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Posted to issues@spark.apache.org by "Davies Liu (JIRA)" <ji...@apache.org> on 2016/09/09 21:27:21 UTC
[jira] [Resolved] (SPARK-17354) java.lang.ClassCastException:
java.lang.Integer cannot be cast to java.sql.Date
[ https://issues.apache.org/jira/browse/SPARK-17354?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Davies Liu resolved SPARK-17354.
--------------------------------
Resolution: Fixed
Fix Version/s: 2.1.0
2.0.1
Issue resolved by pull request 14919
[https://github.com/apache/spark/pull/14919]
> java.lang.ClassCastException: java.lang.Integer cannot be cast to java.sql.Date
> -------------------------------------------------------------------------------
>
> Key: SPARK-17354
> URL: https://issues.apache.org/jira/browse/SPARK-17354
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: Amit Baghel
> Priority: Minor
> Fix For: 2.0.1, 2.1.0
>
>
> Hive database has one table with column type Date. While running select query using Spark 2.0.0 SQL and calling show() function on DF throws ClassCastException. Same code is working fine on Spark 1.6.2. Please see the sample code below.
> {code}
> import java.util.Calendar
> val now = Calendar.getInstance().getTime()
> case class Order(id : Int, customer : String, city : String, pdate : java.sql.Date)
> val orders = Seq(
> Order(1, "John S", "San Mateo", new java.sql.Date(now.getTime)),
> Order(2, "John D", "Redwood City", new java.sql.Date(now.getTime))
> )
> orders.toDF.createOrReplaceTempView("orders1")
> spark.sql("CREATE TABLE IF NOT EXISTS order(id INT, customer String,city String)PARTITIONED BY (pdate DATE)STORED AS PARQUETFILE")
> spark.sql("set hive.exec.dynamic.partition.mode=nonstrict")
> spark.sql("INSERT INTO TABLE order PARTITION(pdate) SELECT * FROM orders1")
> spark.sql("SELECT * FROM order").show()
> {code}
> Exception details
> {code}
> 16/09/01 10:30:07 ERROR Executor: Exception in task 0.0 in stage 5.0 (TID 6)
> java.lang.ClassCastException: java.lang.Integer cannot be cast to java.sql.Date
> at org.apache.spark.sql.execution.vectorized.ColumnVectorUtils.populate(ColumnVectorUtils.java:89)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.initBatch(VectorizedParquetRecordReader.java:185)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.initBatch(VectorizedParquetRecordReader.java:204)
> at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$buildReader$1.apply(ParquetFileFormat.scala:362)
> at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$buildReader$1.apply(ParquetFileFormat.scala:339)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:116)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:91)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.scan_nextBatch$(Unknown Source)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
> at org.apache.spark.scheduler.Task.run(Task.scala:85)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> {code}
> Expected output
> {code}
> +---+--------+------------+----------+
> | id|customer| city| pdate|
> +---+--------+------------+----------+
> | 1| John S| San Mateo|2016-09-01|
> | 2| John D|Redwood City|2016-09-01|
> +---+--------+------------+----------+
> {code}
> Workaround for Spark 2.0.0
> Setting enableVectorizedReader=false before show() method on DF returns expected result.
> {code}
> spark.sql("set spark.sql.parquet.enableVectorizedReader=false")
> {code}
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