一些背景信息：
我正在使用Dataiku DSS，HDFS和分区数据集。我有一个特定的作业正在运行（Hive查询），它有两个输入数据集-一个是非常大的分区数据集，另一个是小的（〜250行，2列）非分区数据集。我们叫分区表A和未分区表B。
问题：
查询具有以下格式，
SELECT a.f1, f2, ..., fn
FROM A as a
LEFT JOIN B as b
ON a.f1 = b.f1
WHERE {PARTITION_FILTER}
这是MapReduce作业的当前输出（请记住该作业仍在运行）：
[09:05:53] [INFO] [dku.utils] - INFO : Total jobs = 4
[09:05:53] [INFO] [dku.utils] - INFO : Starting task [Stage-10:CONDITIONAL] in serial mode
[09:05:53] [INFO] [dku.utils] - INFO : Stage-11 is filtered out by condition resolver.
[09:05:53] [INFO] [dku.utils] - INFO : Stage-1 is selected by condition resolver.
[09:05:53] [INFO] [dku.utils] - INFO : Launching Job 1 out of 4
[09:05:53] [INFO] [dku.utils] - INFO : Starting task [Stage-1:MAPRED] in serial mode
[09:05:53] [INFO] [dku.utils] - INFO : Number of reduce tasks not specified. Estimated from input data size: 307
[09:05:53] [INFO] [dku.utils] - INFO : In order to change the average load for a reducer (in bytes):
[09:05:53] [INFO] [dku.utils] - INFO : set hive.exec.reducers.bytes.per.reducer=<number>
[09:05:53] [INFO] [dku.utils] - INFO : In order to limit the maximum number of reducers:
[09:05:53] [INFO] [dku.utils] - INFO : set hive.exec.reducers.max=<number>
[09:05:53] [INFO] [dku.utils] - INFO : In order to set a constant number of reducers:
[09:05:53] [INFO] [dku.utils] - INFO : set mapreduce.job.reduces=<number>
[09:05:53] [INFO] [dku.utils] - INFO : number of splits:75
[09:05:53] [INFO] [dku.utils] - INFO : Submitting tokens for job: job_1529508387832_10211
[09:05:53] [INFO] [dip.hiveserver2.log.sniffer] - sniffed applicationId => application_1529508387832_10211/
[09:05:53] [INFO] [dku.utils] - INFO : Kill Command = /opt/cloudera/parcels/CDH-5.12.1-1.cdh5.12.1.p0.3/lib/hadoop/bin/hadoop job -kill job_1529508387832_10211
[09:05:53] [INFO] [dku.utils] - INFO : Hadoop job information for Stage-1: number of mappers: 75; number of reducers: 307
[09:05:53] [INFO] [dku.utils] - INFO : 2018-06-27 09:05:47,749 Stage-1 map = 0%, reduce = 0%
[09:06:48] [INFO] [dku.utils] - INFO : 2018-06-27 09:06:48,444 Stage-1 map = 0%, reduce = 0%, Cumulative CPU 278.18 sec
[09:06:53] [INFO] [dku.utils] - INFO : 2018-06-27 09:06:51,682 Stage-1 map = 1%, reduce = 0%, Cumulative CPU 373.35 sec
[09:07:03] [INFO] [dku.utils] - INFO : 2018-06-27 09:07:00,159 Stage-1 map = 2%, reduce = 0%, Cumulative CPU 501.46 sec
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...
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[10:22:20] [INFO] [dku.utils] - INFO : 2018-06-27 10:22:17,545 Stage-1 map = 100%, reduce = 99%, Cumulative CPU 26857.28 sec
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[10:26:23] [INFO] [dku.utils] - INFO : 2018-06-27 10:26:19,404 Stage-1 map = 100%, reduce = 99%, Cumulative CPU 27287.58 sec
如您所见，还原阶段的最后1％花费了一段时间（到目前为止，总共1h32min过去了1h27min）。
我在查找资源时遇到了麻烦，这些资源可以清楚，简洁地说明为什么会这样。值得一提的是，我对MapReduce有一个非常基本的了解，但是目前正在研究HDFS上下文中的MapReduce。根据我的阅读，由于执行JOIN的性质，大部分的reduce任务可能固定在一个reducer上。
如果任何人都可以对正在发生的事情进行高层次的解释，请向我指出一些好的资源，并提供一些技巧，告诉我如何在将来的工作中通过更改Hive查询来避免这种情况，我们将不胜感激
答案 0 :(得分：2)
如果最终的reducer是一个join，那么它看起来像是join key中的歪斜。 首先检查两件事：
检查b.f1连接密钥是否重复：
select b.f1, count(*) cnt from B b
group by b.f1
having count(*)>1 order by cnt desc;
检查a.f1的分布：
select a.f1, count(*) cnt from A a
group by a.f1
order by cnt desc
limit 10;
此查询将显示倾斜的键。
如果存在偏斜（具有相同值的太多行），则分别连接偏斜键，请使用并集所有：
SELECT a.f1, f2, ..., fn
FROM ( select * from A where f1 = skewed_value) as a --skewed
LEFT JOIN B as b
ON a.f1 = b.f1
WHERE {PARTITION_FILTER}
UNION ALL
SELECT a.f1, f2, ..., fn
FROM ( select * from A where f1 != skewed_value) as a --all other
LEFT JOIN B as b
ON a.f1 = b.f1
WHERE {PARTITION_FILTER}
最后，如果不存在歪斜和重复的问题，请尝试增加reducer的并行性： 获取每个reducer配置的当前字节数
设置hive.exec.reducers.bytes.per.reducer;
通常，这将返回大约1G的值。尝试除以二，在查询之前设置新值，并检查它将启动多少个减速器和性能。成功的标准是更多的减速器已经开始使用并且性能得到了提高。
set hive.exec.reducers.bytes.per.reducer=67108864;
每个减速器的字节数越少，启动的减速器越多，从而增加了并行度；
更新：尝试启用map-join ，您的第二张表足够小以适合内存，mapjoin将完全不使用reducer，并且在reducer上倾斜也没有问题。
如何启用mapjoin：https://stackoverflow.com/a/49154414/2700344