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From Jihoon Son <jihoon...@apache.org>
Subject Re: Feedback for tajo-0.10.0
Date Mon, 16 Mar 2015 11:34:24 GMT
Even I haven't tried, it seems to be not possible because their
implementation is in C.

On Mon, Mar 16, 2015 at 6:49 PM Azuryy Yu <azuryyyu@gmail.com> wrote:

> Does that possible Tajo reuse Parquet bundle from Impala?
>
>
> On Mon, Mar 16, 2015 at 5:35 PM, Jihoon Son <jihoonson@apache.org> wrote:
>
> > Honestly, even if there are sufficiently many input files, Impala may be
> > faster than Tajo becuase their optimization for Parquet if better than
> that
> > of Tajo.
> >
> > On Mon, Mar 16, 2015 at 6:16 PM Jihoon Son <jihoonson@apache.org> wrote:
> >
> > > Interesting.
> > > Here are some reasons I think.
> > >
> > > Impala can process Parquet files in a distributed manner even if their
> > > size is very large. This is becuase of its DBMS-like architecture.
> Impala
> > > has a storage manager and a query executor, which have totally
> separated
> > > roles during query processing. Once a query is executed, the storage
> > > manager continuously loads data from HDFS into memory. Query executors
> > just
> > > process data loaded in memory according to the query plan. As a result,
> > > even though the number of data files is small, huge amounts of query
> > > executors can process them simultaneously.
> > >
> > > However, in Tajo, each query executor (i.e., task) has a scanner to
> read
> > > data from HDFS. During query processing, their roles are closely
> related.
> > > Thus, the number of tasks is mainly decided based on the number of
> files.
> > > (Of course, when input data are dispersed on the entire cluster nodes,
> > the
> > > number of tasks is decided based on the number of cpu cores and disks
> per
> > > worker.)
> > >
> > > So, I expect that Tajo's performance with Parquet will be improved when
> > > there are sufficiently many input files.
> > > As I aforementioned, this is just my suspection.
> > > I will investigate further.
> > >
> > > Thanks,
> > > Jihoon
> > >
> > >
> > > On Mon, Mar 16, 2015 at 5:45 PM Azuryy Yu <azuryyyu@gmail.com> wrote:
> > >
> > >> 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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