You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2018/06/25 04:40:00 UTC
[jira] [Assigned] (SPARK-24645) Skip parsing when csvColumnPruning
enabled and partitions scanned only
[ https://issues.apache.org/jira/browse/SPARK-24645?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-24645:
------------------------------------
Assignee: Apache Spark
> Skip parsing when csvColumnPruning enabled and partitions scanned only
> ----------------------------------------------------------------------
>
> Key: SPARK-24645
> URL: https://issues.apache.org/jira/browse/SPARK-24645
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.3.1
> Reporter: Takeshi Yamamuro
> Assignee: Apache Spark
> Priority: Minor
>
> I hit the bug below when parsing csv data;
> {code:java}
> scala> val dir = "/tmp/spark-csv/csv"
> scala> spark.range(10).selectExpr("id % 2 AS p", "id").write.mode("overwrite").partitionBy("p").csv(dir)
> scala> spark.read.csv(dir).selectExpr("sum(p)").collect()
> 18/06/25 13:12:51 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 5)
> java.lang.NullPointerException
> at org.apache.spark.sql.execution.datasources.csv.UnivocityParser.org$apache$spark$sql$execution$datasources$csv$UnivocityParser$$convert(UnivocityParser.scala:197)
> at org.apache.spark.sql.execution.datasources.csv.UnivocityParser.parse(UnivocityParser.scala:190)
> at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$5.apply(UnivocityParser.scala:309)
> at org.apache.spark.sql.execution.datasources.csv.UnivocityParser$$anonfun$5.apply(UnivocityParser.scala:309)
> at org.apache.spark.sql.execution.datasources.FailureSafeParser.parse(FailureSafeParser.scala:61)
> ...
> {code}
>
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org