flinksql 获取kafka的key flink 读取kafka

转载

技术博主 2024-06-25 17:21:56

Flink程序部署

本地部署

package com.baizhi.jsy.deploy
import org.apache.flink.streaming.api.scala._
object FlinkWordCountCreateLocal {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    val env = StreamExecutionEnvironment.createLocalEnvironment(3)
    //2.创建DataStream - 细化
    val text = env.socketTextStream("Centos",9999)
    //3.执⾏行行DataStream的转换算⼦子
    val counts = text.flatMap(line=>line.split("\\s+"))
      .map(word=>(word,1))
      .keyBy(0)
      .sum(1)
    //4.将计算的结果在控制打印
    counts.print()
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }
}

flinksql 获取kafka的key flink 读取kafka_apache

flinksql 获取kafka的key flink 读取kafka_apache_02

远程部署

[root@CentOS ~]# cd /usr/flink-1.10.0/
[root@CentOS flink-1.10.0]# ./bin/flink run
--class com.baizhi.quickstart.FlinkWordCountQiuckStart
--detached # 后台提交
--parallelism 4 #指定程序默认并行度
--jobmanager CentOS:8081 # 提交目标主机
/root/flink-datastream-1.0-SNAPSHOT.jar
Job has been submitted with JobID f2019219e33261de88a1678fdc78c696

StreamExecutionEnvironment.getExecutionEnvironment自动识别运行环境，如果运行环境是idea，
系统会自动切换成本地模式，默认系统的并行度使⽤用系统最大线程数，等价于Spark中设置的
local[*] ,如果是生产环境，需要用户在提交任务的时候指定并行度 --parallelism

查看现有执行任务

[root@Centos flink-1.10.0]# ./bin/flink list --running --jobmanager Centos:8081
Waiting for response...
------------------ Running/Restarting Jobs -------------------
05.03.2020 12:38:51 : 9563c3b5c7bc9b0f36326eaa51e27d95 : Window Stream WordCount (RUNNING)
--------------------------------------------------------------
No scheduled jobs.

查看所有执行任务（包括执行过的)

[root@Centos flink-1.10.0]# ./bin/flink list --all --jobmanager Centos:8081
Waiting for response...
No running jobs.
No scheduled jobs.
---------------------- Terminated Jobs -----------------------
05.03.2020 12:38:51 : 9563c3b5c7bc9b0f36326eaa51e27d95 : Window Stream WordCount (CANCELED)
--------------------------------------------------------------

取消指定任务

[root@Centos flink-1.10.0]#./bin/flink cancel --jobmanager Centos:8081 9563c3b5c7bc9b0f36326eaa51e27d95
Cancelling job 9563c3b5c7bc9b0f36326eaa51e27d95.
Cancelled job 9563c3b5c7bc9b0f36326eaa51e27d95.

查看程序执行计划

[root@CentOS flink-1.10.0]# ./bin/flink info --class
com.baizhi.quickstart.FlinkWordCountQiuckStart --parallelism 4 /root/flink-
datastream-1.0-SNAPSHOT.jar
----------------------- Execution Plan -----------------------
{"nodes":[{"id":1,"type":"Source: Socket Stream","pact":"Data
Source","contents":"Source: Socket Stream","parallelism":1},{"id":2,"type":"Flat
Map","pact":"Operator","contents":"Flat Map","parallelism":4,"predecessors":
[{"id":1,"ship_strategy":"REBALANCE","side":"second"}]},
{"id":3,"type":"Map","pact":"Operator","contents":"Map","parallelism":4,"predecessors"
:[{"id":2,"ship_strategy":"FORWARD","side":"second"}]},
{"id":5,"type":"aggregation","pact":"Operator","contents":"aggregation","parallelism":
4,"predecessors":[{"id":3,"ship_strategy":"HASH","side":"second"}]},
{"id":6,"type":"Sink: Print to Std. Out","pact":"Data Sink","contents":"Sink: Print to
Std. Out","parallelism":4,"predecessors":
[{"id":5,"ship_strategy":"FORWARD","side":"second"}]}]}
--------------------------------------------------------------
No description provided.

用户可以访问：https://flink.apache.org/visualizer/将json数据粘贴过去，查看Flink执行计划图

跨平台发布

在运行之前需要使用mvn重新打包程序。直接运行main函数即可

package com.baizhi.jsy.deploy
import org.apache.flink.streaming.api.scala._
object FlinkWordCountCorssplatRemove {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    //copy path打包后的jar
    var jars="D:\\idea_code\\flink\\flink_wordCount\\target\\flink_wordCount-1.0-SNAPSHOT.jar"
    val env = StreamExecutionEnvironment.createRemoteEnvironment("Centos",8081,jars)
    //设置默认并行度
    env.setParallelism(4)
    //2.创建DataStream - 细化
    val text = env.socketTextStream("Centos",9999)
    //3.执⾏行行DataStream的转换算⼦子
    val counts = text.flatMap(line=>line.split("\\s+"))
      .map(word=>(word,1))
      .keyBy(0)
      .sum(1)
    //4.将计算的结果在控制打印
    counts.print()
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }

}

Streaming (DataStream API)

DataSource

数据源是程序读取数据的来源，用户可以通过 env.addSource(SourceFunction) ,将SourceFunction添加到程序中。Flink内置许多已知实现的SourceFunction，但是用户可以自定义实现SourceFunction （非并行化的接口）接口或者实现 ParallelSourceFunction （并行化）接口，如果需要有状态管理理还可以继承RichParallelSourceFunction .

File-based

只读取一次在/demo/words的文件

readTextFile(path) - Reads(once) text files, i.e. files that respect the TextInputFormatspecification, line-by-line and returns them as Strings.

package com.baizhi.jsy.sources
import org.apache.flink.streaming.api.scala._
object FlinkWordCountFileSources {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //2.创建DataStream - 细化 下面两个都是读取一次
    val text:DataStream[String] = env.readTextFile("hdfs://Centos:9000/demo/words")
    //3.执⾏行行DataStream的转换算⼦子
    val counts = text.flatMap(line=>line.split("\\s+"))
      .map(word=>(word,1))
      .keyBy(0)
      .sum(1)
    //4.将计算的结果在控制打印
    counts.print()
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }
}

readFile(fileInputFormat, path) - Reads (once) files as dictated by the specified file inputformat.

package com.baizhi.jsy.sources
import org.apache.flink.api.common.io.FileInputFormat
import org.apache.flink.api.java.io.TextInputFormat
import org.apache.flink.streaming.api.scala._
object FlinkWordCountFileSources {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //2.创建DataStream - 细化 下面两个都是读取一次
    var inputFormat:FileInputFormat[String]=new TextInputFormat(null)//下面有路径 可以不给了
    val text = env.readFile(inputFormat,"hdfs://Centos:9000/demo/words")
    //3.执⾏行行DataStream的转换算⼦子
    val counts = text.flatMap(line=>line.split("\\s+"))
      .map(word=>(word,1))
      .keyBy(0)
      .sum(1)
    //4.将计算的结果在控制打印
    counts.print()
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }
}

以上两种方式只读取一次在/demo/words的文件。

readFile（fileInputFormat，path，watchType，interval，pathFilter，typeInfo）-

这是前两个内部调用的方法。它根据给定fileInputFormat。根据提供的watchType，此来源可能会定期监视（每间隔毫秒）新数据的路径（FileProcessingMode.PROCESS_CONTINUOUSLY），或处理一次路径中当前的数据并退出（FileProcessingMode.PROCESS_ONCE）。使用pathFilter，用户可以进一步排除文件被处理。

该方法会检查采集目录下的文件，如果文件发生变化系统会重新采集。此时可能会导致文件的重复计算。⼀一般来说不建议修改文件内容，直接上传新文件即可

package com.baizhi.jsy.sources
import org.apache.flink.api.common.io.FileInputFormat
import org.apache.flink.api.java.io.TextInputFormat
import org.apache.flink.streaming.api.functions.source.FileProcessingMode
import org.apache.flink.streaming.api.scala._
object FlinkWordCountFileSources {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //2.创建DataStream - 细化 下面两个都是读取一次
    var inputFormat:FileInputFormat[String]=new TextInputFormat(null)
    val text = env.readFile(inputFormat,"hdfs://Centos:9000/demo/words"
    ,FileProcessingMode.PROCESS_CONTINUOUSLY,1000)
    //3.执⾏行行DataStream的转换算⼦子
    val counts = text.flatMap(line=>line.split("\\s+"))
      .map(word=>(word,1))
      .keyBy(0)
      .sum(1)
    //4.将计算的结果在控制打印
    counts.print()
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }
}

Socket Based

socketTextStream - Reads from a socket. Elements can be separated by a delimiter.

package com.baizhi.jsy.sources
import org.apache.flink.streaming.api.scala._
object FlinkWordCountSocketTextStream {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //2.创建DataStream - 细化
    val text = env.socketTextStream("Centos",9999,'\n',3)
    //3.执⾏行行DataStream的转换算⼦子
    val counts = text.flatMap(line=>line.split("\\s+"))
      .map(word=>(word,1))
      .keyBy(0)
      .sum(1)
    //4.将计算的结果在控制打印
    counts.print()
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }
}

Collection-based

package com.baizhi.jsy.sources
import org.apache.flink.streaming.api.scala._
object FlinkWordCountCollection {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //2.创建DataStream - 细化
    val text = env.fromCollection(List("this is a demo","hello world jiang si yu"))
    //3.执⾏行行DataStream的转换算⼦子
    val counts = text.flatMap(line=>line.split("\\s+"))
      .map(word=>(word,1))
      .keyBy(0)
      .sum(1)
    //4.将计算的结果在控制打印
    counts.print()
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }
}

UserDefinedSource

SourceFunction

package com.baizhi.jsy.sources
import org.apache.flink.streaming.api.scala._
object FlinkWordCountFileSourcesUserDefineNonParallel {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(4)
    //2.创建DataStream - 细化 下面两个都是读取一次
    val text = env.addSource[String](new UserDefineNonParallelSourceFunction)
    //3.执⾏行行DataStream的转换算⼦子
    val counts = text.flatMap(line=>line.split("\\s+"))
      .map(word=>(word,1))
      .keyBy(0)
      .sum(1)

    //4.将计算的结果在控制打印
    counts.print()
    println(env.getExecutionPlan)
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }
}

package com.baizhi.jsy.sources
import org.apache.flink.streaming.api.functions.source.SourceFunction
import scala.util.Random
class UserDefineNonParallelSourceFunction extends SourceFunction[String] {
  @volatile //防⽌止线程拷⻉贝变量量
  var isRunning:Boolean=true
  val lines:Array[String] = Array("this is a demo","hello world","ni hao me")
  //在该⽅方法中启动线程，通过sourceContext的collect⽅方法发送数据
  override def run(sourceContext: SourceFunction.SourceContext[String]): Unit = {
    while (isRunning){
      Thread.sleep(100)
      //输送数据给下游
      sourceContext.collect(lines(new Random().nextInt(lines.size)))
    }
  }
  //释放资源
  override def cancel(): Unit = {
    isRunning=false
  }
}

flinksql 获取kafka的key flink 读取kafka_流计算_04

ParallelSourceFunction

package com.baizhi.jsy.sources
import org.apache.flink.streaming.api.scala._
object FlinkWordCountFileSourcesUserDefineParallel {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(4)
    //2.创建DataStream - 细化 下面两个都是读取一次
    val text = env.addSource[String](new UserDefineParallelSourceFunction)
    //3.执⾏行行DataStream的转换算⼦子
    val counts = text.flatMap(line=>line.split("\\s+"))
      .map(word=>(word,1))
      .keyBy(0)
      .sum(1)
    //4.将计算的结果在控制打印
    counts.print()
    println(env.getExecutionPlan)
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }
}

package com.baizhi.jsy.sources
import org.apache.flink.streaming.api.functions.source.{ParallelSourceFunction, SourceFunction}
import scala.util.Random
class UserDefineParallelSourceFunction extends ParallelSourceFunction[String] {
  @volatile //防⽌止线程拷⻉贝变量量
  var isRunning:Boolean=true
  val lines:Array[String] = Array("this is a demo","hello world","ni hao")
  //在该⽅方法中启动线程，通过sourceContext的collect⽅方法发送数据
  override def run(sourceContext: SourceFunction.SourceContext[String]): Unit = {
    while (isRunning){
      Thread.sleep(100)
      //输送数据给下游
      sourceContext.collect(lines(new Random().nextInt(lines.size)))
    }
  }
  override def cancel(): Unit = {
    isRunning=false
  }
}

flinksql 获取kafka的key flink 读取kafka_flink_05

√Kafka集成

引入maven

<dependency>
	<groupId>org.apache.flink</groupId>
	<artifactId>flink-connector-kafka_2.11</artifactId>
	<version>1.10.0</version>
</dependency>

SimpleStringSchema

该SimpleStringSchema方案只会反序列列化kafka中的value

启动hdfs zk kafka

package com.baizhi.jsy.kafka
import java.util.Properties
import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
object FlinkWordCountKafkaSource {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //2.创建DataStream - 细化
    val props = new Properties()
    props.setProperty("bootstrap.servers", "Centos:9092")
    props.setProperty("group.id", "g1")
    val text = env.addSource(new FlinkKafkaConsumer[String]("topic01",new SimpleStringSchema(),props))
    //3.执行DataStream的转换算子
    val counts = text.flatMap(line=>line.split("\\s+"))
      .map(word=>(word,1))
      .keyBy(0)
      .sum(1)
    //4.将计算的结果在控制打印
    counts.print()
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }
}

自定义Schema

KafkaDeserializationSchema

package com.baizhi.jsy.kafka
import java.util.Properties
import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
object FlinkWordCountKafkaSourceDeserializationSchema {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //2.创建DataStream - 细化
    val props = new Properties()
    props.setProperty("bootstrap.servers", "Centos:9092")
    props.setProperty("group.id", "g1")
    val text = env.addSource(new FlinkKafkaConsumer[(String,String,Int,Long)]("topic01",new UserDefinedKafkaDeserializationSchema(),props))
    //3.执⾏行行DataStream的转换算⼦子
    val counts = text.flatMap(t=>{
      println(t)
      t._2.split("\\s+")
    })
      .map(word=>(word,1))
      .keyBy(0)
      .sum(1)
    //4.将计算的结果在控制打印
    counts.print()
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }
}

package com.baizhi.jsy.kafka
import org.apache.flink.api.common.typeinfo.TypeInformation
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.flink.api.scala._
class UserDefinedKafkaDeserializationSchema extends KafkaDeserializationSchema[(String,String,Int,Long)]{
  //是否流截止
  override def isEndOfStream(t: (String, String, Int, Long)): Boolean = false
  override def deserialize(consumerRecord: ConsumerRecord[Array[Byte], Array[Byte]]): (String, String, Int, Long) = {
    if(consumerRecord.key()!=null){
      (new String(consumerRecord.key()),new String(consumerRecord.value()),consumerRecord.partition(),consumerRecord.offset())
    }else{
      ("",new String(consumerRecord.value()),consumerRecord.partition(),consumerRecord.offset())
    }
  }
  override def getProducedType: TypeInformation[(String, String, Int, Long)] = {
    createTypeInformation[(String, String, Int, Long)]
  }
}

JSONKeyValueNodeDeserializationSchema

要求Kafka中的topic的key和value都必须是json格式，也可以在使用的时候，指定是否读取元数据
（topic、分区、o!set等）

package com.baizhi.jsy.kafka
import java.util.Properties
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.node.ObjectNode
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
import org.apache.flink.streaming.util.serialization.JSONKeyValueDeserializationSchema
object FlinkWordCountKafkaSourceJson {
  def main(args: Array[String]): Unit = {
    //1.创建流计算执⾏行行环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //2.创建DataStream - 细化
    val props = new Properties()
    props.setProperty("bootstrap.servers", "Centos:9092")
    props.setProperty("group.id", "g1")
    val text = env.addSource(new FlinkKafkaConsumer[ObjectNode]("topic01",new JSONKeyValueDeserializationSchema(true),props))
    //输入数据格式 {"id":1,"name":"zhangsan"}
    //t:{"value":{"id":1,"name":"zhangsan"},"metadata":{"offset":0,"topic":"topic01","partition":13}}
    text.map(t=>(t.get("value").get("id").asInt(),t.get("value").get("name").asText())).print()
    //5.执⾏行行流计算任务
    env.execute("Window Stream WordCount")
  }
}

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