[
https://issues.apache.org/jira/browse/SPARK-18107?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15612047#comment-15612047
]
J.P Feng commented on SPARK-18107:
----------------------------------
Here is the execution logs of Hive 1.2.1, [Insert into]:
0: jdbc:hive2://master.mydata.com:23250> insert into table login4game
partition(pt='mix_en',dt='2016-10-21') select distinct
account_name,role_id,server,'1476979200' as recdate, 'mix' as platform, 'mix'
as pid, 'mix' as dev from tbllog_login where pt='mix_en' and dt='2016-10-21' ;
INFO : Number of reduce tasks not specified. Estimated from input data size: 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:3
INFO : Submitting tokens for job: job_1472611548204_72608
INFO : The url to track the job:
http://master.mydata.com:9378/proxy/application_1472611548204_72608/
INFO : Starting Job = job_1472611548204_72608, Tracking URL =
http://master.mydata.com:9378/proxy/application_1472611548204_72608/
INFO : Kill Command = /usr/local/hadoop/bin/hadoop job -kill
job_1472611548204_72608
INFO : Hadoop job information for Stage-1: number of mappers: 3; number of
reducers: 1
INFO : 2016-10-27 21:51:37,717 Stage-1 map = 0%, reduce = 0%
INFO : 2016-10-27 21:51:46,455 Stage-1 map = 33%, reduce = 0%, Cumulative CPU
3.17 sec
INFO : 2016-10-27 21:51:48,576 Stage-1 map = 100%, reduce = 0%, Cumulative
CPU 16.16 sec
INFO : 2016-10-27 21:51:56,945 Stage-1 map = 100%, reduce = 100%, Cumulative
CPU 22.7 sec
INFO : MapReduce Total cumulative CPU time: 22 seconds 700 msec
INFO : Ended Job = job_1472611548204_72608
INFO : Loading data to table my_log.login4game partition (pt=mix_en,
dt=2016-10-21) from
hdfs://master.mydata.com:45660/data/warehouse/staging/.hive-staging_hive_2016-10-27_21-51-26_264_2085348807080462789-1/-ext-10000
INFO : Partition my_log.login4game{pt=mix_en, dt=2016-10-21} stats:
[numFiles=2, numRows=132276, totalSize=971551, rawDataSize=82804776]
No rows affected (32.183 seconds)
> Insert overwrite statement runs much slower in spark-sql than it does in
> hive-client
> ------------------------------------------------------------------------------------
>
> Key: SPARK-18107
> URL: https://issues.apache.org/jira/browse/SPARK-18107
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0
> Environment: spark 2.0.0
> hive 2.0.1
> Reporter: J.P Feng
>
> I find insert overwrite statement running in spark-sql or spark-shell spends
> much more time than it does in hive-client (i start it in
> apache-hive-2.0.1-bin/bin/hive ), where spark costs about ten minutes but
> hive-client just costs less than 20 seconds.
> These are the steps I took.
> Test sql is :
> insert overwrite table login4game partition(pt='mix_en',dt='2016-10-21')
> select distinct account_name,role_id,server,'1476979200' as recdate, 'mix' as
> platform, 'mix' as pid, 'mix' as dev from tbllog_login where pt='mix_en' and
> dt='2016-10-21' ;
> there are 257128 lines of data in tbllog_login with
> partition(pt='mix_en',dt='2016-10-21')
> ps:
> I'm sure it must be "insert overwrite" costing a lot of time in spark, may be
> when doing overwrite, it need to spend a lot of time in io or in something
> else.
> I also compare the executing time between insert overwrite statement and
> insert into statement.
> 1. insert overwrite statement and insert into statement in spark:
> insert overwrite statement costs about 10 minutes
> insert into statement costs about 30 seconds
> 2. insert into statement in spark and insert into statement in hive-client:
> spark costs about 30 seconds
> hive-client costs about 20 seconds
> the difference is little that we can ignore
>
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
