[jira] [Commented] (SPARK-17728) UDFs are run too many times

["Josh Rosen (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-17728?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17557756#comment-17557756 ] 

Josh Rosen commented on SPARK-17728:
------------------------------------

As of SPARK-36718 in Spark 3.3 I think the {{explode(array(udf()))}} trick should no longer be needed: Spark will avoid collapsing projections which would lead to duplication of expensive-to-evaluate expressions. 

There still might be some rare cases where you might need that trick (e.g. to work around SPARK-38485), but I think most cases should be addressed by Spark 3.3's improved CollapseProject optimizer rule.

> UDFs are run too many times
> ---------------------------
>
>                 Key: SPARK-17728
>                 URL: https://issues.apache.org/jira/browse/SPARK-17728
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 2.0.0
>         Environment: Databricks Cloud / Spark 2.0.0
>            Reporter: Jacob Eisinger
>            Priority: Minor
>         Attachments: over_optimized_udf.html
>
>
> h3. Background
> Llonger running processes that might run analytics or contact external services from UDFs. The response might not just be a field, but instead a structure of information. When attempting to break out this information, it is critical that query is optimized correctly.
> h3. Steps to Reproduce
> # Create some sample data.
> # Create a UDF that returns a multiple attributes.
> # Run UDF over some data.
> # Create new columns from the multiple attributes.
> # Observe run time.
> h3. Actual Results
> The UDF is executed *multiple times* _per row._
> h3. Expected Results
> The UDF should only be executed *once* _per row._
> h3. Workaround
> Cache the Dataset after UDF execution.
> h3. Details
> For code and more details, see [^over_optimized_udf.html]



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