hive本地mr

如果在hive中运行的sql本身数据量很小，那么使用本地mr的效率要比分布式的快很多。。

 

比如： 

 

hive> select 1 from dual;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_201208151631_2040444, Tracking URL = http://jt.dc.sh-wgq.sdo.com:50030/jobdetails.jsp?jobid=job_201208151631_2040444
Kill Command = /home/hdfs/hadoop-current/bin/hadoop job  -Dmapred.job.tracker=10.133.10.103:50020 -kill job_201208151631_2040444
2012-10-23 10:55:17,646 Stage-1 map = 0%,  reduce = 0%
2012-10-23 10:55:27,807 Stage-1 map = 100%,  reduce = 0%
Ended Job = job_201208151631_2040444
OK
1
Time taken: 17.853 seconds

 

 

 

set hive.exec.mode.local.auto=true;  //开启本地mr

 

//设置local mr的最大输入数据量,当输入数据量小于这个值的时候会采用local  mr的方式

set hive.exec.mode.local.auto.inputbytes.max=50000000;

 

//设置local mr的最大输入文件个数,当输入文件个数小于这个值的时候会采用local mr的方式

set hive.exec.mode.local.auto.tasks.max=10;

 

当这三个参数同时成立时候，才会采用本地mr

 

hive> select 1 from dual;             
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Execution log at: /tmp/liuxiaowen/liuxiaowen_20121023105757_31c966be-ee79-4c23-a467-648290b338ac.log
Job running in-process (local Hadoop)
2012-10-23 10:58:03,728 null map = 100%,  reduce = 0%
Ended Job = job_local_0001
OK
1
Time taken: 4.842 seconds

 

更多大数据Hadoop、Spark、Hive的文章，请关注 我的博客