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 >> >
