大数据-Flink编程

2022-10-04 21:35:27 浏览数 (1)

加载数据

代码中加载

代码语言：javascript复制
import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment, _}
object WordCount {
  case class Student(id: String, name: String, sex: String, age: Int, department: String)

  def main(args: Array[String]): Unit = {
    //设置用户名，避免权限错误
    System.setProperty("HADOOP_USER_NAME", "hadoop");
    val env = ExecutionEnvironment.getExecutionEnvironment
    val stu: DataSet[(Int, String, Double)] = env.fromElements(
      (19, "Wilson", 178.8),
      (17, "Edith", 168.8),
      (18, "Joyce", 174.8),
      (18, "May", 195.8),
      (18, "Gloria", 182.7),
      (21, "Jessie", 184.8)
    )
    stu.print
  }
}

从文件中加载

代码语言：javascript复制
import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment, _}
object WordCount {
  case class Student(id: String, name: String, sex: String, age: Int, department: String)
  def main(args: Array[String]): Unit = {
    //设置用户名，避免权限错误
    System.setProperty("HADOOP_USER_NAME", "hadoop");
    val environment = ExecutionEnvironment.getExecutionEnvironment
    val stu_list: DataSet[Student] = environment.readCsvFile[Student](
      filePath = "file:///D:/bigdata_study/stu_list.txt",
      lineDelimiter = "n",
      fieldDelimiter = ",",
      quoteCharacter = null,
      ignoreFirstLine = false,
      ignoreComments = "#",
      lenient = false,
      includedFields = Array[Int](0, 1, 2, 3, 4),
      pojoFields = Array[String]("id", "name", "sex", "age", "department")
    )
    println("-------------原数据----------")
    stu_list.print
  }
}

Kafka中加载

代码语言：javascript复制
import java.util
import java.util.Properties

import com.google.gson.Gson
import org.apache.flink.api.common.functions.RuntimeContext
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.elasticsearch.{ElasticsearchSinkFunction, RequestIndexer}
import org.apache.flink.streaming.connectors.elasticsearch7.{ElasticsearchSink, RestClientFactory}
import org.apache.flink.streaming.util.serialization.SimpleStringSchema
import org.apache.http.HttpHost
import org.apache.http.auth.{AuthScope, UsernamePasswordCredentials}
import org.apache.http.impl.client.BasicCredentialsProvider
import org.apache.http.impl.nio.client.HttpAsyncClientBuilder
import org.elasticsearch.action.index.IndexRequest
import org.elasticsearch.client.RestClientBuilder.HttpClientConfigCallback
import org.elasticsearch.client.{Requests, RestClientBuilder}

/**
* 红尘丶世界
*
*/
object FLink_Kafka_ES {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    // 非常关键，一定要设置启动检查点！！
    env.enableCheckpointing(1000)

    //设置kafka topic
    val topic: String = "test"
    //配置kafka参数
    val props: Properties = new Properties
    props.setProperty("bootstrap.servers", "hadoop01:9092,hadoop02:9092,hadoop03:9092")
    props.setProperty("group.id", "test01")
    props.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
    props.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")

    //导入隐式转换
    import org.apache.flink.streaming.connectors.kafka._
    import org.apache.flink.api.scala._
    import scala.collection.JavaConverters._

    val consumer: FlinkKafkaConsumer011[String] = new FlinkKafkaConsumer011[String](topic, new SimpleStringSchema(), props)
    //设置最新的数据进行消费
    consumer.setStartFromLatest()
    //构建数据源
    val kafkaSource: DataStream[String] = env.addSource(consumer)
    //进行转换
    val mapDS: DataStream[Map[String, AnyRef]] = kafkaSource.map(x => {
      //创建Gson解析对象, 把json转化成map
      (new Gson).fromJson(x, classOf[util.Map[String, AnyRef]]).asScala.toMap
    })

    //配置节点信息
    val httpHosts: util.ArrayList[HttpHost] = new java.util.ArrayList[HttpHost]
    httpHosts.add(new HttpHost("192.168.100.111", 9200, "http"))
    //构建es sink
    val esSinkBuilder: ElasticsearchSink.Builder[Map[String, AnyRef]] = new ElasticsearchSink.Builder[Map[String, AnyRef]](
      httpHosts,
      new ElasticsearchSinkFunction[Map[String, AnyRef]] {
        override def process(t: Map[String, AnyRef], runtimeContext: RuntimeContext, requestIndexer: RequestIndexer): Unit = {
          val map: util.Map[String, AnyRef] = t.asJava
          val indexRequest: IndexRequest = Requests
          .indexRequest()
          .index("flink_kafka")
          //.`type`("kafka_data") //非必选项ES 7.x中不需要再设置文档
          //.create(false) //是否自动创建索引,不推荐使用,最好提前在es中进行Mapping映射,当然如果你的时间字段能够被ES自动识别可以让它自动创建
          //因为ES命名的问题,无法直接使用ES的命名
          //如需使用 x.x 命名格式, 可以考虑嵌套map或者json
          //如使用嵌套map需注意把所有的 map 都需要转化成 java.util.map 否则会爆类型异常
          .source(map)
          //发送请求,写入数据
          requestIndexer.add(indexRequest)
          //写入数据成功输出一下
          println("data saved successfully")
        }
      })
    //以下的一些配置可作为生产环境使用, es容错需配合flink 检查点使用
    //设置最大并行度
    //esSinkBuilder.setBulkFlushMaxActions(1)
    //设置es sink 的参数
    esSinkBuilder.setRestClientFactory(
      new RestClientFactory {
        override def configureRestClientBuilder(restClientBuilder: RestClientBuilder): Unit = {
          restClientBuilder.setHttpClientConfigCallback(new HttpClientConfigCallback {
            override def customizeHttpClient(httpClientBuilder: HttpAsyncClientBuilder): HttpAsyncClientBuilder = {
              val provider: BasicCredentialsProvider = new BasicCredentialsProvider()
              //设置用户名和密码
              val credentials: UsernamePasswordCredentials = new UsernamePasswordCredentials("elastic", "123456")
              provider.setCredentials(AuthScope.ANY, credentials)
              httpClientBuilder.setDefaultCredentialsProvider(provider)
            }
          })
        }
      })
    //进行重试的时间间隔。对于指数型则表示起始的基数
    esSink.setBulkFlushBackoffDelay(1)
    //失败重试的次数
    esSink.setBulkFlushBackoffRetries(3)
    //重试策略，又可以分为以下两种类型
    //a、指数型，表示多次重试之间的时间间隔按照指数方式进行增长。eg:2 -> 4 -> 8 ...
    //b、常数型，表示多次重试之间的时间间隔为固定常数。eg:2 -> 2 -> 2 ...
    esSink.setBulkFlushBackoffType(ElasticsearchSinkBase.FlushBackoffType.EXPONENTIAL)
    //设置批量提交时间间隔
    //esSink.setBulkFlushInterval(100)
    //该配置表示批量写入ES时的记录条数
    esSink.setBulkFlushMaxActions(1)
    //设置批量提交的最大字节 以MB为单位
    //esSink.setBulkFlushMaxSizeMb(16)
    //es 容错处理
    esSink.setFailureHandler(
      new ActionRequestFailureHandler {
        override def onFailure(actionRequest: ActionRequest, throwable: Throwable, i: Int, requestIndexer: RequestIndexer): Unit = {
          if (ExceptionUtils.findThrowable(throwable, classOf[EsRejectedExecutionException]).isPresent) {
            // full queue; re-add document for indexing
            requestIndexer.add(actionRequest)
          } else if (ExceptionUtils.findThrowable(throwable, classOf[EsRejectedExecutionException]).isPresent) {
            // malformed document; simply drop request without failing sink
            println("WARN   数据格式出错了")
          } else {
            // for all other failures, fail the sink;
            // here the failure is simply rethrown, but users can also choose to throw custom exceptions
            println("ES 出问题了")
            throw throwable
          }
        }
      }
    )
    esSink
  }
  //设置最大并行度
  mapDS.setMaxParallelism(1)
  //把数据sink到es
  mapDS.addSink(esSinkBuilder.build())

  //生产数据命令如下
  // $KAFKA_HOME/bin/kafka-console-producer.sh --broker-list hadoop01:9092,hadoop02:9092,hadoop03:9092 --topic test
  //kafka中输入的测试数据 
  // {"id":1,"completed":false,"title":"delectus aut autem","userId":1}

  //查看索引
  //Get _cat/indices
  //查看索引中的内容
  //Get flink_kafka/_search
  //批量请求的配置；这将指示接收器在每个元素之后发出请求，否则将对它们进行缓冲。
  env.execute("Kafka_Flink")
}
}

依赖

代码语言：javascript复制
<?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>cn.cloudfall</groupId>
  <artifactId>flink_elk</artifactId>
  <version>1.0-SNAPSHOT</version>

  <!-- 版本管理 -->
  <properties>
    <maven.compiler.source>1.8</maven.compiler.source>
    <maven.compiler.target>1.8</maven.compiler.target>
    <encoding>UTF-8</encoding>
    <scala.version>2.12.15</scala.version>
    <scala.compat.version>2.11</scala.compat.version>
    <flink.version>1.9.3</flink.version>
    <scala.binary.version>2.12.15</scala.binary.version>
  </properties>

  <dependencies>
    <!-- 导入scala的依赖 -->
    <dependency>
      <groupId>org.scala-lang</groupId>
      <artifactId>scala-library</artifactId>
      <version>${scala.version}</version>
    </dependency>
    <!-- 导入flink streaming 和 scala的依赖 -->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-streaming-scala_${scala.compat.version}</artifactId>
      <version>${flink.version}</version>
      <exclusions>
        <exclusion>
          <artifactId>scala-library</artifactId>
          <groupId>org.scala-lang</groupId>
        </exclusion>
      </exclusions>
    </dependency>
    <!-- 导入flink和scala的依赖 -->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-scala_${scala.compat.version}</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <!-- 指定flink-client API的版本 -->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-clients_${scala.compat.version}</artifactId>
      <version>${flink.version}</version>
      <exclusions>
        <exclusion>
          <artifactId>scala-parser-combinators_${scala.compat.version}</artifactId>
          <groupId>org.scala-lang.modules</groupId>
        </exclusion>
        <exclusion>
          <artifactId>slf4j-api</artifactId>
          <groupId>org.slf4j</groupId>
        </exclusion>
      </exclusions>
    </dependency>

    <!-- 指定flink-connector-elasticsearch的依赖 -->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-connector-elasticsearch7_${scala.compat.version}</artifactId>
      <version>${flink.version}</version>
      <exclusions>
        <exclusion>
          <artifactId>slf4j-api</artifactId>
          <groupId>org.slf4j</groupId>
        </exclusion>
      </exclusions>
    </dependency>

    <!-- 指定flink-connector-kafka的依赖 -->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-connector-kafka-0.11_${scala.compat.version}</artifactId>
      <version>${flink.version}</version>
      <exclusions>
        <exclusion>
          <artifactId>slf4j-api</artifactId>
          <groupId>org.slf4j</groupId>
        </exclusion>
        <exclusion>
          <artifactId>snappy-java</artifactId>
          <groupId>org.xerial.snappy</groupId>
        </exclusion>
      </exclusions>
    </dependency>

    <!--       指定fast json的依赖 -->
    <!--        <dependency>-->
    <!--            <groupId>com.alibaba</groupId>-->
    <!--            <artifactId>fastjson</artifactId>-->
    <!--            <version>1.2.60</version>-->
    <!--        </dependency>-->
    
    <!-- 指定Google json 的依赖 -->
    <dependency>
      <groupId>com.google.code.gson</groupId>
      <artifactId>gson</artifactId>
      <version>2.3.1</version>
    </dependency>

  </dependencies>
  <!-- 打包插件-->
  <build>
    <sourceDirectory>src/main/scala</sourceDirectory>
    <testSourceDirectory>src/test/scala</testSourceDirectory>
    <plugins>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-compiler-plugin</artifactId>
        <version>2.5.1</version>
        <configuration>
          <source>${maven.compiler.source}</source>
          <target>${maven.compiler.target}</target>
          <!--<encoding>${project.build.sourceEncoding}</encoding>-->
        </configuration>
      </plugin>

      <plugin>
        <groupId>net.alchim31.maven</groupId>
        <artifactId>scala-maven-plugin</artifactId>
        <version>3.2.0</version>
        <executions>
          <execution>
            <goals>
              <goal>compile</goal>
              <goal>testCompile</goal>
            </goals>
            <configuration>
              <args>
                <!--<arg>-make:transitive</arg>-->
                <arg>-dependencyfile</arg>
                <arg>${project.build.directory}/.scala_dependencies</arg>
              </args>

            </configuration>
          </execution>
        </executions>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-surefire-plugin</artifactId>
        <version>2.18.1</version>
        <configuration>
          <useFile>false</useFile>
          <disableXmlReport>true</disableXmlReport>
          <includes>
            <include>**/*Test.*</include>
            <include>**/*Suite.*</include>
          </includes>
        </configuration>
      </plugin>

      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-shade-plugin</artifactId>
        <version>2.3</version>
        <executions>
          <execution>
            <phase>package</phase>
            <goals>
              <goal>shade</goal>
            </goals>
            <configuration>
              <filters>
                <filter>
                  <artifact>*:*</artifact>
                  <excludes>
                    <exclude>META-INF/*.SF</exclude>
                    <exclude>META-INF/*.DSA</exclude>
                    <exclude>META-INF/*.RSA</exclude>
                  </excludes>
                </filter>
              </filters>
              <transformers>
                <transformer
                             implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                  <mainClass>cn.cloudFall.FLinkKafka</mainClass>
                </transformer>
              </transformers>
            </configuration>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>
</project>

数据导出

导出到HDFS

代码语言：javascript复制
//写入到HDFS
val output2 = "hdfs://bdedev/flink/Student002.csv"
ds2.writeAsCsv(output2, rowDelimiter = "n", fieldDelimiter = "|||", WriteMode.OVERWRITE)
env.execute()

导出到文件

代码语言：javascript复制
//写入到文件
val output2 = "file:///D:/bigdata_study/result001.txt"
ds3.writeAsCsv(output2, rowDelimiter = "n", fieldDelimiter = ",", WriteMode.OVERWRITE)
env.execute()

值转换

Flink的Transformation转换主要包括四种：

  1. 单数据流基本转换
  2. 基于Key的分组转换
  3. 多数据流转换
  4. 数据重分布转换

单数据流基本转换

基于Key的分组转换

对数据分组主要是为了进行后续的聚合操作，即对同组数据进行聚合分析。

groupBy会将一个DataSet转化为一个GroupedDataSet，聚合操作会将GroupedDataSet转化为DataSet。如果聚合前每个元素数据类型是T，聚合后的数据类型仍为T。

aggregation

常见的聚合操作有sum、max、min等，这些聚合操作统称为aggregation。aggregation需要一个参数来指定按照哪个字段进行聚合。跟groupBy相似，我们可以使用数字位置来指定对哪个字段进行聚合，也可以使用字段名。

与批处理不同，这些聚合函数是对流数据进行数据，流数据是依次进入Flink的，聚合操作是对之前流入的数据进行统计聚合。sum算子的功能对该字段进行加和，并将结果保存在该字段上。min操作无法确定其他字段的数值。

代码语言：javascript复制
val tupleStream = env.fromElements(
  (0, 0, 0), (0, 1, 1), (0, 2, 2),
  (1, 0, 6), (1, 1, 7), (1, 2, 8)
)
tupleStream.groupBy(0).sum(1).print()

第0个分组，第1个求和

结果

(1,3,8) (0,3,2)

reduce

代码语言：javascript复制
import org.apache.flink.api.common.operators.Order
import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment, _}

object WordCount {
  def main(args: Array[String]): Unit = {
    //设置用户名，避免权限错误
    System.setProperty("HADOOP_USER_NAME", "hadoop");
    val env = ExecutionEnvironment.getExecutionEnvironment
    val ds1: DataSet[String] = env.fromElements(
      "good good study", "day day up"
    )
    val group_ds  = ds1.flatMap(line => line.split(" ")).map(word => (word, 1)).groupBy(0)
    val ds3 = group_ds.reduce((a, b) => (a._1, a._2   b._2))
    ds3.sortPartition(0, Order.ASCENDING).print
  }
}

结果

(up,1) (day,2) (good,2) (study,1)

机器学习Alink

Spark对应的机器学习框架SparkML

Flink对应的机器学习框架FlinkML/Alink

FlinkML

https://github.com/apache/flink-ml

代码语言：javascript复制
<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flink-ml_2.12</artifactId>
  <version>1.9.3</version>
</dependency>

Alink

Alink与SparkML算法相比，Alink算法更全面，性能更优异，场景更丰富（同时支持流批），本地化更出色（支持中文分词）是快速搭建在线机器学习系统的不二之选。

镜像仓库:Alink

教程：https://www.yuque.com/pinshu/alink_tutorial/book_java

机器学习 神经网络 深度学习 人工智能 flink

0 人点赞