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https://issues.apache.org/jira/browse/SPARK-48989?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
ASF GitHub Bot updated SPARK-48989:
-----------------------------------
Labels: pull-request-available (was: )
> WholeStageCodeGen error resulting in NumberFormatException when calling
> SUBSTRING_INDEX
> ---------------------------------------------------------------------------------------
>
> Key: SPARK-48989
> URL: https://issues.apache.org/jira/browse/SPARK-48989
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 4.0.0
> Environment: This was tested from the {{spark-shell}}, in local mode.
> All Spark versions were run with default settings.
> Spark 4.0 SNAPSHOT: Exception.
> Spark 4.0 Preview: Exception.
> Spark 3.5.1: Success.
> Reporter: Mithun Radhakrishnan
> Priority: Major
> Labels: pull-request-available
>
> One seems to run into a {{NumberFormatException}}, possibly from an error in
> WholeStageCodeGen, when I exercise {{SUBSTRING_INDEX}} with a null row, thus:
> {code:scala}
> // Create integer table with one null.
> sql( " SELECT num FROM VALUES (1), (2), (3), (NULL) AS (num)
> ").repartition(1).write.mode("overwrite").parquet("/tmp/mytable")
> // Exercise substring-index.
> sql( " SELECT num, SUBSTRING_INDEX('a_a_a', '_', num) AS subs FROM
> PARQUET.`/tmp/mytable` ").show()
> {code}
> On Spark 4.0 (HEAD, as of today, and with the preview-1), I see the following
> exception:
> {code}
> java.lang.NumberFormatException: For input string: "columnartorow_value_0"
> at
> java.base/java.lang.NumberFormatException.forInputString(NumberFormatException.java:67)
> at java.base/java.lang.Integer.parseInt(Integer.java:668)
> at
> org.apache.spark.sql.catalyst.expressions.SubstringIndex.$anonfun$doGenCode$29(stringExpressions.scala:1660)
> at
> org.apache.spark.sql.catalyst.expressions.TernaryExpression.$anonfun$defineCodeGen$3(Expression.scala:869)
> at
> org.apache.spark.sql.catalyst.expressions.TernaryExpression.nullSafeCodeGen(Expression.scala:888)
> at
> org.apache.spark.sql.catalyst.expressions.TernaryExpression.defineCodeGen(Expression.scala:868)
> at
> org.apache.spark.sql.catalyst.expressions.SubstringIndex.doGenCode(stringExpressions.scala:1659)
> at
> org.apache.spark.sql.catalyst.expressions.Expression.$anonfun$genCode$3(Expression.scala:207)
> at scala.Option.getOrElse(Option.scala:201)
> at
> org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:202)
> at
> org.apache.spark.sql.catalyst.expressions.ToPrettyString.doGenCode(ToPrettyString.scala:62)
> at
> org.apache.spark.sql.catalyst.expressions.Expression.$anonfun$genCode$3(Expression.scala:207)
> at scala.Option.getOrElse(Option.scala:201)
> at
> org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:202)
> at
> org.apache.spark.sql.catalyst.expressions.Alias.genCode(namedExpressions.scala:162)
> at
> org.apache.spark.sql.execution.ProjectExec.$anonfun$doConsume$2(basicPhysicalOperators.scala:74)
> at scala.collection.immutable.List.map(List.scala:247)
> at scala.collection.immutable.List.map(List.scala:79)
> at
> org.apache.spark.sql.execution.ProjectExec.$anonfun$doConsume$1(basicPhysicalOperators.scala:74)
> at
> org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext.withSubExprEliminationExprs(CodeGenerator.scala:1085)
> at
> org.apache.spark.sql.execution.ProjectExec.doConsume(basicPhysicalOperators.scala:74)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume(WholeStageCodegenExec.scala:200)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume$(WholeStageCodegenExec.scala:153)
> at
> org.apache.spark.sql.execution.ColumnarToRowExec.consume(Columnar.scala:68)
> at
> org.apache.spark.sql.execution.ColumnarToRowExec.doProduce(Columnar.scala:193)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:99)
> {code}
> The same query seems to run alright on Spark 3.5.x:
> {code}
> +----+-----+
> | num| subs|
> +----+-----+
> | 1| a|
> | 2| a_a|
> | 3|a_a_a|
> |NULL| NULL|
> +----+-----+
> {code}
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