[WIP][SPARK-56661] Introducing logical and physical planning nodes for language-agnostic Spark UDFs#55768
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…-agnostic MapPartition Spark UDFs
| session.close() | ||
| // TODO [SPARK-55278]: Stream rows to/from the worker | ||
| // via session.process(). | ||
| rows |
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maybe throws an exception here to be correct?
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| /** | ||
| * :: Experimental :: | ||
| * Builds a [[UDFWorkerSpecification]] for Python UDFs from a |
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do we have tests that this converted worker spec can run with direct worker dispatcher?
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no need to run udf, just worker starts/stops
| * `[className]`. Useful for identifying which class produced a | ||
| * log line. | ||
| */ | ||
| def forClass(clazz: Class[_]): WorkerLogger = { |
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this looks a little bit weird to me - why we don't make this a static function from a singleton class, and make the prefix a required field in the WorkerLogger?
| Dataset.ofRows( | ||
| sparkSession, | ||
| MapPartitionsExternalUDF( | ||
| workerSpec, udf, output, logicalPlan)) |
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we would need isBarrier, too
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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.