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performance - Why is the final reduce step extremely slow in this MapReduce? (HiveQL, HDFS MapReduce)

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performance - Why is the final reduce step extremely slow in this MapReduce? (HiveQL, HDFS MapReduce)

Some background information:

I'm working with Dataiku DSS, HDFS, and partitioned datasets. I have a particular job running (Hive query) which has two input datasets - one a very large, partitioned dataset, the other a small (~250 rows, 2 columns), non-partitioned dataset. Let's call the partitioned table A, and the non-partitioned table B.

Question:

The query is of the following format,

SELECT a.f1, f2, ..., fn
  FROM A as a
  LEFT JOIN B as b
  ON a.f1 = b.f1
WHERE {PARTITION_FILTER}

Here is the current ouput from the MapReduce job (keep in mind this job is still running):

[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
[09:07:03] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:02,235 Stage-1 map = 3%,  reduce = 0%, Cumulative CPU 539.6 sec
[09:07:08] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:07,472 Stage-1 map = 4%,  reduce = 0%, Cumulative CPU 1389.81 sec
[09:07:13] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:10,605 Stage-1 map = 5%,  reduce = 0%, Cumulative CPU 2172.52 sec
[09:07:13] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:12,574 Stage-1 map = 6%,  reduce = 0%, Cumulative CPU 2577.81 sec
[09:07:13] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:13,604 Stage-1 map = 8%,  reduce = 0%, Cumulative CPU 2865.44 sec
[09:07:18] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:15,747 Stage-1 map = 9%,  reduce = 0%, Cumulative CPU 3110.21 sec
[09:07:23] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:19,898 Stage-1 map = 10%,  reduce = 0%, Cumulative CPU 4080.2 sec
[09:07:23] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:21,988 Stage-1 map = 11%,  reduce = 0%, Cumulative CPU 4522.48 sec
[09:07:23] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:23,015 Stage-1 map = 12%,  reduce = 0%, Cumulative CPU 4755.96 sec
[09:07:33] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:29,335 Stage-1 map = 13%,  reduce = 0%, Cumulative CPU 5710.85 sec
[09:07:33] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:31,407 Stage-1 map = 14%,  reduce = 0%, Cumulative CPU 5948.34 sec
[09:07:38] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:34,555 Stage-1 map = 15%,  reduce = 0%, Cumulative CPU 6399.6 sec
[09:07:38] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:37,663 Stage-1 map = 16%,  reduce = 0%, Cumulative CPU 6811.22 sec
[09:07:43] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:38,695 Stage-1 map = 19%,  reduce = 0%, Cumulative CPU 7087.68 sec
[09:07:43] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:39,729 Stage-1 map = 20%,  reduce = 0%, Cumulative CPU 7288.22 sec
[09:07:43] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:40,769 Stage-1 map = 22%,  reduce = 0%, Cumulative CPU 7520.54 sec
[09:07:43] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:41,903 Stage-1 map = 24%,  reduce = 0%, Cumulative CPU 7771.37 sec
[09:07:43] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:42,930 Stage-1 map = 25%,  reduce = 0%, Cumulative CPU 7936.9 sec
[09:07:48] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:45,035 Stage-1 map = 27%,  reduce = 0%, Cumulative CPU 8254.78 sec
[09:07:48] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:46,075 Stage-1 map = 29%,  reduce = 0%, Cumulative CPU 8428.35 sec
[09:07:48] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:47,111 Stage-1 map = 30%,  reduce = 0%, Cumulative CPU 8661.23 sec
[09:07:48] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:48,153 Stage-1 map = 31%,  reduce = 0%, Cumulative CPU 8834.37 sec
[09:07:53] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:49,193 Stage-1 map = 33%,  reduce = 0%, Cumulative CPU 8983.68 sec
[09:07:53] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:50,227 Stage-1 map = 35%,  reduce = 0%, Cumulative CPU 9149.94 sec
[09:07:53] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:51,263 Stage-1 map = 37%,  reduce = 0%, Cumulative CPU 9268.9 sec
[09:07:53] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:52,301 Stage-1 map = 38%,  reduce = 0%, Cumulative CPU 9415.86 sec
[09:07:53] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:53,352 Stage-1 map = 39%,  reduce = 0%, Cumulative CPU 9540.63 sec
[09:07:58] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:54,381 Stage-1 map = 41%,  reduce = 0%, Cumulative CPU 9711.54 sec
[09:07:58] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:55,421 Stage-1 map = 43%,  reduce = 0%, Cumulative CPU 9823.52 sec
[09:07:58] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:56,453 Stage-1 map = 46%,  reduce = 0%, Cumulative CPU 10010.83 sec
[09:07:58] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:57,492 Stage-1 map = 49%,  reduce = 0%, Cumulative CPU 10081.9 sec
[09:07:58] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:58,532 Stage-1 map = 53%,  reduce = 0%, Cumulative CPU 10230.13 sec
[09:08:03] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:07:59,576 Stage-1 map = 56%,  reduce = 0%, Cumulative CPU 10392.61 sec
[09:08:03] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:00,607 Stage-1 map = 58%,  reduce = 0%, Cumulative CPU 10483.38 sec
[09:08:03] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:01,649 Stage-1 map = 63%,  reduce = 0%, Cumulative CPU 10618.16 sec
[09:08:03] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:02,672 Stage-1 map = 66%,  reduce = 0%, Cumulative CPU 10684.82 sec
[09:08:08] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:03,695 Stage-1 map = 67%,  reduce = 0%, Cumulative CPU 10701.95 sec
[09:08:08] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:04,720 Stage-1 map = 70%,  reduce = 0%, Cumulative CPU 10767.21 sec
[09:08:08] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:05,750 Stage-1 map = 71%,  reduce = 0%, Cumulative CPU 10849.92 sec
[09:08:08] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:06,780 Stage-1 map = 73%,  reduce = 0%, Cumulative CPU 10924.45 sec
[09:08:08] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:07,902 Stage-1 map = 76%,  reduce = 0%, Cumulative CPU 11000.21 sec
[09:08:13] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:09,965 Stage-1 map = 77%,  reduce = 0%, Cumulative CPU 11013.58 sec
[09:08:13] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:10,991 Stage-1 map = 78%,  reduce = 0%, Cumulative CPU 11057.76 sec
[09:08:18] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:14,216 Stage-1 map = 80%,  reduce = 0%, Cumulative CPU 11157.51 sec
[09:08:18] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:17,362 Stage-1 map = 82%,  reduce = 0%, Cumulative CPU 11392.85 sec
[09:08:23] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:20,460 Stage-1 map = 83%,  reduce = 0%, Cumulative CPU 11610.7 sec
[09:08:28] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:26,106 Stage-1 map = 84%,  reduce = 0%, Cumulative CPU 11781.65 sec
[09:08:28] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:28,163 Stage-1 map = 85%,  reduce = 0%, Cumulative CPU 11788.58 sec
[09:08:38] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:35,410 Stage-1 map = 86%,  reduce = 0%, Cumulative CPU 12167.24 sec
[09:08:38] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:38,540 Stage-1 map = 86%,  reduce = 1%, Cumulative CPU 12317.09 sec
[09:08:43] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:39,583 Stage-1 map = 86%,  reduce = 2%, Cumulative CPU 12329.8 sec
[09:08:43] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:40,631 Stage-1 map = 86%,  reduce = 3%, Cumulative CPU 12333.61 sec
[09:08:48] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:47,927 Stage-1 map = 87%,  reduce = 3%, Cumulative CPU 12651.77 sec
[09:08:53] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:48,970 Stage-1 map = 88%,  reduce = 4%, Cumulative CPU 12826.37 sec
[09:08:53] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:51,092 Stage-1 map = 88%,  reduce = 5%, Cumulative CPU 12857.19 sec
[09:08:58] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:54,214 Stage-1 map = 90%,  reduce = 5%, Cumulative CPU 13037.63 sec
[09:09:03] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:08:59,396 Stage-1 map = 91%,  reduce = 5%, Cumulative CPU 13117.71 sec
[09:09:03] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:00,440 Stage-1 map = 92%,  reduce = 5%, Cumulative CPU 13238.06 sec
[09:09:03] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:01,485 Stage-1 map = 93%,  reduce = 5%, Cumulative CPU 13249.8 sec
[09:09:08] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:05,660 Stage-1 map = 94%,  reduce = 5%, Cumulative CPU 13306.0 sec
[09:09:08] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:06,706 Stage-1 map = 97%,  reduce = 5%, Cumulative CPU 13393.5 sec
[09:09:08] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:07,751 Stage-1 map = 97%,  reduce = 6%, Cumulative CPU 13409.12 sec
[09:09:08] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:08,795 Stage-1 map = 98%,  reduce = 6%, Cumulative CPU 13433.07 sec
[09:09:13] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:09,835 Stage-1 map = 98%,  reduce = 8%, Cumulative CPU 13474.03 sec
[09:09:13] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:10,874 Stage-1 map = 98%,  reduce = 9%, Cumulative CPU 13484.64 sec
[09:09:18] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:14,004 Stage-1 map = 100%,  reduce = 11%, Cumulative CPU 13580.71 sec
[09:09:18] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:15,118 Stage-1 map = 100%,  reduce = 13%, Cumulative CPU 13619.15 sec
[09:09:18] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:16,160 Stage-1 map = 100%,  reduce = 16%, Cumulative CPU 13707.2 sec
[09:09:18] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:17,210 Stage-1 map = 100%,  reduce = 33%, Cumulative CPU 14456.75 sec
[09:09:18] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:18,258 Stage-1 map = 100%,  reduce = 39%, Cumulative CPU 14708.07 sec
[09:09:23] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:19,291 Stage-1 map = 100%,  reduce = 40%, Cumulative CPU 14768.29 sec
[09:09:23] [INFO] [dku.utils]  - INFO  : 2018-06-27 09:09:20,329 Stage-1 map = 100%,  reduce = 41%, Cumulative

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If final reducer is a join then it looks like skew in join key. First of all check two things:

check that b.f1 join key has no duplicates:

select b.f1, count(*) cnt from B b 
 group by b.f1 
having count(*)>1 order by cnt desc;

check the distribution of a.f1:

select a.f1, count(*) cnt from A a
 group by a.f1  
order by cnt desc
limit 10;

This query will show skewed keys.

If there is a skew (too many rows with the same value) then join skewed keys separately, use union all:

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}

And finally if there is no issues with skew and duplication, then try to increase reducers parallelism: Get current bytes per reducer configuration

set hive.exec.reducers.bytes.per.reducer;

typically this will return some value about 1G. Try to divide by two, set new value before your query and check how many reducers will it start and performance. Success criteria is more reducers has started and performance improved. 

set hive.exec.reducers.bytes.per.reducer=67108864;

The less the bytes per reducer the more reducers will be started, increasing parallelism;

UPDATE: Try to enable map-join, your second table is small enough to fit in memory, mapjoin will work without reducers at all and it will be no problem with skew on reducers.

How to enable mapjoin: https://stackoverflow.com/a/49154414/2700344


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