Hi Jihoon, Here is an example: My data: (Parquet file is 1GB limited) hadoop fs -ls /data/basetable/par/dt=20150301/pf=pc
-rw-r--r-- 9 hadoop tajo 1062932057 2015-03-12 15:08 /data/basetable/par/dt=20150301/pf=pc/cc456c9d427c88a3-3ead7e35ecf0da8_448517166_data.0.parq -rw-r--r-- 9 hadoop tajo 1063205684 2015-03-12 15:11 /data/basetable/par/dt=20150301/pf=pc/cc456c9d427c88a3-3ead7e35ecf0da8_448517166_data.1.parq -rw-r--r-- 9 hadoop tajo 1063236005 2015-03-12 15:14 /data/basetable/par/dt=20150301/pf=pc/cc456c9d427c88a3-3ead7e35ecf0da8_448517166_data.2.parq -rw-r--r-- 9 hadoop tajo 543786632 2015-03-12 15:16 /data/basetable/par/dt=20150301/pf=pc/cc456c9d427c88a3-3ead7e35ecf0da8_448517166_data.3.parq hadoop fs -ls /data/basetable/snappy/dt=20150301/pf=pc -rw-r--r-- 9 tajo tajo 144059045 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00000 -rw-r--r-- 9 tajo tajo 144178118 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00001 -rw-r--r-- 9 tajo tajo 143642438 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00002 -rw-r--r-- 9 tajo tajo 143553142 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00003 -rw-r--r-- 9 tajo tajo 143849627 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00004 -rw-r--r-- 9 tajo tajo 144648456 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00005 -rw-r--r-- 9 tajo tajo 144647502 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00006 -rw-r--r-- 9 tajo tajo 144551053 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00007 -rw-r--r-- 9 tajo tajo 144017287 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00008 -rw-r--r-- 9 tajo tajo 144205111 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00009 -rw-r--r-- 9 tajo tajo 145066506 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00010 -rw-r--r-- 9 tajo tajo 144740791 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00011 -rw-r--r-- 9 tajo tajo 144198266 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00012 -rw-r--r-- 9 tajo tajo 143575440 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00013 -rw-r--r-- 9 tajo tajo 143922343 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00014 -rw-r--r-- 9 tajo tajo 143930019 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00015 -rw-r--r-- 9 tajo tajo 144253019 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00016 -rw-r--r-- 9 tajo tajo 144175506 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00017 -rw-r--r-- 9 tajo tajo 143072995 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00018 -rw-r--r-- 9 tajo tajo 143818118 2015-03-16 11:48 /data/basetable/snappy/dt=20150301/pf=pc/part-r-00019 Result: default> select sum (cast(movie_vv as bigint)), sum(cast(movie_cv as bigint)),sum(cast(movie_pt as bigint)) from snappy where pf='pc'; Progress: 19%, response time: 1.87 sec Progress: 19%, response time: 1.873 sec Progress: 19%, response time: 2.276 sec Progress: 100%, response time: 2.372 sec ?sum_3, ?sum_4, ?sum_5 ------------------------------- 6928463, 6183665, 6055494385 (1 rows, 2.372 sec, 27 B selected) default> select sum (cast(movie_vv as bigint)), sum(cast(movie_cv as bigint)),sum(cast(movie_pt as bigint)) from par where pf='pc'; Progress: 0%, response time: 0.751 sec Progress: 0%, response time: 0.753 sec Progress: 0%, response time: 1.155 sec Progress: 0%, response time: 1.959 sec Progress: 0%, response time: 2.962 sec Progress: 0%, response time: 3.965 sec Progress: 0%, response time: 4.968 sec Progress: 0%, response time: 5.97 sec Progress: 12%, response time: 6.974 sec Progress: 12%, response time: 7.977 sec Progress: 12%, response time: 8.979 sec Progress: 12%, response time: 9.982 sec Progress: 25%, response time: 10.985 sec Progress: 100%, response time: 11.14 sec ?sum_3, ?sum_4, ?sum_5 ------------------------------- 6928463, 6183665, 6055494385 (1 rows, 11.14 sec, 27 B selected) On Mon, Mar 16, 2015 at 2:58 PM, Jihoon Son <[email protected]> wrote: > Azuryy, thanks for your feedbacks. > They are very interesting results. > Would you mind telling me how Tajo with Parquet is slower than Tajo with > RCFile? > > Thanks, > Jihoon > > On Mon, Mar 16, 2015 at 3:39 PM Hyunsik Choi <[email protected]> wrote: > > > Hi Azuryy, > > > > Thank for sharing the test results. They are very inspiring to us. > > Also, I'll make some jira about the problems that you found. > > > > Best regards, > > Hyunsik > > > > On Sun, Mar 15, 2015 at 10:58 PM, Azuryy Yu <[email protected]> wrote: > > > Another fix: > > > My test result is unfair during compare Imapla-2.1.2 and Tajo-0.10.0, > > > because I used Parquet with Impala and RCFILE snappy with Tajo. I > should > > > use the same file format to compare. > > > > > > because I've got a clear conclusion that Imapala works better on > Parquet > > > than Tajo, so I use RCFILE as the test data. > > > > > > *Tajo*: > > > default> select sum (cast(movie_vv as bigint)), sum(cast(movie_cv as > > > bigint)),sum(cast(movie_pt as bigint)) from snappy; > > > Progress: 0%, response time: 1.598 sec > > > Progress: 0%, response time: 1.6 sec > > > Progress: 0%, response time: 2.003 sec > > > Progress: 0%, response time: 2.806 sec > > > Progress: 37%, response time: 3.808 sec > > > Progress: 100%, response time: 4.792 sec > > > ?sum_3, ?sum_4, ?sum_5 > > > ------------------------------- > > > 22557920, 19648838, 2005366694576 > > > (1 rows, 4.792 sec, 32 B selected) > > > > > > *Impala*: > > > > select sum (cast(movie_vv as bigint)), sum(cast(movie_cv as > > > bigint)),sum(cast(movie_pt as bigint)) from snappy; > > > +-------------------------------+--------------------------- > > ----+-------------------------------+ > > > | sum(cast(movie_vv as bigint)) | sum(cast(movie_cv as bigint)) | > > > sum(cast(movie_pt as bigint)) | > > > +-------------------------------+--------------------------- > > ----+-------------------------------+ > > > | 22557920 | 19648838 | > > > 2005366694576 | > > > +-------------------------------+--------------------------- > > ----+-------------------------------+ > > > Fetched 1 row(s) in 11.12s > > > > > > > > > > > > On Mon, Mar 16, 2015 at 1:49 PM, Azuryy Yu <[email protected]> wrote: > > > > > >> There is a typo in my Email. I corrected here: > > >> > > >> for example: > > >> > > >> <property> > > >> <name>tajo.master.umbilical-rpc.address</name> > > >> <value>1-1-1-1:26001</value> > > >> </property> > > >> > > >> which does work under tajo-0.9.0, but it complain "1-1-1-1:2601" is > not > > a > > >> valid network address under tajo-0.10.0. > > >> > > >> I have to change to: > > >> <property> > > >> <name>tajo.master.umbilical-rpc.address</name> > > >> <value>1.1.1.1:26001</value> > > >> </property> > > >> > > >> > > >> On Mon, Mar 16, 2015 at 1:44 PM, Azuryy Yu <[email protected]> > wrote: > > >> > > >>> Hi, > > >>> I compiled tajo-0.10 source based on hadoop-2.6.0, then post some > > >>> feedback here. > > >>> > > >>> My cluster: > > >>> 1 tajo-master, 9 tajo-worker > > >>> 24 CPU(logic), 64GB mem, 4TB*12 HDD > > >>> > > >>> Feedback: > > >>> 1) tajo task progress estimate is normal on partitioned table, which > is > > >>> incorrect sometimes in tajo-0.9.0 > > >>> 2) Tajo configuration doesn't support hostname in tajo-site.xml. > > >>> for example: > > >>> > > >>> <property> > > >>> <name>tajo.master.umbilical-rpc.address</name> > > >>> <value>1-1-1-1:26001</value> > > >>> </property> > > >>> > > >>> which does work under tajo-0.9.0, but it complain "1-1-1-1:2601" is > > not a > > >>> valid network address. > > >>> > > >>> I have to change to: > > >>> <property> > > >>> <name>tajo.master.umbilical-rpc.address</name> > > >>> <value>1.1.1.1:26001</value> > > >>> </property> > > >>> > > >>> but we don't use IP in our cluster, only hostname. so I did a little > in > > >>> the code: > > >>> org.apache.tajo.validation.NetworkAddressValidator.java: > > >>> hostnamePattern = Pattern.compile("\\d*-\\d*-\\d*-\\d"); > > >>> then It works. > > >>> > > >>> 3) I did some test on the parquet, RCFILE(snappy compressed), > > >>> RCFILE(GZIP compressed) > > >>> > > >>> they are the same data, only different from file format. > > >>> the table has six partitions, 20 RCFILES, each parquet file is 1GB. > > >>> > > >>> then rcfile with snappy's performance is similiar to rcfile with > gzip. > > >>> but they are all two~three times better than parquet. > > >>> > > >>> 4) I compared tajo-0.10 and Impala-2.1.2, > > >>> Impala can provide very good support for parquet. more better than > > Tajo. > > >>> > > >>> but impala is more *slow *with other format than Tajo. > > >>> such as(I don't use WHERE because I want query all six partitions > > >>> together): > > >>> > > >>> *Impala*: > > >>> > select sum (cast(movie_vv as bigint)), sum(cast(movie_cv as > > >>> bigint)),sum(cast(movie_pt as bigint)) from par; > > >>> > > >>> +-------------------------------+--------------------------- > > ----+-------------------------------+ > > >>> | sum(cast(movie_vv as bigint)) | sum(cast(movie_cv as bigint)) | > > >>> sum(cast(movie_pt as bigint)) | > > >>> > > >>> +-------------------------------+--------------------------- > > ----+-------------------------------+ > > >>> | 22557920 | 19648838 | > > >>> 2005366694576 | > > >>> > > >>> +-------------------------------+--------------------------- > > ----+-------------------------------+ > > >>> Fetched 1 row(s) in 6.02s > > >>> > > >>> *Tajo:* > > >>> > > >>> *default*> select sum (cast(movie_vv as bigint)), sum(cast(movie_cv > as > > >>> bigint)),sum(cast(movie_pt as bigint)) from snappy; > > >>> Progress: 0%, response time: 1.598 sec > > >>> Progress: 0%, response time: 1.6 sec > > >>> Progress: 0%, response time: 2.003 sec > > >>> Progress: 0%, response time: 2.806 sec > > >>> Progress: 37%, response time: 3.808 sec > > >>> Progress: 100%, response time: 4.792 sec > > >>> ?sum_3, ?sum_4, ?sum_5 > > >>> ------------------------------- > > >>> 22557920, 19648838, 2005366694576 > > >>> (1 rows, 4.792 sec, 32 B selected) > > >>> > > >>> > > >>> > > >>> > > >>> > > >>> > > >>> > > >>> > > >>> > > >>> > > >> > > >
