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From Thomas Weise <...@apache.org>
Subject Re: open/close ports and active/inactive streams
Date Mon, 10 Apr 2017 13:59:00 GMT
I don't think this fully covers the the scenario of limited resources. You
describe a case of 3 operators, but when you consider just 2 operators that
both have to hold a large data set in memory, then the suggested approach
won't work. Let's say the first operator is outer join and the second
operator topN. Both are blocking and cannot emit before all input is seen.

To deallocate the outer join, all results need to be drained. It's a
resource swap and you need a temporary space to hold the data. Also, if the
requirement is to be able to recover and retry from results of stage one,
then you need a fault tolerant swap space. If the cluster does not have
enough memory, then disk is a good option (SLA vs. memory tradeoff).

I would also suggest to think beyond the single DAG scenario. Often users
need to define pipelines that are composed of multiple smaller flows (which
they may also want to reuse in multiple pipelines). APEXCORE-408 gives you
an option to compose such flows within a single Apex application, in
addition of covering the simplified use case that we discuss there.


On Thu, Apr 6, 2017 at 5:52 PM, Vlad Rozov <v.rozov@datatorrent.com> wrote:

> It is exactly the same use case with the exception that it is not
> necessary to write data to files. Consider 3 operators, an input operator,
> an aggregate operator and an output operator. When the application starts,
> the output port of the aggregate operator should be in the closed state,
> the stream between the second and the third would be inactive and the
> output operator does not need to be allocated. After the input operator
> process all data, it can close the output port and the input operator may
> be de-allocated. Once the aggregator receives EOS on it's input port, it
> should open the output port and start writing to it. At this point, the
> output operator needs to be deployed and the stream between the last two
> operators (aggregator and output) becomes active.
> In a real batch use case, it is preferable to have full application DAG to
> be statically defined and delegate to platform activation/de-activation of
> stages. It is also preferable not to write intermediate files to disk/HDFS,
> but instead pass data in-memory.
> Thank you,
> Vlad
> On 4/6/17 09:37, Thomas Weise wrote:
>> You would need to provide more specifics of the use case you are thinking
>> to address to make this a meaningful discussion.
>> An example for APEXCORE-408 (based on real batch use case): I have two
>> stages, first stage produces a set of files that second stage needs as
>> input. Stage 1 operators to be released and stage 2 operators deployed
>> when
>> stage 2 starts. These can be independent operators, they don't need to be
>> connected through a stream.
>> Thomas
>> On Thu, Apr 6, 2017 at 9:21 AM, Vlad Rozov <v.rozov@datatorrent.com>
>> wrote:
>> It is not about a use case difference. My proposal and APEXCORE-408
>>> address the same use case - how to re-allocate resources for batch
>>> applications or applications where processing happens in stages. The
>>> difference between APEXCORE-408 and the proposal is shift in complexity
>>> from application logic to the platform. IMO, supporting batch
>>> applications
>>> using APEXCORE-408 will require more coding on the application side.
>>> Thank you,
>>> Vlad
>>> On 4/5/17 21:57, Thomas Weise wrote:
>>> I think this needs more input on a use case level. The ability to
>>>> dynamically alter the DAG internally will also address the resource
>>>> allocation for operators:
>>>> https://issues.apache.org/jira/browse/APEXCORE-408
>>>> It can be used to implement stages of a batch pipeline and is very
>>>> flexible
>>>> in general. Considering the likely implementation complexity for the
>>>> proposed feature I would like to understand what benefits it provides to
>>>> the user (use cases that cannot be addressed otherwise)?
>>>> Thanks,
>>>> Thomas
>>>> On Sat, Apr 1, 2017 at 12:23 PM, Vlad Rozov <v.rozov@datatorrent.com>
>>>> wrote:
>>>> Correct, a statefull downstream operator can only be undeployed at a
>>>>> checkpoint window after it consumes all data emitted by upstream
>>>>> operator
>>>>> on the closed port.
>>>>> It will be necessary to distinguish between closed port and inactive
>>>>> stream. After port is closed, stream may still be active and after port
>>>>> is
>>>>> open, stream may still be inactive (not yet ready).
>>>>> The more contributors participate in the discussion and implementation,
>>>>> the more solid the feature will be.
>>>>> Thank you,
>>>>> Vlad
>>>>> Отправлено с iPhone
>>>>> On Apr 1, 2017, at 11:03, Pramod Immaneni <pramod@datatorrent.com>
>>>>> wrote:
>>>>> Generally a good idea. Care should be taken around fault tolerance and
>>>>>> idempotency. Close stream would need to stop accepting new data but
>>>>>> still
>>>>>> can't actually close all the streams and un-deploy operators till
>>>>>> committed. Idempotency might require the close stream to take effect
>>>>>> at
>>>>>> the
>>>>> end of the window. What would it then mean for re-opening streams
>>>>>> within
>>>>>> a
>>>>> window? Also, looks like a larger undertaking, as Ram suggested would
>>>>>> be
>>>>>> good to understand the use cases and I also suggest that multiple
>>>>>> folks
>>>>>> participate in the implementation effort to ensure that we are able
>>>>>> address all the scenarios and minimize chances of regression in
>>>>>> existing
>>>>>> behavior.
>>>>>> Thanks
>>>>>> On Sat, Apr 1, 2017 at 8:12 AM, Vlad Rozov <v.rozov@datatorrent.com>
>>>>>> wrote:
>>>>>> All,
>>>>>>> Currently Apex assumes that an operator can emit on any defined
>>>>>>> output
>>>>>>> port and all streams defined by a DAG are active. I'd like to
>>>>>>> an
>>>>>>> ability for an operator to open and close output ports. By default
>>>>>>> all
>>>>>>> ports defined by an operator will be open. In the case an operator
>>>>>>> for
>>>>>>> any
>>>>>> reason decides that it will not emit tuples on the output port, it
>>>>>>> close it. This will make the stream inactive and the application
>>>>>>> master
>>>>>>> may
>>>>>> undeploy the downstream (for that input stream) operators. If this
>>>>>> leads to
>>>>>> containers that don't have any active operators, those containers
>>>>>> be
>>>>>>> undeployed as well leading to better cluster resource utilization
>>>>>>> better Apex elasticity. Later, the operator may be in a state
>>>>>>> it
>>>>>>> needs to emit tuples on the closed port. In this case, it needs
>>>>>>> re-open
>>>>>> the port and wait till the stream becomes active again before emitting
>>>>>>> tuples on that port. Making inactive stream active again, requires
>>>>>>> the
>>>>>>> application master to re-allocate containers and re-deploy the
>>>>>>> downstream
>>>>>> operators.
>>>>>>> It should be also possible for an application designer to mark
>>>>>>> streams
>>>>>>> as
>>>>>> inactive when an application starts. This will allow the application
>>>>>> master
>>>>>> avoid reserving all containers when the application starts. Later,
>>>>>> port
>>>>>> can be open and inactive stream become active.
>>>>>>> Thank you,
>>>>>>> Vlad

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