【总结】spark按文本格式和Lzo格式处理Lzo压缩文件的比较

1、描述spark中怎么加载lzo压缩格式的文件

2、比较lzo格式文件以textFile方式和LzoTextInputFormat方式计算数据，Running Tasks个数的影响

a.确保lzo文件所在文件夹中生成lzo.index索引文件

（对该lzo压缩文件进行index操作，生成lzo.index文件，map操作才可以进行split

hadoop jar ${HADOOP_HOME}/lib/hadoop-lzo.jar com.hadoop.compression.lzo.DistributedLzoIndexer /wh/source/）

b.以LzoTextInputFormat处理，能够正常按分块数分配Tasks

查看文件块数量

[tech@dx2 ~]$ hdfs fsck /wh/source/hotel.2017-08-07.txt_10.10.10.10_20170807.lzoConnecting to namenode via http://nn1.zdp.ol:50070FSCK started by bwtech (auth:SIMPLE) from /10.10.10.10 for path /wh/source/hotel.2017-08-07.txt_10.10.16.105_20170807.lzo at Tue Aug 08 15:27:52 CST 2017.Status: HEALTHY Total size:2892666412 B Total dirs:0 Total files:1 Total symlinks:0 Total blocks (validated):11 (avg. block size 262969673 B) Minimally replicated blocks:11 (100.0 %) Over-replicated blocks:0 (0.0 %) Under-replicated blocks:0 (0.0 %) Mis-replicated blocks:0 (0.0 %) Default replication factor:3 Average block replication:3.0 Corrupt blocks:0 Missing replicas:0 (0.0 %) Number of data-nodes:21 Number of racks:2FSCK ended at Tue Aug 08 15:27:52 CST 2017 in 3 milliseconds

Spark源代码可以参考https://github.com/chocolateBlack/LearningSpark/blob/master/src/main/scala-2.11/SparkLzoFile.scala

import com.hadoop.mapreduce.LzoTextInputFormatimport org.apache.hadoop.io.{Text, LongWritable}import org.apache.spark.{SparkContext, SparkConf}object SparkLzoFile{  def main(args:Array[String]){    val conf = new SparkConf().setAppName("Spark_Lzo_File")    val sc = new SparkContext(conf)    //文件路径    val filePath = "/wh/source/hotel.2017-08-07.txt_10.10.10.10_20170807.lzo"    //按textFile方式加载文件    val textFile = sc.textFile(filePath)    //按lzoTextInputFormat加载数据文件    val lzoFile = sc.newAPIHadoopFile[LongWritable, Text, LzoTextInputFormat](filePath)    println(textFile.partitions.length)// partitions个数输出 1    println(lzoFile.partitions.length)//  partitions个数输出 11    //两种方式计算word count查看后台任务    lzoFile.map(_._2.toString).flatMap(x=>x.split("-")).map((_,1)).reduceByKey(_+_).collect    textFile.flatMap(x=>x.split("\t")).map((_,1)).reduceByKey(_+_).collect  }}