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Posted to dev@beam.apache.org by Robert Bradshaw <ro...@google.com.INVALID> on 2016/08/02 18:10:48 UTC
Re: Proposal: Dynamic PIpelineOptions
Being able to "late-bind" parameters like input paths to a
pre-constructed program would be a very useful feature, and I think is
worth adding to Beam.
Of the four API proposals, I have a strong preference for (4).
Further, it seems that these need not be bound to the PipelineOptions
object itself (i.e. a named RuntimeValueSupplier could be constructed
off of a pipeline object), which the Python API makes less heavy use
of (encouraging the user to use familiar, standard libraries for
argument parsing), though of course such integration is useful to
provide for convenience.
- Robert
On Fri, Jul 29, 2016 at 12:14 PM, Sam McVeety <sg...@google.com.invalid> wrote:
> During the graph construction phase, the given SDK generates an initial
> execution graph for the program. At execution time, this graph is
> executed, either locally or by a service. Currently, Beam only supports
> parameterization at graph construction time. Both Flink and Spark supply
> functionality that allows a pre-compiled job to be run without SDK
> interaction with updated runtime parameters.
>
> In its current incarnation, Dataflow can read values of PipelineOptions at
> job submission time, but this requires the presence of an SDK to properly
> encode these values into the job. We would like to build a common layer
> into the Beam model so that these dynamic options can be properly provided
> to jobs.
>
> Please see
> https://docs.google.com/document/d/1I-iIgWDYasb7ZmXbGBHdok_IK1r1YAJ90JG5Fz0_28o/edit
> for the high-level model, and
> https://docs.google.com/document/d/17I7HeNQmiIfOJi0aI70tgGMMkOSgGi8ZUH-MOnFatZ8/edit
> for
> the specific API proposal.
>
> Cheers,
> Sam
Re: Proposal: Dynamic PIpelineOptions
Posted by Amit Sela <am...@gmail.com>.
+1 sounds like a good idea.
Spark's driver actually takes all dynamic parameters starting with "spark."
and propagates them into SparkConf which is propagated onto the Executors
and is available via the environment's SparkEnv.
I'm wondering, does this mean that PipelineOption will be available to the
PTransform, or only the ValueSupplier (yes, (4) for me too please) ?
Thanks,
Amit
On Fri, Aug 5, 2016 at 5:41 PM Aljoscha Krettek <al...@apache.org> wrote:
> +1
>
> It's true that Flink provides a way to pass dynamic parameters to operator
> instances. That's not used in any of the built-in sources and operators,
> however. They are instantiated with their parameters when the graph is
> constructed. So what you are suggesting for Beam would actually provide
> more functionality than what we currently have in Flink. :-)
>
> Out of the options I think (4) would be the best. (1) and (2) are not type
> safe, correct? and (3) seems very boilerplate-y.
>
> Cheers,
> Aljoscha
>
> On Thu, 4 Aug 2016 at 21:53 Frances Perry <fj...@google.com.invalid> wrote:
>
> > +Amit, Aljoscha, Manu
> >
> > Any comments from folks on the Flink, Spark, or Gearpump runners?
> >
> > On Tue, Aug 2, 2016 at 11:10 AM, Robert Bradshaw <
> > robertwb@google.com.invalid> wrote:
> >
> > > Being able to "late-bind" parameters like input paths to a
> > > pre-constructed program would be a very useful feature, and I think is
> > > worth adding to Beam.
> > >
> > > Of the four API proposals, I have a strong preference for (4).
> > > Further, it seems that these need not be bound to the PipelineOptions
> > > object itself (i.e. a named RuntimeValueSupplier could be constructed
> > > off of a pipeline object), which the Python API makes less heavy use
> > > of (encouraging the user to use familiar, standard libraries for
> > > argument parsing), though of course such integration is useful to
> > > provide for convenience.
> > >
> > > - Robert
> > >
> > > On Fri, Jul 29, 2016 at 12:14 PM, Sam McVeety <sgmc@google.com.invalid
> >
> > > wrote:
> > > > During the graph construction phase, the given SDK generates an
> initial
> > > > execution graph for the program. At execution time, this graph is
> > > > executed, either locally or by a service. Currently, Beam only
> > supports
> > > > parameterization at graph construction time. Both Flink and Spark
> > supply
> > > > functionality that allows a pre-compiled job to be run without SDK
> > > > interaction with updated runtime parameters.
> > > >
> > > > In its current incarnation, Dataflow can read values of
> PipelineOptions
> > > at
> > > > job submission time, but this requires the presence of an SDK to
> > properly
> > > > encode these values into the job. We would like to build a common
> > layer
> > > > into the Beam model so that these dynamic options can be properly
> > > provided
> > > > to jobs.
> > > >
> > > > Please see
> > > > https://docs.google.com/document/d/1I-iIgWDYasb7ZmXbGBHdok_I
> > > K1r1YAJ90JG5Fz0_28o/edit
> > > > for the high-level model, and
> > > > https://docs.google.com/document/d/17I7HeNQmiIfOJi0aI70tgGMM
> > > kOSgGi8ZUH-MOnFatZ8/edit
> > > > for
> > > > the specific API proposal.
> > > >
> > > > Cheers,
> > > > Sam
> > >
> >
>
Re: Proposal: Dynamic PIpelineOptions
Posted by Aljoscha Krettek <al...@apache.org>.
+1
It's true that Flink provides a way to pass dynamic parameters to operator
instances. That's not used in any of the built-in sources and operators,
however. They are instantiated with their parameters when the graph is
constructed. So what you are suggesting for Beam would actually provide
more functionality than what we currently have in Flink. :-)
Out of the options I think (4) would be the best. (1) and (2) are not type
safe, correct? and (3) seems very boilerplate-y.
Cheers,
Aljoscha
On Thu, 4 Aug 2016 at 21:53 Frances Perry <fj...@google.com.invalid> wrote:
> +Amit, Aljoscha, Manu
>
> Any comments from folks on the Flink, Spark, or Gearpump runners?
>
> On Tue, Aug 2, 2016 at 11:10 AM, Robert Bradshaw <
> robertwb@google.com.invalid> wrote:
>
> > Being able to "late-bind" parameters like input paths to a
> > pre-constructed program would be a very useful feature, and I think is
> > worth adding to Beam.
> >
> > Of the four API proposals, I have a strong preference for (4).
> > Further, it seems that these need not be bound to the PipelineOptions
> > object itself (i.e. a named RuntimeValueSupplier could be constructed
> > off of a pipeline object), which the Python API makes less heavy use
> > of (encouraging the user to use familiar, standard libraries for
> > argument parsing), though of course such integration is useful to
> > provide for convenience.
> >
> > - Robert
> >
> > On Fri, Jul 29, 2016 at 12:14 PM, Sam McVeety <sg...@google.com.invalid>
> > wrote:
> > > During the graph construction phase, the given SDK generates an initial
> > > execution graph for the program. At execution time, this graph is
> > > executed, either locally or by a service. Currently, Beam only
> supports
> > > parameterization at graph construction time. Both Flink and Spark
> supply
> > > functionality that allows a pre-compiled job to be run without SDK
> > > interaction with updated runtime parameters.
> > >
> > > In its current incarnation, Dataflow can read values of PipelineOptions
> > at
> > > job submission time, but this requires the presence of an SDK to
> properly
> > > encode these values into the job. We would like to build a common
> layer
> > > into the Beam model so that these dynamic options can be properly
> > provided
> > > to jobs.
> > >
> > > Please see
> > > https://docs.google.com/document/d/1I-iIgWDYasb7ZmXbGBHdok_I
> > K1r1YAJ90JG5Fz0_28o/edit
> > > for the high-level model, and
> > > https://docs.google.com/document/d/17I7HeNQmiIfOJi0aI70tgGMM
> > kOSgGi8ZUH-MOnFatZ8/edit
> > > for
> > > the specific API proposal.
> > >
> > > Cheers,
> > > Sam
> >
>
Re: Proposal: Dynamic PIpelineOptions
Posted by Frances Perry <fj...@google.com.INVALID>.
+Amit, Aljoscha, Manu
Any comments from folks on the Flink, Spark, or Gearpump runners?
On Tue, Aug 2, 2016 at 11:10 AM, Robert Bradshaw <
robertwb@google.com.invalid> wrote:
> Being able to "late-bind" parameters like input paths to a
> pre-constructed program would be a very useful feature, and I think is
> worth adding to Beam.
>
> Of the four API proposals, I have a strong preference for (4).
> Further, it seems that these need not be bound to the PipelineOptions
> object itself (i.e. a named RuntimeValueSupplier could be constructed
> off of a pipeline object), which the Python API makes less heavy use
> of (encouraging the user to use familiar, standard libraries for
> argument parsing), though of course such integration is useful to
> provide for convenience.
>
> - Robert
>
> On Fri, Jul 29, 2016 at 12:14 PM, Sam McVeety <sg...@google.com.invalid>
> wrote:
> > During the graph construction phase, the given SDK generates an initial
> > execution graph for the program. At execution time, this graph is
> > executed, either locally or by a service. Currently, Beam only supports
> > parameterization at graph construction time. Both Flink and Spark supply
> > functionality that allows a pre-compiled job to be run without SDK
> > interaction with updated runtime parameters.
> >
> > In its current incarnation, Dataflow can read values of PipelineOptions
> at
> > job submission time, but this requires the presence of an SDK to properly
> > encode these values into the job. We would like to build a common layer
> > into the Beam model so that these dynamic options can be properly
> provided
> > to jobs.
> >
> > Please see
> > https://docs.google.com/document/d/1I-iIgWDYasb7ZmXbGBHdok_I
> K1r1YAJ90JG5Fz0_28o/edit
> > for the high-level model, and
> > https://docs.google.com/document/d/17I7HeNQmiIfOJi0aI70tgGMM
> kOSgGi8ZUH-MOnFatZ8/edit
> > for
> > the specific API proposal.
> >
> > Cheers,
> > Sam
>