{
    override def isEndOfStream(t: (Int, Long, String, String, String)): Boolean = {
        return false;
    }

    override def deserialize(r: ConsumerRecord[Array[Byte], Array[Byte]]): (Int, Long, String, String, String) = {
        if(r.key()==null){
            (r.partition(),r.offset(),r.topic(),"",new String(r.value()))
        }else{
            (r.partition(),r.offset(),r.topic(),StringUtils.arrayToString(r.key()),new String(r.value()))
        }
    }
    //告知返回值类型
    override def getProducedType: TypeInformation[(Int, Long, String, String, String)] = {
        createTypeInformation[(Int, Long, String, String, String)]
    }
}

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

//2.创建DataStream -细化
val props = new Properties()
props.setProperty(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,
                  "Spark:9092")
props.setProperty(ConsumerConfig.GROUP_ID_CONFIG, "g1")

val flinkKafkaConsumer = new FlinkKafkaConsumer[(Int,Long,String,String,String)]("topic01",
                                                                                 new UserDefineKafkaDeserializationSchema(),props)

val dataStream: DataStream[(Int,Long,String,String,String)] = fsEnv.addSource(flinkKafkaConsumer)

dataStream.print()

fsEnv.execute("FlinkWordCountsQuickStart")

Data Sink

Data sinks负责消费Data Stream的数据，将数据写出到外围系统，例如：文件/网络/NoSQL/RDBMS/Message Queue等。Flink底层也预定义了一些常用的Sinks，同时用户也可以根据实际需求定制Data Sink通过集成SinkFunction或者RichSinkFunction。

File Based（测试）

• writeAsText()|writeAsCsv(…)|writeUsingOutputFormat() at-least-once

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

val output = new CsvOutputFormat[Tuple2[String, Int]](new Path("file:///D:/fink-results"))
//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
    .map((_,1))
    .keyBy(0)
    .sum(1)
    .map(t=> new Tuple2(t._1,t._2))
    .writeUsingOutputFormat(output)

fsEnv.execute("FlinkWordCountsQuickStart")

• Bucketing File Sink (exactly-once)

<dependency>
    <groupId>org.apache.flinkgroupId>
    <artifactId>flink-connector-filesystem_2.11artifactId>
    <version>1.8.1version>
dependency>

<dependency>
    <groupId>org.apache.hadoopgroupId>
    <artifactId>hadoop-commonartifactId>
    <version>2.9.2version>
dependency>
<dependency>
    <groupId>org.apache.hadoopgroupId>
    <artifactId>hadoop-hdfsartifactId>
    <version>2.9.2version>
dependency>

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

val bucketSink = new BucketingSink[String]("hdfs://Spark:9000/bucketSink")
bucketSink.setBucketer(new DateTimeBucketer("yyyy-MM-dd-HH", ZoneId.of("Asia/Shanghai")))

//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.sum(1)
.map(t=>t._1+"\t"+t._2)
.addSink(bucketSink)

fsEnv.execute("FlinkWordCountsQuickStart")

print()/ printToErr()

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.sum(1)
.print("error")

fsEnv.execute("FlinkWordCountsQuickStart")

自定义Sink（熟练）

class UserDefineRichSinkFunction extends RichSinkFunction[(String,Int)]{
    override def open(parameters: Configuration): Unit = {
        println("open")
    }
    override def invoke(value: (String, Int)): Unit = {
        println("insert into xxx "+value)
    }

    override def close(): Unit = {
        println("close")
    }
}

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",7788)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.sum(1)
.addSink(new UserDefineRichSinkFunction)

fsEnv.execute("FlinkWordCountsQuickStart")

Redis Sink(掌握)

参考：https://bahir.apache.org/docs/flink/current/flink-streaming-redis/

<dependency>
    <groupId>org.apache.bahirgroupId>
    <artifactId>flink-connector-redis_2.11artifactId>
    <version>1.0version>
dependency>

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment


val conf = new FlinkJedisPoolConfig.Builder()
.setHost("Spark")
.setPort(6379).build()

//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",7788)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.sum(1)
.addSink(new RedisSink(conf,new UserDefineRedisMapper))

fsEnv.execute("FlinkWordCountsQuickStart")

class UserDefineRedisMapper extends RedisMapper[(String,Int)]{
  override def getCommandDescription: RedisCommandDescription = {
    new RedisCommandDescription(RedisCommand.HSET,"word-count")
  }

  override def getKeyFromData(t: (String, Int)): String = {
    t._1
  }

  override def getValueFromData(t: (String, Int)): String = {
    t._2.toString
  }
}

在安装Redis如果访问不到，需要关闭Redis protect-model：no

Kafka Sink(掌握)

<dependency>
    <groupId>org.apache.flinkgroupId>
    <artifactId>flink-connector-kafka_2.11artifactId>
    <version>1.8.1version>
dependency>

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

val props = new Properties()
props.setProperty(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"Spark:9092")
//不建议覆盖
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,classOf[ByteArraySerializer])
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,classOf[ByteArraySerializer])


props.put(ProducerConfig.RETRIES_CONFIG,"3")
props.put(ProducerConfig.ACKS_CONFIG,"-1")
props.put(ProducerConfig.ENABLE_IDEMPOTENCE_CONFIG,"true")
props.put(ProducerConfig.BATCH_SIZE_CONFIG,"100")
props.put(ProducerConfig.LINGER_MS_CONFIG,"500")


//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",7788)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.sum(1)
.addSink(new FlinkKafkaProducer[(String, Int)]("topicxx",
                                               new UserDefineKeyedSerializationSchema,
                                               props))

fsEnv.execute("FlinkWordCountsQuickStart")

class UserDefineKeyedSerializationSchema extends KeyedSerializationSchema[(String,Int)]{
  override def serializeKey(t: (String, Int)): Array[Byte] = {
    t._1.getBytes()
  }

  override def serializeValue(t: (String, Int)): Array[Byte] = {
    t._2.toString.getBytes()
  }

  override def getTargetTopic(t: (String, Int)): String = "topic01"
}

Operator（会用）

DataStream Transformations

Datastream -> Datasteam

Map

Takes one element and produces one element. A map function that doubles the values of the input stream:

dataStream.map { x => x * 2 }

FlatMap

Takes one element and produces zero, one, or more elements. A flatmap function that splits sentences to words:

dataStream.flatMap { str => str.split(" ") }

Filter

Evaluates a boolean function for each element and retains those for which the function returns true. A filter that filters out zero values:

dataStream.filter { _ != 0 }

Union

Union of two or more data streams creating a new stream containing all the elements from all the streams. Note: If you union a data stream with itself you will get each element twice in the resulting stream.

dataStream.union(otherStream1, otherStream2,  ...)

DataStream,DataStream → ConnectedStreams

Connect

“Connects” two data streams retaining their types, allowing for shared state between the two streams.

someStream : DataStream[Int] = ...
otherStream : DataStream[String] = ...

val connectedStreams = someStream.connect(otherStream)

CoMap, CoFlatMap

Similar to map and flatMap on a connected data stream

connectedStreams.map(
    (_ : Int) => true,
    (_ : String) => false
)
connectedStreams.flatMap(
    (_ : Int) => true,
    (_ : String) => false
)

案例小节

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

val s1 = fsEnv.socketTextStream("Spark",9999)
val s2 = fsEnv.socketTextStream("Spark",8888)
s1.connect(s2).flatMap(
    (line:String)=>line.split("\\s+"),//s1流转换逻辑
    (line:String)=>line.split("\\s+")//s2流转换逻辑
)
.map((_,1))
.keyBy(0)
.sum(1)
.print()

fsEnv.execute("ConnectedStream")

DataStream → SplitStream

Split

Split the stream into two or more streams according to some criterion.

val split = someDataStream.split(
  (num: Int) =>
    (num % 2) match {
      case 0 => List("even")
      case 1 => List("odd")
    }
)

Select

Select one or more streams from a split stream.

val even = split select "even"
val odd = split select "odd"
val all = split.select("even","odd")

案例小节(过期了)

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

val logStream = fsEnv.socketTextStream("Spark",9999)

val splitStream: SplitStream[String] = logStream.split(new OutputSelector[String] {
    override def select(out: String): lang.Iterable[String] = {
        if (out.startsWith("INFO")) {
            val array = new util.ArrayList[String]()
            array.add("info")
            return array
        } else  {
            val array = new util.ArrayList[String]()
            array.add("error")
            return array
        }
    }
})

splitStream.select("info").print("info")
splitStream.select("error").printToErr("error")

fsEnv.execute("ConnectedStream")

用法二（优先）

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

val logStream = fsEnv.socketTextStream("Spark",9999)


val errorTag = new OutputTag[String]("error")

val dataStream = logStream.process(new ProcessFunction[String, String] {
    override def processElement(line: String,
                                context: ProcessFunction[String, String]#Context,
                                collector: Collector[String]): Unit = {
        if (line.startsWith("INFO")) {
            collector.collect(line)
        }else{
            context.output(errorTag,line)//分支输出
        }
    }
})

dataStream.print("正常信息")
dataStream.getSideOutput(errorTag).print("错误信息")

fsEnv.execute("ConnectedStream")

DataStream → KeyedStream

KeyBy

Logically partitions a stream into disjoint partitions, each partition containing elements of the same key. Internally, this is implemented with hash partitioning.

dataStream.keyBy("someKey") // Key by field "someKey"
dataStream.keyBy(0) // Key by the first element of a Tuple

Reduce

A “rolling” reduce on a keyed data stream. Combines the current element with the last reduced value and emits the new value.

keyedStream.reduce { _ + _ }

Fold

A “rolling” fold on a keyed data stream with an initial value. Combines the current element with the last folded value and emits the new value.

val result: DataStream[String] =
keyedStream.fold("start")((str, i) => { str + "-" + i })

Aggregations

Rolling aggregations on a keyed data stream. The difference between min and minBy is that min returns the minimum value, whereas minBy returns the element that has the minimum value in this field (same for max and maxBy).

keyedStream.sum(0)
keyedStream.sum("key")
keyedStream.min(0)
keyedStream.min("key")
keyedStream.max(0)
keyedStream.max("key")
keyedStream.minBy(0)
keyedStream.minBy("key")
keyedStream.maxBy(0)
keyedStream.maxBy("key")

Physical partitioning

Flink提供了一些分区方案，可供用户选择，分区目的是为了任务之间数据的能够均衡分布。

分区方案	说明
Custom partitioning	需要用户实现分区策略 dataStream.partitionCustom(partitioner, “someKey”)
Random partitioning	将当前的数据随机分配给下游任务 dataStream.shuffle()
Rebalancing (Round-robin partitioning)	轮询将上游的数据均分下游任务 dataStream.rebalance()
Rescaling	缩放分区数据，例如上游2个并行度/下游4个 ，上游会将1个分区的数据发送给下游前两个分区，后1个分区，会发送下游后两个。 dataStream.rescale()
Broadcasting	上游会将分区所有数据，广播给下游的所有任务分区。 dataStream.broadcast()

Task chaining and resource groups(了解)

连接两个Operator 转换，尝试将两个Operator 转换放置到一个线程当中，可以减少线程消耗，避免不必要的线程通信。用户可以通过 StreamExecutionEnvironment.disableOperatorChaining()禁用chain操作。

val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)

    //3.对数据做转换
    dataStream.filter(line => line.startsWith("INFO"))
    .flatMap(_.split("\\s+"))
    .map((_,1))
    .map(t=>WordPair(t._1,t._2))
    .print()

为了方便，Flink提供一下算子用于修改chain的行为

算子	操作	说明
Start new chain	someStream.filter(…).map(…).startNewChain().map(…)	开启新chain，将当前算子和filter断开
Disable chaining	someStream.map(…).disableChaining()	当前算子和前后都要断开chain操作
Set slot sharing group	someStream.filter(…).slotSharingGroup(“name”)	设置操作任务所属资源Group，影响任务对TaskSlots占用。

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)

//3.对数据做转换
dataStream.filter(line => line.startsWith("INFO"))
.flatMap(_.split("\\s+"))
.startNewChain()
.slotSharingGroup("g1")
.map((_,1))
.map(t=>WordPair(t._1,t._2))
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

State & Fault Tolerance（重点）

Flink将流计算的状态分为两类：Keyed Sate \ Opertator State.其中Keyed Sate状态是操作符中key绑定的，而 Operator State只可以和操作符绑定。无论是Keyed state还是Operator State Flink对状态的管理分为两种形式 Managed State 和 Raw Sate。

Managed State - 由于状态处于被管理，因此状态结构和信息都是被Flink预制好的，因此使用Managed State Flink可以更好的对存储做优化。

Raw Sate - 该状态是原生的数据，只有在用户自定义Operator实现的时候，才开会用到，并且Flink在存储原生状态的时候，仅仅存储了字节数组，因此Flink无法获取有关注状态任何信息，因此在实际的开发中基本不用。

All datastream functions can use managed state, but the raw state interfaces can only be used when implementing operators. Using managed state (rather than raw state) is recommended, since with managed state Flink is able to automatically redistribute state when the parallelism is changed, and also do better memory management.

Managed Keyed State

针对于Keyed Sate状态flink提供了丰富的状态变量，一遍用户完成状态存储。目前有以下几种状态：

类型	说明	方法
ValueState	这个状态主要存储一个可以用作更新的值	update(T) T value() clear()
ListState	这将存储List集合元素	add(T) addAll(List) Iterable get() update(List) clear()
ReducingState	这将保留一个值，该值表示添加到状态的所有值的汇总 需要用户提供ReduceFunction	add(T) T get() clear()
AggregatingState	这将保留一个值，该值表示添加到状态的所有值的汇总 需要用户提供AggregateFunction	add(IN) T get() clear()
FoldingState	这将保留一个值，该值表示添加到状态的所有值的汇总 需要用户提供FoldFunction	add(IN) T get() clear()
MapState	这个状态会保留一个Map集合元素	put(UK, UV) putAll(Map) entries() keys() values() clear()

如果想拿到一个State的引用，必须创建相应SateDescriptor,Flink提供了以下的以下SateDescriptor

ValueStateDescriptor, ListStateDescriptor, ReducingStateDescriptor, FoldingStateDescriptor MapStateDescriptor,AggregatingStateDescriptor

创建完SateDescriptor用户需要在Rich Function获取RuntimeConext对象，然后调用该对象的相应方法获取Sate对象

• ValueState getState(ValueStateDescriptor)
• ReducingState getReducingState(ReducingStateDescriptor)
• ListState getListState(ListStateDescriptor)
• AggregatingState getAggregatingState(AggregatingStateDescriptor)
• FoldingState getFoldingState(FoldingStateDescriptor)
• MapState getMapState(MapStateDescriptor)

ValueState

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
fsEnv.disableOperatorChaining()//
//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.map(new RichMapFunction[(String,Int),(String,Int)] {
    var valueState:ValueState[Int]=_

    override def open(parameters: Configuration): Unit = {
        val vsd = new ValueStateDescriptor[Int]("wordcount",createTypeInformation[Int])
        valueState= getRuntimeContext.getState(vsd)
    }

    override def map(value: (String, Int)): (String, Int) = {
        var historyValue = valueState.value()
        if(historyValue==null){
            historyValue=0
        }
        //更新历史
        valueState.update(historyValue+value._2)
        (value._1,valueState.value())
    }
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

ReduceState

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
fsEnv.disableOperatorChaining()//
//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.map(new RichMapFunction[(String,Int),(String,Int)] {
    var reduceState:ReducingState[Int]=_

    override def open(parameters: Configuration): Unit = {
        val rsd = new ReducingStateDescriptor[Int]("wordcount",new ReduceFunction[Int] {
            override def reduce(value1: Int, value2: Int): Int = value1+value2
        },createTypeInformation[Int])
        reduceState= getRuntimeContext.getReducingState(rsd)
    }

    override def map(value: (String, Int)): (String, Int) = {
        reduceState.add(value._2)
        (value._1,reduceState.get())
    }
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

AggregatingState

 //1.创建StreamExecutionEnvironment
    val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
    fsEnv.disableOperatorChaining()//
    //2.创建DataStream -细化
    val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)
    //3.对数据做转换 1 zhangsan 销售部 10000
    dataStream.map(_.split("\\s+"))
      .map(ts=>Employee(ts(0),ts(1),ts(2),ts(3).toDouble))
      .keyBy("dept")
      .map(new RichMapFunction[Employee,(String,Double)] {
        var aggregatingState:AggregatingState[Double,Double]= _

        override def open(parameters: Configuration): Unit = {
          val asd=new AggregatingStateDescriptor[Double,(Double,Int),Double]("agggstate",
            new AggregateFunction[Double,(Double,Int),Double] {
              override def createAccumulator(): (Double, Int) = (0.0,0)

              override def add(value: Double, accumulator: (Double, Int)): (Double, Int) = {
                var total=accumulator._1
                var count=accumulator._2
                (total+value,count+1)
              }
              override def merge(a: (Double, Int), b: (Double, Int)): (Double, Int) = {
                (a._1+b._1,a._2+b._2)
              }
              override def getResult(accumulator: (Double, Int)): Double = {
                accumulator._1/accumulator._2
              }

            }
            ,createTypeInformation[(Double,Int)])

          aggregatingState=getRuntimeContext.getAggregatingState(asd)
        }

        override def map(value: Employee): (String, Double) = {
          aggregatingState.add(value.salary)
          (value.dept,aggregatingState.get())
        }
      })
      .print()
    
    fsEnv.execute("FlinkWordCountsQuickStart")

List State

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)
//3.对数据做转换 zhangsan 123456
dataStream.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1)))
.keyBy(0)
.map(new RichMapFunction[(String,String),String] {
    var historyPasswords:ListState[String]=_

    override def open(parameters: Configuration): Unit = {
        val lsd = new ListStateDescriptor[String]("pwdstate",createTypeInformation[String])
        historyPasswords=getRuntimeContext.getListState(lsd)
    }
    override def map(value: (String, String)): String = {
        var list = historyPasswords.get().asScala.toList
        list= list.::(value._2)
        list = list.distinct //去重
        historyPasswords.update(list.asJava)

        value._1+"\t"+list.mkString(",")
    }
})
.print()
fsEnv.execute("FlinkWordCountsQuickStart")
}

Managed Operator State

如果用户想去使用Operator State，用户可以实现一个通用接口CheckpointedFunction 或者实现ListCheckpointed

CheckpointFunction

目前CheckpointFunction仅仅支持List风格状态，每个Operator实例维护者一个SubList，真个系统会将所有的Operator实例sublist进行逻辑拼接。在系统恢复的时候，系统可以在多个Operator实例中进行分发状态，在状态分发时，遵循两种策略：Even-Split(均分)/Union(联合|广播)

public interface CheckpointedFunction {
    void snapshotState(FunctionSnapshotContext var1) throws Exception;
    void initializeState(FunctionInitializationContext var1) throws Exception;
}

class UserDefineBufferSink(threshold: Int = 0) extends SinkFunction[String] with CheckpointedFunction {
    @transient
    private var checkpointedState: ListState[String] = _
    private val bufferedElements = ListBuffer[String]()

    override def invoke(value: String): Unit = {
        bufferedElements += value
        println(bufferedElements.size+ " " + (bufferedElements.size >= threshold))
        if(bufferedElements.size == threshold){
            bufferedElements.foreach(item => println(item))
            bufferedElements.clear()
        }
    }
    //将数据保存起来
    override def snapshotState(functionSnapshotContext: FunctionSnapshotContext): Unit = {
        checkpointedState.clear()
        for(i <- bufferedElements){
            checkpointedState.add(i)
        }
    }

    override def initializeState(context: FunctionInitializationContext): Unit = {
        val descriptor = new ListStateDescriptor[String]("buffered-elements",createTypeInformation[String])
        checkpointedState = context.getOperatorStateStore.getListState(descriptor)
        if(context.isRestored){
            for(element <- checkpointedState.get().asScala) {
                bufferedElements += element
            }
        }
    }
}

ListCheckpointed

该接口是CheckpointFunction一个变体，仅仅支持List style风格状态的Even-Split方案。

public interface ListCheckpointed {
    //返回值即是需要存储的状态
    List snapshotState(long var1, long var3) throws Exception;
    //状态初始化|恢复逻辑
    void restoreState(List var1) throws Exception;
}

class UserDefineCounterSource extends RichParallelSourceFunction[Long] with ListCheckpointed[JLong]{

    @volatile
    private var isRunning = true
    private var offset = 0L

    override def snapshotState(checkpointId: Long, timestamp: Long): util.List[JLong] = {
        println("存储:"+offset)
        Collections.singletonList(offset)//存储的是 offset
    }

    override def restoreState(list: util.List[JLong]): Unit = {
        for (i <- list.asScala){
            println("状态恢复："+i)
            offset=i
        }
    }

    override def run(ctx: SourceFunction.SourceContext[Long]): Unit = {
        val lock = ctx.getCheckpointLock

        while (isRunning) {
            Thread.sleep(1000)
            lock.synchronized({
                ctx.collect(offset) //向下游输出offset
                offset += 1
            })

        }
    }
    override def cancel(): Unit =  isRunning=false
}

//1.创建StreamExecutionEnvironment
val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

//2.创建DataStream -细化
val dataStream: DataStream[Long] = fsEnv.addSource(new UserDefineCounterSource)

dataStream.map(counter => "offset: "+counter)
.print()
fsEnv.execute("FlinkWordCountsQuickStart")

State Time-To-Live（TTL）

keyed state任意类型都可以指定TTL存活时间（配置状态时效性），如果状态配置TTL，并且该状态已经失效了，Flink将尽最大努力清除过期的状态。TTL除了支持单一值的TTL时效，针对集合类型例如 MapState|ListState中的元素，每一个元素都有自己的TTL失效时间。

基本使用

val vsd = new ValueStateDescriptor[Int]("wordcount",createTypeInformation[Int])

val ttlConfig = StateTtlConfig
.newBuilder(Time.seconds(5))//状态存活时间
.setUpdateType(StateTtlConfig.UpdateType.OnCreateAndWrite)//更新时机 默认
.setStateVisibility(StateTtlConfig.StateVisibility.NeverReturnExpired)//永不反回过期数据 默认
.build
//开启TTL特性
vsd.enableTimeToLive(ttlConfig)

Cleanup of Expired State

过期的数据当且仅当用户在读取State数据时候，系统才会将过期的数据删除。

This means that by default if expired state is not read, it won’t be removed, possibly leading to ever growing state.

• Cleanup in full snapshot

系统在重启的时候，会去读取磁盘中的备份的状态数据，这个时候可以过滤掉过期的数据，但是在计算过程中依然无法剔除过期的数据，因此需要用户定期的重启流计算服务，以释放内存空间。

 val ttlConfig = StateTtlConfig
    .newBuilder(Time.seconds(5))//状态存活时间
    .setUpdateType(StateTtlConfig.UpdateType.OnCreateAndWrite)//更新时机
    .setStateVisibility(StateTtlConfig.StateVisibility.NeverReturnExpired)//永不反回过期数据
    .cleanupFullSnapshot()
    .build

• Cleanup in background

除了在启动的时候在做full snapshot的时候清除过期数据，还可以在运行时，后台清理过期数据，可以使用.cleanupInBackground()开启该特性，但是具体的清理策略，取决于用户state backend后端的实现。

 val ttlConfig = StateTtlConfig
    .newBuilder(Time.seconds(5))//状态存活时间
    .setUpdateType(StateTtlConfig.UpdateType.OnCreateAndWrite)//更新时机
    .setStateVisibility(StateTtlConfig.StateVisibility.NeverReturnExpired)//永不反回过期数据
    .cleanupFullSnapshot()
    .cleanupInBackground()//根据State backend实现，选择默认清除策略
    .build

当然用户如果知道自己的statebackend实现，可以自己定制后端清除策略，目前基于内存实现只支持Incremental cleanup和基于RocksDB的 compaction filter的清理策略.

Incremental cleanup

val ttlConfig = StateTtlConfig
    .newBuilder(Time.seconds(5))//状态存活时间
    .setUpdateType(StateTtlConfig.UpdateType.OnCreateAndWrite)//更新时机
    .setStateVisibility(StateTtlConfig.StateVisibility.NeverReturnExpired)//永不反回过期数据
    .cleanupFullSnapshot()
    // .cleanupInBackground()
    //一次增量处理10条 ，每一次有Record过来就清理，默认值 5，false
    .cleanupIncrementally(10,true)
    .build
    //开启TTL特性
vsd.enableTimeToLive(ttlConfig)

Cleanup during RocksDB compaction

如果使用RocksDB状态后端，则另一种清除策略是激活Flink特定的压缩过滤器。 RocksDB定期运行异步压缩以合并状态更新并减少存储。Flink压缩过滤器使用TTL检查状态条目的到期时间戳记，并排除到期值。默认情况compaction特性禁用此功能。 首先必须通过设置Flink配置选项state.backend.rocksdb.ttl.compaction.filter.enabled或调用RocksDBStateBackend :: enableTtlCompactionFilter为RocksDB后端激活

val ttlConfig = StateTtlConfig
    .newBuilder(Time.seconds(5))//状态存活时间
    .setUpdateType(StateTtlConfig.UpdateType.OnCreateAndWrite)//更新时机
    .setStateVisibility(StateTtlConfig.StateVisibility.NeverReturnExpired)//永不反回过期数据
    .cleanupFullSnapshot()
    // .cleanupInBackground()
    //  .cleanupIncrementally(10,true)
    //当RocksDB 累计合并1000条记录的时候，查询一次过期的记录，并且将过期的记录清理掉
    .cleanupInRocksdbCompactFilter(1000)
    .build
//开启TTL特性
vsd.enableTimeToLive(ttlConfig)

Broadcast State（状态广播）

在Flink中除了Operator Sate或者Keyed Sate，还存在第三种状态，称为广播状态，该广播状态可以将A流中的计算结果，广播给B流。B流只可以通过只读的方式读取A流状态。A流状态可以在A流实时更新。

• non-keyed ：DataStream 连接 BroadcastStream
• keyed：KeydStream 连接 BroadcastStream

Checkpoint & Savepoint

Chackpoint是一种机制，Flink会定期存储流计算的状态信息，该检查点的协调任务由JobManager负责协调。JobManager会定期给下游的任务发送barrier（栅栏）信号给下游的节点，下游的任务收到barrier信号之后会预先提交自己的状态，并且将该barrier继续传递下游，下游接受信号后也会预先提交自己的状态，并且会通知JobManager状态持久化情况，只有当所有下游的状态提交都是ok状态时候，JobManager才会标记当前一次checkpoint是成功的。(自动触发过程，无需人工干预)

Savepoint一种手动触发的checkpoint机制。需要人工干预。flink cancel --wirthSavepoint

默认checkpoint没有开启的，需要用户去配置对应的job作业。

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment

// checkpoint 频率 12次/分钟
fsEnv.enableCheckpointing(5000,CheckpointingMode.EXACTLY_ONCE)
// 每次Checkpoint时长不得超过4s
fsEnv.getCheckpointConfig.setCheckpointTimeout(4000)
// 此次chk距离上一次chk时间不得少于2s，同一时刻只能有一个chk
fsEnv.getCheckpointConfig.setMinPauseBetweenCheckpoints(2000);
// 如果用取消任务，但是没有添加--withSavepoint，系统保留checkpoint数据
fsEnv.getCheckpointConfig.enableExternalizedCheckpoints(ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
// 如果检查点恢复失败，放弃任务执行
fsEnv.getCheckpointConfig.setFailOnCheckpointingErrors(true);

val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)
//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.map(new RichMapFunction[(String,Int),(String,Int)] {
    var valueState:ValueState[Int]=_

    override def open(parameters: Configuration): Unit = {
        val vsd = new ValueStateDescriptor[Int]("wordcount",createTypeInformation[Int])
        valueState= getRuntimeContext.getState(vsd)
    }

    override def map(value: (String, Int)): (String, Int) = {
        var historyValue = valueState.value()
        if(historyValue==null){
            historyValue=0
        }
        //更新历史
        valueState.update(historyValue+value._2)
        (value._1,valueState.value())
    }
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

State backend（状态后端：状态后端，用来做状态的持久化）

参考：https://ci.apache.org/projects/flink/flink-docs-release-1.9/ops/state/state_backends.html

MemoryStateBackend- 将快照数据存储在JobManager的内存中，每个State大小，默认不得超过5MB，总的State大小不· 得大于JobManager的内存。一般用于开发测试阶段，状态数据比较小。

FsStateBackend- 将程序计算的状态数据存储在TaskManager的内存中，当系统做checkpoint的时候，系统会将数据异步 写进文件系统。JobManager在内存中存储少许的元数据信息。一般用在生产环境，大state需要存储。

RocksDBStateBackend- 将程序计算的状态数据存储在TaskManager运行所在RocksDB的数据库文件中，系统会以增量 方式将完成检查点。在chk的时候，TaskManager会将本地的RocksDB的数据库数据信息异步写入到 远程 文件系统。JobManager在内存中存储少许的元数据信息。一般用在生产环境，超大state需要存 （key,value 不可以大于 2^31 bytes）

FsStateBackend VS RocksDBStateBackend：FsStateBackend 受限于TaskManager内存，效率高。RocksDBStateBackend仅仅受限于TaskManager本地磁盘，同时由于数据是存储在磁盘中可能序列化和反序列化，因此性能可能有所下降。

Window(窗口计算)

窗口计算是流计算的核心，通过使用窗口对无限的流数据划分成固定大小的 buckets，然后基于落入同一个bucket（窗口）中的元素执行计算。Flink将窗口计算分为两大类。

一类基于keyed-stream窗口计算。

stream
       .keyBy(...)               <-  分组
       .window(...)              <-  必须: "assigner" 窗口分配器
      [.trigger(...)]            <-  可选: "trigger" 每一种类型的窗口系统都有默认触发器
      [.evictor(...)]            <-  可选: "evictor" 可以剔除窗口中元素
      [.allowedLateness(...)]    <-  可选: "lateness" 可以处理迟到数据
      [.sideOutputLateData(...)] <-  可选: "output tag" 可以Side Out获取迟到的元素
       .reduce/aggregate/fold/apply()      <-  必须: "function"
      [.getSideOutput(...)]      <-  可选: 获取Sideout数据 例如迟到数据

直接对non-keyed Stream窗口计算

stream
       .windowAll(...)           <-  required: "assigner"
      [.trigger(...)]            <-  optional: "trigger" (else default trigger)
      [.evictor(...)]            <-  optional: "evictor" (else no evictor)
      [.allowedLateness(...)]    <-  optional: "lateness" (else zero)
      [.sideOutputLateData(...)] <-  optional: "output tag" (else no side output for late data)
       .reduce/aggregate/fold/apply()      <-  required: "function"
      [.getSideOutput(...)]      <-  optional: "output tag"

Window Lifecycle

简而言之，一旦应属于该窗口的第一个元素到达，就会创建一个窗口，并且当时间|WaterMarker（Event Tme或Process Time）超过其Window End 时间加上用户指定的允许延迟时，该窗口将被完全删除。窗口触发计算前提 水位线 没过窗口的End Time这个时候窗口处于Ready状态，这个时候Flink才会对窗口做真正的输出计算。

Trigger：负责监控窗口，只有满足触发器的条件，窗口才会触发。（例如 水位线计算）

evictor： 在窗口触发之后在应用聚合函数之前或之后剔除窗口中的元素。

Window Assigners

Window Assigners定义了如何将元素分配给窗口。在定义完窗口之后，用户可以使用reduce/aggregate/folder/apply等算子实现对窗口的聚合计算。

• Tumbling Windows ：滚动，窗口长度和滑动间隔相等，窗口之间没有重叠。（时间）

dataStream.flatMap(_.split("\\s+"))
    .map((_,1))
    .keyBy(0)
    .window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
    .reduce((v1,v2)=>(v1._1,v1._2+v2._2))
    .print()

• Sliding Windows：滑动，窗口长度 大于 滑动间隔，窗口之间存在数据重叠。（时间）

dataStream.flatMap(_.split("\\s+"))
    .map((_,1))
    .keyBy(0)
    .window(SlidingProcessingTimeWindows.of(Time.seconds(4),Time.seconds(2)))
    .fold(("",0))((z,v)=>(v._1,z._2+v._2))
    .print()

• Session Windows: 会话窗口，窗口没有固定大小，每个元素都会形成一个新窗口，如果窗口的间隔小于指定时间，这些窗口会进行合并。（时间）

dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(0)
.window(ProcessingTimeSessionWindows.withGap(Time.seconds(5)))
.aggregate(new AggregateFunction[(String,Int),(String,Int),(String,Int)] {
    override def createAccumulator(): (String, Int) = {
        ("",0)
    }

    override def add(value: (String, Int), accumulator: (String, Int)): (String, Int) = {
        (value._1,value._2+accumulator._2)
    }

    override def getResult(accumulator: (String, Int)): (String, Int) = {
        accumulator
    }

    override def merge(a: (String, Int), b: (String, Int)): (String, Int) = {
        (a._1,a._2+b._2)
    }
})
.print()

• Global Windows：全局窗口，窗口并不是基于时间划分窗口，因此不存在窗口长度和时间概念。需要用户定制触发策略，窗口才会触发。

dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(_._1)
.window(GlobalWindows.create())
.trigger(CountTrigger.of(4))
.apply(new WindowFunction[(String,Int),(String,Int),String, GlobalWindow] {
    override def apply(key: String, window: GlobalWindow, inputs: Iterable[(String, Int)],
                       out: Collector[(String, Int)]): Unit = {
        println("key:"+key+" w:"+window)
        inputs.foreach(t=>println(t))
        out.collect((key,inputs.map(_._2).sum))
    }
})
.print()

Window Function

定义Window Assigners后，我们需要指定要在每个窗口上执行的计算。 这是Window Function的职责，一旦系统确定某个窗口已准备好进行处理，该Window Function将用于处理每个窗口的元素。Flink提供了以下Window Function处理函数：

• ReduceFunction

new ReduceFunction[(String, Int)] {
    override def reduce(v1: (String, Int), v2: (String, Int)): (String, Int) = {
        (v1._1,v1._2+v2._2)
    }
}

• AggregateFunction

new AggregateFunction[(String,Int),(String,Int),(String,Int)] {
    override def createAccumulator(): (String, Int) = {
        ("",0)
    }
    override def add(value: (String, Int), accumulator: (String, Int)): (String, Int) = {
        (value._1,value._2+accumulator._2)
    }
    override def getResult(accumulator: (String, Int)): (String, Int) = {
        accumulator
    }
    override def merge(a: (String, Int), b: (String, Int)): (String, Int) = {
        (a._1,a._2+b._2)
    }
}

• FoldFunction(废弃)

new FoldFunction[(String,Int),(String,Int)] {
    override def fold(accumulator: (String, Int), value: (String, Int)): (String, Int) = {
        (value._1,accumulator._2+value._2)
    }
}

不能用在Merger window中，不可用在SessionWindows中。

• apply/WindowFunction（旧版-一般不推荐）

可以获取窗口的中的所有元素，并且可以拿到一些元数据信息，无法操作窗口状态。

new WindowFunction[(String,Int),(String,Int),String, GlobalWindow] {
    override def apply(key: String, window: GlobalWindow, inputs: Iterable[(String, Int)],
                       out: Collector[(String, Int)]): Unit = {
        println("key:"+key+" w:"+window)
        inputs.foreach(t=>println(t))
        out.collect((key,inputs.map(_._2).sum))
    }
}

在keyBy的时候，不能使用下标，只能使用keyBy(_._1)

• ProcessWindowFunction(重点掌握)

可以获取窗口的中的所有元素，并且拿到一些元数据信息。是WindowFunction的替代方案，因为该接口可以直接操作窗口的State|全局State

获取窗口状态

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)

//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(_._1)
.window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
.process(new ProcessWindowFunction[(String,Int),(String,Int),String,TimeWindow] {

    override def process(key: String,
                         context: Context,
                         elements: Iterable[(String, Int)],
                         out: Collector[(String, Int)]): Unit = {

        val w = context.window
        val sdf = new SimpleDateFormat("HH:mm:ss")

        println(sdf.format(w.getStart)+" ~ "+ sdf.format(w.getEnd))

        val total = elements.map(_._2).sum
        out.collect((key,total))
    }
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

配合Reduce|Aggregate|FoldFunction

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)

//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(_._1)
.window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
.reduce((v1:(String,Int),v2:(String,Int))=>(v1._1,v1._2+v2._2),
        new ProcessWindowFunction[(String,Int),(String,Int),String,TimeWindow] {

    override def process(key: String,
                         context: Context,
                         elements: Iterable[(String, Int)],
                         out: Collector[(String, Int)]): Unit = {

        val w = context.window
        val sdf = new SimpleDateFormat("HH:mm:ss")

        println(sdf.format(w.getStart)+" ~ "+ sdf.format(w.getEnd))

        val total = elements.map(_._2).sum
        out.collect((key,total))
    }
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

操作WindowState|GlobalState

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)

//3.对数据做转换
dataStream.flatMap(_.split("\\s+"))
.map((_,1))
.keyBy(_._1)
.window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
.reduce((v1:(String,Int),v2:(String,Int))=>(v1._1,v1._2+v2._2),
        new ProcessWindowFunction[(String,Int),String,String,TimeWindow] {
            var windowStateDescriptor:ReducingStateDescriptor[Int]=_
            var globalStateDescriptor:ReducingStateDescriptor[Int]=_

            override def open(parameters: Configuration): Unit = {
                windowStateDescriptor = new ReducingStateDescriptor[Int]("wcs",new ReduceFunction[Int] {
                    override def reduce(value1: Int, value2: Int): Int = value1+value2
                },createTypeInformation[Int])
                globalStateDescriptor = new ReducingStateDescriptor[Int]("gcs",new ReduceFunction[Int] {
                    override def reduce(value1: Int, value2: Int): Int = value1+value2
                },createTypeInformation[Int])
            }

            override def process(key: String,
                                 context: Context,
                                 elements: Iterable[(String, Int)],
                                 out: Collector[String]): Unit = {

                val w = context.window
                val sdf = new SimpleDateFormat("HH:mm:ss")

                val windowState = context.windowState.getReducingState(windowStateDescriptor)
                val globalState = context.globalState.getReducingState(globalStateDescriptor)

                elements.foreach(t=>{
                    windowState.add(t._2)
                    globalState.add(t._2)
                })
                out.collect(key+"\t"+windowState.get()+"\t"+globalState.get())
            }
        })
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

Trigger (触发器)

Trigger确定窗口（由Window Assigner形成）何时准备好由Window Function处理。 每个Window Assigner都带有一个默认Trigger。 如果默认Trigger不适合您的需求，则可以使用trigger（…）指定自定义触发器。

窗口类型	触发器	触发时机
event-time window（Tumbling/Sliding/Session）	EventTimeTrigger	一旦watermarker没过窗口的末端，该触发器便会触发
processing-time window（Tumbling/Sliding/Session）	ProcessingTimeTrigger	一旦系统时间没过窗口末端，该触发器便会触发
GlobalWindow 并不是基于时间的窗口	NeverTrigger	永远不会触发。

public class UserDefineDeltaTrigger extends Trigger {

    private final DeltaFunction deltaFunction;
    private final double threshold;
    private final ValueStateDescriptor stateDesc;

    private UserDefineDeltaTrigger(double threshold, DeltaFunction deltaFunction, TypeSerializer stateSerializer) {
        this.deltaFunction = deltaFunction;
        this.threshold = threshold;
        this.stateDesc = new ValueStateDescriptor("last-element", stateSerializer);
    }

    public TriggerResult onElement(T element, long timestamp, W window, TriggerContext ctx) throws Exception {
        ValueState lastElementState = (ValueState)ctx.getPartitionedState(this.stateDesc);
        if (lastElementState.value() == null) {
            lastElementState.update(element);
            return TriggerResult.CONTINUE;
        } else if (this.deltaFunction.getDelta(lastElementState.value(), element) > this.threshold) {
            lastElementState.update(element);
            return TriggerResult.FIRE_AND_PURGE;
        } else {
            return TriggerResult.CONTINUE;
        }
    }

    public TriggerResult onEventTime(long time, W window, TriggerContext ctx) {
        return TriggerResult.CONTINUE;
    }

    public TriggerResult onProcessingTime(long time, W window, TriggerContext ctx) throws Exception {
        return TriggerResult.CONTINUE;
    }

    public void clear(W window, TriggerContext ctx) throws Exception {
        ((ValueState)ctx.getPartitionedState(this.stateDesc)).clear();
    }

    public String toString() {
        return "DeltaTrigger(" + this.deltaFunction + ", " + this.threshold + ")";
    }

    public static  UserDefineDeltaTrigger of(double threshold, DeltaFunction deltaFunction, TypeSerializer stateSerializer) {
        return new UserDefineDeltaTrigger(threshold, deltaFunction, stateSerializer);
    }
}

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)

var deltaTrigger=UserDefineDeltaTrigger.of[(String,Double),GlobalWindow](10.0,new DeltaFunction[(String, Double)] {
    override def getDelta(lastData: (String, Double), newData: (String, Double)): Double = {
        newData._2-lastData._2
    }
},createTypeInformation[(String,Double)].createSerializer(fsEnv.getConfig))

//3.对数据做转换  10
// a  100.0
dataStream.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1).toDouble))
.keyBy(_._1)
.window(GlobalWindows.create())
.trigger(deltaTrigger)
.apply(new WindowFunction[(String,Double),(String,Int),String, GlobalWindow] {
    override def apply(key: String, window: GlobalWindow, inputs: Iterable[(String, Double)],
                       out: Collector[(String, Int)]): Unit = {
        println("key:"+key+" w:"+window)
        inputs.foreach(t=>println(t))
    }
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

Evictors（剔除器）

Evictors可以在触发器触发后，应用Window Function之前 和/或 之后从窗口中删除元素。 为此，Evictor界面有两种方法：

public interface Evictor extends Serializable {

	/**
	 * Optionally evicts elements. Called before windowing function.
	 *
	 * @param elements The elements currently in the pane.
	 * @param size The current number of elements in the pane.
	 * @param window The {@link Window}
	 * @param evictorContext The context for the Evictor
     */
	void evictBefore(Iterable> elements, int size, W window, EvictorContext evictorContext);

	/**
	 * Optionally evicts elements. Called after windowing function.
	 *
	 * @param elements The elements currently in the pane.
	 * @param size The current number of elements in the pane.
	 * @param window The {@link Window}
	 * @param evictorContext The context for the Evictor
	 */
	void evictAfter(Iterable> elements, int size, W window, EvictorContext evictorContext);
	}
}

public class UserDefineErrorEvictor implements Evictor {
    private  boolean isEvictorBefore;
    private  String  content;

    public UserDefineErrorEvictor(boolean isEvictorBefore, String content) {
        this.isEvictorBefore = isEvictorBefore;
        this.content=content;
    }

    public void evictBefore(Iterable> elements, int size, W window, EvictorContext evictorContext) {
        if(isEvictorBefore){
            evict(elements,  size,  window,  evictorContext);
        }
    }

    public void evictAfter(Iterable> elements, int size, W window, EvictorContext evictorContext) {
        if(!isEvictorBefore){
            evict(elements,  size,  window,  evictorContext);
        }
    }
    private  void evict(Iterable> elements, int size, W window, EvictorContext evictorContext) {
        Iterator> iterator = elements.iterator();
        while(iterator.hasNext()){
            TimestampedValue next = iterator.next();
            String value = next.getValue();
            if(value.contains(content)){
                iterator.remove();
            }
        }
    }
}

EventTime Window

Flink在流式传输程序中支持不同的时间概念。包含：Processing Time/Event Time/Ingestion Time

如果用户不指定Flink处理时间属性，默认使用的是ProcessingTime.其中Ingestion和Processing Time都是系统产生的，不同的是Ingestion Time是Source Function产生，而Processing Time由计算节点产生，无需用户指定时间抽取策略。

Flink中用于衡量事件时间进度的机制是水印。 水印作为数据流的一部分流动，并带有时间戳t。 Watermark（t）声明事件时间已在该流中达到时间t，这意味着该流中不应再有时间戳t’<= t的元素。

watermarker(T)= max Event time seen by Process Node  - maxOrderness 时间(乱序时间)  

水位线计算

 .assignTimestampsAndWatermarks(AssignerWithPeriodicWatermarks|AssignerWithPunctuatedWatermarks)

• AssignerWithPeriodicWatermarks:会定期的计算watermarker的值

//设置水位线计算频率 1s
fsEnv.getConfig.setAutoWatermarkInterval(1000)

class UserDefineAssignerWithPeriodicWatermarks extends AssignerWithPeriodicWatermarks[(String,Long)] {

    var  maxOrderness=2000L
    var  maxSeenTime=0L
    var sdf=new SimpleDateFormat("HH:mm:ss")
    override def getCurrentWatermark: Watermark = {
        // println("watermarker:"+sdf.format(maxSeenTime-maxOrderness))
        new Watermark(maxSeenTime-maxOrderness)
    }

    override def extractTimestamp(element: (String, Long), previousElementTimestamp: Long): Long = {
        maxSeenTime=Math.max(element._2,maxSeenTime)
        element._2
    }
}

• AssignerWithPunctuatedWatermarks：系统每接收一个元素，就会触发水位线的计算

class UserDefineAssignerWithPunctuatedWatermarks extends AssignerWithPunctuatedWatermarks[(String,Int)]{
    var  maxOrderness=2000L //最大乱序时间
    var  maxSeenTime=0L
    override def checkAndGetNextWatermark(lastElement: (String, Int), extractedTimestamp: Long): Watermark = {
        new Watermark(maxSeenTime-maxOrderness)
    }

    override def extractTimestamp(element: (String, Int), previousElementTimestamp: Long): Long = {
        maxSeenTime=Math.max(element._2,maxSeenTime)
        element._2
    }
}

基本案例

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
fsEnv.setParallelism(1)
//设置时间特性
fsEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
//设置水位线计算频率 1s
fsEnv.getConfig.setAutoWatermarkInterval(1000)
//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)
//3.对数据做转换
//a 时间戳
dataStream.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1).toLong))
.assignTimestampsAndWatermarks(new UserDefineAssignerWithPeriodicWatermarks)
.windowAll(TumblingEventTimeWindows.of(Time.seconds(5)))
.apply(new AllWindowFunction[(String,Long),String,TimeWindow] {
    var sdf=new SimpleDateFormat("HH:mm:ss")

    override def apply(window: TimeWindow,
                       input: Iterable[(String, Long)],
                       out: Collector[String]): Unit = {
        println(sdf.format(window.getStart)+" ~ "+ sdf.format(window.getEnd))
        out.collect(input.map(t=>t._1+"->" +sdf.format(t._2)).reduce((v1,v2)=>v1+" | "+v2))
    }
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

迟到数据处理

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
fsEnv.setParallelism(1)
//设置时间特性
fsEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
//设置水位线计算频率 1s
fsEnv.getConfig.setAutoWatermarkInterval(1000)

//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)

//3.对数据做转换
//a 时间戳
dataStream.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1).toLong))
.assignTimestampsAndWatermarks(new UserDefineAssignerWithPeriodicWatermarks)
.windowAll(TumblingEventTimeWindows.of(Time.seconds(5)))
.allowedLateness(Time.seconds(2)) // w - window End < 2 数据还可以参与计算
.apply(new AllWindowFunction[(String,Long),String,TimeWindow] {
    var sdf=new SimpleDateFormat("HH:mm:ss")

    override def apply(window: TimeWindow,
                       input: Iterable[(String, Long)],
                       out: Collector[String]): Unit = {
        println(sdf.format(window.getStart)+" ~ "+ sdf.format(window.getEnd))
        out.collect(input.map(t=>t._1+"->" +sdf.format(t._2)).reduce((v1,v2)=>v1+" | "+v2))
    }
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

当 窗口end时间

太迟的数据

如果当前水位线的时间T - 窗口的End时间 >= 最大迟到的时间，此时如果有数据落入到窗口中，该数据默认Flink是丢弃的，如果需要获取这些没有参与计算的数据用户可以通过sideout手段获取，这些太迟的数据。

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
fsEnv.setParallelism(1)
//设置时间特性
fsEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
//设置水位线计算频率 1s
fsEnv.getConfig.setAutoWatermarkInterval(1000)

//2.创建DataStream -细化
val dataStream: DataStream[String] = fsEnv.socketTextStream("Spark",9999)
val lateTag = new OutputTag[(String,Long)]("late")
//3.对数据做转换
//a 时间戳
val stream = dataStream.map(_.split("\\s+"))
.map(ts => (ts(0), ts(1).toLong))
.assignTimestampsAndWatermarks(new UserDefineAssignerWithPeriodicWatermarks)
.windowAll(TumblingEventTimeWindows.of(Time.seconds(5)))
.allowedLateness(Time.seconds(2)) // w - window end time < 2 数据还可以参与计算
.sideOutputLateData(lateTag) //将太迟的数据，输出到
.apply(new AllWindowFunction[(String, Long), String, TimeWindow] {
    var sdf = new SimpleDateFormat("HH:mm:ss")

    override def apply(window: TimeWindow,
                       input: Iterable[(String, Long)],
                       out: Collector[String]): Unit = {
        println(sdf.format(window.getStart) + " ~ " + sdf.format(window.getEnd))
        out.collect(input.map(t => t._1 + "->" + sdf.format(t._2)).reduce((v1, v2) => v1 + " | " + v2))
    }
})
stream.print("窗口")
stream.getSideOutput(lateTag).print("迟到数据：")

fsEnv.execute("FlinkWordCountsQuickStart")

Watermarks in Parallel Streams

watermarker 在Source Function 之后直接生成。 Source Function 的每个并行子任务通常独立生成其watermarker 。随着watermarker 在流程序中的流动，它们会增加计算节点的EventTime。 每当Operator更新了事件时间，该事件事件都会为其后Operator在下游生成新的watermarker。当下游操作符接收到多个watermarker的值得时候，系统会选择最小的watermarker。‘

Join(连接)

Window Join

stream.join(otherStream)
    .where()
    .equalTo()
    .window()
    .apply()

Tumbling Window Join

当执行滚动窗口联接时，所有具有公共键和公共滚动窗口的元素都按成对组合联接，并传递给“ JoinFunction”或“ FlatJoinFunction”。 因为它的行为就像一个内部联接，所以不会发出在滚动窗口中没有来自另一个流的元素的一个流的元素！

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
fsEnv.setParallelism(1)
//设置时间特性
fsEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
//设置水位线计算频率 1s
fsEnv.getConfig.setAutoWatermarkInterval(1000)

// 001 zhangsan 时间戳
val userStrem: DataStream[(String,String,Long)] = fsEnv.socketTextStream("Spark",9999)
.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1),ts(2).toLong))
.assignTimestampsAndWatermarks(new UserAssignerWithPunctuatedWatermarks)
// 001 100.0 时间戳
val orderStream: DataStream[(String,Double,Long)] = fsEnv.socketTextStream("Spark",8888)
.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1).toDouble,ts(2).toLong))
.assignTimestampsAndWatermarks(new OrderAssignerWithPunctuatedWatermarks)

userStrem.join(orderStream)
.where(_._1)
.equalTo(_._1)
.window(TumblingEventTimeWindows.of(Time.seconds(2)))
.apply((v1,v2,out:Collector[String])=>{
    out.collect(v1._1+"\t"+v1._2+"\t"+v2._2)
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

Sliding Window Join

当执行滑动窗口连接时，所有具有公共键和公共滑动窗口的元素都以成对组合的方式连接在一起，并传递给“ JoinFunction”或“ FlatJoinFunction”。 在当前滑动窗口中，一个流中没有其他流元素的元素不会被发出！

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val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
fsEnv.setParallelism(1)
//设置时间特性
fsEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
//设置水位线计算频率 1s
fsEnv.getConfig.setAutoWatermarkInterval(1000)

// 001 zhangsan 时间戳
val userStrem: DataStream[(String,String,Long)] = fsEnv.socketTextStream("Spark",9999)
.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1),ts(2).toLong))
.assignTimestampsAndWatermarks(new UserAssignerWithPunctuatedWatermarks)
// 001 100.0 时间戳
val orderStream: DataStream[(String,Double,Long)] = fsEnv.socketTextStream("Spark",8888)
.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1).toDouble,ts(2).toLong))
.assignTimestampsAndWatermarks(new OrderAssignerWithPunctuatedWatermarks)

userStrem.join(orderStream)
.where(_._1)
.equalTo(_._1)
.window(SlidingEventTimeWindows.of(Time.seconds(2),Time.seconds(1)))
.apply((v1,v2,out:Collector[String])=>{
    out.collect(v1._1+"\t"+v1._2+"\t"+v2._2)
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

Session Window Join

当执行会话窗口连接时，所有具有*“组合” *时满足会话条件的相同键的元素将以成对组合的方式连接在一起，并传递给“ JoinFunction”或“ FlatJoinFunction”。 再次执行内部联接，因此，如果有一个会话窗口仅包含一个流中的元素，则不会发出任何输出！

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
fsEnv.setParallelism(1)
//设置时间特性
fsEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
//设置水位线计算频率 1s
fsEnv.getConfig.setAutoWatermarkInterval(1000)

// 001 zhangsan 时间戳
val userStrem: DataStream[(String,String,Long)] = fsEnv.socketTextStream("Spark",9999)
.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1),ts(2).toLong))
.assignTimestampsAndWatermarks(new UserAssignerWithPunctuatedWatermarks)
// 001 100.0 时间戳
val orderStream: DataStream[(String,Double,Long)] = fsEnv.socketTextStream("Spark",8888)
.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1).toDouble,ts(2).toLong))
.assignTimestampsAndWatermarks(new OrderAssignerWithPunctuatedWatermarks)

userStrem.join(orderStream)
.where(_._1)
.equalTo(_._1)
.window(EventTimeSessionWindows.withGap(Time.seconds(2)))
.apply((v1,v2,out:Collector[String])=>{
    out.collect(v1._1+"\t"+v1._2+"\t"+v2._2)
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

Interval Join

间隔连接使用公共密钥连接两个流（现在将它们分别称为A和B）的元素，并且流B的元素具有与流A的元素时间戳相对时间间隔的时间戳。

这也可以更正式地表示为b.timestamp∈[a.timestamp + lowerBound; a.timestamp + upperBound]或a.timestamp + lowerBound <= b.timestamp <= a.timestamp + upperBound

其中a和b是A和B的元素，它们共享一个公共密钥。 只要下限始终小于或等于上限，则下限和上限都可以为负或正。 间隔联接当前仅执行内部联接。

当一对元素传递给ProcessJoinFunction时，它们将被分配两个元素的较大时间戳（可通过ProcessJoinFunction.Context访问）。

val fsEnv = StreamExecutionEnvironment.getExecutionEnvironment
fsEnv.setParallelism(1)
//设置时间特性
fsEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
//设置水位线计算频率 1s
fsEnv.getConfig.setAutoWatermarkInterval(1000)

// 001 zhangsan 时间戳
val userkeyedStrem: KeyedStream[(String,String,Long),String] = fsEnv.socketTextStream("Spark",9999)
.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1),ts(2).toLong))
.assignTimestampsAndWatermarks(new UserAssignerWithPunctuatedWatermarks)
.keyBy(t=>t._1)
// 001 100.0 时间戳
val orderStream: KeyedStream[(String,Double,Long),String] = fsEnv.socketTextStream("Spark",8888)
.map(_.split("\\s+"))
.map(ts=>(ts(0),ts(1).toDouble,ts(2).toLong))
.assignTimestampsAndWatermarks(new OrderAssignerWithPunctuatedWatermarks)
.keyBy(t=>t._1)

userkeyedStrem.intervalJoin(orderStream)
.between(Time.seconds(-2),Time.seconds(2))
.process(new ProcessJoinFunction[(String,String,Long),(String,Double,Long),String] {
    override def processElement(left: (String, String, Long),
                                right: (String, Double, Long),
                                ctx: ProcessJoinFunction[(String, String, Long), (String, Double, Long), String]#Context,
                                out: Collector[String]): Unit = {
        val leftTimestamp = ctx.getLeftTimestamp
        val rightTimestamp = ctx.getRightTimestamp
        val timestamp = ctx.getTimestamp
        println(s"left:${leftTimestamp},right:${rightTimestamp},timestamp:${timestamp}")
        out.collect(left._1+"\t"+left._2+"\t"+right._2)
    }
})
.print()

fsEnv.execute("FlinkWordCountsQuickStart")

Flink HA搭建

独立群集的JobManager高可用性的总体思想是，随时有一个领先的JobManager，并且有多个备用JobManager可以在领导者失败的情况下接管领导。 这样可以确保没有单点故障，并且只要待机JobManager处于领导地位，程序就可以取得进展。 备用JobManager实例和主JobManager实例之间没有明显区别。 每个JobManager都可以充当主角色或备用角色

准备工作

• 时钟同步
• IP和主机映射
• SSH免密登陆
• 关闭防火墙
• 安装JDK8
• 安装zookeeeper
• 安装HDFS -HA
• 搭建Flink -HA