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Posted to dev@spark.apache.org by invkrh <in...@gmail.com> on 2014/12/03 17:50:23 UTC
scala.MatchError on SparkSQL when creating ArrayType of StructType
Hi,
I am using SparkSQL on 1.1.0 branch.
The following code leads to a scala.MatchError
at
org.apache.spark.sql.catalyst.expressions.Cast.cast$lzycompute(Cast.scala:247)
val scm = StructType(*inputRDD*.schema.fields.init :+
StructField("list",
ArrayType(
StructType(
Seq(StructField("*date*", StringType, nullable = *false*),
StructField("*nbPurchase*", IntegerType, nullable =
*false*)))),
nullable = false))
// *purchaseRDD* is RDD[sql.ROW] whose schema is corresponding to scm. It is
transformed from *inputRDD*
val schemaRDD = hiveContext.applySchema(purchaseRDD, scm)
schemaRDD.registerTempTable("t_purchase")
Here's the stackTrace:
scala.MatchError: ArrayType(StructType(List(StructField(date,StringType,
*true* ), StructField(n_reachat,IntegerType, *true* ))),true) (of class
org.apache.spark.sql.catalyst.types.ArrayType)
at
org.apache.spark.sql.catalyst.expressions.Cast.cast$lzycompute(Cast.scala:247)
at org.apache.spark.sql.catalyst.expressions.Cast.cast(Cast.scala:247)
at org.apache.spark.sql.catalyst.expressions.Cast.eval(Cast.scala:263)
at
org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:84)
at
org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:66)
at
org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:50)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.org$apache$spark$sql$hive$execution$InsertIntoHiveTable$$writeToFile$1(InsertIntoHiveTable.scala:149)
at
org.apache.spark.sql.hive.execution.InsertIntoHiveTable$$anonfun$saveAsHiveFile$1.apply(InsertIntoHiveTable.scala:158)
at
org.apache.spark.sql.hive.execution.InsertIntoHiveTable$$anonfun$saveAsHiveFile$1.apply(InsertIntoHiveTable.scala:158)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62)
at org.apache.spark.scheduler.Task.run(Task.scala:54)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:177)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:744)
The strange thing is that *nullable* of *date* and *nbPurchase* field are
set to true while it were false in the code. If I set both to *true*, it
works. But, in fact, they should not be nullable.
Here's what I find at Cast.scala:247 on 1.1.0 branch
private[this] lazy val cast: Any => Any = dataType match {
case StringType => castToString
case BinaryType => castToBinary
case DecimalType => castToDecimal
case TimestampType => castToTimestamp
case BooleanType => castToBoolean
case ByteType => castToByte
case ShortType => castToShort
case IntegerType => castToInt
case FloatType => castToFloat
case LongType => castToLong
case DoubleType => castToDouble
}
Any idea? Thank you.
Hao
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Re: scala.MatchError on SparkSQL when creating ArrayType of StructType
Posted by Yin Huai <hu...@gmail.com>.
Seems you hit https://issues.apache.org/jira/browse/SPARK-4245. It was
fixed in 1.2.
Thanks,
Yin
On Wed, Dec 3, 2014 at 11:50 AM, invkrh <in...@gmail.com> wrote:
> Hi,
>
> I am using SparkSQL on 1.1.0 branch.
>
> The following code leads to a scala.MatchError
> at
>
> org.apache.spark.sql.catalyst.expressions.Cast.cast$lzycompute(Cast.scala:247)
>
> val scm = StructType(*inputRDD*.schema.fields.init :+
> StructField("list",
> ArrayType(
> StructType(
> Seq(StructField("*date*", StringType, nullable = *false*),
> StructField("*nbPurchase*", IntegerType, nullable =
> *false*)))),
> nullable = false))
>
> // *purchaseRDD* is RDD[sql.ROW] whose schema is corresponding to scm. It
> is
> transformed from *inputRDD*
> val schemaRDD = hiveContext.applySchema(purchaseRDD, scm)
> schemaRDD.registerTempTable("t_purchase")
>
> Here's the stackTrace:
> scala.MatchError: ArrayType(StructType(List(StructField(date,StringType,
> *true* ), StructField(n_reachat,IntegerType, *true* ))),true) (of class
> org.apache.spark.sql.catalyst.types.ArrayType)
> at
>
> org.apache.spark.sql.catalyst.expressions.Cast.cast$lzycompute(Cast.scala:247)
> at
> org.apache.spark.sql.catalyst.expressions.Cast.cast(Cast.scala:247)
> at
> org.apache.spark.sql.catalyst.expressions.Cast.eval(Cast.scala:263)
> at
>
> org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:84)
> at
>
> org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:66)
> at
>
> org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:50)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at
> org.apache.spark.sql.hive.execution.InsertIntoHiveTable.org
> $apache$spark$sql$hive$execution$InsertIntoHiveTable$$writeToFile$1(InsertIntoHiveTable.scala:149)
> at
>
> org.apache.spark.sql.hive.execution.InsertIntoHiveTable$$anonfun$saveAsHiveFile$1.apply(InsertIntoHiveTable.scala:158)
> at
>
> org.apache.spark.sql.hive.execution.InsertIntoHiveTable$$anonfun$saveAsHiveFile$1.apply(InsertIntoHiveTable.scala:158)
> at
> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62)
> at org.apache.spark.scheduler.Task.run(Task.scala:54)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:177)
> at
>
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
>
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:744)
>
> The strange thing is that *nullable* of *date* and *nbPurchase* field are
> set to true while it were false in the code. If I set both to *true*, it
> works. But, in fact, they should not be nullable.
>
> Here's what I find at Cast.scala:247 on 1.1.0 branch
>
> private[this] lazy val cast: Any => Any = dataType match {
> case StringType => castToString
> case BinaryType => castToBinary
> case DecimalType => castToDecimal
> case TimestampType => castToTimestamp
> case BooleanType => castToBoolean
> case ByteType => castToByte
> case ShortType => castToShort
> case IntegerType => castToInt
> case FloatType => castToFloat
> case LongType => castToLong
> case DoubleType => castToDouble
> }
>
> Any idea? Thank you.
>
> Hao
>
>
>
> --
> View this message in context:
> http://apache-spark-developers-list.1001551.n3.nabble.com/scala-MatchError-on-SparkSQL-when-creating-ArrayType-of-StructType-tp9623.html
> Sent from the Apache Spark Developers List mailing list archive at
> Nabble.com.
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: dev-unsubscribe@spark.apache.org
> For additional commands, e-mail: dev-help@spark.apache.org
>
>