Ah yes, I didn't send over the filter benchmarks ..
Num files : 500
Num rows per file: 10,000
*Benchmark
Mode Cnt Score Error Units*
IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterFileSourceNonVectorized
ss 5 3.837 ± 0.424 s/op
IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterFileSourceVectorized
ss 5 3.964 ± 1.891 s/op
IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterIceberg
ss 5 0.272 ± 0.039 s/op
IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterIcebergVect100k
ss 5 0.274 ± 0.013 s/op
IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterIcebergVect10k
ss 5 0.275 ± 0.040 s/op
IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterIcebergVect5k
ss 5 0.273 ± 0.031 s/op
On Wed, Jul 31, 2019 at 2:35 PM Anjali Norwood <[email protected]>
wrote:
> Hi Gautam,
>
> You wrote: ' - The filters are not being applied in columnar fashion they
> are being applied row by row as in Iceberg each filter visitor is stateless
> and applied separately on each row's column. ' .. this should not be a
> problem for this particular benchmark as IcebergSourceFlatParquetDataRe
> adBenchmark does not apply filters.
>
> -Anjali.
>
> On Wed, Jul 31, 2019 at 1:44 PM Gautam <[email protected]> wrote:
>
>> Hey Samarth,
>> Sorry bout the delay. I ran into some bottlenecks for which
>> I had to add more code to be able to run benchmarks. I'v checked in my
>> latest changes to my fork's *vectorized-read* branch [0].
>>
>> Here's the early numbers on the initial implementation...
>>
>> *Benchmark Data:*
>> - 10 files
>> - 9MB each
>> - 1Millon rows (1 RowGroup)
>>
>> Ran benchmark using the jmh benchmark tool within
>> incubator-iceberg/spark/src/jmh
>> using batch different sizes and compared it to spark's vectorization and
>> non-vectorized reader.
>>
>> *Command: *
>> ./gradlew clean :iceberg-spark:jmh
>> -PjmhIncludeRegex=IcebergSourceFlatParquetDataReadBenchmark
>> -PjmhOutputPath=benchmark/iceberg-source-flat-parquet-data-read-benchmark-result.txt
>>
>>
>>
>> *Benchmark
>> Mode Cnt Score Error Units*
>> IcebergSourceFlatParquetDataReadBenchmark.readFileSourceNonVectorized
>> ss 5 16.172 ± 0.750 s/op
>> IcebergSourceFlatParquetDataReadBenchmark.readFileSourceVectorized
>> ss 5 6.430 ± 0.136 s/op
>> IcebergSourceFlatParquetDataReadBenchmark.readIceberg
>> ss 5 15.287 ± 0.212 s/op
>>
>>
>>
>> *IcebergSourceFlatParquetDataReadBenchmark.readIcebergVectorized100k
>> ss 5 18.310 ± 0.498
>> s/opIcebergSourceFlatParquetDataReadBenchmark.readIcebergVectorized10k
>> ss 5 18.020 ± 0.378
>> s/opIcebergSourceFlatParquetDataReadBenchmark.readIcebergVectorized5k
>> ss 5 17.769 ± 0.412
>> s/op*IcebergSourceFlatParquetDataReadBenchmark.readWithProjectionFileSourceNonVectorized
>> ss 5 2.794 ± 0.141 s/op
>> IcebergSourceFlatParquetDataReadBenchmark.readWithProjectionFileSourceVectorized
>> ss 5 1.063 ± 0.140 s/op
>> IcebergSourceFlatParquetDataReadBenchmark.readWithProjectionIceberg
>> ss 5 2.966 ± 0.133 s/op
>>
>>
>> *IcebergSourceFlatParquetDataReadBenchmark.readWithProjectionIcebergVectorized100k
>> ss 5 2.015 ± 0.261
>> s/opIcebergSourceFlatParquetDataReadBenchmark.readWithProjectionIcebergVectorized10k
>> ss 5 1.972 ± 0.105
>> s/opIcebergSourceFlatParquetDataReadBenchmark.readWithProjectionIcebergVectorized5k
>> ss 5 2.065 ± 0.079 s/op*
>>
>>
>>
>> So seems like there's no improvement that vectorization is adding over
>> the non-vectorized reading. I'm currently trying to profile where the time
>> is being spent.
>>
>> *Here is my initial speculation of why this is slow:*
>> - There's too much overhead that seems to be from creating the batches.
>> i'm creating new instance of ColumnarBatch on each read [1] . This should
>> prolly be re-used.
>> - Although I am reusing the *FieldVector* across batched reads [2] I
>> wrap them in new *ArrowColumnVector*s [3] on each read call. I didn't
>> think this would be a big deal but maybe it is.
>> - The filters are not being applied in columnar fashion they are being
>> applied row by row as in Iceberg each filter visitor is stateless and
>> applied separately on each row's column.
>> - I'm trying to re-use the BufferAllocator that Arrow provides [4] ..
>> Dunno if there are other strategies to using this. Will look more into this.
>> - I'm batching until the rowgroup ends and restricting the last batch to
>> the Rowgroup boundary. I should prolly spill over to the next rowgroup to
>> fill that batch. Dunno if this would help as from what i can tell I don't
>> think *VectorizedParquetRecordReader *does this.
>>
>> I'l try and provide more insights once i improve my code. But if there's
>> other insights folks have on where we can improve on things, i'd gladly try
>> them.
>>
>> Cheers,
>> - Gautam.
>>
>> [0] - https://github.com/prodeezy/incubator-iceberg/tree/vectorized-read
>> [1] -
>> https://github.com/prodeezy/incubator-iceberg/blob/vectorized-read/parquet/src/main/java/org/apache/iceberg/parquet/ParquetValueReaders.java#L655
>> [2] -
>> https://github.com/prodeezy/incubator-iceberg/blob/vectorized-read/spark/src/main/java/org/apache/iceberg/spark/data/vector/VectorizedParquetValueReaders.java#L108
>> [3] -
>> https://github.com/prodeezy/incubator-iceberg/blob/vectorized-read/parquet/src/main/java/org/apache/iceberg/parquet/ParquetValueReaders.java#L651
>> [4] -
>> https://github.com/prodeezy/incubator-iceberg/blob/vectorized-read/spark/src/main/java/org/apache/iceberg/spark/data/vector/VectorizedSparkParquetReaders.java#L92
>>
>>
>> On Tue, Jul 30, 2019 at 5:13 PM Samarth Jain <[email protected]>
>> wrote:
>>
>>> Hey Gautam,
>>>
>>> Wanted to check back with you and see if you had any success running the
>>> benchmark and if you have any numbers to share.
>>>
>>>
>>>
>>> On Fri, Jul 26, 2019 at 4:34 PM Gautam <[email protected]> wrote:
>>>
>>>> Got it. Commented out that module and it works. Was just curious why it
>>>> doesn't work on master branch either.
>>>>
>>>> On Fri, Jul 26, 2019 at 3:49 PM Daniel Weeks <[email protected]>
>>>> wrote:
>>>>
>>>>> 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 <[email protected]>
>>>>> 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 <[email protected]>
>>>>>> 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 <[email protected]>
>>>>>>> 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 <[email protected]>
>>>>>>>> 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 <[email protected]>
>>>>>>>>> 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
>>>>>>>>>> <[email protected]> . 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 <[email protected]>
>>>>>>>>>> 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 <[email protected]>
>>>>>>>>>>> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Would it be possible to put the work in progress code in open
>>>>>>>>>>>> source?
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> *From: *Gautam <[email protected]>
>>>>>>>>>>>> *Reply-To: *"[email protected]" <[email protected]>
>>>>>>>>>>>> *Date: *Monday, July 22, 2019 at 9:46 AM
>>>>>>>>>>>> *To: *Daniel Weeks <[email protected]>
>>>>>>>>>>>> *Cc: *Ryan Blue <[email protected]>, Iceberg Dev List <
>>>>>>>>>>>> [email protected]>
>>>>>>>>>>>> *Subject: *Re: Approaching Vectorized Reading in Iceberg ..
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> That would be great!
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, Jul 22, 2019 at 9:12 AM Daniel Weeks <
>>>>>>>>>>>> [email protected]> 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 <
>>>>>>>>>>>> [email protected]> 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 <[email protected]>
>>>>>>>>>>>> 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 <[email protected]>
>>>>>>>>>>>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> Replies inline.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Fri, Jun 14, 2019 at 1:11 AM Gautam <[email protected]>
>>>>>>>>>>>> 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
>>>>>>>>>
>>>>>>>>
