Any ideas ?
On Thu, Apr 16, 2015 at 5:04 PM, Jaonary Rabarisoa <jaon...@gmail.com>
wrote:
> Dear all,
>
> Here is an issue that gets me mad. I wrote a UserDefineType in order to be
> able to store a custom type in a parquet file. In my code I just create a
> DataFrame with my custom data type and write in into a parquet file. When I
> run my code directly inside idea every thing works like a charm. But when I
> create the assembly jar with sbt assembly and run the same code with
> spark-submit I get the following error :
>
> *15/04/16 17:02:17 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID
> 0)*
> *java.lang.IllegalArgumentException: Unsupported dataType:
> {"type":"struct","fields":[{"name":"metadata","type":{"type":"udt","class":"org.apache.spark.vision.types.ImageMetadataUDT","pyClass":null,"sqlType":{"type":"struct","fields":[{"name":"name","type":"string","nullable":true,"metadata":{}},{"name":"encoding","type":"string","nullable":true,"metadata":{}},{"name":"cameraId","type":"string","nullable":true,"metadata":{}},{"name":"timestamp","type":"string","nullable":true,"metadata":{}},{"name":"frameId","type":"string","nullable":true,"metadata":{}}]}},"nullable":true,"metadata":{}}]},
> [1.1] failure: `TimestampType' expected but `{' found*
>
>
> *{"type":"struct","fields":[{"name":"metadata","type":{"type":"udt","class":"org.apache.spark.vision.types.ImageMetadataUDT","pyClass":null,"sqlType":{"type":"struct","fields":[{"name":"name","type":"string","nullable":true,"metadata":{}},{"name":"encoding","type":"string","nullable":true,"metadata":{}},{"name":"cameraId","type":"string","nullable":true,"metadata":{}},{"name":"timestamp","type":"string","nullable":true,"metadata":{}},{"name":"frameId","type":"string","nullable":true,"metadata":{}}]}},"nullable":true,"metadata":{}}]}*
> *^*
> * at
> org.apache.spark.sql.types.DataType$CaseClassStringParser$.apply(dataTypes.scala:163)*
> * at
> org.apache.spark.sql.types.DataType$.fromCaseClassString(dataTypes.scala:98)*
> * at
> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$6.apply(ParquetTypes.scala:402)*
> * at
> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$6.apply(ParquetTypes.scala:402)*
> * at scala.util.Try.getOrElse(Try.scala:77)*
> * at
> org.apache.spark.sql.parquet.ParquetTypesConverter$.convertFromString(ParquetTypes.scala:402)*
> * at
> org.apache.spark.sql.parquet.RowWriteSupport.init(ParquetTableSupport.scala:145)*
> * at
> parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:278)*
> * at
> parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:252)*
> * at org.apache.spark.sql.parquet.ParquetRelation2.org
> <http://org.apache.spark.sql.parquet.ParquetRelation2.org>$apache$spark$sql$parquet$ParquetRelation2$$writeShard$1(newParquet.scala:691)*
> * at
> org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$insert$2.apply(newParquet.scala:713)*
> * at
> org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$insert$2.apply(newParquet.scala:713)*
> * at
> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)*
> * at org.apache.spark.scheduler.Task.run(Task.scala:64)*
> * at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:210)*
> * 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:745)*
>
>
