十年网站开发经验 + 多家企业客户 + 靠谱的建站团队
量身定制 + 运营维护+专业推广+无忧售后,网站问题一站解决
这篇文章给大家介绍Spark 中怎么读取本地日志文件,内容非常详细,感兴趣的小伙伴们可以参考借鉴,希望对大家能有所帮助。
成都创新互联公司是一家集网站建设,灵寿企业网站建设,灵寿品牌网站建设,网站定制,灵寿网站建设报价,网络营销,网络优化,灵寿网站推广为一体的创新建站企业,帮助传统企业提升企业形象加强企业竞争力。可充分满足这一群体相比中小企业更为丰富、高端、多元的互联网需求。同时我们时刻保持专业、时尚、前沿,时刻以成就客户成长自我,坚持不断学习、思考、沉淀、净化自己,让我们为更多的企业打造出实用型网站。
import java.io.{FileWriter, BufferedWriter, File} import com.alvinalexander.accesslogparser.{AccessLogRecord, AccessLogParser} import org.apache.spark.{SparkContext, SparkConf} import scala.collection.immutable.ListMap /** * Spark 读取本地日志文件,抽取最高的访问地址,排序,并保存到本地文件 * Created by eric on 16/6/29. */ object LogAnalysisSparkFile { def getStatusCode(line: Option[AccessLogRecord]) = { line match { case Some(l) => l.httpStatusCode case None => "0" } } def main(agrs: Array[String]): Unit = { //设置本地运行,在Vm options:上填写:-Dspark.master=local ,Program arguments上填写:local val sparkConf = new SparkConf().setMaster("local[1]").setAppName("StreamingTest") val sc = new SparkContext(sparkConf) val p = new AccessLogParser val log = sc.textFile("/var/log/nginx/www.eric.aysaas.com-access.log") println(log.count())//68591 val log1 = log.filter(line => getStatusCode(p.parseRecord(line)) == "404").count() println(log1) val nullObject = AccessLogRecord("", "", "", "", "GET /foo HTTP/1.1", "", "", "", "") val recs = log.filter(p.parseRecord(_).getOrElse(nullObject).httpStatusCode == "404") .map(p.parseRecord(_).getOrElse(nullObject).request) val wordCounts = log.flatMap(line => line.split(" ")) .map(word => (word, 1)) .reduceByKey((a, b) => a + b) val uriCounts = log.map(p.parseRecord(_).getOrElse(nullObject).request) .map(_.split(" ")(1)) .map(uri => (uri, 1)) .reduceByKey((a, b) => a + b) val uriToCount = uriCounts.collect // (/foo, 3), (/bar, 10), (/baz, 1) ...//无序 val uriHitCount = ListMap(uriToCount.toSeq.sortWith(_._2 > _._2):_*) // (/bar, 10), (/foo, 3), (/baz, 1),降序 uriCounts.take(10).foreach(println) println("**************************") val logSave = uriHitCount.take(10).foreach(println) // this is a decent way to print some sample data uriCounts.takeSample(false, 100, 1000) //输出保存到本地文件,由于ListMap,导致 saveAsTextFile 不能用 // logSave.saveAsTextFile("UriHitCount") val file = new File("UriHitCount.out") val bw = new BufferedWriter(new FileWriter(file)) for { record <- uriHitCount val uri = record._1 val count = record._2 } bw.write(s"$count => $uri\n") bw.close } }
关于Spark 中怎么读取本地日志文件就分享到这里了,希望以上内容可以对大家有一定的帮助,可以学到更多知识。如果觉得文章不错,可以把它分享出去让更多的人看到。