Do you suggest that in a streaming use case join operator also pass data
to downstream using files or that there are two different join operators
one for streaming and one for batch? If not, it means that the join
operator needs to emit data to a separate file output operator, so it
still needs to read data from a temporary space before emitting, why not
to emit directly to topN in this case?
Is not pipeline reuse already supported by Apex modules?
Thank you,
Vlad
On 4/10/17 06:59, Thomas Weise wrote:
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.
Thomas
On Thu, Apr 6, 2017 at 5:52 PM, Vlad Rozov <v.ro...@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.ro...@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.ro...@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 <pra...@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 to
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.ro...@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 propose
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 may
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 may
be
undeployed as well leading to better cluster resource utilization and
better Apex elasticity. Later, the operator may be in a state where
it
needs to emit tuples on the closed port. In this case, it needs to
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, the
port
can be open and inactive stream become active.
Thank you,
Vlad
