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009 优化&新特性&HA

作者:互联网

1、Hadoop数据压缩

压缩算法原始文件大小压缩文件大小压缩速度解压速度自带切分改程序
gzip8.3GB1.8GB17.5MB/s58MB/s
bzip28.3GB1.1GB2.4MB/s9.5MB/s
LZO8.3GB2.9GB49.3MB/s74.6MB/s

PS:LZO格式是基于GPL许可的,不能通过Apache来分发许可,基于此,它的hadoop编码/解码器必须单独下载,Linux上安装编译lzo详解。lzop编码/解码器兼容干lzop工具,它其实就是LZO 格式,但额外还有头部,它正是我们想要的。还有一个纯LZO格式的编码/解码器LzoCodec,它使用.lzo_deflate作为扩展名(根据 DEFLATE类推,是没有头部的gzip格式)。

1.1、数据流的压缩和解压缩

//获取压缩编解码器codec
CompressionCodecFactory factory = new CompressionCodecFactory(new Configuration());
CompressionCodec codec = factory.getCodecByName(method);
//获取普通输出流,文件后面需要加上压缩后缀
FileOutputStream fos = new FileOutputStream(new File(filename + codec.getDefaultExtension()));
//获取压缩输出流,用压缩解码器对fos进行压缩
CompressionOutputStream cos = codec.createOutputStream(fos);
//获取压缩编解码器codec
CompressionCodecFactory factory = new CompressionCodecFactory(new Configuration());
CompressionCodec codec = factory.getCodec(new Path(filename));
//获取普通输入流
FileInputStream fis = new FileInputStream(new File(filename));
//获取压缩输出流,用压缩解码器对fis进行解压
CompressionInputStream cis = codec.createInputStream(fis);

1.2、Map、Reduce输出端采用压缩

Mapper和Reducer不变

// 开启map端输出压缩
conf.setBoolean("mapreduce.map.output.compress", true);
// 设置map端输出压缩方式
conf.setClass("mapreduce.map.output.compress.codec", BZip2Codec.class,CompressionCodec.class);
// 设置reduce端输出压缩开启
FileOutputFormat.setCompressOutput(job, true);
// 设置压缩的方式
FileOutputFormat.setOutputCompressorClass(job, BZip2Codec.class);

2、Hadoop企业优化

2.1、MapReduce程序效率的瓶颈

1)计算机性能:CPU、内存、硬盘、网络
2)I/O操作优化:数据倾斜、MapTask和ReduceTask数不合理、小文件、压缩文件不可切分、切片数过多、Merge数过多、Reduce时间过长

解决方案:
1)输入阶段:CombineTextInputFormat合并输入端大量的小文件
2)Map阶段:减少溢写次数、减少合并次数、加入Combine
mapred-default.xml

<!-- 增大触发Spill的内存上限-->
<property>
  <name>mapreduce.task.io.sort.mb</name>
  <value>100</value>
  <description>The total amount of buffer memory to use while sorting
  files, in megabytes.  By default, gives each merge stream 1MB, which
  should minimize seeks.</description>
</property>
<property>
  <name>mapreduce.map.sort.spill.percent</name>
  <value>0.80</value>
  <description>The soft limit in the serialization buffer. Once reached, a
  thread will begin to spill the contents to disk in the background. Note that
  collection will not block if this threshold is exceeded while a spill is
  already in progress, so spills may be larger than this threshold when it is
  set to less than .5</description>
</property>

<!--增大Merge的文件数目-->
<property>
  <name>mapreduce.task.io.sort.factor</name>
  <value>10</value>
  <description>The number of streams to merge at once while sorting
  files.  This determines the number of open file handles.</description>
</property>

3)Reduce阶段:合理设置MapTask和ReduceTask数(太少task会等待,太多task会竞争)、设置Map和Reduce共存(Map运行到一定程度后,开始运行Reduce)、减少Reduce(Reduce获取数据产生大量的网络消耗)
mapred-default.xml

<property>
  <name>mapreduce.job.reduce.slowstart.completedmaps</name>
  <value>0.05</value>
  <description>Fraction of the number of maps in the job which should be
  complete before reduces are scheduled for the job.
  </description>
</property>

<property>
  <name>mapreduce.reduce.input.buffer.percent</name>
  <value>0.0</value>
  <description>The percentage of memory- relative to the maximum heap size- to
  retain map outputs during the reduce. When the shuffle is concluded, any
  remaining map outputs in memory must consume less than this threshold before
  the reduce can begin.
  </description>
</property>

4)I/O阶段:使用Snappy和LZO压缩编码器、使用SequenceFile二进制文件对hive二进制存储格式,即SequenceFile和RCFile的思考总结
5)数据倾斜:抽样和范围分区(数据抽样预设分区)、自定义分区、Combiner精简数据、避免Reduce Join(尽量Map Join)

2.2、hadoop常用的调优参数

2.3、Hadoop小文件优化方法

补充:SequenceFile是由一系列的二进制k/v组成,如果为key为文件名,value为文件内容,可将大批小文件合并成一个大文件

3、Hadoop新特性

3.1、采用distcp命令实现两个Hadoop集群之间的递归数据复制

[atguigu@hadoop102 hadoop-3.1.3]$  bin/hadoop distcp hdfs://hadoop102:9820/user/atguigu/hello.txt hdfs://hadoop105:9820/user/atguigu/hello.txt

3.2、小文件存档

# 归档文件
[atguigu@hadoop102 hadoop-3.1.3]$ hadoop archive -archiveName input.har -p  /user/atguigu/input   /user/atguigu/output
# 查看归档
[atguigu@hadoop102 hadoop-3.1.3]$ hadoop fs -ls /user/atguigu/output/input.har
[atguigu@hadoop102 hadoop-3.1.3]$ hadoop fs -ls har:///user/atguigu/output/input.har
# 解归档文件
[atguigu@hadoop102 hadoop-3.1.3]$ hadoop fs -cp har:/// user/atguigu/output/input.har/*    /user/atguigu

3.3、Hadoop Trash回收站使用指南

补充:通过程序删除的文件不会经过回收站,需要调用moveToTrash()才进入回收站

Trash trash = New Trash(conf);
trash.moveToTrash(path);

3.4、Hadoop3.x新特性

PS:纠删码Erasure Coding (分布式存储系统)

标签:压缩,compress,hadoop,io,atguigu,new,009,HA,优化
来源: https://blog.csdn.net/qq_24964575/article/details/122179406