[jira] [Updated] (SPARK-29881) Introduce API for manually breaking up dataset plan

["Devyn Cairns (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-29881?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Devyn Cairns updated SPARK-29881:
---------------------------------
    Description: 
I have an interesting situation where I'm calling functions that are relatively expensive from Spark SQL, and then using the result several times in a loop through {{transform}}.

Although the WholeStageCodegen is usually helpful, it always calls expressions as they're used, which means that in the case of, for example:

{{SELECT transform(sequence(0, 32), x -> expensive_result * x)}}
 {{FROM (}}
 {{  SELECT expensive_operation(foo) AS expensive_result FROM source}}
 {{)}}

the expensive_operation function will almost certainly be called 32 times for each source row, without any explicit way to cache that value intermediately.

I've found a workaround for now is to insert something like {{.filter \{ _ => true }}} in the middle, which will create a barrier to whole-stage codegen without much negative impact, aside from preventing other optimizations like PushDown. This does indeed produce the intended result and expensive_operation is only run once.

But it would be great to have an API on Dataset like {{.barrier()}} to introduce an explicit barrier to whole-stage codegen without adding any additional behavior or getting in the way of any PushDown optimizations.

  was:
I have an interesting situation where I'm calling functions that are relatively expensive from Spark SQL, and then using the result several times in a loop through {{transform}}.

Although the WholeStageCodegen is usually helpful, it always calls expressions as they're used, which means that in the case of, for example:

{{SELECT transform(sequence(0, 32), x -> expensive_result * x)}}
{{FROM (}}
{{  SELECT expensive_operation(foo) AS expensive_result FROM source}}
{{)}}

the expensive_operation function will almost certainly be called 32 times for each source row, without any explicit way to cache that value intermediately.

I've found a workaround for now is to insert something like {{.filter \{ _ => true }}} in the middle, which will create a barrier to whole-stage codegen without much negative impact, aside from preventing other optimizations like PushDown. This does indeed produce the intended result and expensive_operation is only run once.

But it would be great to have an API on Dataset like {{.barrier()}} to introduce an explicit barrier to whole-stage codegen without adding any additional behavior or getting in the way of any PushDown optimizations.


> Introduce API for manually breaking up dataset plan
> ---------------------------------------------------
>
>                 Key: SPARK-29881
>                 URL: https://issues.apache.org/jira/browse/SPARK-29881
>             Project: Spark
>          Issue Type: Wish
>          Components: SQL
>    Affects Versions: 2.4.4
>            Reporter: Devyn Cairns
>            Priority: Trivial
>
> I have an interesting situation where I'm calling functions that are relatively expensive from Spark SQL, and then using the result several times in a loop through {{transform}}.
> Although the WholeStageCodegen is usually helpful, it always calls expressions as they're used, which means that in the case of, for example:
> {{SELECT transform(sequence(0, 32), x -> expensive_result * x)}}
>  {{FROM (}}
>  {{  SELECT expensive_operation(foo) AS expensive_result FROM source}}
>  {{)}}
> the expensive_operation function will almost certainly be called 32 times for each source row, without any explicit way to cache that value intermediately.
> I've found a workaround for now is to insert something like {{.filter \{ _ => true }}} in the middle, which will create a barrier to whole-stage codegen without much negative impact, aside from preventing other optimizations like PushDown. This does indeed produce the intended result and expensive_operation is only run once.
> But it would be great to have an API on Dataset like {{.barrier()}} to introduce an explicit barrier to whole-stage codegen without adding any additional behavior or getting in the way of any PushDown optimizations.



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