As everyone has pointed out there will be a small overhead added by the abstraction but in my own experience its totally worth it.
Almost two years ago we decided to jump into the beam wagon, by first deploying into an on-premises hadoop cluster with the Spark engine (just because spark was already available and we didn't want to introduce a new stack in our hadoop cluster), then we moved to a Flink cluster (due to others reason) and few months later we moved 90% of our streaming processing to Dataflow (in order to migrate the on-premises cluster to the cloud), all that wouldn't have been possible without the beam abstraction. In conclusion beam abstraction rocks, it's not perfect, but it's really good. Just my 2 cents. Matt Casters <mattcast...@gmail.com> schrieb am Mo., 6. Mai 2019, 15:33: > I've dealt with responses like this for a number of decades. With Kettle > Beam I could say: "here, in 20 minutes of visual programming you have your > pipeline up and running". It's easy to set up, maintain, debug, unit test, > version control... the whole thing. And then someone would say: Naaah, if I > don't code it myself I don't trust it. Usually it's worded differently but > that's what it comes down to. > Some people think in terms of impossibilities instead of possibilities and > will always find some reason why they fall in that 0.1% of the cases. > > > Lets say Beam came up with the abstractions long before other runners > but to map things to runners it is going to take time (that's where things > are today). so its always a moving target. > > Any scaleable data processing problem you might have that can't be solved > by Spark, Flink or DataFlow is pretty obscure don't you think? > > Great discussion :-) > > Cheers, > Matt > --- > Matt Casters <m <mcast...@pentaho.org>attcast...@gmail.com> > Senior Solution Architect, Kettle Project Founder > > > > Op zo 5 mei 2019 om 00:18 schreef kant kodali <kanth...@gmail.com>: > >> I believe this comes down to more of abstractions vs execution engines >> and I am sure people can take on both sides. I think both are important >> however It is worth noting that the execution framework themselves have a >> lot of abstractions but sure more generic ones can be built on top. Are >> abstractions always good?! I will just point to this book >> <https://www.amazon.com/Philosophy-Software-Design-John-Ousterhout/dp/1732102201/ref=sr_1_1?keywords=john+ousterhout+book&qid=1557008185&s=gateway&sr=8-1> >> >> I tend to lean more on the execution engines side because I can build >> something on top. I am also not sure if Beam is the first one to come up >> with these ideas since Frameworks like Cascading had existed long before. >> >> Lets say Beam came up with the abstractions long before other runners but >> to map things to runners it is going to take time (that's where things are >> today). so its always a moving target. >> >> >> >> >> >> >> On Tue, Apr 30, 2019 at 3:15 PM Kenneth Knowles <k...@apache.org> wrote: >> >>> It is worth noting that Beam isn't solely a portability layer that >>> exposes underlying API features, but a feature-rich layer in its own right, >>> with carefully coherent abstractions. For example, quite early on the >>> SparkRunner supported streaming aspects of the Beam model - watermarks, >>> windowing, triggers - that were not really available any other way. Beam's >>> various features sometimes requires just a pass-through API and sometimes >>> requires clever new implementation. And everything is moving constantly. I >>> don't see Beam as following the features of any engine, but rather coming >>> up with new needed data processing abstractions and figuring out how to >>> efficiently implement them on top of various architectures. >>> >>> Kenn >>> >>> On Tue, Apr 30, 2019 at 8:37 AM kant kodali <kanth...@gmail.com> wrote: >>> >>>> Staying behind doesn't imply one is better than the other and I didn't >>>> mean that in any way but I fail to see how an abstraction framework like >>>> Beam can stay ahead of the underlying execution engines? >>>> >>>> For example, If a new feature is added into the underlying execution >>>> engine that doesn't fit the interface of Beam or breaks then I would think >>>> the interface would need to be changed. Another example would say the >>>> underlying execution engines take different kind's of parameters for the >>>> same feature then it isn't so straight forward to come up with an interface >>>> since there might be very little in common in the first place so, in that >>>> sense, I fail to see how Beam can stay ahead. >>>> >>>> "Of course the API itself is Spark-specific, but it borrows heavily >>>> (among other things) on ideas that Beam itself pioneered long before Spark >>>> 2.0" Good to know. >>>> >>>> "one of the things Beam has focused on was a language portability >>>> framework" Sure but how important is this for a typical user? Do people >>>> stop using a particular tool because it is in an X language? I personally >>>> would put features first over language portability and it's completely fine >>>> that may not be in line with Beam's priorities. All said I can agree Beam >>>> focus on language portability is great. >>>> >>>> On Tue, Apr 30, 2019 at 2:48 AM Maximilian Michels <m...@apache.org> >>>> wrote: >>>> >>>>> > I wouldn't say one is, or will always be, in front of or behind >>>>> another. >>>>> >>>>> That's a great way to phrase it. I think it is very common to jump to >>>>> the conclusion that one system is better than the other. In reality >>>>> it's >>>>> often much more complicated. >>>>> >>>>> For example, one of the things Beam has focused on was a language >>>>> portability framework. Do I get this with Flink? No. Does that mean >>>>> Beam >>>>> is better than Flink? No. Maybe a better question would be, do I want >>>>> to >>>>> be able to run Python pipelines? >>>>> >>>>> This is just an example, there are many more factors to consider. >>>>> >>>>> Cheers, >>>>> Max >>>>> >>>>> On 30.04.19 10:59, Robert Bradshaw wrote: >>>>> > Though we all certainly have our biases, I think it's fair to say >>>>> that >>>>> > all of these systems are constantly innovating, borrowing ideas from >>>>> > one another, and have their strengths and weaknesses. I wouldn't say >>>>> > one is, or will always be, in front of or behind another. >>>>> > >>>>> > Take, as the given example Spark Structured Streaming. Of course the >>>>> > API itself is spark-specific, but it borrows heavily (among other >>>>> > things) on ideas that Beam itself pioneered long before Spark 2.0, >>>>> > specifically the unification of batch and streaming processing into a >>>>> > single API, and the event-time based windowing (triggering) model for >>>>> > consistently and correctly handling distributed, out-of-order data >>>>> > streams. >>>>> > >>>>> > Of course there are also operational differences. Spark, for example, >>>>> > is very tied to the micro-batch style of execution whereas Flink is >>>>> > fundamentally very continuous, and Beam delegates to the underlying >>>>> > runner. >>>>> > >>>>> > It is certainly Beam's goal to keep overhead minimal, and one of the >>>>> > primary selling points is the flexibility of portability (of both the >>>>> > execution runtime and the SDK) as your needs change. >>>>> > >>>>> > - Robert >>>>> > >>>>> > >>>>> > On Tue, Apr 30, 2019 at 5:29 AM <kanth...@gmail.com> wrote: >>>>> >> >>>>> >> Ofcourse! I suspect beam will always be one or two step backwards >>>>> to the new functionality that is available or yet to come. >>>>> >> >>>>> >> For example: Spark Structured Streaming is still not available, no >>>>> CEP apis yet and much more. >>>>> >> >>>>> >> Sent from my iPhone >>>>> >> >>>>> >> On Apr 30, 2019, at 12:11 AM, Pankaj Chand < >>>>> pankajchanda...@gmail.com> wrote: >>>>> >> >>>>> >> Will Beam add any overhead or lack certain API/functions available >>>>> in Spark/Flink? >>>>> >>>>
