hive中count(distinct) 的原理
参考博客:
https://blog.csdn.net/oracle8090/article/details/80760233
https://www.cnblogs.com/ling1995/p/7339424.html(看我)
-- 这种set mapreduce.job.reduces=5; 中的5是生效的(局部去重); SELECT dept_num ,COUNT(DISTINCT name) AS name_cnt FROM emp_ct GROUP BY dept_num; -- 这种set mapreduce.job.reduces=5; 中的5不会生效的,只有一个reducer task(全局去重); select count(distinct dept_num) from emp_ct;
count(distinct id)的原理
count(distinct id)从执行计划上面来看:只有一个reducer任务(即使你设置reducer任务为100个,实际上也没有用),所有的id都
会聚集到同一个reducer任务进行去重然后在聚合,这非常容易造成数据倾斜.
STAGE DEPENDENCIES: Stage-1 is a root stage Stage-0 depends on stages: Stage-1 STAGE PLANS: Stage: Stage-1 Map Reduce Map Operator Tree: TableScan alias: emp_ct Statistics: Num rows: 42 Data size: 171 Basic stats: COMPLETE Column stats: NONE Select Operator expressions: dept_num (type: int) outputColumnNames: _col0 Statistics: Num rows: 42 Data size: 171 Basic stats: COMPLETE Column stats: NONE Reduce Output Operator key expressions: _col0 (type: int) sort order: + Statistics: Num rows: 42 Data size: 171 Basic stats: COMPLETE Column stats: NONE Reduce Operator Tree: Group By Operator aggregations: count(DISTINCT KEY._col0:0._col0) mode: complete outputColumnNames: _col0 Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE File Output Operator compressed: false Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table: input format: org.apache.hadoop.mapred.TextInputFormat output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Stage: Stage-0 Fetch Operator limit: -1 Processor Tree: ListSink
运行示例:注意设置的reducer任务数量实际上是不生效的。
hive> set mapreduce.job.reduces=5; hive> > select count(distinct dept_num) > from emp_ct; Query ID = mart_fro_20200320233947_4f60c190-4967-4da6-bf3e-97db786fbc6c Total jobs = 1 Launching Job 1 out of 1 Number of reduce tasks determined at compile time: 1 In order to change the average load for a reducer (in bytes): set hive.exec.reducers.bytes.per.reducer=<number> In order to limit the maximum number of reducers: set hive.exec.reducers.max=<number> In order to set a constant number of reducers: set mapreduce.job.reduces=<number> Start submit job ! Start GetSplits GetSplits finish, it costs : 32 milliseconds Submit job success : job_1584341089622_358496 Starting Job = job_1584341089622_358496, Tracking URL = http://BJHTYD-Hope-25-11.hadoop.jd.local:50320/proxy/application_1584341089622_358496/ Kill Command = /data0/hadoop/hadoop_2.100.31_2019090518/bin/hadoop job -kill job_1584341089622_358496 Hadoop job(job_1584341089622_358496) information for Stage-1: number of mappers: 2; number of reducers: 1 2020-03-20 23:39:58,215 Stage-1(job_1584341089622_358496) map = 0%, reduce = 0% 2020-03-20 23:40:09,628 Stage-1(job_1584341089622_358496) map = 50%, reduce = 0%, Cumulative CPU 2.74 sec 2020-03-20 23:40:16,849 Stage-1(job_1584341089622_358496) map = 100%, reduce = 0%, Cumulative CPU 7.43 sec 2020-03-20 23:40:29,220 Stage-1(job_1584341089622_358496) map = 100%, reduce = 100%, Cumulative CPU 10.64 sec MapReduce Total cumulative CPU time: 10 seconds 640 msec Stage-1 Elapsed : 40533 ms job_1584341089622_358496 Ended Job = job_1584341089622_358496 MapReduce Jobs Launched: Stage-1: Map: 2 Reduce: 1 Cumulative CPU: 10.64 sec HDFS Read: 0.000 GB HDFS Write: 0.000 GB SUCCESS Elapsed : 40s533ms job_1584341089622_358496 Total MapReduce CPU Time Spent: 10s640ms Total Map: 2 Total Reduce: 1 Total HDFS Read: 0.000 GB Written: 0.000 GB OK 3 Time taken: 43.025 seconds, Fetched: 1 row(s)
count(distinct id)的解决方案
看我:https://zhuanlan.zhihu.com/p/400830935
https://baijiahao.baidu.com/s?id=1709215794913205586&wfr=spider&for=pc
该怎么解决这个问题呢?实际上解决方法非常巧妙:
我们利用Hive对嵌套语句的支持,将原来一个MapReduce作业转换为两个作业,在第一阶段选出全部的非重复id,在第二阶段再对
这些已消重的id进行计数。这样在第一阶段我们可以通过增大Reduce的并发数,并发处理Map输出。在第二阶段,由于id已经消重,
因此COUNT(*)操作在Map阶段不需要输出原id数据,只输出一个合并后的计数即可。这样即使第二阶段Hive强制指定一个Reduce Task,
极少量的Map输出数据也不会使单一的Reduce Task成为瓶颈。改进后的SQL语句如下:
查看一下执行计划:
STAGE DEPENDENCIES: Stage-1 is a root stage Stage-2 depends on stages: Stage-1 Stage-0 depends on stages: Stage-2 STAGE PLANS: Stage: Stage-1 Map Reduce Map Operator Tree: TableScan alias: emp_ct Statistics: Num rows: 42 Data size: 171 Basic stats: COMPLETE Column stats: NONE Select Operator expressions: dept_num (type: int) outputColumnNames: dept_num Statistics: Num rows: 42 Data size: 171 Basic stats: COMPLETE Column stats: NONE Reduce Output Operator key expressions: dept_num (type: int) sort order: + Map-reduce partition columns: dept_num (type: int) Statistics: Num rows: 42 Data size: 171 Basic stats: COMPLETE Column stats: NONE Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: int) mode: complete outputColumnNames: _col0 Statistics: Num rows: 21 Data size: 85 Basic stats: COMPLETE Column stats: NONE File Output Operator compressed: false table: input format: org.apache.hadoop.mapred.SequenceFileInputFormat output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat serde: org.apache.hadoop.hive.serde2.lazybinary.LazyBinarySerDe Stage: Stage-2 Map Reduce Map Operator Tree: TableScan Reduce Output Operator sort order: Statistics: Num rows: 21 Data size: 85 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: int) Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) mode: complete outputColumnNames: _col0 Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE File Output Operator compressed: false Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table: input format: org.apache.hadoop.mapred.TextInputFormat output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Stage: Stage-0 Fetch Operator limit: -1 Processor Tree: ListSink
具体看一下执行结果:注意看reducer任务的数量,第一个reducer任务是5个,第二个是1个.
hive> set mapreduce.job.reduces=5; hive> > select count(dept_num) > from ( > select distinct dept_num > from emp_ct > ) t1; Query ID = mart_fro_20200320234453_68ad3780-c3e5-44bc-94df-58a8f2b01f59 Total jobs = 2 Launching Job 1 out of 2 Number of reduce tasks not specified. Defaulting to jobconf value of: 5 In order to change the average load for a reducer (in bytes): set hive.exec.reducers.bytes.per.reducer=<number> In order to limit the maximum number of reducers: set hive.exec.reducers.max=<number> In order to set a constant number of reducers: set mapreduce.job.reduces=<number> Start submit job ! Start GetSplits GetSplits finish, it costs : 13 milliseconds Submit job success : job_1584341089622_358684 Starting Job = job_1584341089622_358684, Tracking URL = http://BJHTYD-Hope-25-11.hadoop.jd.local:50320/proxy/application_1584341089622_358684/ Kill Command = /data0/hadoop/hadoop_2.100.31_2019090518/bin/hadoop job -kill job_1584341089622_358684 Hadoop job(job_1584341089622_358684) information for Stage-1: number of mappers: 2; number of reducers: 5 2020-03-20 23:45:02,920 Stage-1(job_1584341089622_358684) map = 0%, reduce = 0% 2020-03-20 23:45:23,533 Stage-1(job_1584341089622_358684) map = 50%, reduce = 0%, Cumulative CPU 3.48 sec 2020-03-20 23:45:25,596 Stage-1(job_1584341089622_358684) map = 100%, reduce = 0%, Cumulative CPU 7.08 sec 2020-03-20 23:45:32,804 Stage-1(job_1584341089622_358684) map = 100%, reduce = 20%, Cumulative CPU 9.43 sec 2020-03-20 23:45:34,861 Stage-1(job_1584341089622_358684) map = 100%, reduce = 40%, Cumulative CPU 12.39 sec 2020-03-20 23:45:36,923 Stage-1(job_1584341089622_358684) map = 100%, reduce = 80%, Cumulative CPU 18.47 sec 2020-03-20 23:45:40,011 Stage-1(job_1584341089622_358684) map = 100%, reduce = 100%, Cumulative CPU 23.23 sec MapReduce Total cumulative CPU time: 23 seconds 230 msec Stage-1 Elapsed : 46404 ms job_1584341089622_358684 Ended Job = job_1584341089622_358684 Launching Job 2 out of 2 Number of reduce tasks determined at compile time: 1 In order to change the average load for a reducer (in bytes): set hive.exec.reducers.bytes.per.reducer=<number> In order to limit the maximum number of reducers: set hive.exec.reducers.max=<number> In order to set a constant number of reducers: set mapreduce.job.reduces=<number> Start submit job ! Start GetSplits GetSplits finish, it costs : 47 milliseconds Submit job success : job_1584341089622_358729 Starting Job = job_1584341089622_358729, Tracking URL = http://BJHTYD-Hope-25-11.hadoop.jd.local:50320/proxy/application_1584341089622_358729/ Kill Command = /data0/hadoop/hadoop_2.100.31_2019090518/bin/hadoop job -kill job_1584341089622_358729 Hadoop job(job_1584341089622_358729) information for Stage-2: number of mappers: 5; number of reducers: 1 2020-03-20 23:45:48,353 Stage-2(job_1584341089622_358729) map = 0%, reduce = 0% 2020-03-20 23:46:05,846 Stage-2(job_1584341089622_358729) map = 20%, reduce = 0%, Cumulative CPU 2.62 sec 2020-03-20 23:46:06,873 Stage-2(job_1584341089622_358729) map = 60%, reduce = 0%, Cumulative CPU 8.49 sec 2020-03-20 23:46:08,931 Stage-2(job_1584341089622_358729) map = 80%, reduce = 0%, Cumulative CPU 11.53 sec 2020-03-20 23:46:09,960 Stage-2(job_1584341089622_358729) map = 100%, reduce = 0%, Cumulative CPU 15.23 sec 2020-03-20 23:46:35,639 Stage-2(job_1584341089622_358729) map = 100%, reduce = 100%, Cumulative CPU 20.37 sec MapReduce Total cumulative CPU time: 20 seconds 370 msec Stage-2 Elapsed : 54552 ms job_1584341089622_358729 Ended Job = job_1584341089622_358729 MapReduce Jobs Launched: Stage-1: Map: 2 Reduce: 5 Cumulative CPU: 23.23 sec HDFS Read: 0.000 GB HDFS Write: 0.000 GB SUCCESS Elapsed : 46s404ms job_1584341089622_358684 Stage-2: Map: 5 Reduce: 1 Cumulative CPU: 20.37 sec HDFS Read: 0.000 GB HDFS Write: 0.000 GB SUCCESS Elapsed : 54s552ms job_1584341089622_358729 Total MapReduce CPU Time Spent: 43s600ms Total Map: 7 Total Reduce: 6 Total HDFS Read: 0.000 GB Written: 0.000 GB OK 3 Time taken: 103.692 seconds, Fetched: 1 row(s)
这个解决方案有点类似于set hive.groupby.skew.indata参数的作用!
实际测试:
select count(distinct dept_num) from emp_ct select count(*) from ( select distinct dept_num from emp_ct )