Structrued Streaming业务数据实时分析

先启动spark-shell,记得启动nc服务

输入以下代码

scala> import org.apache.spark.sql.functions._

import org.apache.spark.sql.functions._

scala> import org.apache.spark.sql.SparkSession

import org.apache.spark.sql.SparkSession

scala> import spark.implicits._

import spark.implicits._

scala> val lines = spark.readStream.format("socket").option("host", "bigdata-pro01.kfk.com").option("port", ).load()

// :: WARN TextSocketSourceProvider: The socket source should not be used for production applications! It does not support recovery.

lines: org.apache.spark.sql.DataFrame = [value: string]

scala> val words = lines.as[String].flatMap(_.split(" "))

words: org.apache.spark.sql.Dataset[String] = [value: string]

scala> val wordCounts = words.groupBy("value").count()

wordCounts: org.apache.spark.sql.DataFrame = [value: string, count: bigint]

scala> val query = wordCounts.writeStream.outputMode("complete").format("console").start()

query: org.apache.spark.sql.streaming.StreamingQuery = org.apache.spark.sql.execution.streaming.StreamingQueryWrapper@4e260e04

在nc输入几个单词

我们再输入一些单词

我们改一下代码换成update模式

首先重新启动一次spark-shell,记得启动nc

换成append模式

scala> import org.apache.spark.sql.functions._

import org.apache.spark.sql.functions._

scala> import org.apache.spark.sql.SparkSession

import org.apache.spark.sql.SparkSession

scala> import spark.implicits._

import spark.implicits._

scala> val lines = spark.readStream.format("socket").option("host", "bigdata-pro01.kfk.com").option("port", ).load()

// :: WARN TextSocketSourceProvider: The socket source should not be used for production applications! It does not support recovery.

lines: org.apache.spark.sql.DataFrame = [value: string]

scala> val words = lines.as[String].flatMap(_.split(" "))

words: org.apache.spark.sql.Dataset[String] = [value: string]

scala> val query = words.writeStream.outputMode("append").format("console").start()

query: org.apache.spark.sql.streaming.StreamingQuery = org.apache.spark.sql.execution.streaming.StreamingQueryWrapper@19d85bbe

因为我们之前的kafka的版本低了，我下载一个0.10.0版本的

下载地址 http://kafka.apache.org/downloads

我们把kafka0.9版本的配置文件直接复制过来

为了快一点我直接在虚拟机里操作了

复制这几个配置文件

把kafka0.10的覆盖掉

修改一下配置文件

把kafka分发都另外的两个节点去

在节点2和节点3也把相应的配置文件修改一下

server.properties

在idea里重新建一个scala类

加上如下代码

package com.spark.test

import org.apache.spark

import org.apache.spark.sql.SparkSession

object StructuredStreamingKafka {

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

     val spark=SparkSession.builder().master("local[2]").appName("streaming").getOrCreate()

    val df = spark

      .readStream

      .format("kafka")

      .option("kafka.bootstrap.servers", "bigdata-pro01.kfk.com:9092")

      .option("subscribe", "weblogs")

      .load()

    import spark.implicits._

   val lines= df.selectExpr("CAST(value AS STRING)").as[String]

    val words = lines.flatMap(_.split(" "))

    val wordCounts = words.groupBy("value").count()

    val query = wordCounts.writeStream

      .outputMode("complete")

      .format("console")

      .start()

    query.awaitTermination()

  }

}

跑一下我们的程序

如果报错了提示需要0.10版本的可以先不用管

我们启动一下kafka

可以看到程序已经在跑了

我们在kafak里创建一个生产者

bin/kafka-console-producer.sh --broker-list bigdata-pro01.kfk.com: --topic weblogs

我们输入几个单词

可以看到idea这边的结果

我们可以换成update模式

程序跑起来了

输入单词

这个是运行的结果

我们把包上传上来（3个节点都这样做）

启动spark-shell

把代码拷贝进来

 val df = spark

      .readStream

      .format("kafka")

      .option("kafka.bootstrap.servers", "bigdata-pro01.kfk.com:9092")

      .option("subscribe", "weblogs")

      .load()

    import spark.implicits._

   val lines= df.selectExpr("CAST(value AS STRING)").as[String]

    val words = lines.flatMap(_.split(" "))

    val wordCounts = words.groupBy("value").count()

    val query = wordCounts.writeStream

      .outputMode("update")

      .format("console")

      .start()

    query.awaitTermination()

这个时候一定要保持kafka和生产者是开启的

我在生产者这边输入几个单词

回到spark-shell界面可以看到统计结果

我们先把mysqld的test数据库的webCount的表的内容清除

打开idea,我们编写两个程序

package com.spark.test

import org.apache.spark.sql.SparkSession

import org.apache.spark.sql.streaming.ProcessingTime

/**

  * Created by Administrator on 2017/10/16.

  */

object StructuredStreamingKafka {

  case class Weblog(datatime:String,

                    userid:String,

                    searchname:String,

                    retorder:String,

                    cliorder:String,

                    cliurl:String)

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

    val spark  = SparkSession.builder()

      .master("local[2]")

      .appName("streaming").getOrCreate()

    val df = spark

      .readStream

      .format("kafka")

      .option("kafka.bootstrap.servers", "bigdata-pro01.kfk.com:9092")

      .option("subscribe", "weblogs")

      .load()

    import spark.implicits._

    val lines = df.selectExpr("CAST(value AS STRING)").as[String]

    val weblog = lines.map(_.split(","))

      .map(x => Weblog(x(), x(), x(),x(),x(),x()))

    val titleCount = weblog

      .groupBy("searchname").count().toDF("titleName","count")

    val url ="jdbc:mysql://bigdata-pro01.kfk.com:3306/test"

    val username="root"

    val password="root"

    val writer = new JDBCSink(url,username,password)

    val query = titleCount.writeStream

      .foreach(writer)

      .outputMode("update")

        //.format("console")

      .trigger(ProcessingTime("5 seconds"))

      .start()

    query.awaitTermination()

  }

}

package com.spark.test

import java.sql._

import java.sql.{Connection, DriverManager}

import org.apache.spark.sql.{ForeachWriter, Row}

/**

  * Created by Administrator on 2017/10/17.

  */

class JDBCSink(url:String, username:String,password:String) extends ForeachWriter[Row]{

  var statement : Statement =_

  var resultSet : ResultSet =_

  var connection : Connection=_

  override def open(partitionId: Long, version: Long): Boolean = {

    Class.forName("com.mysql.jdbc.Driver")

    //  connection = new MySqlPool(url,username,password).getJdbcConn();

    connection = DriverManager.getConnection(url,username,password);

      statement = connection.createStatement()

      return true

  }

  override def process(value: Row): Unit = {

    val titleName = value.getAs[String]("titleName").replaceAll("[\\[\\]]","")

    val count = value.getAs[Long]("count");

    val querySql = "select 1 from webCount " +

      "where titleName = '"+titleName+"'"

    val updateSql = "update webCount set " +

      "count = "+count+" where titleName = '"+titleName+"'"

    val insertSql = "insert into webCount(titleName,count)" +

      "values('"+titleName+"',"+count+")"

    try{

      var resultSet = statement.executeQuery(querySql)

      if(resultSet.next()){

        statement.executeUpdate(updateSql)

      }else{

        statement.execute(insertSql)

      }

    }catch {

      case ex: SQLException => {

        println("SQLException")

      }

      case ex: Exception => {

        println("Exception")

      }

      case ex: RuntimeException => {

        println("RuntimeException")

      }

      case ex: Throwable => {

        println("Throwable")

      }

    }

  }

  override def close(errorOrNull: Throwable): Unit = {

//    if(resultSet.wasNull()){

//      resultSet.close()

//    }

    if(statement==null){

      statement.close()

    }

    if(connection==null){

      connection.close()

    }

  }

}

在pom.xml文件里添加这个依赖包

<dependency>

      <groupId>mysql</groupId>

      <artifactId>mysql-connector-java</artifactId>

      <version>5.1.</version>

    </dependency>

我在这里说一下这个依赖包版本的选择上最好要跟你集群里面的依赖包版本一样，不然可能会报错的，可以参考hive里的Lib路径下的版本

保持集群的dfs,hbase,yarn,zookeeper,都是启动的状态

启动我们节点1和节点2的flume,在启动之前我们先修改一下flume的配置，因为我们把jdk版本和kafka版本后面更换了，所以我们要修改配置文件（3个节点的都改）

启动节点1的flume

启动节点1的kafka

bin/kafka-server-start.sh config/server.properties

启动节点2的flume

在节点2上把数据启动起来，实时产生数据

回到idea我们把程序运行一下

注意了，现在程序是没有报错的，因为我前期工作做得不是太好，给idea分配的内存小了，所以跑得很慢

回到mysql里面查看webCount表，已经有数据进来了

我们把配置文件修改如下

[client]

socket=/var/lib/mysql/mysql.sock

default-character-set=utf8

[mysqld]

character-set-server=utf8

datadir=/var/lib/mysql

socket=/var/lib/mysql/mysql.sock

user=mysql

# Disabling symbolic-links is recommended to prevent assorted security risks

symbolic-links=

[mysql]

default-character-set=utf8

[mysqld_safe]

log-error=/var/log/mysqld.log

pid-file=/var/run/mysqld/mysqld.pid

把表删除了

重新创建表

create table webCount( titleName varchar() CHARACTER SET utf8 DEFAULT NULL, count int() DEFAULT NULL )ENGINE=lnnoDB DEFAULT CHARSET=utf8;

重新在运行一次程序

可以看到没有中文乱码了。

同时我们通过可视化工具连接mysql查看

巴特西

Structrued Streaming业务数据实时分析

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