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https://issues.apache.org/jira/browse/SPARK-37971?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-37971:
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Priority: Major (was: Critical)
> Apply and evaluate expressions row-wise in a Spark DataFrame
> ------------------------------------------------------------
>
> Key: SPARK-37971
> URL: https://issues.apache.org/jira/browse/SPARK-37971
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 3.2.0
> Reporter: Carlos Gameiro
> Priority: Major
>
> This functionality would serve very specific use cases.
> Consider a DataFrame with a column of SQL expressions encoded as strings.
> Individually it's possible to evaluate each string and obtain the
> corresponding result with PySpark's expr function. However it is not possible
> to apply the expr function row-wise to a dataframe (UDF or map), and evaluate
> all expressions efficiently.
> {code:java}
> id | sql_expression
> --------------------------
> 1 | abs(-1) + 12
> 2 | decode(1,2,3,4) - 1
> 3 | 30 * 20 - 5
> df = df.withColumn('sql_eval', f.expr_row('sql_expression')) {code}
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