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https://issues.apache.org/jira/browse/SPARK-48666?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon resolved SPARK-48666.
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Fix Version/s: 4.0.0
Resolution: Fixed
Issue resolved by pull request 47313
[https://github.com/apache/spark/pull/47313]
> A filter should not be pushed down if it contains Unevaluable expression
> ------------------------------------------------------------------------
>
> Key: SPARK-48666
> URL: https://issues.apache.org/jira/browse/SPARK-48666
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 4.0.0
> Reporter: Wei Zheng
> Assignee: Wei Zheng
> Priority: Major
> Labels: pull-request-available
> Fix For: 4.0.0
>
>
> We should avoid pushing down Unevaluable expression as it can cause
> unexpected failures. For example, the code snippet below (assuming there is a
> table {{_t_}} with a partition column {{{_}p{_})}}
> {code:java}
> from pyspark import SparkConf
> from pyspark.sql import SparkSession
> from pyspark.sql.types import StringType
> import pyspark.sql.functions as f
> def getdata(p: str) -> str:
> return "data"
> NEW_COLUMN = 'new_column'
> P_COLUMN = 'p'
> f_getdata = f.udf(getdata, StringType())
> rows = spark.sql("select * from default.t")
> table = rows.withColumn(NEW_COLUMN, f_getdata(f.col(P_COLUMN)))
> df = table.alias('t1').join(table.alias('t2'), (f.col(f"t1.{NEW_COLUMN}") ==
> f.col(f"t2.{NEW_COLUMN}")), how='inner')
> df.show(){code}
> will cause an error like:
> {code:java}
> org.apache.spark.SparkException: [INTERNAL_ERROR] Cannot evaluate expression:
> getdata(input[0, string, true])#16
> at org.apache.spark.SparkException$.internalError(SparkException.scala:92)
> at org.apache.spark.SparkException$.internalError(SparkException.scala:96)
> at
> org.apache.spark.sql.errors.QueryExecutionErrors$.cannotEvaluateExpressionError(QueryExecutionErrors.scala:66)
> at
> org.apache.spark.sql.catalyst.expressions.Unevaluable.eval(Expression.scala:391)
> at
> org.apache.spark.sql.catalyst.expressions.Unevaluable.eval$(Expression.scala:390)
> at
> org.apache.spark.sql.catalyst.expressions.PythonUDF.eval(PythonUDF.scala:71)
> at
> org.apache.spark.sql.catalyst.expressions.IsNotNull.eval(nullExpressions.scala:384)
> at
> org.apache.spark.sql.catalyst.expressions.InterpretedPredicate.eval(predicates.scala:52)
> at
> org.apache.spark.sql.catalyst.catalog.ExternalCatalogUtils$.$anonfun$prunePartitionsByFilter$1(ExternalCatalogUtils.scala:166)
> at
> org.apache.spark.sql.catalyst.catalog.ExternalCatalogUtils$.$anonfun$prunePartitionsByFilter$1$adapted(ExternalCatalogUtils.scala:165)
> {code}
>
>
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