Spark-Streaming与Spark-Sql整合实现实时股票排行---通过kafka列队数据

Spark-Streaming与Spark-Sql整合实现实时股票排行---通过kafka列队数据，前端数据通过 kafka队列传递，外层还有flume的实时收集。

1、mvn构建工程，指定好依赖的库，这里用的是spark1.4.1

 <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>4.0.0modelVersion>

     <groupId>com.hexungroupId>
     <artifactId>spark-streaming-javaartifactId>
     <version>0.0.1-SNAPSHOTversion>
     <packaging>jarpackaging>

     <name>spark-study-javaname>
     <url>http://maven.apache.orgurl>

     <properties>
         <project.build.sourceEncoding>UTF-8project.build.sourceEncoding>
     properties>

     <dependencies>
         <dependency>
             <groupId>junitgroupId>
             <artifactId>junitartifactId>
             <version>3.8.1version>
             <scope>testscope>
         dependency>
         <dependency>
             <groupId>org.apache.sparkgroupId>
             <artifactId>spark-core_2.10artifactId>
             <version>1.4.1version>
         dependency>
         <dependency>
             <groupId>org.apache.sparkgroupId>
             <artifactId>spark-sql_2.10artifactId>
             <version>1.4.1version>
         dependency>
         <dependency>
             <groupId>org.apache.sparkgroupId>
             <artifactId>spark-hive_2.10artifactId>
             <version>1.4.1version>
         dependency>
         <dependency>
             <groupId>org.apache.sparkgroupId>
             <artifactId>spark-streaming_2.10artifactId>
             <version>1.4.1version>
         dependency>
         <dependency>
             <groupId>org.apache.hadoopgroupId>
             <artifactId>hadoop-clientartifactId>
             <version>2.6.0version>
         dependency>
         <dependency>
             <groupId>org.apache.sparkgroupId>
             <artifactId>spark-streaming-kafka_2.10artifactId>
             <version>1.4.1version>
         dependency>
         <dependency>
             <groupId>mysqlgroupId>
             <artifactId>mysql-connector-javaartifactId>
             <version>5.1.6version>
         dependency>
     dependencies>

     <build>
         <sourceDirectory>src/main/javasourceDirectory>
         <testSourceDirectory>src/main/testtestSourceDirectory>

         <plugins>
             <plugin>
                 <artifactId>maven-assembly-pluginartifactId>
                 <configuration>
                     <descriptorRefs>
                         <descriptorRef>jar-with-dependenciesdescriptorRef>
                     descriptorRefs>
                     <archive>
                         <manifest>
                             <mainClass>mainClass>
                         manifest>
                     archive>
                 configuration>
                 <executions>
                     <execution>
                         <id>make-assemblyid>
                         <phase>packagephase>
                         <goals>
                             <goal>singlegoal>
                         goals>
                     execution>
                 executions>
             plugin>

             <plugin>
                 <groupId>org.codehaus.mojogroupId>
                 <artifactId>exec-maven-pluginartifactId>
                 <version>1.2.1version>
                 <executions>
                     <execution>
                         <goals>
                             <goal>execgoal>
                         goals>
                     execution>
                 executions>
                 <configuration>
                     <executable>javaexecutable>
                     <includeProjectDependencies>trueincludeProjectDependencies>
                     <includePluginDependencies>falseincludePluginDependencies>
                     <classpathScope>compileclasspathScope>
                 configuration>
             plugin>

             <plugin>
                 <groupId>org.apache.maven.pluginsgroupId>
                 <artifactId>maven-compiler-pluginartifactId>
                 <configuration>
                     <source>1.6source>
                     <target>1.6target>
                 configuration>
             plugin>

         plugins>
     build>
 project>

2、实现过程：

1）通过kafka队列获取数据，createDirectStream实现

2）映射pojo与数据的关系，注册成sparksql的表

3）实现sql中的函数，这里大部分的函数都要自己实现udf，甚至length简单的函数

4)  编写sql语实现

5）保存入MySQL数据，供前端展示

具体代码如下（Scala版本）：

 package com.hexun.streaming

 import java.sql.{DriverManager, Connection}
 import java.util.Date
 import java.util.regex.Pattern

 import kafka.serializer.StringDecoder
 import org.apache.commons.lang.time.DateFormatUtils
 import org.apache.spark.streaming.kafka.KafkaUtils
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.{SparkContext, SparkConf}

 import scala.collection.mutable

 import scala.collection.immutable.ListMap


 /**
  * Created by Administrator on 2015/11/26.
  */
 object StockCntSumKafkaLPcnt {


   case class Tracklog(dateday: String, datetime: String, ip: String, cookieid: String, userid: String, logserverip: String, referer: String, requesturl: String, remark1: String,
                       remark2: String, alexaflag: String, ua: String)


   def main(args: Array[String]) {
     val smap = new mutable.HashMap[String, Integer]()

     val url = "jdbc:mysql://10.130.3.211:3306/charts"
     val user = "dbcharts"
     val password = "Abcd1234"

     val conf = new SparkConf().setAppName("stocker") //.setMaster("local[2]")
     val sc = new SparkContext(conf)

     val ssc = new StreamingContext(sc, Seconds(15))

     // Kafka configurations

     val topics = Set("teststreaming")

     val brokers = "bdc46.hexun.com:9092,bdc53.hexun.com:9092,bdc54.hexun.com:9092"

     val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers, "serializer.class" -> "kafka.serializer.StringEncoder")

     // Create a direct stream
     val kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)

     val events = kafkaStream.flatMap(line => {
       Some(line.toString())
     })

     try {
       val tmpdf = events.map(_.split(" ")).filter(_.length >= 11).map(x => Tracklog(x(0), x(1), x(2), x(3), x(4), x(5), x(6), x(7), x(8), x(9), x(10), x(11)))

       tmpdf.foreachRDD { rdd =>
         val sqlContext = new org.apache.spark.sql.SQLContext(sc)

         import sqlContext.implicits._
         val df = rdd.toDF().registerTempTable("tracklog")

         sqlContext.udf.register("strLen", (str: String) => str.length())

         sqlContext.udf.register("concat", (str1: String, str2: String, str3: String) => str1 + str2 + str3)

         sqlContext.udf.register("regexp_extract", (str: String, pattern: String) => {
           val matcher = Pattern.compile(pattern, 1).matcher(str)
           var res = ""
           while (matcher.find()) {
             res = matcher.group()
           }
           res
         })

         val rcount = sqlContext.sql("SELECT  substring(t.requesturl,strLen(regexp_extract(t.requesturl,'(.*?[^0-9][0|3|6][0][0-9][0-9][0-9][0-9]).*?'))-5,6) stock_code," +
           "concat('http://stockdata.stock.hexun.com/', substring(t.requesturl,strLen(regexp_extract(t.requesturl,'(.*?[^0-9][0|3|6][0][0-9][0-9][0-9][0-9]).*?'))-5,6),'.shtml') url," +
           "count(*) clickcnt " +
           "FROM " +
           "(select distinct dateday,datetime,ip,cookieid,userid,logserverip,referer,requesturl,remark1,remark2,alexaflag,ua from  tracklog where strLen(datetime)=12) t " +
           "WHERE  " +
           "regexp_extract(t.requesturl,'(.*?[^0-9][0|3|6][0][0-9][0-9][0-9][0-9]).*?') <>'' " +
           "and t.requesturl like 'http://stockdata.stock.hexun.com/%shtml' " +
           "and t.requesturl not like '%index%' " +
           "and t.requesturl not like '%fund%' " +
           "group by substring(t.requesturl,strLen(regexp_extract(t.requesturl,'(.*?[^0-9][0|3|6][0][0-9][0-9][0-9][0-9]).*?'))-5,6)  " +
           "order by clickcnt desc " +
           "limit 150")

         var flag:Int = 0


         rcount.collect().foreach(data => {
           flag = 1;
           val stockerId = data.get(0).toString;
           val cnt = smap.get(stockerId)

           println("stockerId: " + stockerId + ", cnt:" + cnt)

           if (cnt == null || cnt.toString.equals("None")) {
             smap += (stockerId -> Integer.parseInt(data.get(2).toString))
           } else if (cnt != null && !cnt.toString.equals("None")) {
             val cntI = smap(stockerId)
             val sum: Integer = Integer.parseInt(data.get(2).toString) + cntI
             smap += (stockerId -> sum)
           }
         })


         if(flag == 1){
           // sort by value
           var idx: Int = 1

           val sortMap = ListMap(smap.toSeq.sortWith(_._2 > _._2): _*)

           val stattime = DateFormatUtils.format(new Date, "yyyy-MM-dd HH:mm:ss")

           val conn: Connection = DriverManager.getConnection(url, user, password)
           val pstat = conn.prepareStatement("INSERT INTO  stock_realtime_analysis_spark (stockId,url,clickcnt,type,recordtime) VALUES (?,?,?,?,?)")

           sortMap foreach {
             case (key, value) =>
               if (idx <= 150) {
                 println(key + ",http://stockdata.stock.hexun.com/" + key + ".shtml," + value + "," + stattime)

                 pstat.setString(1, key)
                 pstat.setString(2, "http://stockdata.stock.hexun.com/" + key + ".shtml")
                 pstat.setInt(3, value)
                 pstat.setString(4, "01")
                 pstat.setString(5, stattime)

                 pstat.executeUpdate()
               }
               idx = idx + 1
           }

           pstat.close
           conn.close

           flag == 0
         }


       }
     } catch {
       case e: Exception =>
     }

     ssc.start()
     ssc.awaitTermination()
   }

 }

3、任务提交执行脚本如下：

 #!/bin/bash
 source /etc/profile

 stocker=`ps -ef | grep spark |grep SparkStreaming.jar | awk '{print $2}'`
 echo $stocker

 kill -9 $stocker

 nohup /opt/modules/spark/bin/spark-submit \
 --master spark://10.130.2.20:7077 \
 --driver-memory 3g \
 --executor-memory 3g \
 --total-executor-cores 24 \
 --conf spark.ui.port=56689  \
 --jars /opt/bin/sparkJars/kafka_2.10-0.8.2.1.jar,/opt/bin/sparkJars/spark-streaming-kafka_2.10-1.4.1.jar,/opt/bin/sparkJars/metrics-core-2.2.0.jar,/opt/bin/sparkJars/mysql-connector-java-5.1.26-bin.jar,/opt/bin/sparkJars/spark-streaming-
 kafka_2.10-1.4.1.jar \
 --class com.hexun.streaming.StockCntSumKafkaLPcnt \
 /opt/bin/UDF/SparkStreaming.jar \
  >/opt/bin/initservice/stock.log 2>&1 & \