guangdong created HIVE-20289:
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Summary: The function of row_number have different result
Key: HIVE-20289
URL: https://issues.apache.org/jira/browse/HIVE-20289
Project: Hive
Issue Type: Bug
Components: Hive
Affects Versions: 2.3.0
Environment: hive 2.3.3
hadoop 2.7.6
Reporter: guangdong
Fix For: 2.3.0
1. Create table like this:
create table src(
name string
,buy_time string
,consumption int );
2.Then insert data：
insert into src values('zzz','2018-08-01',20),('zzz','2018-08-01',10);
3.When i execute sql in hive 2.3.3. The result is :
hive> select consumption, row_number() over(distribute by name sort by buy_time
desc) from src;
Query ID = dwetl_20180801210808_692d5d70-a136-4525-9cdb-b6269e6c3069
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 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>
Starting Job = job_1531984581474_944267, Tracking URL =
http://hadoop-jr-nn02.pekdc1.jdfin.local:8088/proxy/application_1531984581474_944267/
Kill Command = /soft/hadoop/bin/hadoop job -kill job_1531984581474_944267
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2018-08-01 21:09:08,855 Stage-1 map = 0%, reduce = 0%
2018-08-01 21:09:16,026 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.12
sec
2018-08-01 21:09:22,210 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 4.09
sec
MapReduce Total cumulative CPU time: 4 seconds 90 msec
Ended Job = job_1531984581474_944267
MapReduce Jobs Launched:
Stage-Stage-1: Map: 2 Reduce: 1 Cumulative CPU: 4.09 sec HDFS Read: 437
HDFS Write: 10 SUCCESS
Total MapReduce CPU Time Spent: 4 seconds 90 msec
OK
20 1
10 2
Time taken: 80.135 seconds, Fetched: 2 row(s)
4.When i execute sql in hive 0.14. The result is :
> select consumption, row_number() over(distribute by name sort by buy_time
> desc) from src;
Query ID = dwetl_20180801212222_7812d9f0-328d-4125-ba99-0f577f4cca9a
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 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>
Starting Job = job_1531984581474_944597, Tracking URL =
http://hadoop-jr-nn02.pekdc1.jdfin.local:8088/proxy/application_1531984581474_944597/
Kill Command = /soft/hadoop/bin/hadoop job -kill job_1531984581474_944597
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2018-08-01 21:22:26,467 Stage-1 map = 0%, reduce = 0%
2018-08-01 21:22:34,839 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.13
sec
2018-08-01 21:22:40,984 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 3.28
sec
MapReduce Total cumulative CPU time: 3 seconds 280 msec
Ended Job = job_1531984581474_944597
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.28 sec HDFS Read: 233
HDFS Write: 10 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 280 msec
OK
I hope have the common result . How could i can do?
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