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https://issues.apache.org/jira/browse/SPARK-38904?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17523636#comment-17523636
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Rafal Wojdyla commented on SPARK-38904:
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[~hyukjin.kwon] ok, will give it a shot, and ping you if I get stuck. Also if
you have any immediate tips would appreciate it.
> Low cost DataFrame schema swap util
> -----------------------------------
>
> Key: SPARK-38904
> URL: https://issues.apache.org/jira/browse/SPARK-38904
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 3.2.1
> Reporter: Rafal Wojdyla
> Priority: Major
>
> This question is related to [https://stackoverflow.com/a/37090151/1661491].
> Let's assume I have a pyspark DataFrame with certain schema, and I would like
> to overwrite that schema with a new schema that I *know* is compatible, I
> could do:
> {code:python}
> df: DataFrame
> new_schema = ...
> df.rdd.toDF(schema=new_schema)
> {code}
> Unfortunately this triggers computation as described in the link above. Is
> there a way to do that at the metadata level (or lazy), without eagerly
> triggering computation or conversions?
> Edit, note:
> * the schema can be arbitrarily complicated (nested etc)
> * new schema includes updates to description, nullability and additional
> metadata (bonus points for updates to the type)
> * I would like to avoid writing a custom query expression generator,
> *unless* there's one already built into Spark that can generate query based
> on the schema/{{{}StructType{}}}
> Copied from:
> [https://stackoverflow.com/questions/71610435/how-to-overwrite-pyspark-dataframe-schema-without-data-scan]
> See POC of workaround/util in
> [https://github.com/ravwojdyla/spark-schema-utils]
> Also posted in
> [https://lists.apache.org/thread/5ds0f7chzp1s3h10tvjm3r96g769rvpj]
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