[WIP][SPARK-56661] Introducing logical and physical planning nodes for language-agnostic Spark UDFs#55768
Draft
sven-weber-db wants to merge 2 commits intoapache:masterfrom
Draft
Conversation
…-agnostic MapPartition Spark UDFs
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
What changes were proposed in this pull request?
This PR introduces new logical and physical Catalyst nodes for language-agnostic User Defined Functions (UDF) as part of SPIP SPARK-55278, which proposes language-agnostic UDFs.
As a first step towards the goal of language-agnostic UDFs, we want to target mapPartition UDFs like
pyspark.sql.DataFrame.mapInArrow,pyspark.RDD.mapPartitions, orpyspark.sql.DataFrame.mapInArrow. The overarching goal is to deprecate the current, language-specific Catalyst nodes (likemapInArrow). However, for now, the new nodes will exist in addition to the old ones until the new framework has reach maturity.In summary, this PR introduces:
ExternalUDFExpression, which captures language-agnostic UDF properties (payload, name, etc.)ExternalUDF, which serves as a base class for all language-agnostic UDF nodesMapPartitionExternalUDF, which is the new, language-agnostic map partition nodeWorkerDispatcherManager- A manager class which manages UDF Dispatchers based on the targetUDFWorkerSpecificationNone of the changes introduced above are currently consumed in Spark.
Why are the changes needed?
This is the first step toward language-agnostic UDF execution for Spark. Existing physical and logical planning nodes need to be replaced eventually to achieve this goal as they make language-specific assumptions.
Does this PR introduce any user-facing change?
No
How was this patch tested?
New unit-tests were added.
Was this patch authored or co-authored using generative AI tooling?
Partially. However, the code was manually reviewed and adjusted.