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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-20978) CSV emits NPE when the number of tokens is less than given schema and corrupt column is given
Date Mon, 05 Jun 2017 05:43:04 GMT

     [ https://issues.apache.org/jira/browse/SPARK-20978?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-20978:
------------------------------------

    Assignee: Apache Spark

> CSV emits NPE when the number of tokens is less than given schema and corrupt column
is given
> ---------------------------------------------------------------------------------------------
>
>                 Key: SPARK-20978
>                 URL: https://issues.apache.org/jira/browse/SPARK-20978
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.2.0, 2.3.0
>            Reporter: Hyukjin Kwon
>            Assignee: Apache Spark
>
> Currently, if the number of tokens is less than the given schema, CSV datasource throws
an NPE as below:
> {code}
> scala> spark.read.schema("a string, b string, unparsed string").option("columnNameOfCorruptRecord",
"unparsed").csv(Seq("a").toDS).show()
> 17/06/05 13:59:26 ERROR Executor: Exception in task 0.0 in stage 3.0 (TID 3)
> java.lang.NullPointerException
> 	at scala.collection.immutable.StringLike$class.stripLineEnd(StringLike.scala:89)
> 	at scala.collection.immutable.StringOps.stripLineEnd(StringOps.scala:29)
> 	at org.apache.spark.sql.execution.datasources.csv.UnivocityParser.org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$getCurrentInput(UnivocityParser.scala:56)
> 	at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:211)
> 	at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert$1.apply(UnivocityParser.scala:211)
> 	at org.apache.spark.sql.execution.datasources.FailureSafeParser$$anonfun$2.apply(FailureSafeParser.scala:50)
> 	at org.apache.spark.sql.execution.datasources.FailureSafeParser$$anonfun$2.apply(FailureSafeParser.scala:43)
> 	at org.apache.spark.sql.execution.datasources.FailureSafeParser.parse(FailureSafeParser.scala:64)
> 	at org.apache.spark.sql.DataFrameReader$$anonfun$11$$anonfun$apply$4.apply(DataFrameReader.scala:471)
> 	at org.apache.spark.sql.DataFrameReader$$anonfun$11$$anonfun$apply$4.apply(DataFrameReader.scala:471)
> 	at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
> 	at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
> 	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> {code}
> If this is not given, it works as below:
> {code}
> scala> spark.read.schema("a string, b string, unparsed string").csv(Seq("a").toDS).show()
> +---+----+--------+
> |  a|   b|unparsed|
> +---+----+--------+
> |  a|null|    null|
> +---+----+--------+
> {code}



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