Robert Joseph Evans created SPARK-27396:
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Summary: SPIP: Public APIs for extended Columnar Processing Support
Key: SPARK-27396
URL: https://issues.apache.org/jira/browse/SPARK-27396
Project: Spark
Issue Type: Improvement
Components: SQL
Affects Versions: 3.0.0
Reporter: Robert Joseph Evans
*Q1.* What are you trying to do? Articulate your objectives using absolutely no
jargon.
The Dataset/DataFrame API in Spark currently only exposes to users one row at a
time when processing data. The goals of this are to
# Expose to end users a new option of processing the data in a columnar
format, multiple rows at a time, with the data organized into contiguous arrays
in memory.
# Make any transitions between the columnar memory layout and a row based
layout transparent to the end user.
# Allow for simple data exchange with other systems, DL/ML libraries, pandas,
etc. by having clean APIs to transform the columnar data into an Apache Arrow
compatible layout.
# Provide a plugin mechanism for columnar processing support so an advanced
user could avoid data transition between columnar and row based processing even
through shuffles. This means we should at least support pluggable APIs so an
advanced end user can implement the columnar partitioning themselves, and
provide the glue necessary to shuffle the data still in a columnar format.
# Expose new APIs that allow advanced users or frameworks to implement
columnar processing either as UDFs, or by adjusting the physical plan to do
columnar processing. If the latter is too controversial we can move it to
another SPIP, but we plan to implement some accelerated computing in parallel
with this feature to be sure the APIs work, and without this feature it makes
that impossible.
Not Requirements, but things that would be nice to have.
# Provide default implementations for partitioning columnar data, so users
don’t have to.
# Transition the existing in memory columnar layouts to be compatible with
Apache Arrow. This would make the transformations to Apache Arrow format a
no-op. The existing formats are already very close to those layouts in many
cases. This would not be using the Apache Arrow java library, but instead
being compatible with the memory
[layout|https://arrow.apache.org/docs/format/Layout.html] and possibly only a
subset of that layout.
# Provide a clean transition from the existing code to the new one. The
existing APIs which are public but evolving are not that far off from what is
being proposed. We should be able to create a new parallel API that can wrap
the existing one. This means any file format that is trying to support columnar
can still do so until we make a conscious decision to deprecate and then turn
off the old APIs.
*Q2.* What problem is this proposal NOT designed to solve?
This is not trying to implement any of the processing itself in a columnar way,
with the exception of examples for documentation, and possibly default
implementations for partitioning of columnar shuffle.
*Q3.* How is it done today, and what are the limits of current practice?
The current columnar support is limited to 3 areas.
# Input formats, optionally can return a ColumnarBatch instead of rows. The
code generation phase knows how to take that columnar data and iterate through
it as rows for stages that wants rows, which currently is almost everything.
The limitations here are mostly implementation specific. The current standard
is to abuse Scala’s type erasure to return ColumnarBatches as the elements of
an RDD[InternalRow]. The code generation can handle this because it is
generating java code, so it bypasses scala’s type checking and just casts the
InternalRow to the desired ColumnarBatch. This makes it difficult for others
to implement the same functionality for different processing because they can
only do it through code generation. There really is no clean separate path in
the code generation for columnar vs row based. Additionally because it is only
supported through code generation if for any reason code generation would fail
there is no backup. This is typically fine for input formats but can be
problematic when we get into more extensive processing.
# When caching data it can optionally be cached in a columnar format if the
input is also columnar. This is similar to the first area and has the same
limitations because the cache acts as an input, but it is the only piece of
code that also consumes columnar data as an input.
# Pandas vectorized processing. To be able to support Pandas UDFs Spark will
build up a batch of data and send it python for processing, and then get a
batch of data back as a result. The format of the data being sent to python
can either be pickle, which is the default, or optionally Arrow. The result
returned is the same format. The limitations here really are around
performance. Transforming the data back and forth can be very expensive.
*Q4.* What is new in your approach and why do you think it will be successful?
Conceptually what we are primarily doing is cleaning up a lot of existing
functionality and getting it ready to be exposed as public facing APIs. We
think we can be successful because we have already completed a proof of concept
that shows columnar processing can be efficiently done in Spark.
*Q5.* Who cares? If you are successful, what difference will it make?
Anyone who wants to integrate Spark with other data processing systems will
care, as it provides a cleaner, more standards-based way of transferring the
data. Anyone who wants to experiment with accelerated computing, either on a
CPU or GPU, will benefit as it provides a supported way to make this work
instead of trying to hack something in that was not available before.
*Q6.* What are the risks?
Technologically I don’t see many risks. We have done a proof of concept
implementation that shows it can be done. This biggest risks are if Apache
Arrow loses favor as an interchange format between different systems. In the
worst cast that means we may have to support transforming the data into other
types, but because Arrow as the internal format is not a requirement and there
really are not that many ways to lay out columnar data, it should require minor
changes if anything.
The second risk is if we didn’t setup the API in a powerful/flexible enough way
for users to really take full advantage of it. This is really going to come
with time, and if we start off fairly conservatively in our API we can
hopefully expand it in the future.
The final risk is around dictionaries. The current columnar layout exposes the
dictionary as just a java class. When we go to an Arrow compatible layout the
dictionary has a specific format. The Parquet APIs don’t give raw access to the
layout of the dictionary. This is not something that we cannot overcome, it
just means we may have less than optimal translations, in terms of performance,
when going from Parquet formatted data to Arrow until we can get some changes
into the Parquet API. This may be true of Orc as well. It is no worse off
than it is today already.
*Q7.* How long will it take?
I suspect that we can put together a patch with tests in a month. Adding
documentation and iterating on the APIs I would suspect would put it at a month
and a half to two months. So one quarter would give us enough time to get
through everything, including reviews if things go well.
*Q8.* What are the mid-term and final “exams” to check for success?
The first check for success would be to successfully transition the existing
columnar code over to using this. That would be transforming and sending the
data to python for processing by Pandas UDFs, and the file formats and caching
code to use a cleaner more explicit columnar API.
The final success is really about adoption and seeing if the follow on work
that we, and hopefully others, have shows that it cleanly enables something
that was not possible before.
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