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From Atri Sharma <atri.j...@gmail.com>
Subject Re: Parallel Aggregates
Date Thu, 18 Jun 2015 09:38:46 GMT
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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