I'll link the two. On Thu, Apr 11, 2019 at 12:34 PM Reynold Xin <r...@databricks.com> wrote:
> I just realized we had an earlier SPIP on a similar topic: > https://issues.apache.org/jira/browse/SPARK-24579 > > Perhaps we should tie the two together. IIUC, you'd want to expose the > existing ColumnBatch API, but also provide utilities to directly convert > from/to Arrow. > > > On Thu, Apr 11, 2019 at 7:13 AM, Bobby Evans <bo...@apache.org> wrote: > >> The SPIP has been up for almost 6 days now with really no discussion on >> it. I am hopeful that means it's okay and we are good to call a vote on >> it, but I want to give everyone one last chance to take a look and >> comment. If there are no comments by tomorrow I this we will start a vote >> for this. >> >> Thanks, >> >> Bobby >> >> On Fri, Apr 5, 2019 at 2:24 PM Bobby Evans <bo...@apache.org> wrote: >> >> I just filed SPARK-27396 as the SPIP for this proposal. Please use that >> JIRA for further discussions. >> >> Thanks for all of the feedback, >> >> Bobby >> >> On Wed, Apr 3, 2019 at 7:15 PM Bobby Evans <bo...@apache.org> wrote: >> >> I am still working on the SPIP and should get it up in the next few >> days. I have the basic text more or less ready, but I want to get a >> high-level API concept ready too just to have something more concrete. I >> have not really done much with contributing new features to spark so I am >> not sure where a design document really fits in here because from >> http://spark.apache.org/improvement-proposals.html and >> http://spark.apache.org/contributing.html it does not mention a design >> anywhere. I am happy to put one up, but I was hoping the API concept would >> cover most of that. >> >> Thanks, >> >> Bobby >> >> On Tue, Apr 2, 2019 at 9:16 PM Renjie Liu <liurenjie2...@gmail.com> >> wrote: >> >> Hi, Bobby: >> Do you have design doc? I'm also interested in this topic and want to >> help contribute. >> >> On Tue, Apr 2, 2019 at 10:00 PM Bobby Evans <bo...@apache.org> wrote: >> >> Thanks to everyone for the feedback. >> >> Overall the feedback has been really positive for exposing columnar as a >> processing option to users. I'll write up a SPIP on the proposed changes >> to support columnar processing (not necessarily implement it) and then ping >> the list again for more feedback and discussion. >> >> Thanks again, >> >> Bobby >> >> On Mon, Apr 1, 2019 at 5:09 PM Reynold Xin <r...@databricks.com> wrote: >> >> I just realized I didn't make it very clear my stance here ... here's >> another try: >> >> I think it's a no brainer to have a good columnar UDF interface. This >> would facilitate a lot of high performance applications, e.g. GPU-based >> accelerations for machine learning algorithms. >> >> On rewriting the entire internals of Spark SQL to leverage columnar >> processing, I don't see enough evidence to suggest that's a good idea yet. >> >> >> >> >> On Wed, Mar 27, 2019 at 8:10 AM, Bobby Evans <bo...@apache.org> wrote: >> >> Kazuaki Ishizaki, >> >> Yes, ColumnarBatchScan does provide a framework for doing code generation >> for the processing of columnar data. I have to admit that I don't have a >> deep understanding of the code generation piece, so if I get something >> wrong please correct me. From what I had seen only input formats currently >> inherent from ColumnarBatchScan, and from comments in the trait >> >> /** >> * Generate [[ColumnVector]] expressions for our parent to consume as >> rows. >> * This is called once per [[ColumnarBatch]]. >> */ >> >> https://github.com/apache/spark/blob/956b52b1670985a67e49b938ac1499ae65c79f6e/sql/core/src/main/scala/org/apache/spark/sql/execution/ColumnarBatchScan.scala#L42-L43 >> >> It appears that ColumnarBatchScan is really only intended to pull out the >> data from the batch, and not to process that data in a columnar fashion. >> The Loading stage that you mentioned. >> >> > The SIMDzation or GPUization capability depends on a compiler that >> translates native code from the code generated by the whole-stage codegen. >> To be able to support vectorized processing Hive stayed with pure java >> and let the JVM detect and do the SIMDzation of the code. To make that >> happen they created loops to go through each element in a column and remove >> all conditionals from the body of the loops. To the best of my knowledge >> that would still require a separate code path like I am proposing to make >> the different processing phases generate code that the JVM can compile down >> to SIMD instructions. The generated code is full of null checks for each >> element which would prevent the operations we want. Also, the intermediate >> results are often stored in UnsafeRow instances. This is really fast for >> row-based processing, but the complexity of how they work I believe would >> prevent the JVM from being able to vectorize the processing. If you have a >> better way to take java code and vectorize it we should put it into OpenJDK >> instead of spark so everyone can benefit from it. >> >> Trying to compile directly from generated java code to something a GPU >> can process is something we are tackling but we decided to go a different >> route from what you proposed. From talking with several compiler experts >> here at NVIDIA my understanding is that IBM in partnership with NVIDIA >> attempted in the past to extend the JVM to run at least partially on GPUs, >> but it was really difficult to get right, especially with how java does >> memory management and memory layout. >> >> To avoid that complexity we decided to split the JITing up into two >> separate pieces. I didn't mention any of this before because this >> discussion was intended to just be around the memory layout support, and >> not GPU processing. The first part would be to take the Catalyst AST and >> produce CUDA code directly from it. If properly done we should be able to >> do the selection and projection phases within a single kernel. The biggest >> issue comes with UDFs as they cannot easily be vectorized for the CPU or >> GPU. So to deal with that we have a prototype written by the compiler team >> that is trying to tackle SPARK-14083 which can translate basic UDFs into >> catalyst expressions. If the UDF is too complicated or covers operations >> not yet supported it will fall back to the original UDF processing. I >> don't know how close the team is to submit a SPIP or a patch for it, but I >> do know that they have some very basic operations working. The big issue >> is that it requires java 11+ so it can use standard APIs to get the byte >> code of scala UDFs. >> >> We split it this way because we thought it would be simplest to >> implement, and because it would provide a benefit to more than just GPU >> accelerated queries. >> >> Thanks, >> >> Bobby >> >> On Tue, Mar 26, 2019 at 11:59 PM Kazuaki Ishizaki <ishiz...@jp.ibm.com> >> wrote: >> >> Looks interesting discussion. >> Let me describe the current structure and remaining issues. This is >> orthogonal to cost-benefit trade-off discussion. >> >> The code generation basically consists of three parts. >> 1. Loading >> 2. Selection (map, filter, ...) >> 3. Projection >> >> 1. Columnar storage (e.g. Parquet, Orc, Arrow , and table cache) is well >> abstracted by using ColumnVector ( >> https://github.com/apache/spark/blob/master/sql/core/src/main/java/org/apache/spark/sql/vectorized/ColumnVector.java) >> class. By combining with ColumnarBatchScan, the whole-stage code generation >> generate code to directly get valus from the columnar storage if there is >> no row-based operation. >> Note: The current master does not support Arrow as a data source. >> However, I think it is not technically hard to support Arrow. >> >> 2. The current whole-stage codegen generates code for element-wise >> selection (excluding sort and join). The SIMDzation or GPUization >> capability depends on a compiler that translates native code from the code >> generated by the whole-stage codegen. >> >> 3. The current Projection assume to store row-oriented data, I think that >> is a part that Wenchen pointed out >> >> My slides >> https://www.slideshare.net/ishizaki/making-hardware-accelerator-easier-to-use/41 >> <https://www.slideshare.net/ishizaki/making-hardware-accelerator-easier-to-use>may >> simplify the above issue and possible implementation. >> >> >> >> FYI. NVIDIA will present an approach to exploit GPU with Arrow thru >> Python at SAIS 2019 >> https://databricks.com/sparkaisummit/north-america/sessions-single-2019?id=110. >> I think that it uses Python UDF support with Arrow in Spark. >> >> P.S. I will give a presentation about in-memory data storages for SPark >> at SAIS 2019 >> https://databricks.com/sparkaisummit/north-america/sessions-single-2019?id=40 >> :) >> >> Kazuaki Ishizaki >> >> >> >> From: Wenchen Fan <cloud0...@gmail.com> >> To: Bobby Evans <bo...@apache.org> >> Cc: Spark dev list <dev@spark.apache.org> >> Date: 2019/03/26 13:53 >> Subject: Re: [DISCUSS] Spark Columnar Processing >> ------------------------------ >> >> >> >> Do you have some initial perf numbers? It seems fine to me to remain >> row-based inside Spark with whole-stage-codegen, and convert rows to >> columnar batches when communicating with external systems. >> >> On Mon, Mar 25, 2019 at 1:05 PM Bobby Evans <*bo...@apache.org* >> <bo...@apache.org>> wrote: >> This thread is to discuss adding in support for data frame processing >> using an in-memory columnar format compatible with Apache Arrow. My main >> goal in this is to lay the groundwork so we can add in support for GPU >> accelerated processing of data frames, but this feature has a number of >> other benefits. Spark currently supports Apache Arrow formatted data as an >> option to exchange data with python for pandas UDF processing. There has >> also been discussion around extending this to allow for exchanging data >> with other tools like pytorch, tensorflow, xgboost,... If Spark supports >> processing on Arrow compatible data it could eliminate the >> serialization/deserialization overhead when going between these systems. >> It also would allow for doing optimizations on a CPU with SIMD instructions >> similar to what Hive currently supports. Accelerated processing using a GPU >> is something that we will start a separate discussion thread on, but I >> wanted to set the context a bit. >> Jason Lowe, Tom Graves, and I created a prototype over the past few >> months to try and understand how to make this work. What we are proposing >> is based off of lessons learned when building this prototype, but we really >> wanted to get feedback early on from the community. We will file a SPIP >> once we can get agreement that this is a good direction to go in. >> >> The current support for columnar processing lets a Parquet or Orc file >> format return a ColumnarBatch inside an RDD[InternalRow] using Scala’s type >> erasure. The code generation is aware that the RDD actually holds >> ColumnarBatchs and generates code to loop through the data in each batch as >> InternalRows. >> >> >> Instead, we propose a new set of APIs to work on an >> RDD[InternalColumnarBatch] instead of abusing type erasure. With this we >> propose adding in a Rule similar to how WholeStageCodeGen currently works. >> Each part of the physical SparkPlan would expose columnar support through a >> combination of traits and method calls. The rule would then decide when >> columnar processing would start and when it would end. Switching between >> columnar and row based processing is not free, so the rule would make a >> decision based off of an estimate of the cost to do the transformation and >> the estimated speedup in processing time. >> >> >> This should allow us to disable columnar support by simply disabling the >> rule that modifies the physical SparkPlan. It should be minimal risk to >> the existing row-based code path, as that code should not be touched, and >> in many cases could be reused to implement the columnar version. This also >> allows for small easily manageable patches. No huge patches that no one >> wants to review. >> >> >> As far as the memory layout is concerned OnHeapColumnVector and >> OffHeapColumnVector are already really close to being Apache Arrow >> compatible so shifting them over would be a relatively simple change. >> Alternatively we could add in a new implementation that is Arrow compatible >> if there are reasons to keep the old ones. >> >> >> Again this is just to get the discussion started, any feedback is >> welcome, and we will file a SPIP on it once we feel like the major changes >> we are proposing are acceptable. >> >> Thanks, >> >> Bobby Evans >> >> >> >> >> -- >> Renjie Liu >> Software Engineer, MVAD >> >> >
