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From Timothy Farkas <...@datatorrent.com>
Subject Re: Supporting iterations in Apex
Date Tue, 03 Nov 2015 23:34:48 GMT
+1 for option 2

Also would it be possible to chain delay operators? A lot of stochastic and
adaptive methods depend on finding the correlations between the current
time step n and previous k time steps  (n, n - 1), (n, n - 2), (n, n - 3)
... (n, n - k)

Here is a picture of a model that uses delay operators (in the picture
these are represented by z^-1) and is used for time series prediction.

https://upload.wikimedia.org/wikipedia/commons/thumb/d/d2/IIRFilter2.svg/250px-IIRFilter2.svg.png

On Tue, Nov 3, 2015 at 3:14 PM, Sasha Parfenov <sasha@datatorrent.com>
wrote:

> +1 for option 2.  Although option 2 doesn't mirror the current unifiers
> like option 1, and may look more complicated when viewing logical plan, I
> think the benefits of flexibility of specifying locality and ability to
> bring multiple downstream operators into a single delay operators may be
> important for some projects.  For me the added flexibility wins,
> particularly in light of efforts towards a simpler high level API.
>
>
>
> On Mon, Nov 2, 2015 at 11:25 AM, David Yan <david@datatorrent.com> wrote:
>
> > Please share your thoughts using the dev mailing list on this topic if
> you
> > can.  Thanks.
> >
> > ---------- Forwarded message ----------
> > From: David Yan <david@datatorrent.com>
> > Date: Thu, Oct 29, 2015 at 11:11 AM
> > Subject: Re: Supporting iterations in Apex
> > To: dev@apex.incubator.apache.org
> >
> >
> > This delay operator will act as an input operator for the first window
> and
> > act as a regular operator after that.
> > The engine will increment the window id of the windows from all the
> output
> > ports of the delay operator.
> >
> > We will need a new interface for the delay operator, extending the
> > existing Operator interface.  The interface will probably include:
> >
> > - Emitting the tuples for the first window
> > - Emitting the tuples after recovery
> >
> > We will provide a default implementation of the delay operator with a
> > write-ahead log that stores the tuples for the window before each
> > checkpoint for recovery.  We will also probably support the number of
> > windows to delay using an operator property.
> >
> > Let's look at this DAG with an iteration loop:
> >
> > upstream --> A --> B --> downstream
> >              ^     |
> >              |-----|
> >
> > With the delay operator, the physical view of the DAG looks like this
> with
> > D being the delay operator:
> >
> > upstream --> A --> B --> downstream
> >              ^     |
> >              |-D<--|
> >
> > There are two approaches for specifying the delay operator.
> >
> > 1) As discussed earlier on this thread, the delay operator can be
> > specified as an *input port attribute* of A. The delay operator D will
> > not appear in the logical DAG.  The engine will do the +1 on the window
> ID
> > based on the presence of the input port attribute.  In this case, the
> delay
> > operator does not need to specify any input port, just like the unifier,
> > with the process(tuple) method implicitly taking in the tuples from the
> > output port of B, which logically connects to the input port of A.
> >
> > 2) The delay operator is specified and connected *as any other operator*
> > in the logical DAG.  The engine will do the +1 on the window ID if the
> > operator implements the delay operator interface.  In this case, the
> delay
> > operator D will need to specify at least one input port (just like a
> > regular operator), and can actually have multiple input ports.
> >
> > I'm leaning toward the 2nd approach.
> >
> > Please share your thoughts.  Which one you think is better?  Or maybe
> > suggest a different approach altogether?
> >
> > Thanks!
> >
> > David
> >
> > David
> >
> > On Wed, Oct 7, 2015 at 10:51 AM, Thomas Weise <thomas@datatorrent.com>
> > wrote:
> >
> >> Why not set the the delay operator as attribute? We already support
> >> partitioners and stream codecs as attribute.
> >>
> >>
> >> On Wed, Oct 7, 2015 at 10:09 AM, Pramod Immaneni <
> pramod@datatorrent.com>
> >> wrote:
> >>
> >> > How about making just the window delay an attribute on the input port.
> >> The
> >> > operator connection is just like a normal DAG stream creation. We
> could
> >> > also support connecting same operator to multiple input ports with
> >> > different delay and handle fault recovery accordingly.
> >> >
> >> > On Wed, Oct 7, 2015 at 9:53 AM, David Yan <david@datatorrent.com>
> >> wrote:
> >> >
> >> > > The iteration operator actually resembles the usage of unifiers. 
We
> >> have
> >> > > getUnifier in the interface of OutputPort.
> >> > >
> >> > > But if we add getDelayOperator in the interface of InputPort, that
> >> would
> >> > > introduce backward incompatibility especially since we can't use the
> >> > > default implementation feature of interfaces that is in Java 8.
> >> > >
> >> > > Putting the class object as an attribute of the InputPort is not
> good
> >> > > either because you can't configure the delay operator itself.
> >> > >
> >> > > Thoughts?
> >> > >
> >> > > David
> >> > >
> >> > > On Fri, Sep 25, 2015 at 10:10 AM, David Yan <david@datatorrent.com>
> >> > wrote:
> >> > >
> >> > > > This is a very good idea.  This way, we can have a default
> >> > implementation
> >> > > > of that operator and the user can control how the tuples are
> stored
> >> by
> >> > > > having his/her own implementation.  How many windows the operator
> >> > delays
> >> > > is
> >> > > > part of the implementation of that operator.
> >> > > >
> >> > > > I am thinking of getting rid of the ITERATION_WINDOW_OFFSET
> >> attribute
> >> > and
> >> > > > introduce a DELAY_OPERATOR_CLASS attribute so that the user can
> >> specify
> >> > > the
> >> > > > delay operator class to be used.
> >> > > >
> >> > > > More thoughts?
> >> > > >
> >> > > > David
> >> > > >
> >> > > > On Thu, Sep 17, 2015 at 7:16 PM, Gaurav Gupta <
> >> gaurav@datatorrent.com>
> >> > > > wrote:
> >> > > >
> >> > > >> Hey David,
> >> > > >>
> >> > > >> I was thinking can we add another operator in front of the
input
> >> port
> >> > > that
> >> > > >> has ITERATION_WINDOW_COUNT set. The new additional operator
will
> >> have
> >> > > >> property whose value  will be set equal to ITERATION_WINDOW_COUNT
> >> and
> >> > it
> >> > > >> will be responsible for caching the data for those many windows
> and
> >> > > >> delaying the data. This operator can act as unifier cum iterator
> >> > > operator.
> >> > > >> For this you may not need any external storage agent as platform
> >> > > >> checkpointing should help you here.
> >> > > >>
> >> > > >> We are doing something similar for Sliding window.
> >> > > >>
> >> > > >> Thanks
> >> > > >> -Gaurav
> >> > > >>
> >> > > >> On Wed, Sep 16, 2015 at 1:44 PM, David Yan <
> david@datatorrent.com>
> >> > > wrote:
> >> > > >>
> >> > > >> > Hi all,
> >> > > >> >
> >> > > >> > One current disadvantage of Apex is the inability to
do
> >> iterations
> >> > and
> >> > > >> > machine learning algorithms because we don't allow loops
in the
> >> > > >> application
> >> > > >> > DAG (hence the name DAG).  I am proposing that we allow
loops
> in
> >> the
> >> > > >> DAG if
> >> > > >> > the loop advances the window ID by a configured amount.
 A JIRA
> >> > ticket
> >> > > >> has
> >> > > >> > been created:
> >> > > >> >
> >> > > >> > https://malhar.atlassian.net/browse/APEX-60
> >> > > >> >
> >> > > >> > I have started this work in my fork at
> >> > > >> > https://github.com/davidyan74/incubator-apex-core/tree/APEX-60
> .
> >> > > >> >
> >> > > >> > The current progress is that a simple test case works.
 Major
> >> work
> >> > > still
> >> > > >> > needs to be done with respect to recovery and partitioning.
> >> > > >> >
> >> > > >> > The value ITERATION_WINDOW_COUNT is an attribute to
an input
> >> port of
> >> > > an
> >> > > >> > operator.  If the value of the attribute is greater
than or
> >> equal to
> >> > > 1,
> >> > > >> any
> >> > > >> > tuples sent to the input port are treated to be
> >> > ITERATION_WINDOW_COUNT
> >> > > >> > windows ahead of what they are.
> >> > > >> >
> >> > > >> > For recovery, we will need to checkpoint all the tuples
between
> >> > ports
> >> > > >> with
> >> > > >> > the to replay the looped tuples.  During the recovery,
if the
> >> > operator
> >> > > >> has
> >> > > >> > an input port, with ITERATION_WINDOW_COUNT=2, is recovering
> from
> >> > > >> checkpoint
> >> > > >> > window 14, the tuples for that input port from window
13 and
> >> window
> >> > 14
> >> > > >> need
> >> > > >> > to be replayed to be treated as window 15 and window
16
> >> respectively
> >> > > >> (13+2
> >> > > >> > and 14+2).
> >> > > >> >
> >> > > >> > In other words, we need to store all the tuples from
window
> with
> >> ID
> >> > > >> > committedWindowId minus ITERATION_WINDOW_COUNT for recovery
and
> >> > purge
> >> > > >> the
> >> > > >> > tuples earlier than that window.
> >> > > >> > We can optimize this by only storing the tuples for
> >> > > >> ITERATION_WINDOW_COUNT
> >> > > >> > windows prior to any checkpoint.
> >> > > >> >
> >> > > >> > For that, we need a storage mechanism for the tuples.
 Chandni
> >> > already
> >> > > >> has
> >> > > >> > something that fits this usage case in Apex Malhar.
 The class
> is
> >> > > >> > IdempotentStorageManager.  In order for this to be used
in Apex
> >> > core,
> >> > > we
> >> > > >> > need to deprecate the class in Apex Malhar and move
it to Apex
> >> Core.
> >> > > >> >
> >> > > >> > A JIRA ticket has been created for this particular work:
> >> > > >> >
> >> > > >> > https://malhar.atlassian.net/browse/APEX-128
> >> > > >> >
> >> > > >> > Some of the above has been discussed among Thomas, Chetan,
> >> Chandni,
> >> > > and
> >> > > >> > myself.
> >> > > >> >
> >> > > >> > For partitioning, we have not started any discussion
or
> >> > brainstorming.
> >> > > >> We
> >> > > >> > appreciate any feedback on this and any other aspect
related to
> >> > > >> supporting
> >> > > >> > iterations in general.
> >> > > >> >
> >> > > >> > Thanks!
> >> > > >> >
> >> > > >> > David
> >> > > >> >
> >> > > >>
> >> > > >
> >> > > >
> >> > >
> >> >
> >>
> >
> >
> >
>

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