Hyukjin Kwon created SPARK-20978:
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Summary: 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
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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