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From Azuryy Yu <azury...@gmail.com>
Subject Re: Feedback for tajo-0.10.0
Date Mon, 16 Mar 2015 08:43:46 GMT
HDFS block size is also 1GB


On Mon, Mar 16, 2015 at 4:18 PM, Jihoon Son <jihoonson@apache.org> wrote:

> Right. A large file size can improves the sequential scan on Parquet.
> However, if you want to use the large file size, it is recommended to also
> increase the HDFS block size to reduce the remote read cost.
> How large size did you set for HDFS blocks?
>
> On Impala's good performance, I will also investigate it.
> It seems to be related with Impala's storage manager.
>
> Best,
> Jihoon
>
> On Mon, Mar 16, 2015 at 5:05 PM Azuryy Yu <azuryyyu@gmail.com> wrote:
>
> > Hi Jihoon,
> >
> > Impala works on Parquet is more faster than other file formats. and
> Impala
> > advice don't make more small parquet files. 1GB would be better.
> >
> >
> >
> >
> > On Mon, Mar 16, 2015 at 3:57 PM, Jihoon Son <jihoonson@apache.org>
> wrote:
> >
> > > 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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