How would this work with: Codegen – how does one generate code given a user’s UDF? Would the user be able to specify the code that is generated that represents their function? In practice that’s pretty hard to get right. Row serialization and representation – Will the UDF receive catalyst rows with optimized internal representations, or will Spark have to convert to something more easily consumed by a UDF?
Otherwise +1 for trying to get this to work without Hive. I think even having
something without codegen and optimized row formats is worthwhile if only
because it’s easier to use than Hive UDFs.
-Matt Cheah
From: Reynold Xin <r...@databricks.com>
Date: Friday, December 14, 2018 at 1:49 PM
To: "rb...@netflix.com" <rb...@netflix.com>
Cc: Spark Dev List <dev@spark.apache.org>
Subject: Re: [DISCUSS] Function plugins
Having a way to register UDFs that are not using Hive APIs would be great!
On Fri, Dec 14, 2018 at 1:30 PM, Ryan Blue <rb...@netflix.com.invalid> wrote:
Hi everyone,
I’ve been looking into improving how users of our Spark platform register and
use UDFs and I’d like to discuss a few ideas for making this easier.
The motivation for this is the use case of defining a UDF from SparkSQL or
PySpark. We want to make it easy to write JVM UDFs and use them from both SQL
and Python. Python UDFs work great in most cases, but we occasionally don’t
want to pay the cost of shipping data to python and processing it there so we
want to make it easy to register UDFs that will run in the JVM.
There is already syntax to create a function from a JVM class
[docs.databricks.com] in SQL that would work, but this option requires using
the Hive UDF API instead of Spark’s simpler Scala API. It also requires
argument translation and doesn’t support codegen. Beyond the problem of the API
and performance, it is annoying to require registering every function
individually with a CREATE FUNCTION statement.
The alternative that I’d like to propose is to add a way to register a named
group of functions using the proposed catalog plugin API.
For anyone unfamiliar with the proposed catalog plugins, the basic idea is to
load and configure plugins using a simple property-based scheme. Those plugins
expose functionality through mix-in interfaces, like TableCatalog to
create/drop/load/alter tables. Another interface could be UDFCatalog that can
load UDFs.
interface UDFCatalog extends CatalogPlugin {
UserDefinedFunction loadUDF(String name)
}
To use this, I would create a UDFCatalog class that returns my Scala functions
as UDFs. To look up functions, we would use both the catalog name and the
function name.
This would allow my users to write Scala UDF instances, package them using a
UDFCatalog class (provided by me), and easily use them in Spark with a few
configuration options to add the catalog in their environment.
This would also allow me to expose UDF libraries easily in my configuration,
like brickhouse [community.cloudera.com], without users needing to ensure the
Jar is loaded and register individual functions.
Any thoughts on this high-level approach? I know that this ignores things like
creating and storing functions in a FunctionCatalog, and we’d have to solve
challenges with function naming (whether there is a db component). Right now
I’d like to think through the overall idea and not get too focused on those
details.
Thanks,
rb
--
Ryan Blue
Software Engineer
Netflix
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