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From Jihoon Son <jihoon...@apache.org>
Subject Re: Parallel Aggregates
Date Thu, 18 Jun 2015 11:21:37 GMT
It seems that there aren't ongoing issues for distinct aggregation.

2015년 6월 18일 (목) 오전 10:50, Atri Sharma <atri.jiit@gmail.com>님이 작성:

> And for DISTINCT issues?
> On 18 Jun 2015 15:16, "Jihoon Son" <ghoonson@gmail.com> wrote:
>
> > As far as I know, TAJO-256, TAJO-259, and TAJO-1420 are issues for data
> > cube and grouping sets.
> > You can create any issues if you want. Even though some issues can be
> > duplicated, it's ok.
> > 2015년 6월 18일 (목) 오전 10:39, Atri Sharma <atri.jiit@gmail.com>님이
작성:
> >
> > > Do we have a ticket around that?
> > > On 18 Jun 2015 15:07, "Jihoon Son" <jihoonson@apache.org> wrote:
> > >
> > > > It looks good to start.
> > > > Any questions welcome!
> > > >
> > > > Jihoon
> > > >
> > > > 2015년 6월 18일 (목) 오전 3:39, Atri Sharma <atri.jiit@gmail.com>님이
작성:
> > > >
> > > > > So distinct aggregation is one area, thanks.
> > > > >
> > > > > I am trying to get enough knowledge of Internals of aggregation
> > engine
> > > > and
> > > > > query planner to be able to work on rollup and cube so picking
> > smaller
> > > > > tickets first.
> > > > > On 18 Jun 2015 02:42, "Jihoon Son" <jihoonson@apache.org> wrote:
> > > > >
> > > > > > As far as I know, there aren't any plans for improvement except
> in
> > > > > distinct
> > > > > > aggregation. I think that our code for distinct aggregation
is
> too
> > > > > > complicated, and the performance also should be improved.
> > > > > >
> > > > > > So, when you design the implementation of your algorithm on
Tajo,
> > you
> > > > > don't
> > > > > > have to consider distinct aggregation part, I think.
> > > > > >
> > > > > > 2015년 6월 18일 (목) 오전 2:16, Atri Sharma <atri.jiit@gmail.com>님이
> 작성:
> > > > > >
> > > > > > > Thank you.
> > > > > > >
> > > > > > > Is there any improvement in aggregates that we are looking
at
> > > please?
> > > > > > > On 16 Jun 2015 17:07, "Jihoon Son" <jihoonson@apache.org>
> wrote:
> > > > > > >
> > > > > > > > In Tajo, aggregation is very similar to that in Hadoop
> > MapReduce.
> > > > > > > > Let me consider an example. Given a query of "select
*k*,
> > > count(*)
> > > > > from
> > > > > > > *t*
> > > > > > > > group by *k*", Tajo generates a LogicalPlan as follows.
> > > > > > > >
> > > > > > > > group by (k)
> > > > > > > >        |
> > > > > > > >    scan (t)
> > > > > > > >
> > > > > > > > This LogicalPlan is translated into a MasterPlan as
follows.
> > > > > > > >
> > > > > > > > -----------------
> > > > > > > >      Stage2
> > > > > > > >   group by *k*
> > > > > > > > -----------------
> > > > > > > >           |
> > > > > > > > shuffle tuples with *k*
> > > > > > > >           |
> > > > > > > > -----------------
> > > > > > > >      Stage1
> > > > > > > >   group by *k*
> > > > > > > >          |
> > > > > > > >     scan *t*
> > > > > > > > -----------------
> > > > > > > >
> > > > > > > > As you can see in this example, the query plan consists
of 2
> > > > stages.
> > > > > > Each
> > > > > > > > stage is executed subsequently because the result
of Stage 1
> is
> > > > used
> > > > > as
> > > > > > > the
> > > > > > > > input of Stage 2. Each stage is divided into multiple
tasks
> for
> > > > each
> > > > > > > input
> > > > > > > > split as follows.
> > > > > > > >
> > > > > > > > Stage1
> > > > > > > >
> > > > > > > > Task 1
> > > > > > > > group by *k*
> > > > > > > >        |
> > > > > > > >   scan *t* (0 - 99)
> > > > > > > >
> > > > > > > > Task 2
> > > > > > > > group by *k*
> > > > > > > >        |
> > > > > > > >   scan *t* (100 - 199)
> > > > > > > > ...
> > > > > > > >
> > > > > > > > Each task is executed by a TajoWorker. As you can
see, tasks
> of
> > > the
> > > > > > first
> > > > > > > > stage execute a local aggregation after scanning input
split.
> > > This
> > > > > > local
> > > > > > > > aggregation result is shuffled among TajoWorkers with
the
> > > > aggregation
> > > > > > key
> > > > > > > > *k*. Then, the final aggregation is computed at the
second
> > stage.
> > > > > > > >
> > > > > > > > Stage1 and Stage2 are similar to Map and Reduce of
MapReduce.
> > The
> > > > > local
> > > > > > > > aggregation of Stage1 is similar to the Combiner of
Hadoop
> > > > MapReduce.
> > > > > > > >
> > > > > > > > I hope that this will be helpful to you.
> > > > > > > > If you have any further questions, please feel free
to ask.
> > > > > > > > Jihoon
> > > > > > > >
> > > > > > > > 2015년 6월 16일 (화) 오전 7:28, Atri Sharma
<atri.jiit@gmail.com
> >님이
> > > 작성:
> > > > > > > >
> > > > > > > > Thanks.
> > > > > > > > >
> > > > > > > > > What are your thoughts on parallel aggregation?
Generating
> > > query
> > > > > > plans
> > > > > > > > that
> > > > > > > > > allow states to be generated which can be executed
> > > independently
> > > > > and
> > > > > > > then
> > > > > > > > > states recombined?
> > > > > > > > > On 16 Jun 2015 05:25, "Jihoon Son" <jihoonson@apache.org>
> > > wrote:
> > > > > > > > >
> > > > > > > > > > Hi Atri, thanks for your question.
> > > > > > > > > >
> > > > > > > > > > First of all, maybe you already did, I recommend
that you
> > > read
> > > > > this
> > > > > > > > > article
> > > > > > > > > > <
> > > > > > > > > >
> > > > > > > > >
> > > > > > > >
> > > > > > >
> > > > > >
> > > > >
> > > >
> > >
> >
> http://www.hadoopsphere.com/2015/02/technical-deep-dive-into-apache-tajo.html
> > > > > > > > > > >
> > > > > > > > > > before you start implementation. This is
written by
> > Hyunsik,
> > > > and
> > > > > > > > contains
> > > > > > > > > > the description of Tajo's overall infrastructure.
> > > Afterwards, I
> > > > > > think
> > > > > > > > > that
> > > > > > > > > > you may ask more detailed question.
> > > > > > > > > >
> > > > > > > > > > Here, I'll roughly list some important classes
for
> > aggregate
> > > > > > > > > > implementation.
> > > > > > > > > >
> > > > > > > > > >    - SQLParser.g4 contains our SQL parsing
rules. It is
> > > written
> > > > > in
> > > > > > > > antlr.
> > > > > > > > > >    - SQLAnalyzer is our parser based on
rules defined at
> > > > > > > SQLParser.g4.
> > > > > > > > > >    - SQLAnalyzer translates a SQL query
into a tree of
> Expr
> > > > which
> > > > > > > > > >    represents an algebraic expression.
> > > > > > > > > >    - LogicalPlanner translates the Expr
tree into a
> > > LogicalPlan
> > > > > > that
> > > > > > > > > >    logically describes how the given query
will be
> > executed.
> > > > > > > > > >    - GlobalPlanner translates the LogicalPlan
into a
> > > MasterPlan
> > > > > > > > > >    (distributed query execution plan) that
describes how
> > the
> > > > > given
> > > > > > > > query
> > > > > > > > > > will
> > > > > > > > > >    be executed in distributed cluster.
> > > > > > > > > >    - Once a MasterPlan is created, QueryMaster
starts to
> > > > execute
> > > > > > > query
> > > > > > > > > >    processing. A query consists of multiple
stages, which
> > are
> > > > > > > > > individually
> > > > > > > > > >    processed in some order.
> > > > > > > > > >       - For example, a simple aggregation
query is
> executed
> > > in
> > > > > two
> > > > > > > > > stages,
> > > > > > > > > >       each of which is for parallel aggregation
and
> > combining
> > > > > > > > aggregates.
> > > > > > > > > > These
> > > > > > > > > >       stages are executed sequentially.
> > > > > > > > > >    - A stage is concurrently processed by
multiple tasks,
> > and
> > > > is
> > > > > > > > executed
> > > > > > > > > >    by TajoWorker.
> > > > > > > > > >    - Each task contains meta information
for input data
> > and a
> > > > > > > > LogicalPlan
> > > > > > > > > >    of the stage. This LogicalPlan is translated
into
> > > > PhysicalExec
> > > > > > by
> > > > > > > > > >    PhysicalPlanner.
> > > > > > > > > >    - PhysicalExec describes how the query
is actually
> > > executed.
> > > > > > > > > >       - For example, there are two types
of
> > AggregationExec,
> > > > > > > > > >       i.e., HashAggregateExec and SortAggregateExec,
for
> > > > > hash-based
> > > > > > > > > > aggregation
> > > > > > > > > >       and sort-based aggregation, respectively.
> > > > > > > > > >
> > > > > > > > > > Best regards,
> > > > > > > > > > Jihoon
> > > > > > > > > >
> > > > > > > > > > 2015년 6월 15일 (월) 오후 11:32, Atri
Sharma <
> > atri.jiit@gmail.com
> > > >님이
> > > > > 작성:
> > > > > > > > > >
> > > > > > > > > > > Folks,
> > > > > > > > > > >
> > > > > > > > > > > I am looking into parallel aggregates/combining
> > > aggregates. I
> > > > > > have
> > > > > > > a
> > > > > > > > > plan
> > > > > > > > > > > around it which I think can work.
> > > > > > > > > > >
> > > > > > > > > > > Please update me on current infrastructure
and point me
> > > > around
> > > > > > the
> > > > > > > > > > existing
> > > > > > > > > > > code base. Also, ideas would be most
welcome around it.
> > > > > > > > > > >
> > > > > > > > > > > --
> > > > > > > > > > > Regards,
> > > > > > > > > > >
> > > > > > > > > > > Atri
> > > > > > > > > > > *l'apprenant*
> > > > > > > > > > >
> > > > > > > > > >
> > > > > > > > >
> > > > > > > >
> > > > > > >
> > > > > >
> > > > >
> > > >
> > >
> >
>

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