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From Azuryy Yu <azury...@gmail.com>
Subject Re: Feedback for tajo-0.10.0
Date Tue, 17 Mar 2015 01:00:42 GMT
Impala using C wrapper, which start JVM internal,  you can use jps to find
this JVM after start Impala.

BTW, I used the same JVM options and HDFS configuration during my test on
Impala and Tajo.


On Mon, Mar 16, 2015 at 7:34 PM, Jihoon Son <jihoonson@apache.org> wrote:

> 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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