Flink 2020尚硅谷

客官°小女子只卖身不卖艺 2022-12-23 11:19 129阅读 0赞

# Flink简介 #

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![在这里插入图片描述][20201129131709565.png]  
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Flink底层主要书java实现

**Apache Flink 是一个框架和分布式处理引擎，用于对无界和有界数据流进行状态计算。**

**flink是要解决的问题就是，在高吞吐量的前提下实现实时的数据处理**

**flink是真的流式处理(实时处理)，Spark Streaming是假的流式处理()实时处理，SpringStream是批处理，只是每批的数据比较小。**

Spring Stream一般是几秒的延迟(有参数可以设置)

如今的大数据技术应用场景，对实时性的要求已经越来越高。作为新一代大数据流处理框架，由于非常好的实时性，Flink 独树一帜，在近些年引起了业内极大的兴趣和关注。Flink能够提供毫秒级别的延迟，同时保证了数据处理的低延迟、高吞吐和结果的正确性，还提供了丰富的时间类型和窗口计算、Exactly-once 语义支持，另外还可以进行状态管理，并提供了 CEP（复杂事件处理）的支持。Flink 在实时分析领域的优势，使得越来越多的公司开始将实时项目向 Flink 迁移，其社区也在快速发展壮大。  
目前，Flink 已经成为各大公司实时领域的发力重点，特别是国内以阿里为代表的一众大厂，都在全力投入，不少公司为 Flink 社区贡献了大量源码。如今 Flink 已被很多人认为是大数据实时处理的方向和未来，很多公司也都在招聘和储备了解掌握 Flink 的人才。

# Flink应用场景 #

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# 传统数据处理架构 #

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# 分析处理 #

用到了数据仓  
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# 流式处理 #

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# 有状态的流式处理 #

有状态主要是指的，把流式数据存储起来(周期性的保存),方便数据处理系统异常是数据恢复。  
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# 流处理的演变 #

老式的lambda机构：批处理+流处理  
先通过流处理保证数据的实时性，后期在通过批处理保证数据的正确性。

# Flink特点 #

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# Flink和Spark Streaming的区别 #

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# Flink运行时架构 #

## 运行是时组件 ##

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### 作业管理器（JobManager） ###

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### 任务管理器（TaskManager） ###

任务管理器是flink中干活的（处理业务逻辑）  
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### 资源管理器（ResourceManager） ###

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### 分发器（Dispatcher） ###

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# 任务提交流程 #

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上图是从一个较为高层级的视角，来看应用中各组件的交互协作。如果部署的集群环境 不同（例如 YARN，Mesos，Kubernetes，standalone 等），其中一些步骤可以被省略，或是 有些组件会运行在同一个 JVM 进程中。

# 任务调度原理 #

# 第五章 Flink 流处理 API #

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## 5.1 Environment ##

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## 5.2 Source(数据来源) ##

### 5.2.3 以 kafka 消息队列的数据作为来源 ###

**kafka主要是做流式数据处理，kafka是flink的最佳拍档，一般都是使用kafka做为flink的数据源。**  
**flink kafka 的客户端可以自动根据topic的分区数启动对应的工作线程数**

<dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-connector-kafka-0.11_2.11</artifactId>
                <version>1.7.2</version>
            </dependency>

**pom.xml**

<?xml version="1.0" encoding="UTF-8"?>
    <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
        <modelVersion>4.0.0</modelVersion>
    
        <groupId>com.atguigu</groupId>
        <artifactId>FlinkTutorial</artifactId>
        <version>1.0-SNAPSHOT</version>
        <dependencies>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-scala_2.11</artifactId>
                <version>1.7.2</version>
            </dependency>
            
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-streaming-scala_2.11</artifactId>
                <version>1.7.2</version>
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-connector-kafka-0.11_2.11</artifactId>
                <version>1.7.2</version>
            </dependency>
            <dependency>
                <groupId>org.apache.bahir</groupId>
                <artifactId>flink-connector-redis_2.11</artifactId>
                <version>1.0</version>
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-connector-elasticsearch6_2.11</artifactId>
                <version>1.7.2</version>
            </dependency>
            <dependency>
                <groupId>mysql</groupId>
                <artifactId>mysql-connector-java</artifactId>
                <version>5.1.44</version>
            </dependency>
            <dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-statebackend-rocksdb_2.11</artifactId>
                <version>1.7.2</version>
            </dependency>
    
    
        </dependencies>
    
        <build>
            <plugins>
                
                <plugin>
                    <groupId>net.alchim31.maven</groupId>
                    <artifactId>scala-maven-plugin</artifactId>
                    <version>3.4.6</version>
                    <executions>
                        <execution>
                            
                            <goals>
                                <goal>testCompile</goal>
                            </goals>
                        </execution>
                    </executions>
                </plugin>
                <plugin>
                    <groupId>org.apache.maven.plugins</groupId>
                    <artifactId>maven-assembly-plugin</artifactId>
                    <version>3.0.0</version>
                    <configuration>
                        <descriptorRefs>
                            <descriptorRef>jar-with-dependencies</descriptorRef>
                        </descriptorRefs>
                    </configuration>
                    <executions>
                        <execution>
                            <id>make-assembly</id>
                            <phase>package</phase>
                            <goals>
                                <goal>single</goal>
                            </goals>
                        </execution>
                    </executions>
                </plugin>
            </plugins>
        </build>
    
    
    </project>

**SourceTest.scala:**

package com.atguigu.apitest
    
    import java.util.Properties
    
    import org.apache.flink.api.common.serialization.SimpleStringSchema
    import org.apache.flink.streaming.api.functions.source.SourceFunction
    import org.apache.flink.streaming.api.scala._
    import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011
    
    import scala.util.Random
    
    /** * Copyright (c) 2018-2028 尚硅谷 All Rights Reserved * * Project: FlinkTutorial * Package: com.atguigu.apitest * Version: 1.0 * * Created by wushengran on 2019/9/17 10:11 */
    
    // 定义传感器数据样例类
    case class SensorReading( id: String, timestamp: Long, temperature: Double )
    
    object SourceTest { 
      def main(args: Array[String]): Unit = { 
        val env = StreamExecutionEnvironment.getExecutionEnvironment
        env.setParallelism(1)
    
        // 1. 从集合中读取数据
        val stream1 = env.fromCollection(List(
          SensorReading("sensor_1", 1547718199, 35.80018327300259),
          SensorReading("sensor_6", 1547718201, 15.402984393403084),
          SensorReading("sensor_7", 1547718202, 6.720945201171228),
          SensorReading("sensor_10", 1547718205, 38.101067604893444)
        ))
    
    // env.fromElements("flink", 1, 32, 3213, 0.324).print("test")
    
        // 2. 从文件中读取数据
        val stream2 = env.readTextFile("D:\\Projects\\BigData\\FlinkTutorial\\src\\main\\resources\\sensor.txt")
    
        // 3. 从kafka中读取数据
        // 创建kafka相关的配置
        val properties = new Properties()
        properties.setProperty("bootstrap.servers", "localhost:9092")
        properties.setProperty("group.id", "consumer-group")
        properties.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
        properties.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
        properties.setProperty("auto.offset.reset", "latest")
    
        val stream3 = env.addSource(new FlinkKafkaConsumer011[String]("topic-sensor", new SimpleStringSchema(), properties))
    
        // sink输出
        stream3.print("stream3")
        
        // 4. 自定义数据源
     // val stream4 = env.addSource(new SensorSource())
    
        // sink输出
       // stream4.print("stream4")
    
        env.execute("source api test")
      }
    }
    
    class SensorSource() extends SourceFunction[SensorReading]{ 
      // 定义一个flag：表示数据源是否还在正常运行
      var running: Boolean = true
      override def cancel(): Unit = running = false
    
      override def run(ctx: SourceFunction.SourceContext[SensorReading]): Unit = { 
        // 创建一个随机数发生器
        val rand = new Random()
    
        // 随机初始换生成10个传感器的温度数据，之后在它基础随机波动生成流数据
        var curTemp = 1.to(10).map(
          i => ( "sensor_" + i, 60 + rand.nextGaussian() * 20 )
        )
    
        // 无限循环生成流数据，除非被cancel
        while(running){ 
          // 更新温度值
          curTemp = curTemp.map(
            t => (t._1, t._2 + rand.nextGaussian())
          )
          // 获取当前的时间戳
          val curTime = System.currentTimeMillis()
          // 包装成SensorReading，输出
          curTemp.foreach(
            t => ctx.collect( SensorReading(t._1, curTime, t._2) )
          )
          // 间隔100ms
          Thread.sleep(100)
        }
      }
    }

## 5.3Transform(转换算子->数据转换) ##

### 数据转换操作代码示例 ###

package com.atguigu.apitest
    
    import org.apache.flink.api.common.functions.{ FilterFunction, RichMapFunction}
    import org.apache.flink.configuration.Configuration
    import org.apache.flink.streaming.api.scala._
    
    /** * Copyright (c) 2018-2028 尚硅谷 All Rights Reserved * * Project: FlinkTutorial * Package: com.atguigu.apitest * Version: 1.0 * * Created by wushengran on 2019/9/17 11:41 */
    object TransformTest { 
      def main(args: Array[String]): Unit = { 
        val env = StreamExecutionEnvironment.getExecutionEnvironment
        env.setParallelism(1)
    
        // 读入数据
        val inputStream = env.readTextFile("D:\\Projects\\BigData\\FlinkTutorial\\src\\main\\resources\\sensor.txt")
    
        // Transform操作->转换成样例类类型
    
        val dataStream = inputStream
          .map(
            data => { 
              val dataArray = data.split(",")
              SensorReading( dataArray(0).trim, dataArray(1).trim.toLong, dataArray(2).trim.toDouble )
            }
          )
    
        // 1. 聚合操作
         val stream1 = dataStream
           //以id为key进行分组
           .keyBy("id")
           //分组后再处理
          // .min("temperature")
           //.minBy("temperature")
         // .sum("temperature")
          .reduce( (x, y) => SensorReading(x.id, x.timestamp + 1, y.temperature + 10) )
    
        stream1.print();
    
        // 2. 分流，根据温度是否大于30度划分
        val splitStream = dataStream
          .split( sensorData => { 
            if( sensorData.temperature > 30 ) Seq("high") else Seq("low")
          } )
    
        val highTempStream = splitStream.select("high")
        val lowTempStream = splitStream.select("low")
        val allTempStream = splitStream.select("high", "low")
        highTempStream.print("高")
        lowTempStream.print("低")
        allTempStream.print("所有")
        
        // 3. 合并两条流
        val warningStream = highTempStream.map( sensorData => (sensorData.id, sensorData.temperature) )
        val connectedStreams = warningStream.connect(lowTempStream)
    
        val coMapStream = connectedStreams.map(
          warningData => ( warningData._1, warningData._2, "high temperature warning" ),
          lowData => ( lowData.id, "healthy" )
        )
    
        val unionStream = highTempStream.union(lowTempStream)
    
        // 函数类
        dataStream.filter( new MyFilter() ).print()
    
        // 输出数据
    // dataStream.print()
    // highTempStream.print("high")
    // lowTempStream.print("low")
    // allTempStream.print("all")
    // unionStream.print("union")
    
        env.execute("transform test job")
      }
    }
    
    class MyFilter() extends FilterFunction[SensorReading]{ 
      override def filter(value: SensorReading): Boolean = { 
        value.id.startsWith("sensor_1")
      }
    }
    
    class MyMapper() extends RichMapFunction[SensorReading, String]{ 
      override def map(value: SensorReading): String = { 
        "flink"
      }
    
      override def open(parameters: Configuration): Unit = super.open(parameters)
    }

### 简单转换算子 ###

#### 5.3.1 map ####

![在这里插入图片描述][20201202220616518.png]

// Transform操作->转换成样例类类型
    
        val dataStream = inputStream
          .map(
            data => { 
              val dataArray = data.split(",")
              SensorReading( dataArray(0).trim, dataArray(1).trim.toLong, dataArray(2).trim.toDouble )
            }
          )

#### 5.3.2 flatMap ####

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#### Filter ####

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### 分组后再处理 ###

#### 5.3.4 KeyBy ####

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#### 5.3.5 滚动聚合算子（Rolling Aggregation） ####

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val stream1 = dataStream
           //以id为key进行分组
           .keyBy("id")
           //分组后再处理
          // .min("temperature")
           //.minBy("temperature")
         // .sum("temperature")
          .reduce( (x, y) => SensorReading(x.id, x.timestamp + 1, y.temperature + 10) )
    
        stream1.print();

#### 复杂转换算子 ####

#### 5.3.6 Reduce ####

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![在这里插入图片描述][20201202223321213.png]

### 多流转换算子 ###

#### 5.3.7 Split 和 Select(分流） ####

实际使用场景：kafka流数据进来，只消费我需要的消息，不需要的再写回kafka供其他人消费。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3lhbmdzaGVuZ3dlaTIzMDYxMg_size_16_color_FFFFFF_t_70 21]  
**分流：**  
![在这里插入图片描述][20201202223920996.png]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3lhbmdzaGVuZ3dlaTIzMDYxMg_size_16_color_FFFFFF_t_70 22]

// 2. 分流，根据温度是否大于30度划分
        val splitStream = dataStream
          .split( sensorData => { 
            if( sensorData.temperature > 30 ) Seq("high") else Seq("low")
          } )
    
        val highTempStream = splitStream.select("high")
        val lowTempStream = splitStream.select("low")
        val allTempStream = splitStream.select("high", "low")
        highTempStream.print("高")
        lowTempStream.print("低")
        allTempStream.print("所有")

#### 5.3.8 Connect 和 CoMap(合流) ####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3lhbmdzaGVuZ3dlaTIzMDYxMg_size_16_color_FFFFFF_t_70 23]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3lhbmdzaGVuZ3dlaTIzMDYxMg_size_16_color_FFFFFF_t_70 24]

#### 5.3.9 Union ####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3lhbmdzaGVuZ3dlaTIzMDYxMg_size_16_color_FFFFFF_t_70 25]

## 5.6 Sink (数据输出) ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3lhbmdzaGVuZ3dlaTIzMDYxMg_size_16_color_FFFFFF_t_70 26]

### 5.6.1 Kafka ###

pom.xml

<dependency>
                <groupId>org.apache.flink</groupId>
                <artifactId>flink-connector-kafka-0.11_2.12</artifactId>
                <version>1.10.1</version>
            </dependency>

主函数中添加 sink：

dataStream.addSink( new FlinkKafkaProducer011[String]("localhost:9092", "sinktest-topic", new SimpleStringSchema()) )

package com.atguigu.apitest.sinttest
    
    import java.util.Properties
    
    import com.atguigu.apitest.SensorReading
    import org.apache.flink.api.common.serialization.SimpleStringSchema
    import org.apache.flink.streaming.api.scala._
    import org.apache.flink.streaming.connectors.kafka.{ FlinkKafkaConsumer011, FlinkKafkaProducer011}
    
    /** * Copyright (c) 2018-2028 尚硅谷 All Rights Reserved * * Project: FlinkTutorial * Package: com.atguigu.apitest.sinttest * Version: 1.0 * * Created by wushengran on 2020/8/7 10:12 */
    object KafkaSinkTest { 
      def main(args: Array[String]): Unit = { 
        val env = StreamExecutionEnvironment.getExecutionEnvironment
        env.setParallelism(1)
        // 读取数据
    // val inputPath = "D:\\Projects\\BigData\\FlinkTutorial\\src\\main\\resources\\sensor.txt"
    // val inputStream = env.readTextFile(inputPath)
    
        // 从kafka读取数据
        val properties = new Properties()
        properties.setProperty("bootstrap.servers", "localhost:9092")
        properties.setProperty("group.id", "consumer-group")
        val stream = env.addSource( new FlinkKafkaConsumer011[String]("sensor", new SimpleStringSchema(), properties) )
    
    
        // 先转换成样例类类型（简单转换操作）
        val dataStream = stream
          .map( data => { 
            val arr = data.split(",")
            SensorReading(arr(0), arr(1).toLong, arr(2).toDouble).toString
          } )
    
        dataStream.addSink( new FlinkKafkaProducer011[String]("localhost:9092", "sinktest", new SimpleStringSchema()) )
    
        env.execute("kafka sink test")
      }
    }

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