您的位置：首页 > 数据库

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

2015-12-03 14:54 645 查看

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>4.0.0</modelVersion>

	<groupId>com.hexun</groupId>
	<artifactId>spark-streaming-java</artifactId>
	<version>0.0.1-SNAPSHOT</version>
	<packaging>jar</packaging>

	<name>spark-study-java</name>
	<url>http://maven.apache.org</url>

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

	<dependencies>
		<dependency>
			<groupId>junit</groupId>
			<artifactId>junit</artifactId>
			<version>3.8.1</version>
			<scope>test</scope>
		</dependency>
		<dependency>
			<groupId>org.apache.spark</groupId>
			<artifactId>spark-core_2.10</artifactId>
			<version>1.4.1</version>
		</dependency>
		<dependency>
			<groupId>org.apache.spark</groupId>
			<artifactId>spark-sql_2.10</artifactId>
			<version>1.4.1</version>
		</dependency>
		<dependency>
			<groupId>org.apache.spark</groupId>
			<artifactId>spark-hive_2.10</artifactId>
			<version>1.4.1</version>
		</dependency>
		<dependency>
			<groupId>org.apache.spark</groupId>
			<artifactId>spark-streaming_2.10</artifactId>
			<version>1.4.1</version>
		</dependency>
		<dependency>
			<groupId>org.apache.hadoop</groupId>
			<artifactId>hadoop-client</artifactId>
			<version>2.6.0</version>
		</dependency>
		<dependency>
			<groupId>org.apache.spark</groupId>
			<artifactId>spark-streaming-kafka_2.10</artifactId>
			<version>1.4.1</version>
		</dependency>
		<dependency>
			<groupId>mysql</groupId>
			<artifactId>mysql-connector-java</artifactId>
			<version>5.1.6</version>
		</dependency>
	</dependencies>

	<build>
		<sourceDirectory>src/main/java</sourceDirectory>
		<testSourceDirectory>src/main/test</testSourceDirectory>

		<plugins>
			<plugin>
				<artifactId>maven-assembly-plugin</artifactId>
				<configuration>
					<descriptorRefs>
						<descriptorRef>jar-with-dependencies</descriptorRef>
					</descriptorRefs>
					<archive>
						<manifest>
							<mainClass></mainClass>
						</manifest>
					</archive>
				</configuration>
				<executions>
					<execution>
						<id>make-assembly</id>
						<phase>package</phase>
						<goals>
							<goal>single</goal>
						</goals>
					</execution>
				</executions>
			</plugin>

			<plugin>
				<groupId>org.codehaus.mojo</groupId>
				<artifactId>exec-maven-plugin</artifactId>
				<version>1.2.1</version>
				<executions>
					<execution>
						<goals>
							<goal>exec</goal>
						</goals>
					</execution>
				</executions>
				<configuration>
					<executable>java</executable>
					<includeProjectDependencies>true</includeProjectDependencies>
					<includePluginDependencies>false</includePluginDependencies>
					<classpathScope>compile</classpathScope>
				</configuration>
			</plugin>

			<plugin>
				<groupId>org.apache.maven.plugins</groupId>
				<artifactId>maven-compiler-plugin</artifactId>
				<configuration>
					<source>1.6</source>
					<target>1.6</target>
				</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 & \

内容来自用户分享和网络整理，不保证内容的准确性，如有侵权内容，可联系管理员处理

标签：

相关文章推荐

新的分享

章节导航