Thanks! It is really interesting. I suspect that the large file size of Parquet makes Tajo slower. This is because Parquet is non-splittable, which means that only 4 workers read data from HDFS. In addition, if the HDFS block size is smaller than 1GB, a lot of data can be moved over network during the scan phase.
But, I have no idea why Impala shows good performance. Maybe, its cache scheme improved it. Best regards, Jihoon On Mon, Mar 16, 2015 at 4:16 PM Azuryy Yu <[email protected]> wrote: > PS. my Parquet data was generated by Impala: "Insert into a parquet table > [SHUFFLE] ... AS select .... from a text table" > > On Mon, Mar 16, 2015 at 3:11 PM, Azuryy Yu <[email protected]> wrote: > > > 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) > >> > >>> > >> > >>> > >> > >>> > >> > >>> > >> > >>> > >> > >>> > >> > >>> > >> > >>> > >> > >>> > >> > >>> > >> > >> > >> > > >> > > > > >
