Flink快速入门一(简介以及WC编程）

1、什么是Flink

Flink是架构在有界和无界流数据上的有状态的分布式计算引擎，既可以处理流数据又可以处理批数据（批是流的特例）

1.1 有界无界流

• Unbounded streams：无界流，即流数据，定义了开始，没有定义结束
• Bounded streams：界流，即批数据，定义了开始以及结束。
  将连续的批处理放大即是流处理

1.2 部署模式

Flink可以部署在 Hadoop YARN，Apache Mesos，Kubernetes ，Stand-alone ，Cloud ，Local，和Spark很类似，同Spark一样 on yarn模式 是没有HA的，只作为一个提交程序的客户端。 Stand-alone 模式必须要有部署HA。同一份代码可以在任何环境运行！ 无需更改,开发学习的过程使用local 模式即可

2.Flink的编程模型

2.1编程模型从低到高如下所示：

Stateful streaming process (low) =》DataStream / DataSet API (Core) =》Table API =》SQL

• DataStream API：处理无界数据
• DataSet API：处理有界数据

2.2Time

• Event Time:数据产生的时间
• Ingestion time:数据摄入的时间，即数据进入Flink
• Processing Time：数据处理时间

2.2 常见的计算引擎编程过程

MapRreduce: input ==> map(reduce) ==> output
Spark : input ==> transformations ==> actions ==> output
Storm : input ==>Spout ==>Bolt ==> output
Flink : source ==> transformations ==> sink

3.Flink vs Spark Streaming vs Structured Streaming vs Strom(基本/trident) 简单对比

Strom(基本)是实时流计算， Storm(trident）是微批次计算。storm已过时。

• 实时性：Flink 、Storm、Structured Streaming 是真实时流处理，而Spark是微批次处理
• 延时性：Flink 、Storm 、Structured Streaming 延时较低，Spark延时较高
• 吞吐量： Storm(基本)吞吐量最低

4.WC编程案例

相较于java，使用scala进行编程使得代码更加优美，简洁。编程五步走，非常的有规则的编程。
注意：Flink加载数据以及transformation操作是lazy的，当执行execute时才会真正的触发执行。

4.1依赖的pom.xml文件

<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/maven-v4_0_0.xsd">
  <modelVersion>4.0.0modelVersion>
  <groupId>com.wsk.flinkgroupId>
  <artifactId>flink-trainartifactId>
  <version>1.0-SNAPSHOTversion>
  <inceptionYear>2008inceptionYear>
  <properties>
    <scala.version>2.11.8scala.version>
    <flink.version>1.8.1flink.version>
    <scala.binary.version>2.11scala.binary.version>
  properties>

  <repositories>
    <repository>
      <id>alimavenid>
      <name>aliyun mavenname>
      <url>http://maven.aliyun.com/nexus/content/groups/public/url>
    repository>
    <repository>
      <id>scala-tools.orgid>
      <name>Scala-Tools Maven2 Repositoryname>
      <url>http://scala-tools.org/repo-releasesurl>
    repository>
  repositories>

  <pluginRepositories>
    <pluginRepository>
      <id>scala-tools.orgid>
      <name>Scala-Tools Maven2 Repositoryname>
      <url>http://scala-tools.org/repo-releasesurl>
    pluginRepository>
  pluginRepositories>

  <dependencies>
    <dependency>
      <groupId>org.scala-langgroupId>
      <artifactId>scala-libraryartifactId>
      <version>${scala.version}version>
    dependency>

    <dependency>
      <groupId>org.apache.flinkgroupId>
      <artifactId>flink-scala_${scala.binary.version}artifactId>
      <version>${flink.version}version>
    dependency>
    <dependency>
      <groupId>org.apache.flinkgroupId>
      <artifactId>flink-streaming-scala_${scala.binary.version}artifactId>
      <version>${flink.version}version>
    dependency>

    <dependency>
      <groupId>org.apache.flinkgroupId>
      <artifactId>flink-clients_${scala.binary.version}artifactId>
      <version>${flink.version}version>
    dependency>
  dependencies>

  <build>
    <sourceDirectory>src/main/scalasourceDirectory>
    <testSourceDirectory>src/test/scalatestSourceDirectory>
    <plugins>
      <plugin>
        <groupId>org.scala-toolsgroupId>
        <artifactId>maven-scala-pluginartifactId>
        <executions>
          <execution>
            <goals>
              <goal>compilegoal>
              <goal>testCompilegoal>
            goals>
          execution>
        executions>
        <configuration>
          <scalaVersion>${scala.version}scalaVersion>
          <args>
            <arg>-target:jvm-1.5arg>
          args>
        configuration>
      plugin>
      <plugin>
        <groupId>org.apache.maven.pluginsgroupId>
        <artifactId>maven-eclipse-pluginartifactId>
        <configuration>
          <downloadSources>truedownloadSources>
          <buildcommands>
            <buildcommand>ch.epfl.lamp.sdt.core.scalabuilderbuildcommand>
          buildcommands>
          <additionalProjectnatures>
            <projectnature>ch.epfl.lamp.sdt.core.scalanatureprojectnature>
          additionalProjectnatures>
          <classpathContainers>
            <classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINERclasspathContainer>
            <classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINERclasspathContainer>
          classpathContainers>
        configuration>
      plugin>
    plugins>
  build>
  <reporting>
    <plugins>
      <plugin>
        <groupId>org.scala-toolsgroupId>
        <artifactId>maven-scala-pluginartifactId>
        <configuration>
          <scalaVersion>${scala.version}scalaVersion>
        configuration>
      plugin>
    plugins>
  reporting>
project>

4.2wc代码案列

package com.wsk.flink.streaming

import org.apache.flink.api.java.utils.ParameterTool
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.windowing.time.Time


/**
  * socket 窗口单词统计
  *
  *
  */
object SocketWindowWCApp {

  var hostname : String = _
  var port: Int = 9099


  def main(args: Array[String]): Unit = {

    try {
      val params = ParameterTool.fromArgs(args)
      hostname = if (params.has("hostname")) params.get("hostname") else "10.199.140.143"
      port = params.getInt("port")
    } catch {
      case e: Exception => {
        System.err.println("No port specified. Please run 'SocketWindowWordCount " +
          "--hostname  --port ', where hostname (localhost by default) and port " +
          "is the address of the text server")
        System.err.println("To start a simple text server, run 'netcat -l ' " +
          "and type the input text into the command line")
        //        return
      }

    }

    //step1:获取 运行的ENV 环境
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //step2: source 获取DataStream
//    import org.apache.flink.api.scala._
    val lines = env.socketTextStream(hostname, port)

    //step3:transformations 转换
    val windowCounts = lines
      .flatMap { w => w.split(",") }
      .map { w => WordCount(w, 1) }
      .keyBy("word")
      .timeWindow(Time.seconds(4), Time.seconds(2))
      .sum("count")

    //step4:sink
    windowCounts.print().setParallelism(1)

    //step5: exe
    env.execute("Socket Window WordCount")
  }

  case class WordCount(word: String, count: Long)

}