Just to make sure we don’t move past, I think we haven’t decided yet: - if we’ll replace the current proposal to Wenchen’s approach as the default - if we want to have Wenchen’s approach as an optional mix-in on the top of Ryan’s proposal (SupportsInvoke)
>From what I read, some people pointed out it as a replacement. Please correct me if I misread this discussion thread. As Dongjoon pointed out, it would be good to know rough ETA to make sure making progress in this, and people can compare more easily. FWIW, there’s the saying I like in the zen of Python <https://www.python.org/dev/peps/pep-0020/>: There should be one— and preferably only one —obvious way to do it. If multiple approaches have the way for developers to do the (almost) same thing, I would prefer to avoid it. In addition, I would prefer to focus on what Spark does by default first. 2021년 2월 17일 (수) 오후 2:33, Dongjoon Hyun <dongjoon.h...@gmail.com>님이 작성: > Hi, Wenchen. > > This thread seems to get enough attention. Also, I'm expecting more and > more if we have this on the `master` branch because we are developing > together. > > > Spark SQL has many active contributors/committers and this thread > doesn't get much attention yet. > > So, what's your ETA from now? > > > I think the problem here is we were discussing some very detailed > things without actual code. > > I'll implement my idea after the holiday and then we can have more > effective discussions. > > We can also do benchmarks and get some real numbers. > > In the meantime, we can continue to discuss other parts of this > proposal, and make a prototype if possible. > > I'm looking forward to seeing your PR. I hope we can conclude this thread > and have at least one implementation in the `master` branch this month > (February). > If you need more time (one month or longer), why don't we have Ryan's > suggestion in the `master` branch first and benchmark with your PR later > during Apache Spark 3.2 timeframe. > > Bests, > Dongjoon. > > > On Tue, Feb 16, 2021 at 9:26 AM Ryan Blue <rb...@netflix.com.invalid> > wrote: > >> Andrew, >> >> The proposal already includes an API for aggregate functions and I think >> we would want to implement those right away. >> >> Processing ColumnBatch is something we can easily extend the interfaces >> to support, similar to Wenchen's suggestion. The important thing right now >> is to agree on some basic functionality: how to look up functions and what >> the simple API should be. Like the TableCatalog interfaces, we will layer >> on more support through optional interfaces like `SupportsInvoke` or >> `SupportsColumnBatch`. >> >> On Tue, Feb 16, 2021 at 9:00 AM Andrew Melo <andrew.m...@gmail.com> >> wrote: >> >>> Hello Ryan, >>> >>> This proposal looks very interesting. Would future goals for this >>> functionality include both support for aggregation functions, as well >>> as support for processing ColumnBatch-es (instead of Row/InternalRow)? >>> >>> Thanks >>> Andrew >>> >>> On Mon, Feb 15, 2021 at 12:44 PM Ryan Blue <rb...@netflix.com.invalid> >>> wrote: >>> > >>> > Thanks for the positive feedback, everyone. It sounds like there is a >>> clear path forward for calling functions. Even without a prototype, the >>> `invoke` plans show that Wenchen's suggested optimization can be done, and >>> incorporating it as an optional extension to this proposal solves many of >>> the unknowns. >>> > >>> > With that area now understood, is there any discussion about other >>> parts of the proposal, besides the function call interface? >>> > >>> > On Fri, Feb 12, 2021 at 10:40 PM Chao Sun <sunc...@apache.org> wrote: >>> >> >>> >> This is an important feature which can unblock several other projects >>> including bucket join support for DataSource v2, complete support for >>> enforcing DataSource v2 distribution requirements on the write path, etc. I >>> like Ryan's proposals which look simple and elegant, with nice support on >>> function overloading and variadic arguments. On the other hand, I think >>> Wenchen made a very good point about performance. Overall, I'm excited to >>> see active discussions on this topic and believe the community will come to >>> a proposal with the best of both sides. >>> >> >>> >> Chao >>> >> >>> >> On Fri, Feb 12, 2021 at 7:58 PM Hyukjin Kwon <gurwls...@gmail.com> >>> wrote: >>> >>> >>> >>> +1 for Liang-chi's. >>> >>> >>> >>> Thanks Ryan and Wenchen for leading this. >>> >>> >>> >>> >>> >>> 2021년 2월 13일 (토) 오후 12:18, Liang-Chi Hsieh <vii...@gmail.com>님이 작성: >>> >>>> >>> >>>> Basically I think the proposal makes sense to me and I'd like to >>> support the >>> >>>> SPIP as it looks like we have strong need for the important feature. >>> >>>> >>> >>>> Thanks Ryan for working on this and I do also look forward to >>> Wenchen's >>> >>>> implementation. Thanks for the discussion too. >>> >>>> >>> >>>> Actually I think the SupportsInvoke proposed by Ryan looks a good >>> >>>> alternative to me. Besides Wenchen's alternative implementation, is >>> there a >>> >>>> chance we also have the SupportsInvoke for comparison? >>> >>>> >>> >>>> >>> >>>> John Zhuge wrote >>> >>>> > Excited to see our Spark community rallying behind this important >>> feature! >>> >>>> > >>> >>>> > The proposal lays a solid foundation of minimal feature set with >>> careful >>> >>>> > considerations for future optimizations and extensions. Can't >>> wait to see >>> >>>> > it leading to more advanced functionalities like views with >>> shared custom >>> >>>> > functions, function pushdown, lambda, etc. It has already borne >>> fruit from >>> >>>> > the constructive collaborations in this thread. Looking forward to >>> >>>> > Wenchen's prototype and further discussions including the >>> SupportsInvoke >>> >>>> > extension proposed by Ryan. >>> >>>> > >>> >>>> > >>> >>>> > On Fri, Feb 12, 2021 at 4:35 PM Owen O'Malley < >>> >>>> >>> >>>> > owen.omalley@ >>> >>>> >>> >>>> > > >>> >>>> > wrote: >>> >>>> > >>> >>>> >> I think this proposal is a very good thing giving Spark a >>> standard way of >>> >>>> >> getting to and calling UDFs. >>> >>>> >> >>> >>>> >> I like having the ScalarFunction as the API to call the UDFs. It >>> is >>> >>>> >> simple, yet covers all of the polymorphic type cases well. I >>> think it >>> >>>> >> would >>> >>>> >> also simplify using the functions in other contexts like pushing >>> down >>> >>>> >> filters into the ORC & Parquet readers although there are a lot >>> of >>> >>>> >> details >>> >>>> >> that would need to be considered there. >>> >>>> >> >>> >>>> >> .. Owen >>> >>>> >> >>> >>>> >> >>> >>>> >> On Fri, Feb 12, 2021 at 11:07 PM Erik Krogen < >>> >>>> >>> >>>> > ekrogen@.com >>> >>>> >>> >>>> > > >>> >>>> >> wrote: >>> >>>> >> >>> >>>> >>> I agree that there is a strong need for a FunctionCatalog >>> within Spark >>> >>>> >>> to >>> >>>> >>> provide support for shareable UDFs, as well as make movement >>> towards >>> >>>> >>> more >>> >>>> >>> advanced functionality like views which themselves depend on >>> UDFs, so I >>> >>>> >>> support this SPIP wholeheartedly. >>> >>>> >>> >>> >>>> >>> I find both of the proposed UDF APIs to be sufficiently >>> user-friendly >>> >>>> >>> and >>> >>>> >>> extensible. I generally think Wenchen's proposal is easier for >>> a user to >>> >>>> >>> work with in the common case, but has greater potential for >>> confusing >>> >>>> >>> and >>> >>>> >>> hard-to-debug behavior due to use of reflective method signature >>> >>>> >>> searches. >>> >>>> >>> The merits on both sides can hopefully be more properly >>> examined with >>> >>>> >>> code, >>> >>>> >>> so I look forward to seeing an implementation of Wenchen's >>> ideas to >>> >>>> >>> provide >>> >>>> >>> a more concrete comparison. I am optimistic that we will not >>> let the >>> >>>> >>> debate >>> >>>> >>> over this point unreasonably stall the SPIP from making >>> progress. >>> >>>> >>> >>> >>>> >>> Thank you to both Wenchen and Ryan for your detailed >>> consideration and >>> >>>> >>> evaluation of these ideas! >>> >>>> >>> ------------------------------ >>> >>>> >>> *From:* Dongjoon Hyun < >>> >>>> >>> >>>> > dongjoon.hyun@ >>> >>>> >>> >>>> > > >>> >>>> >>> *Sent:* Wednesday, February 10, 2021 6:06 PM >>> >>>> >>> *To:* Ryan Blue < >>> >>>> >>> >>>> > blue@ >>> >>>> >>> >>>> > > >>> >>>> >>> *Cc:* Holden Karau < >>> >>>> >>> >>>> > holden@ >>> >>>> >>> >>>> > >; Hyukjin Kwon < >>> >>>> >>> >>> >>>> >>> >>>> > gurwls223@ >>> >>>> >>> >>>> >>; Spark Dev List < >>> >>>> >>> >>>> > dev@.apache >>> >>>> >>> >>>> > >; Wenchen Fan >>> >>>> >>> < >>> >>>> >>> >>>> > cloud0fan@ >>> >>>> >>> >>>> > > >>> >>>> >>> *Subject:* Re: [DISCUSS] SPIP: FunctionCatalog >>> >>>> >>> >>> >>>> >>> BTW, I forgot to add my opinion explicitly in this thread >>> because I was >>> >>>> >>> on the PR before this thread. >>> >>>> >>> >>> >>>> >>> 1. The `FunctionCatalog API` PR was made on May 9, 2019 and has >>> been >>> >>>> >>> there for almost two years. >>> >>>> >>> 2. I already gave my +1 on that PR last Saturday because I >>> agreed with >>> >>>> >>> the latest updated design docs and AS-IS PR. >>> >>>> >>> >>> >>>> >>> And, the rest of the progress in this thread is also very >>> satisfying to >>> >>>> >>> me. >>> >>>> >>> (e.g. Ryan's extension suggestion and Wenchen's alternative) >>> >>>> >>> >>> >>>> >>> To All: >>> >>>> >>> Please take a look at the design doc and the PR, and give us >>> some >>> >>>> >>> opinions. >>> >>>> >>> We really need your participation in order to make DSv2 more >>> complete. >>> >>>> >>> This will unblock other DSv2 features, too. >>> >>>> >>> >>> >>>> >>> Bests, >>> >>>> >>> Dongjoon. >>> >>>> >>> >>> >>>> >>> >>> >>>> >>> >>> >>>> >>> On Wed, Feb 10, 2021 at 10:58 AM Dongjoon Hyun < >>> >>>> >>> >>>> > dongjoon.hyun@ >>> >>>> >>> >>>> > > >>> >>>> >>> wrote: >>> >>>> >>> >>> >>>> >>> Hi, Ryan. >>> >>>> >>> >>> >>>> >>> We didn't move past anything (both yours and Wenchen's). What >>> Wenchen >>> >>>> >>> suggested is double-checking the alternatives with the >>> implementation to >>> >>>> >>> give more momentum to our discussion. >>> >>>> >>> >>> >>>> >>> Your new suggestion about optional extention also sounds like a >>> new >>> >>>> >>> reasonable alternative to me. >>> >>>> >>> >>> >>>> >>> We are still discussing this topic together and I hope we can >>> make a >>> >>>> >>> conclude at this time (for Apache Spark 3.2) without being >>> stucked like >>> >>>> >>> last time. >>> >>>> >>> >>> >>>> >>> I really appreciate your leadership in this dicsussion and the >>> moving >>> >>>> >>> direction of this discussion looks constructive to me. Let's >>> give some >>> >>>> >>> time >>> >>>> >>> to the alternatives. >>> >>>> >>> >>> >>>> >>> Bests, >>> >>>> >>> Dongjoon. >>> >>>> >>> >>> >>>> >>> On Wed, Feb 10, 2021 at 10:14 AM Ryan Blue < >>> >>>> >>> >>>> > blue@ >>> >>>> >>> >>>> > > wrote: >>> >>>> >>> >>> >>>> >>> I don’t think we should so quickly move past the drawbacks of >>> this >>> >>>> >>> approach. The problems are significant enough that using invoke >>> is not >>> >>>> >>> sufficient on its own. But, I think we can add it as an optional >>> >>>> >>> extension >>> >>>> >>> to shore up the weaknesses. >>> >>>> >>> >>> >>>> >>> Here’s a summary of the drawbacks: >>> >>>> >>> >>> >>>> >>> - Magic function signatures are error-prone >>> >>>> >>> - Spark would need considerable code to help users find what >>> went >>> >>>> >>> wrong >>> >>>> >>> - Spark would likely need to coerce arguments (e.g., String, >>> >>>> >>> Option[Int]) for usability >>> >>>> >>> - It is unclear how Spark will find the Java Method to call >>> >>>> >>> - Use cases that require varargs fall back to casting; users >>> will >>> >>>> >>> also get this wrong (cast to String instead of UTF8String) >>> >>>> >>> - The non-codegen path is significantly slower >>> >>>> >>> >>> >>>> >>> The benefit of invoke is to avoid moving data into a row, like >>> this: >>> >>>> >>> >>> >>>> >>> -- using invoke >>> >>>> >>> int result = udfFunction(x, y) >>> >>>> >>> >>> >>>> >>> -- using row >>> >>>> >>> udfRow.update(0, x); -- actual: values[0] = x; >>> >>>> >>> udfRow.update(1, y); >>> >>>> >>> int result = udfFunction(udfRow); >>> >>>> >>> >>> >>>> >>> And, again, that won’t actually help much in cases that require >>> varargs. >>> >>>> >>> >>> >>>> >>> I suggest we add a new marker trait for BoundMethod called >>> >>>> >>> SupportsInvoke. >>> >>>> >>> If that interface is implemented, then Spark will look for a >>> method that >>> >>>> >>> matches the expected signature based on the bound input type. >>> If it >>> >>>> >>> isn’t >>> >>>> >>> found, Spark can print a warning and fall back to the >>> InternalRow call: >>> >>>> >>> “Cannot find udfFunction(int, int)”. >>> >>>> >>> >>> >>>> >>> This approach allows the invoke optimization, but solves many >>> of the >>> >>>> >>> problems: >>> >>>> >>> >>> >>>> >>> - The method to invoke is found using the proposed load and >>> bind >>> >>>> >>> approach >>> >>>> >>> - Magic function signatures are optional and do not cause >>> runtime >>> >>>> >>> failures >>> >>>> >>> - Because this is an optional optimization, Spark can be >>> more strict >>> >>>> >>> about types >>> >>>> >>> - Varargs cases can still use rows >>> >>>> >>> - Non-codegen can use an evaluation method rather than >>> falling back >>> >>>> >>> to slow Java reflection >>> >>>> >>> >>> >>>> >>> This seems like a good extension to me; this provides a plan for >>> >>>> >>> optimizing the UDF call to avoid building a row, while the >>> existing >>> >>>> >>> proposal covers the other cases well and addresses how to >>> locate these >>> >>>> >>> function calls. >>> >>>> >>> >>> >>>> >>> This also highlights that the approach used in DSv2 and this >>> proposal is >>> >>>> >>> working: start small and use extensions to layer on more complex >>> >>>> >>> support. >>> >>>> >>> >>> >>>> >>> On Wed, Feb 10, 2021 at 9:04 AM Dongjoon Hyun < >>> >>>> >>> >>>> > dongjoon.hyun@ >>> >>>> >>> >>>> > > >>> >>>> >>> wrote: >>> >>>> >>> >>> >>>> >>> Thank you all for making a giant move forward for Apache Spark >>> 3.2.0. >>> >>>> >>> I'm really looking forward to seeing Wenchen's implementation. >>> >>>> >>> That would be greatly helpful to make a decision! >>> >>>> >>> >>> >>>> >>> > I'll implement my idea after the holiday and then we can have >>> >>>> >>> more effective discussions. We can also do benchmarks and get >>> some real >>> >>>> >>> numbers. >>> >>>> >>> > FYI: the Presto UDF API >>> >>>> >>> < >>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fprestodb.io%2Fdocs%2Fcurrent%2Fdevelop%2Ffunctions.html&data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067978066%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=iMWmHqqXPcT7EK%2Bovyzhy%2BZpU6Llih%2BwdZD53wvobmc%3D&reserved=0> >>> ; >>> >>>> >>> also >>> >>>> >>> takes individual parameters instead of the row parameter. I >>> think this >>> >>>> >>> direction at least worth a try so that we can see the >>> performance >>> >>>> >>> difference. It's also mentioned in the design doc as an >>> alternative >>> >>>> >>> (Trino). >>> >>>> >>> >>> >>>> >>> Bests, >>> >>>> >>> Dongjoon. >>> >>>> >>> >>> >>>> >>> >>> >>>> >>> On Tue, Feb 9, 2021 at 10:18 PM Wenchen Fan < >>> >>>> >>> >>>> > cloud0fan@ >>> >>>> >>> >>>> > > wrote: >>> >>>> >>> >>> >>>> >>> FYI: the Presto UDF API >>> >>>> >>> < >>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fprestodb.io%2Fdocs%2Fcurrent%2Fdevelop%2Ffunctions.html&data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067988024%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=ZSBCR7yx3PpwL4KY9V73JG42Z02ZodqkjxC0LweHt1g%3D&reserved=0> >>> ; >>> >>>> >>> also takes individual parameters instead of the row parameter. >>> I think >>> >>>> >>> this >>> >>>> >>> direction at least worth a try so that we can see the >>> performance >>> >>>> >>> difference. It's also mentioned in the design doc as an >>> alternative >>> >>>> >>> (Trino). >>> >>>> >>> >>> >>>> >>> On Wed, Feb 10, 2021 at 10:18 AM Wenchen Fan < >>> >>>> >>> >>>> > cloud0fan@ >>> >>>> >>> >>>> > > wrote: >>> >>>> >>> >>> >>>> >>> Hi Holden, >>> >>>> >>> >>> >>>> >>> As Hyukjin said, following existing designs is not the >>> principle of DS >>> >>>> >>> v2 >>> >>>> >>> API design. We should make sure the DS v2 API makes sense. >>> AFAIK we >>> >>>> >>> didn't >>> >>>> >>> fully follow the catalog API design from Hive and I believe >>> Ryan also >>> >>>> >>> agrees with it. >>> >>>> >>> >>> >>>> >>> I think the problem here is we were discussing some very >>> detailed things >>> >>>> >>> without actual code. I'll implement my idea after the holiday >>> and then >>> >>>> >>> we >>> >>>> >>> can have more effective discussions. We can also do benchmarks >>> and get >>> >>>> >>> some >>> >>>> >>> real numbers. >>> >>>> >>> >>> >>>> >>> In the meantime, we can continue to discuss other parts of this >>> >>>> >>> proposal, >>> >>>> >>> and make a prototype if possible. Spark SQL has many active >>> >>>> >>> contributors/committers and this thread doesn't get much >>> attention yet. >>> >>>> >>> >>> >>>> >>> On Wed, Feb 10, 2021 at 6:17 AM Hyukjin Kwon < >>> >>>> >>> >>>> > gurwls223@ >>> >>>> >>> >>>> > > wrote: >>> >>>> >>> >>> >>>> >>> Just dropping a few lines. I remember that one of the goals in >>> DSv2 is >>> >>>> >>> to >>> >>>> >>> correct the mistakes we made in the current Spark codes. >>> >>>> >>> It would not have much point if we will happen to just follow >>> and mimic >>> >>>> >>> what Spark currently does. It might just end up with another >>> copy of >>> >>>> >>> Spark >>> >>>> >>> APIs, e.g. Expression (internal) APIs. I sincerely would like >>> to avoid >>> >>>> >>> this >>> >>>> >>> I do believe we have been stuck mainly due to trying to come up >>> with a >>> >>>> >>> better design. We already have an ugly picture of the current >>> Spark APIs >>> >>>> >>> to >>> >>>> >>> draw a better bigger picture. >>> >>>> >>> >>> >>>> >>> >>> >>>> >>> 2021년 2월 10일 (수) 오전 3:28, Holden Karau < >>> >>>> >>> >>>> > holden@ >>> >>>> >>> >>>> > >님이 작성: >>> >>>> >>> >>> >>>> >>> I think this proposal is a good set of trade-offs and has >>> existed in the >>> >>>> >>> community for a long period of time. I especially appreciate >>> how the >>> >>>> >>> design >>> >>>> >>> is focused on a minimal useful component, with future >>> optimizations >>> >>>> >>> considered from a point of view of making sure it's flexible, >>> but actual >>> >>>> >>> concrete decisions left for the future once we see how this API >>> is used. >>> >>>> >>> I >>> >>>> >>> think if we try and optimize everything right out of the gate, >>> we'll >>> >>>> >>> quickly get stuck (again) and not make any progress. >>> >>>> >>> >>> >>>> >>> On Mon, Feb 8, 2021 at 10:46 AM Ryan Blue < >>> >>>> >>> >>>> > blue@ >>> >>>> >>> >>>> > > wrote: >>> >>>> >>> >>> >>>> >>> Hi everyone, >>> >>>> >>> >>> >>>> >>> I'd like to start a discussion for adding a FunctionCatalog >>> interface to >>> >>>> >>> catalog plugins. This will allow catalogs to expose functions >>> to Spark, >>> >>>> >>> similar to how the TableCatalog interface allows a catalog to >>> expose >>> >>>> >>> tables. The proposal doc is available here: >>> >>>> >>> >>> https://docs.google.com/document/d/1PLBieHIlxZjmoUB0ERF-VozCRJ0xw2j3qKvUNWpWA2U/edit >>> >>>> >>> < >>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.google.com%2Fdocument%2Fd%2F1PLBieHIlxZjmoUB0ERF-VozCRJ0xw2j3qKvUNWpWA2U%2Fedit&data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067988024%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=Kyth8%2FhNUZ6GXG2FsgcknZ7t7s0%2BpxnDMPyxvsxLLqE%3D&reserved=0> >>> ; >>> >>>> >>> >>> >>>> >>> Here's a high-level summary of some of the main design choices: >>> >>>> >>> * Adds the ability to list and load functions, not to create or >>> modify >>> >>>> >>> them in an external catalog >>> >>>> >>> * Supports scalar, aggregate, and partial aggregate functions >>> >>>> >>> * Uses load and bind steps for better error messages and simpler >>> >>>> >>> implementations >>> >>>> >>> * Like the DSv2 table read and write APIs, it uses InternalRow >>> to pass >>> >>>> >>> data >>> >>>> >>> * Can be extended using mix-in interfaces to add vectorization, >>> codegen, >>> >>>> >>> and other future features >>> >>>> >>> >>> >>>> >>> There is also a PR with the proposed API: >>> >>>> >>> https://github.com/apache/spark/pull/24559/files >>> >>>> >>> < >>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fapache%2Fspark%2Fpull%2F24559%2Ffiles&data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067988024%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=t3ZCqffdsrmCY3X%2FT8x1oMjMcNUiQ0wQNk%2ByAXQx1Io%3D&reserved=0> >>> ; >>> >>>> >>> >>> >>>> >>> Let's discuss the proposal here rather than on that PR, to get >>> better >>> >>>> >>> visibility. Also, please take the time to read the proposal >>> first. That >>> >>>> >>> really helps clear up misconceptions. >>> >>>> >>> >>> >>>> >>> >>> >>>> >>> >>> >>>> >>> -- >>> >>>> >>> Ryan Blue >>> >>>> >>> >>> >>>> >>> >>> >>>> >>> >>> >>>> >>> -- >>> >>>> >>> Twitter: https://twitter.com/holdenkarau >>> >>>> >>> < >>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftwitter.com%2Fholdenkarau&data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067997978%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fVfSPIyazuUYv8VLfNu%2BUIHdc3ePM1AAKKH%2BlnIicF8%3D&reserved=0> >>> ; >>> >>>> >>> Books (Learning Spark, High Performance Spark, etc.): >>> >>>> >>> https://amzn.to/2MaRAG9 >>> >>>> >>> < >>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Famzn.to%2F2MaRAG9&data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060067997978%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=NbRl9kK%2B6Wy0jWmDnztYp3JCPNLuJvmFsLHUrXzEhlk%3D&reserved=0> >>> ; >>> >>>> >>> YouTube Live Streams: https://www.youtube.com/user/holdenkarau >>> >>>> >>> < >>> https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.youtube.com%2Fuser%2Fholdenkarau&data=04%7C01%7Cekrogen%40linkedin.com%7C0ccf8c15abd74dfc974f08d8ce31ae4d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637486060068007935%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=OWXOBELzO3hBa2JI%2FOSBZ3oNyLq0yr%2FGXMkNn7bqYDM%3D&reserved=0> >>> ; >>> >>>> >>> >>> >>>> >>> -- >>> >>>> >>> Ryan Blue >>> >>>> >>> >>> >>>> >>> >>> >>>> > >>> >>>> > -- >>> >>>> > John Zhuge >>> >>>> >>> >>>> >>> >>>> >>> >>>> >>> >>>> >>> >>>> -- >>> >>>> Sent from: >>> http://apache-spark-developers-list.1001551.n3.nabble.com/ >>> >>>> >>> >>>> >>> --------------------------------------------------------------------- >>> >>>> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org >>> >>>> >>> > >>> > >>> > -- >>> > Ryan Blue >>> > Software Engineer >>> > Netflix >>> >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org >>> >>> >> >> -- >> Ryan Blue >> Software Engineer >> Netflix >> >
