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文章作者邮箱：yugongshiye@sina.cn 地址：广东惠州
▲ 本章节目的
⚪ 掌握网站流量项目的SparkStreaming代码；
⚪ 掌握网站流量项目的HBaseUtil代码；
⚪ 掌握网站流量项目的MysqlUtil代码；
⚪ 掌握网站流量项目的LogBean代码；
⚪ 掌握网站流量项目的TongjiBean代码；
一、SparkStreaming代码
package cn.tedu.kafkasource
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.kafka.common.TopicPartition
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.SparkContext
import cn.tedu.pojo.LogBean
import java.util.Calendar
import cn.tedu.dao.HBaseUtil
import cn.tedu.pojo.TongjiBean
import cn.tedu.dao.MysqlUtil
object SparkStreaming {
def main(args: Array[String]): Unit = {
val conf= new SparkConf().setMaster("local[3]").setAppName("test01")
.set("spark.serializer","org.apache.spark.serializer.KryoSerializer")
val sc=new SparkContext(conf)
val ssc=new StreamingContext(sc, Seconds(5))
val kafkaParams: Map[String, Object] = Map[String, Object](
"bootstrap.servers" -> "hadoop01:9092,hadoop02:9092,hadoop03:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"group.id" -> "gp2"
)
val topics = Array("logdata")
val kafkaSource=KafkaUtils.createDirectStream[String, String](
ssc,
PreferConsistent,
Subscribe[String, String](topics, kafkaParams)
).map(x=>x.value())
kafkaSource.foreachRDD{rdd=>
//lines里存储了当前批次内的所有数据
val lines=rdd.toLocalIterator
//遍历迭代器,对每条数据进行处理
while(lines.hasNext){
val line=lines.next()
//第一步:清洗出所需要的业务字段。url,urlname,uvid,ssid,sscount,sstime,cip
val info=line.split("\\|")
val url=info(0)
val urlname=info(1)
val uvid=info(13)
val ssid=info(14).split("_")(0)
val sscount=info(14).split("_")(1)
val sstime=info(14).split("_")(2)