我在2个表（大小：table_a~100k行，table_b~2亿行）上运行交叉连接，如下面的hive：
select a.id, a.first_name, a.last_name, a.street
, b.pid, b.first_nam, b.last_nam, b.strt
from table_a a
cross join table_b b
where
(a.phone_number in (b.phone_1, b.phone_2)
and a.birth_date in (b.birth_date_1, b.birth_date_2))
or
(
cast(a.zip_code as int) = cast(b.zip_code as int)
and
(
(
upper(a.last_name) = upper(b.last_nam)
and a.birth_date in (b.birth_date_1, b.birth_date_2)
)
or
(
a.phone_number in (b.phone_1, b.phone_2)
)
or
(
a.birth_date in (b.birth_date_1, b.birth_date_2))
and upper(a.street) = upper(b.strt))
)
)
);
作业在群集上启动，并显示选择了160个映射器和1个减速器来运行它。地图部分很快完成（大约3分钟）。减少开始，它缓慢但稳定地从1％，2％，4％，......直到它达到67％。然后它停滞了30多分钟没有任何进展。虽然它表明累计的CPU时间增加了一些（从大约3500秒到4200秒）。但减速机刚刚超过67％。最终我不得不取消这份工作并试图重新运行。我每次都有同样的行为。我尝试通过设置以下选项来增加减少器的数量：
set mapred.job.reduces=10;
但是每当我运行查询时，它仍然只显示使用1个reducer。知道这里发生了什么以及如何解决问题吗？
修改：这是输出
INFO : Stage-1 is selected by condition resolver.
INFO : Number of reduce tasks determined at compile time: 1
INFO : In order to change the average load for a reducer (in bytes):
INFO : set hive.exec.reducers.bytes.per.reducer=<number>
INFO : In order to limit the maximum number of reducers:
INFO : set hive.exec.reducers.max=<number>
INFO : In order to set a constant number of reducers:
INFO : set mapreduce.job.reduces=<number>
INFO : number of splits:160
INFO : Submitting tokens for job: job_1484094252811_0005
INFO : Kind: HDFS_DELEGATION_TOKEN, Service: ha-hdfs:nameservice1, Ident: (HDFS_DELEGATION_TOKEN token 53 for hive)
INFO : The url to track the job: redacted
INFO : Starting Job = job_1484094252811_0005, redacted
INFO : Kill Command = /opt/cloudera/parcels/CDH-5.5.0-1.cdh5.5.0.p0.8/lib/hadoop/bin/hadoop job -kill job_1484094252811_0005
INFO : Hadoop job information for Stage-1: number of mappers: 160; number of reducers: 1
INFO : 2017-01-11 14:11:36,957 Stage-1 map = 0%, reduce = 0%
INFO : 2017-01-11 14:11:49,499 Stage-1 map = 1%, reduce = 0%, Cumulative CPU 10.45 sec
INFO : 2017-01-11 14:11:50,549 Stage-1 map = 2%, reduce = 0%, Cumulative CPU 17.03 sec
INFO : 2017-01-11 14:11:51,610 Stage-1 map = 21%, reduce = 0%, Cumulative CPU 1100.63 sec
INFO : 2017-01-11 14:11:52,674 Stage-1 map = 22%, reduce = 0%, Cumulative CPU 1181.04 sec
INFO : 2017-01-11 14:11:54,773 Stage-1 map = 40%, reduce = 0%, Cumulative CPU 1744.29 sec
INFO : 2017-01-11 14:11:55,819 Stage-1 map = 41%, reduce = 0%, Cumulative CPU 1769.61 sec
INFO : 2017-01-11 14:11:57,903 Stage-1 map = 58%, reduce = 0%, Cumulative CPU 2444.53 sec
INFO : 2017-01-11 14:11:59,988 Stage-1 map = 62%, reduce = 0%, Cumulative CPU 2505.06 sec
INFO : 2017-01-11 14:12:01,032 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2999.97 sec
INFO : 2017-01-11 14:12:12,486 Stage-1 map = 100%, reduce = 2%, Cumulative CPU 3051.65 sec
INFO : 2017-01-11 14:12:21,878 Stage-1 map = 100%, reduce = 3%, Cumulative CPU 3084.96 sec
INFO : 2017-01-11 14:12:27,088 Stage-1 map = 100%, reduce = 4%, Cumulative CPU 3108.88 sec
INFO : 2017-01-11 14:12:36,477 Stage-1 map = 100%, reduce = 5%, Cumulative CPU 3132.38 sec
INFO : 2017-01-11 14:12:45,837 Stage-1 map = 100%, reduce = 6%, Cumulative CPU 3153.54 sec
INFO : 2017-01-11 14:12:55,204 Stage-1 map = 100%, reduce = 7%, Cumulative CPU 3181.5 sec
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INFO : 2017-01-11 14:14:11,142 Stage-1 map = 100%, reduce = 18%, Cumulative CPU 3416.55 sec
INFO : 2017-01-11 14:14:17,365 Stage-1 map = 100%, reduce = 19%, Cumulative CPU 3436.33 sec
INFO : 2017-01-11 14:14:26,710 Stage-1 map = 100%, reduce = 20%, Cumulative CPU 3458.24 sec
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INFO : 2017-01-11 14:14:42,262 Stage-1 map = 100%, reduce = 22%, Cumulative CPU 3504.41 sec
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INFO : 2017-01-11 14:16:07,269 Stage-1 map = 100%, reduce = 59%, Cumulative CPU 3765.56 sec
INFO : 2017-01-11 14:16:10,377 Stage-1 map = 100%, reduce = 67%, Cumulative CPU 3769.25 sec
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