Spark读文本并将多行合并为一行

数据格式

六月 15, 2015 4:28:02 下午

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六月 15, 2015 4:28:03 下午

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六月 15, 2015 4:28:04 下午

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GitHub地址： https://github.com/dankfir/spark-multi-line

import org.apache.log4j.{Level,Logger}

import org.apache.spark.sql.SQLContext

import org.apache.spark.{SparkConf, SparkContext}

import scala.collection.mutable.ArrayBuffer

/**

  * Created by Dank on 2017/8/25.

  */

object logAnalysis_catalina{

 Logger.getLogger(“org”).setLevel(Level.ERROR)

  def main(args: Array[String]): Unit = {

    val root =this.getClass.getResource(“/”)

    val conf = newSparkConf().setAppName(“logAnalysis”).setMaster(“local[*]”)

    val sc = new SparkContext(conf)

    val sqlContext = new SQLContext(sc)

    val sourceRdd = sc.textFile(root +”catalina/catalina*.log”)

    var log = new ArrayBuffer[String]()//用于临时保存要合并的行

    val preprocessRDD = sourceRdd

      .map(line => {

        var tlog = ” “

        if (log.length < 2) {//合并两行

          log += line

          if (log.length == 2) {

            if (log(0).contains(“:”)) tlog = log(1) + ” ” + log(0)

            //由于spark读数据并行的，故需将两行排序，带：的行排在后面

            else tlog = log(0) + ” “+ log(1)

            log = new ArrayBuffer[String]()

          }

        }

        tlog//合并后的行

      })

      .filter(!_.equals(” “))

    import sqlContext.implicits._

    val logDF = preprocessRDD.toDF()

    logDF.show()

    import com.databricks.spark.csv._

    logDF.repartition(1).saveAsCsvFile(root +”catalina.csv”)

  }

}