Spark Streaming从Kafka中拉取数据，并且使用过“窗口函数”统计一些流量信息
一、应用案例场景：
在Spark Streaming中，我们通常计算的是一段时间间隔内的数据。比如http://blog.csdn.net/tototuzuoquan/article/details/75094540这个案例中，统计单词出现次数时，每间隔5秒钟进行实时从Kafka中读取数据。但是当遇到一些其它的场景，比如一些流量计算类的，可能日志信息是30秒收集并被生成一次。但是我们有时候需要计算2.5分钟（150秒）中流量变化情况。所以这时候就要用到Spark Streaming中的窗口函数来实现这种业务需求。
二、关于流量相关的日志文件说明
三、日志格式如下
2016-06-30 00:18:57,10.242.91.130,117.135.250.52,33799,80,1,0,8270,mmbiz.qpic.cn,/mmbiz/3tsIcoqhsab2ybEANKia7va097HPwphsEOU7DjyQGTXA5LGzjJElVMKaRMJFiaAnDLVzN3j5libBbAzPrgky95libQ/0?wx_fmt=gif&tp=webp&wxfrom=5&wx_lazy=1,17,638,0,200,0,9698,618685,0,1,0,0,2,4,1,211,Mozilla/5.0 (Linux; Android 5.1; m1 metal Build/LMY47I) AppleWebKit/537.36 (KHTML%2C like Gecko) Version/4.0 Chrome/37.0.0.0 Mobile MQQBrowser/6.2 TBS/036524 Safari/537.36 MicroMessenger/6.3.18.800 NetType/WIFI Language/zh_CN,601647,0,0,8270,437,219,419
2016-06-30 00:19:00,10.242.149.112,120.198.203.230,64736,80,1,1,5247,szshort.weixin.qq.com,/mmtls/7dd6aa5f,94,20,1,200,0,1316,2940,4,1,5,1,45,5494,341,94,MicroMessenger Client,589,0,1,5247,3093,10,10
2016-06-30 00:18:52,10.242.162.104,117.187.22.24,10032,80,1,0,92,comm.inner.bbk.com,/clientRequest/detectCollectControlMsg?apiVison=2&model=vivo+Y27&netType=WIFI&elapsedtime=177150983&imei=867534026587667&sysvison=PD1410BL_PD1410LMA_1.15.5&plan_switch=1&modelNumber=vivo+Y27&cs=0,78,11,0,500,0,533,509,1,1,1,1,2,10,3,78,IQooAppstore,4,0,0,92,78,5,6
2016-06-30 00:18:33,10.242.237.196,117.135.196.194,59561,80,1,1,1703,117.135.196.194,/videos/v0/20160629/00/0b/e6e8417d9074fa7d262f537980ca4f08.f4v?key=050b684298dc90e6319fd3f97f654a4a5&src=iqiyi.com&qd_tvid=502276800&qd_vipres=0&qd_index=6&qd_aid=204159701&qd_stert=1850267&qd_scc=4ab54c5ef32351913d522de9958615f0&qd_sc=e30b195312b2975a136a0900265bbd40&qd_src=02022001010000000000&qd_ip=7587d460&qd_uid=1296545976&qd_tm=1467216601880&qd_vip=1&qyid=860734037201152&qypid=&la=CMNET|GuiZhou&li=guiyang_cmnet&lsp=-1&lc=18&uuid=7587d460-5773f471-ce,2,3205,1,206,0,75700,2163473,0,1,0,0,2,5,1,2,HCDNClient_ANDROID_MOBILE;0.0.0.0;QK/0.0.0.0,2097152,0,1,1703,1693,1588,1617
2016-06-30 00:14:52,10.242.7.143,117.135.250.56,56012,80,1,0,1,i.gtimg.cn,/qqlive/images/20141217/corner_pic_lianzai.png?rand=1467216908,1,8,0,65535,0,528,0,8,0,0,0,1,1,1,1,Mozilla/4.0 (compatible; MSIE 5.00; Windows 98),0,0,0,1,1,8,0
2016-06-30 00:14:53,10.242.210.84,183.232.10.27,60160,80,1,0,278,i0.letvimg.com,/lc07_isvrs/201606/29/09/49/8609ad16-4a6a-4957-8147-62440bfc1429/thumb/2_400_300.jpg,1,9,0,65535,0,736,0,60,0,0,0,1,1,1,1,Dalvik/2.1.0 (Linux; U; Android 5.0.1; HUAWEI GRA-UL00 Build/HUAWEIGRA-UL00),0,0,0,278,1,9,0
2016-06-30 00:14:54,10.242.210.84,183.232.10.27,32898,80,1,0,1,i1.letvimg.com,/lc07_isvrs/201606/29/18/26/a8975515-5b03-4c75-af89-e1dc3487d04c/thumb/2_400_300.jpg,1,17,0,65535,0,1160,0,17,0,0,0,1,1,1,1,Dalvik/2.1.0 (Linux; U; Android 5.0.1; HUAWEI GRA-UL00 Build/HUAWEIGRA-UL00),0,0,0,1,1,17,0
2016-06-30 00:18:49,10.242.132.12,117.135.252.141,55817,80,1,0,3282,ww3.sinaimg.cn,/or480/eb5de2dfjw1f56d2p47ujj20id5221kx.jpg,3,74,0,200,0,854,85431,0,1,0,0,1,6,8,2,Weibo/7174 CFNetwork/758.2.8 Darwin/15.0.0,82462,0,0,3282,67,15,59
2016-06-30 00:19:05,10.242.72.31,183.232.119.170,37365,80,1,0,398,wap.mpush.qq.com,/push/conn2?mid=0e0e75865dc4c9c44a86b446518fd8714ab8406e&devid=861916035175798&mac=1c%253A77%253Af6%253Ab5%253Ab3%253Ae7&did=861916035175798&qqnetwork=gsm&bid=10001&msgid=66108462&store=303&screen_height=1920&Cookie=%20lskey%3D%3B%20luin%3D%3B%20skey%3D%3B%20uin%3D%3B%20logintype%3D0%20&apptype=android&netstate=4&hw=OPPO_OPPOR9tm&appver=22_android_5.0.1&uid=846d4c65230ae371&screen_width=1080&qn-sig=d74d29d89304463d9d17a7dd1eee91a8&qn-rid=935575748&imsi=460021808763237&auth=3e0c10fff52338d72bc23450,25,10,0,200,0,1022,965,1,1,1,1,25,102,13,25,%E8%85%BE%E8%AE%AF%E6%96%B0%E9%97%BB501(android),589,0,0,398,25,5,5
2016-06-30 00:15:05,10.242.153.62,183.232.90.90,47509,80,1,0,13,monitor.uu.qq.com,/analytics/rqdsync,1,7,0,65535,0,958,0,7,0,0,0,1,1,1,1,,369,0,0,13,1,7,0
2016-06-30 00:12:31,10.242.169.95,117.135.250.120,56104,80,1,1,376073,117.135.250.120,/cache.p4p.com/k0017irptuc.mp4?vkey=345ED8D9B599A30FCF19C84D138517D9BB48CB597C4400D5F204B38101C098F616CCF866B944D6FDAD0DC4A10C3ABA92F2D663EC34B591C64E93754872E4F776A4F5AEE545A3D0A1A6B357F5B91C3AB61BFEF45EBD95ABF3&sdtfrom=v5060&type=mp4&platform=60301&fmt=mp4&level=0&br=60&sp=0,2,37433,1,206,0,706581,36054392,1,1,1,1,2,253,1,2,Mozilla/4.0 (compatible; MSIE 5.00; Windows 98),46926315,0,1,376073,376073,13070,24363
2016-06-30 00:18:47,10.242.98.209,117.135.250.55,50244,80,1,0,161,nlogtj.zuoyebang.cc,/nlogtj/rule/zuoye_android_1.0.0.rule,86,10,0,200,0,459,589,1,1,1,1,2,159,30,86,Dalvik/1.6.0 (Linux; U; Android 4.4.2; find7 Build/JDQ39),215,0,0,161,86,6,4
2016-06-30 00:18:02,10.242.174.78,112.12.6.139,24629,80,1,1,45199,c.f1zd.com,/b/1/656/fid9fid.swf?uid=519327,62,31,1,200,0,796,24305,1,1,1,1,62,2,8459,62,Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML%2C like Gecko) Chrome/45.0.2454.101 Safari/537.36,23168,1,0,45199,128,11,20
2016-06-30 00:15:09,10.242.37.84,117.135.250.55,54828,80,1,0,1,d1.sina.com.cn,/litong/lijun/0525/mm_15890324_8176878_30042709.js?uuid=wap_photo_pics,1,5,0,65535,0,773,0,0,0,0,0,1,1,1,1,Mozilla/5.0 (iPhone; CPU iPhone OS 9_3_2 like Mac OS X) AppleWebKit/601.1.46 (KHTML%2C like Gecko) Version/9.0 Mobile/13F69 Safari/601.1,0,0,0,1,1,5,0
2016-06-30 00:16:30,10.242.151.53,117.187.19.46,47439,80,1,0,7,cfgstatic.51y5.net,/php/configunion.html,3,19,0,200,0,476,1032,1,1,1,1,2,4,2,3,,165,0,0,7,3,8,11三、创建项目
项目创建参考：http://blog.csdn.net/tototuzuoquan/article/details/75094540
四、编写项目代码
package cn.toto.spark
import cn.toto.spark.streams.LoggerLevels
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
/**
* Created by toto on 2017/7/14.
*/
case class DataBean(session_count: Int, //会话数
retrans_count: Long, //重传次数
connect_time: Long, //连接时常
resp_delay: Long, //相应延迟
total_packets: Long, //总包数
retrans_packets: Int, //重传包数
up_bytes: Int, //上行流量字节数
down_bytes: Int //下行流量
) extends Serializable
/**
* Created by ZX on 2016/7/13.
*/
object TrafficCount {
//这里用于累加，把a的DataBean和b的DataBean最后返回给一个新的DataBean
val reduceFunc = (a: DataBean, b: DataBean) => {
DataBean(a.session_count + b.session_count,
a.retrans_count + b.retrans_count,
a.connect_time + b.connect_time,
a.resp_delay + b.resp_delay,
a.total_packets + b.total_packets,
a.retrans_packets + b.retrans_packets,
a.up_bytes + b.up_bytes,
a.down_bytes + b.down_bytes
)
}
def main(args: Array[String]) {
LoggerLevels.setStreamingLogLevels()
val Array(zkQuorum, group, topics, numThreads) = args
val sparkConf = new SparkConf().setAppName("TrafficCount").setMaster("local[2]")
//设置30秒从kafka中拉取数据
val ssc = new StreamingContext(sparkConf, Seconds(30))
ssc.checkpoint("E://workspace//trafficResult")
val topicMap = topics.split(",").map((_, numThreads.toInt)).toMap
val data: ReceiverInputDStream[(String, String)] = KafkaUtils.createStream(ssc, zkQuorum, group, topicMap)
//拿出一行行的数据，_._2就是kafka中的数据
val lines: DStream[String] = data.map(_._2)
val tupleData: DStream[((String, String), DataBean)] = lines.map(line => {
//切分字段
val fields = line.split(",")
//将时间和IP作为Key，其他字段封装到DataBean中作为Value
//fields(0) ：记录生成时间
//fields(1) : 用户IP
//DataBean
((fields(0), fields(1)), DataBean(
fields(5).toInt,
fields(6).toLong,
fields(7).toLong,
fields(10).toLong,
fields(11).toLong,
fields(12).toInt,
fields(16).toInt,
fields(17).toInt
))
})
val result: DStream[((String, String), DataBean)] = tupleData.reduceByKeyAndWindow(
reduceFunc,
Seconds(180), //窗口参数(6个窗口的时间长度)，因为上面的每个窗口长度是30秒
Seconds(150)) //设置滑动的长度是5个窗口的长度
//将结果保存到HBase或者redis等其它的地方。
result.foreachRDD(rdd => {
rdd.foreachPartition(part => {
//将下面的代码修改一下表明就可以了
//val hConf = new HBaseConfiguration()
//val hTable = new HTable(hConf, "table")
part.foreach(x => {
//修改一下对应的列名就可以了
//val thePut = new Put(Bytes.toBytes(row(0)))
//thePut.add(Bytes.toBytes("cf"), Bytes.toBytes(row(0)), Bytes.toBytes(row(0)))
//hTable.put(thePut)
})
//htable.close()
})
})
ssc.start()
ssc.awaitTermination()
}
}
参考：http://blog.csdn.net/tototuzuoquan/article/details/75094540，和这里面的运行方式一样。只需要把上面的url放到kafka中，然后即可进行计算了