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From Jihoon Son <jihoon...@apache.org>
Subject Re: Feedback for tajo-0.10.0
Date Mon, 16 Mar 2015 07:57:29 GMT
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 <azuryyyu@gmail.com> 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 <azuryyyu@gmail.com> 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 <jihoonson@apache.org>
> 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 <hyunsik@apache.org>
> 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 <azuryyyu@gmail.com>
> 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 <azuryyyu@gmail.com>
> >> 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 <azuryyyu@gmail.com>
> >> 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)
> >> > >>>
> >> > >>>
> >> > >>>
> >> > >>>
> >> > >>>
> >> > >>>
> >> > >>>
> >> > >>>
> >> > >>>
> >> > >>>
> >> > >>
> >> >
> >>
> >
> >
>

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