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Posted to issues@spark.apache.org by "Carlos Gameiro (Jira)" <ji...@apache.org> on 2022/01/20 14:53:00 UTC
[jira] [Updated] (SPARK-37971) Apply and evaluate expressiosn row-wise in a Spark DataFrame
[ https://issues.apache.org/jira/browse/SPARK-37971?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Carlos Gameiro updated SPARK-37971:
-----------------------------------
Summary: Apply and evaluate expressiosn row-wise in a Spark DataFrame (was: Apply and evaluate expressiosn row-wise in a DataFrame)
> Apply and evaluate expressiosn 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: Critical
>
> 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. However it is not possible to apply the expr function row-wise (UDF or map), and evaluate all expression 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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