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From Tom Graves <tgraves...@yahoo.com.INVALID>
Subject Re: [VOTE][SPARK-27396] SPIP: Public APIs for extended Columnar Processing Support
Date Sun, 26 May 2019 15:22:59 GMT
More feedback would be great, this has been open a long time though, let's extend til Wednesday
the 29th and see where we are at.
Tom



Sent from Yahoo Mail on Android 
 
  On Sat, May 25, 2019 at 6:28 PM, Holden Karau<holden@pigscanfly.ca> wrote:   Same
I meant to catch up after kubecon but had some unexpected travels.
On Sat, May 25, 2019 at 10:56 PM Reynold Xin <rxin@databricks.com> wrote:

Can we push this to June 1st? I have been meaning to read it but unfortunately keeps traveling...
On Sat, May 25, 2019 at 8:31 PM Dongjoon Hyun <dongjoon.hyun@gmail.com> wrote:

+1
Thanks,Dongjoon.
On Fri, May 24, 2019 at 17:03 DB Tsai <dbtsai@dbtsai.com.invalid> wrote:

+1 on exposing the APIs for columnar processing support.

I understand that the scope of this SPIP doesn't cover AI / ML
use-cases. But I saw a good performance gain when I converted data
from rows to columns to leverage on SIMD architectures in a POC ML
application.

With the exposed columnar processing support, I can imagine that the
heavy lifting parts of ML applications (such as computing the
objective functions) can be written as columnar expressions that
leverage on SIMD architectures to get a good speedup.

Sincerely,

DB Tsai
----------------------------------------------------------
Web: https://www.dbtsai.com
PGP Key ID: 42E5B25A8F7A82C1

On Wed, May 15, 2019 at 2:59 PM Bobby Evans <revans2@gmail.com> wrote:
>
> It would allow for the columnar processing to be extended through the shuffle.  So if
I were doing say an FPGA accelerated extension it could replace the ShuffleExechangeExec with
one that can take a ColumnarBatch as input instead of a Row. The extended version of the ShuffleExchangeExec
could then do the partitioning on the incoming batch and instead of producing a ShuffleRowRDD
for the exchange they could produce something like a ShuffleBatchRDD that would let the serializing
and deserializing happen in a column based format for a faster exchange, assuming that columnar
processing is also happening after the exchange. This is just like providing a columnar version
of any other catalyst operator, except in this case it is a bit more complex of an operator.
>
> On Wed, May 15, 2019 at 12:15 PM Imran Rashid <irashid@cloudera.com.invalid> wrote:
>>
>> sorry I am late to the discussion here -- the jira mentions using this extensions
for dealing with shuffles, can you explain that part?  I don't see how you would use this
to change shuffle behavior at all.
>>
>> On Tue, May 14, 2019 at 10:59 AM Thomas graves <tgraves@apache.org> wrote:
>>>
>>> Thanks for replying, I'll extend the vote til May 26th to allow your
>>> and other people feedback who haven't had time to look at it.
>>>
>>> Tom
>>>
>>> On Mon, May 13, 2019 at 4:43 PM Holden Karau <holden@pigscanfly.ca> wrote:
>>> >
>>> > I’d like to ask this vote period to be extended, I’m interested but
I don’t have the cycles to review it in detail and make an informed vote until the 25th.
>>> >
>>> > On Tue, May 14, 2019 at 1:49 AM Xiangrui Meng <meng@databricks.com>
wrote:
>>> >>
>>> >> My vote is 0. Since the updated SPIP focuses on ETL use cases, I don't
feel strongly about it. I would still suggest doing the following:
>>> >>
>>> >> 1. Link the POC mentioned in Q4. So people can verify the POC result.
>>> >> 2. List public APIs we plan to expose in Appendix A. I did a quick check.
Beside ColumnarBatch and ColumnarVector, we also need to make the following public. People
who are familiar with SQL internals should help assess the risk.
>>> >> * ColumnarArray
>>> >> * ColumnarMap
>>> >> * unsafe.types.CaledarInterval
>>> >> * ColumnarRow
>>> >> * UTF8String
>>> >> * ArrayData
>>> >> * ...
>>> >> 3. I still feel using Pandas UDF as the mid-term success doesn't match
the purpose of this SPIP. It does make some code cleaner. But I guess for ETL use cases, it
won't bring much value.
>>> >>
>>> > --
>>> > Twitter: https://twitter.com/holdenkarau
>>> > Books (Learning Spark, High Performance Spark, etc.): https://amzn.to/2MaRAG9
>>> > YouTube Live Streams: https://www.youtube.com/user/holdenkarau
>>>
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