Actually, it looks like the issue is right there in the error . . . the ErrorProne module is being excluded from the compile stages of the sub-projects here: https://github.com/apache/incubator-iceberg/blob/master/build.gradle#L152
However, it is still being applied to the jmh tasks. I'm not familiar with this module, but you can run the benchmarks by commenting it out here: https://github.com/apache/incubator-iceberg/blob/master/build.gradle#L167 We'll need to fix the build to disable for the jmh tasks. -Dan On Fri, Jul 26, 2019 at 3:34 PM Daniel Weeks <dwe...@netflix.com> wrote: > Gautam, you need to have the jmh-core libraries available to run. I > validated that PR, so I'm guessing I had it configured in my environment. > > I assume there's a way to make that available within gradle, so I'll take > a look. > > On Fri, Jul 26, 2019 at 2:52 PM Gautam <gautamkows...@gmail.com> wrote: > >> This fails on master too btw. Just wondering if i'm doing something wrong >> trying to run this. >> >> On Fri, Jul 26, 2019 at 2:24 PM Gautam <gautamkows...@gmail.com> wrote: >> >>> I'v been trying to run the jmh benchmarks bundled within the project. >>> I'v been running into issues with that .. have other hit this? Am I running >>> these incorrectly? >>> >>> >>> bash-3.2$ ./gradlew :iceberg-spark:jmh >>> -PjmhIncludeRegex=IcebergSourceFlatParquetDataFilterBenchmark >>> -PjmhOutputPath=benchmark/iceberg-source-flat-parquet-data-filter-benchmark-result.txt >>> .. >>> ... >>> > Task :iceberg-spark:jmhCompileGeneratedClasses FAILED >>> error: plug-in not found: ErrorProne >>> >>> FAILURE: Build failed with an exception. >>> >>> >>> >>> Is there a config/plugin I need to add to build.gradle? >>> >>> >>> >>> >>> >>> >>> >>> >>> On Wed, Jul 24, 2019 at 2:03 PM Ryan Blue <rb...@netflix.com> wrote: >>> >>>> Thanks Gautam! >>>> >>>> We'll start taking a look at your code. What do you think about >>>> creating a branch in the Iceberg repository where we can work on improving >>>> it together, before merging it into master? >>>> >>>> Also, you mentioned performance comparisons. Do you have any early >>>> results to share? >>>> >>>> rb >>>> >>>> On Tue, Jul 23, 2019 at 3:40 PM Gautam <gautamkows...@gmail.com> wrote: >>>> >>>>> Hello Folks, >>>>> >>>>> I have checked in a WIP branch [1] with a working version of >>>>> Vectorized reads for Iceberg reader. Here's the diff [2]. >>>>> >>>>> *Implementation Notes:* >>>>> - Iceberg's Reader adds a `SupportsScanColumnarBatch` mixin to >>>>> instruct the DataSourceV2ScanExec to use `planBatchPartitions()` instead >>>>> of >>>>> the usual `planInputPartitions()`. It returns instances of `ColumnarBatch` >>>>> on each iteration. >>>>> - `ArrowSchemaUtil` contains Iceberg to Arrow type conversion. This >>>>> was copied from [3] . Added by @Daniel Weeks <dwe...@netflix.com> . >>>>> Thanks for that! >>>>> - `VectorizedParquetValueReaders` contains ParquetValueReaders used >>>>> for reading/decoding the Parquet rowgroups (aka pagestores as referred to >>>>> in the code) >>>>> - `VectorizedSparkParquetReaders` contains the visitor >>>>> implementations to map Parquet types to appropriate value readers. I >>>>> implemented the struct visitor so that the root schema can be mapped >>>>> properly. This has the added benefit of vectorization support for structs, >>>>> so yay! >>>>> - For the initial version the value readers read an entire row group >>>>> into a single Arrow Field Vector. this i'd imagine will require tuning for >>>>> right batch sizing but i'v gone with one batch per rowgroup for now. >>>>> - Arrow Field Vectors are wrapped using `ArrowColumnVector` which is >>>>> Spark's ColumnVector implementation backed by Arrow. This is the first >>>>> contact point between Spark and Arrow interfaces. >>>>> - ArrowColumnVectors are stitched together into a `ColumnarBatch` by >>>>> `ColumnarBatchReader` . This is my replacement for `InternalRowReader` >>>>> which maps Structs to Columnar Batches. This allows us to have nested >>>>> structs where each level of nesting would be a nested columnar batch. >>>>> Lemme >>>>> know what you think of this approach. >>>>> - I'v added value readers for all supported primitive types listed in >>>>> `AvroDataTest`. There's a corresponding test for vectorized reader under >>>>> `TestSparkParquetVectorizedReader` >>>>> - I haven't fixed all the Checkstyle errors so you will have to turn >>>>> checkstyle off in build.gradle. Also skip tests while building.. sorry! >>>>> :-( >>>>> >>>>> *P.S*. There's some unused code under ArrowReader.java. Ignore this >>>>> as it's not used. This was from my previous impl of Vectorization. I'v >>>>> kept >>>>> it around to compare performance. >>>>> >>>>> Lemme know what folks think of the approach. I'm getting this working >>>>> for our scale test benchmark and will report back with numbers. Feel free >>>>> to run your own benchmarks and share. >>>>> >>>>> Cheers, >>>>> -Gautam. >>>>> >>>>> >>>>> >>>>> >>>>> [1] - >>>>> https://github.com/prodeezy/incubator-iceberg/tree/issue-9-support-arrow-based-reading-WIP >>>>> [2] - >>>>> https://github.com/apache/incubator-iceberg/compare/master...prodeezy:issue-9-support-arrow-based-reading-WIP >>>>> [3] - >>>>> https://github.com/apache/incubator-iceberg/blob/72e3485510e9cbec05dd30e2e7ce5d03071f400d/core/src/main/java/org/apache/iceberg/arrow/ArrowSchemaUtil.java >>>>> >>>>> >>>>> On Mon, Jul 22, 2019 at 2:33 PM Gautam <gautamkows...@gmail.com> >>>>> wrote: >>>>> >>>>>> Will do. Doing a bit of housekeeping on the code and also adding more >>>>>> primitive type support. >>>>>> >>>>>> On Mon, Jul 22, 2019 at 1:41 PM Matt Cheah <mch...@palantir.com> >>>>>> wrote: >>>>>> >>>>>>> Would it be possible to put the work in progress code in open source? >>>>>>> >>>>>>> >>>>>>> >>>>>>> *From: *Gautam <gautamkows...@gmail.com> >>>>>>> *Reply-To: *"dev@iceberg.apache.org" <dev@iceberg.apache.org> >>>>>>> *Date: *Monday, July 22, 2019 at 9:46 AM >>>>>>> *To: *Daniel Weeks <dwe...@netflix.com> >>>>>>> *Cc: *Ryan Blue <rb...@netflix.com>, Iceberg Dev List < >>>>>>> dev@iceberg.apache.org> >>>>>>> *Subject: *Re: Approaching Vectorized Reading in Iceberg .. >>>>>>> >>>>>>> >>>>>>> >>>>>>> That would be great! >>>>>>> >>>>>>> >>>>>>> >>>>>>> On Mon, Jul 22, 2019 at 9:12 AM Daniel Weeks <dwe...@netflix.com> >>>>>>> wrote: >>>>>>> >>>>>>> Hey Gautam, >>>>>>> >>>>>>> >>>>>>> >>>>>>> We also have a couple people looking into vectorized reading (into >>>>>>> Arrow memory). I think it would be good for us to get together and see >>>>>>> if >>>>>>> we can collaborate on a common approach for this. >>>>>>> >>>>>>> >>>>>>> >>>>>>> I'll reach out directly and see if we can get together. >>>>>>> >>>>>>> >>>>>>> >>>>>>> -Dan >>>>>>> >>>>>>> >>>>>>> >>>>>>> On Sun, Jul 21, 2019 at 10:35 PM Gautam <gautamkows...@gmail.com> >>>>>>> wrote: >>>>>>> >>>>>>> Figured this out. I'm returning ColumnarBatch iterator directly >>>>>>> without projection with schema set appropriately in `readSchema() `.. >>>>>>> the >>>>>>> empty result was due to valuesRead not being set correctly on >>>>>>> FileIterator. >>>>>>> Did that and things are working. Will circle back with numbers soon. >>>>>>> >>>>>>> >>>>>>> >>>>>>> On Fri, Jul 19, 2019 at 5:22 PM Gautam <gautamkows...@gmail.com> >>>>>>> wrote: >>>>>>> >>>>>>> Hey Guys, >>>>>>> >>>>>>> Sorry bout the delay on this. Just got back on getting a >>>>>>> basic working implementation in Iceberg for Vectorization on primitive >>>>>>> types. >>>>>>> >>>>>>> >>>>>>> >>>>>>> *Here's what I have so far : * >>>>>>> >>>>>>> >>>>>>> >>>>>>> I have added `ParquetValueReader` implementations for some basic >>>>>>> primitive types that build the respective Arrow Vector (`ValueVector`) >>>>>>> viz. >>>>>>> `IntVector` for int, `VarCharVector` for strings and so on. Underneath >>>>>>> each >>>>>>> value vector reader there are column iterators that read from the >>>>>>> parquet >>>>>>> pagestores (rowgroups) in chunks. These `ValueVector-s` are lined up as >>>>>>> `ArrowColumnVector`-s (which is ColumnVector wrapper backed by Arrow) >>>>>>> and >>>>>>> stitched together using a `ColumnarBatchReader` (which as the name >>>>>>> suggests >>>>>>> wraps ColumnarBatches in the iterator) I'v verified that these pieces >>>>>>> work properly with the underlying interfaces. I'v also made changes to >>>>>>> Iceberg's `Reader` to implement `planBatchPartitions()` (to add the >>>>>>> `SupportsScanColumnarBatch` mixin to the reader). So the reader now >>>>>>> expects ColumnarBatch instances (instead of InternalRow). The query >>>>>>> planning runtime works fine with these changes. >>>>>>> >>>>>>> >>>>>>> >>>>>>> Although it fails during query execution, the bit it's currently >>>>>>> failing at is this line of code : >>>>>>> https://github.com/apache/incubator-iceberg/blob/master/spark/src/main/java/org/apache/iceberg/spark/source/Reader.java#L414 >>>>>>> [github.com] >>>>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_incubator-2Diceberg_blob_master_spark_src_main_java_org_apache_iceberg_spark_source_Reader.java-23L414&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=hzwIMNQ9E99EMYGuqHI0kXhVbvX3nU3OSDadUnJxjAs&m=UW1Nb5KZOPeIqsjzFnKhGQaxYHT_wAI_2PvgFUlfAoY&s=7wzoBoRwCjQjgamnHukQSe0wiATMnGbYhfJQpXfSMks&e=> >>>>>>> >>>>>>> >>>>>>> >>>>>>> This code, I think, tries to apply the iterator's schema projection >>>>>>> on the InternalRow instances. This seems to be tightly coupled to >>>>>>> InternalRow as Spark's catalyst expressions have implemented the >>>>>>> UnsafeProjection for InternalRow only. If I take this out and just >>>>>>> return >>>>>>> the `Iterator<ColumnarBatch>` iterator I built it returns empty result >>>>>>> on >>>>>>> the client. I'm guessing this is coz Spark is unaware of the iterator's >>>>>>> schema? There's a Todo in the code that says "*remove the >>>>>>> projection by reporting the iterator's schema back to Spark*". Is >>>>>>> there a simple way to communicate that to Spark for my new iterator? Any >>>>>>> pointers on how to get around this? >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> Thanks and Regards, >>>>>>> >>>>>>> -Gautam. >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> On Fri, Jun 14, 2019 at 4:22 PM Ryan Blue <rb...@netflix.com> wrote: >>>>>>> >>>>>>> Replies inline. >>>>>>> >>>>>>> >>>>>>> >>>>>>> On Fri, Jun 14, 2019 at 1:11 AM Gautam <gautamkows...@gmail.com> >>>>>>> wrote: >>>>>>> >>>>>>> Thanks for responding Ryan, >>>>>>> >>>>>>> >>>>>>> >>>>>>> Couple of follow up questions on ParquetValueReader for Arrow.. >>>>>>> >>>>>>> >>>>>>> >>>>>>> I'd like to start with testing Arrow out with readers for primitive >>>>>>> type and incrementally add in Struct/Array support, also ArrowWriter [1] >>>>>>> currently doesn't have converters for map type. How can I default these >>>>>>> types to regular materialization whilst supporting Arrow based support >>>>>>> for >>>>>>> primitives? >>>>>>> >>>>>>> >>>>>>> >>>>>>> We should look at what Spark does to handle maps. >>>>>>> >>>>>>> >>>>>>> >>>>>>> I think we should get the prototype working with test cases that >>>>>>> don't have maps, structs, or lists. Just getting primitives working is a >>>>>>> good start and just won't hit these problems. >>>>>>> >>>>>>> >>>>>>> >>>>>>> Lemme know if this makes sense... >>>>>>> >>>>>>> >>>>>>> >>>>>>> - I extend PrimitiveReader (for Arrow) that loads primitive types >>>>>>> into ArrowColumnVectors of corresponding column types by iterating over >>>>>>> underlying ColumnIterator *n times*, where n is size of batch. >>>>>>> >>>>>>> >>>>>>> >>>>>>> Sounds good to me. I'm not sure about extending vs wrapping because >>>>>>> I'm not too familiar with the Arrow APIs. >>>>>>> >>>>>>> >>>>>>> >>>>>>> - Reader.newParquetIterable() maps primitive column types to the >>>>>>> newly added ArrowParquetValueReader but for other types (nested types, >>>>>>> etc.) uses current *InternalRow* based ValueReaders >>>>>>> >>>>>>> >>>>>>> >>>>>>> Sounds good for primitives, but I would just leave the nested types >>>>>>> un-implemented for now. >>>>>>> >>>>>>> >>>>>>> >>>>>>> - Stitch the columns vectors together to create ColumnarBatch, >>>>>>> (Since *SupportsScanColumnarBatch* mixin currently expects this ) >>>>>>> .. *although* *I'm a bit lost on how the stitching of columns >>>>>>> happens currently*? .. and how the ArrowColumnVectors could be >>>>>>> stitched alongside regular columns that don't have arrow based support ? >>>>>>> >>>>>>> >>>>>>> >>>>>>> I don't think that you can mix regular columns and Arrow columns. It >>>>>>> has to be all one or the other. That's why it's easier to start with >>>>>>> primitives, then add structs, then lists, and finally maps. >>>>>>> >>>>>>> >>>>>>> >>>>>>> - Reader returns readTasks as *InputPartition<*ColumnarBatch*> *so >>>>>>> that DataSourceV2ScanExec starts using ColumnarBatch scans >>>>>>> >>>>>>> >>>>>>> >>>>>>> We will probably need two paths. One for columnar batches and one >>>>>>> for row-based reads. That doesn't need to be done right away and what >>>>>>> you >>>>>>> already have in your working copy makes sense as a start. >>>>>>> >>>>>>> >>>>>>> >>>>>>> That's a lot of questions! :-) but hope i'm making sense. >>>>>>> >>>>>>> >>>>>>> >>>>>>> -Gautam. >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> [1] - >>>>>>> https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowWriter.scala >>>>>>> [github.com] >>>>>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_spark_blob_master_sql_core_src_main_scala_org_apache_spark_sql_execution_arrow_ArrowWriter.scala&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=hzwIMNQ9E99EMYGuqHI0kXhVbvX3nU3OSDadUnJxjAs&m=UW1Nb5KZOPeIqsjzFnKhGQaxYHT_wAI_2PvgFUlfAoY&s=8yzJh2S49rbuM06dC5Sy-yMECClqEeLS7tpg45BmDN4&e=> >>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> >>>>>>> Ryan Blue >>>>>>> >>>>>>> Software Engineer >>>>>>> >>>>>>> Netflix >>>>>>> >>>>>>> >>>> >>>> -- >>>> Ryan Blue >>>> Software Engineer >>>> Netflix >>>> >>>
