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https://issues.apache.org/jira/browse/SPARK-17728?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15543969#comment-15543969
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Herman van Hovell commented on SPARK-17728:
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First of all, we implement subexpression elimination (which is a form of
memoization), and this should prevent multiple invocations from happening. I am
quite curious why this is not triggering in your case. Are you on a completely
interpreted path?
Cost functions for a UDF is doable, we would have to this for expression trees
though, and this is a non-trivial thing to implement.
> 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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