MapReduce Job继续运行map = 0％，reduce = 0％for hours

Question

I am running one Hive query which looks like

create table table1 as select split(comments,' ') as words from table2;

comments column has review comments in the form of Strings separated by space.

When I run this query, MapReduce job starts and continues to run with Map 0% for hours. It does not give any error during this process.

hive> create table jw_1 as select split(comments,' ') from removed_null_values;
Query ID = xxx-190418201314_7781cf59-6afb-4e82-ab75-c7e343c4985e
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1555607912038_0013, Tracking URL = http://xxx-VirtualBox:8088/proxy/application_1555607912038_0013/
Kill Command = /usr/local/bin/hadoop-3.2.0/bin/mapred job  -kill job_1555607912038_0013
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2019-04-18 20:13:30,568 Stage-1 map = 0%,  reduce = 0%
2019-04-18 20:14:31,140 Stage-1 map = 0%,  reduce = 0%, Cumulative CPU 39.6 sec
2019-04-18 20:15:31,311 Stage-1 map = 0%,  reduce = 0%, Cumulative CPU 101.64 sec
2019-04-18 20:16:31,451 Stage-1 map = 0%,  reduce = 0%, Cumulative CPU 146.5 sec
2019-04-18 20:17:31,684 Stage-1 map = 0%,  reduce = 0%, Cumulative CPU 212.08 sec

However when I try

select split(comments,' ') from table2;

I can see comments in the form of an array in the shell.

["\"Lauren","was","promptly","responsive","in","advance","of","our","booking.","providing","a","lot","of","helpful","info.","And","she","stayed","in","contact","and","was","readily","available","prior","to","and","during","our","stay.","which","was","awesome.","The","location.","price","and","privacy","were","the","real","benefits."]

I have also run a few other queries where the MapReduce jobs complete and produce the desired result

I am currently using Hive 3.1.1

Basically, I want to create a new table with an array containing words and later on tokenize that column

I am new to Hive and I am working on sentimental analysis on data file of size 35MB.

Answer 1

In your first case, you most likely don't have the resources necessary to complete the Hive query when converted to MapReduce. You would have to look at either YARN or MR1 to determine if you have enough compute resources to run your MapReduce job.

I the second query, some Hive queries trigger don't trigger MapReduce jobs and that is why it comes back. See How does Hive decide when to use map reduce and when not to? for more information.