1）hadoop 本身并不支持 lzo 压缩，故需要使用 twitter 提供的 hadoop-lzo 开源组件。hadoop

lzo 需依赖 hadoop 和 lzo 进行编译，编译步骤如下。

编译参照：

https://www.cnblogs.com/allthewayforward/p/11131218.html

2）将编译好后的 hadoop-lzo-0.4.20.jar 放入 hadoop-3.1.3/share/hadoop/common/

3）同步 hadoop-lzo-0.4.20.jar 到 hadoop103、hadoop104

xsync hadoop-lzo-0.4.20.jar

4）core-site.xml 增加配置支持 LZO 压缩

io.compression.codecs org.apache.hadoop.io.compress.GzipCodec, org.apache.hadoop.io.compress.DefaultCodec, org.apache.hadoop.io.compress.BZip2Codec, org.apache.hadoop.io.compress.SnappyCodec, com.hadoop.compression.lzo.LzoCodec, com.hadoop.compression.lzo.LzopCodec io.compression.codec.lzo.class com.hadoop.compression.lzo.LzoCodec

5）同步 core-site.xml 到 hadoop103、hadoop104

xsync core-site.xml

6）启动及查看集群

[atguigu@hadoop102 hadoop-3.1.3]$ sbin/start-dfs.sh

[atguigu@hadoop103 hadoop-3.1.3]$ sbin/start-yarn.sh

7) 安装成功后进行测试

在命令行中使用lzo 压缩，相关参数可以到官网查找：

https://hadoop.apache.org/docs/stable/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapred-default.xml

hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount -Dmapreduce.output.fileoutputformat.compress=true -Dmapreduce.output.fileoutputformat.compress.codec=

com.hadoop.compression.lzo.LzoCodec /input /output111

LZO创建索引

1）创建LZO文件的索引，LZO压缩文件的可切片特性依赖于其索引，故我们需要手动为LZO压缩文件创建索引。若无索引，则LZO文件的切片只有一个。

hadoop jar /path/to/your/hadoop-lzo.jar com.hadoop.compression.lzo.DistributedLzoIndexer big_file.lzo

2）测试

（1）将bigtable.lzo（200M）上传到集群的根目录

（2）执行wordcount程序

hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount -Dmapreduce.job.inputformat.class=com.hadoop.mapreduce.LzoTextInputFormat /input /output1

（3）对上传的LZO文件建索引

hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/common/hadoop-lzo-0.4.20.jar com.hadoop.compression.lzo.DistributedLzoIndexer /input/bigtable.lzo

（4）再次执行WordCount程序

hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount -Dmapreduce.job.inputformat.class=com.hadoop.mapreduce.LzoTextInputFormat /input /output2

3）注意：如果以上任务，在运行过程中报如下异常

Container [pid=8468,containerID=container_1594198338753_0001_01_000002] is running 318740992B beyond the 'VIRTUAL' memory limit. Current usage: 111.5 MB of 1 GB physical memory used; 2.4 GB of 2.1 GB virtual memory used. Killing container.

Dump of the process-tree for container_1594198338753_0001_01_000002 :

解决办法：在hadoop102的/opt/module/hadoop-3.1.3/etc/hadoop/yarn-site.xml文件中增加如下配置，然后分发到hadoop103、hadoop104服务器上，并重新启动集群。

yarn.nodemanager.pmem-check-enabled

false

yarn.nodemanager.vmem-check-enabled

false