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From Manoj Samel <manojsamelt...@gmail.com>
Subject Re: spark 1.2 - Writing parque fails for timestamp with "Unsupported datatype TimestampType"
Date Mon, 26 Jan 2015 23:05:03 GMT
Thanks Michael. I am sure there have been many requests for this support.

Any release targeted for this?

Thanks,

On Sat, Jan 24, 2015 at 11:47 AM, Michael Armbrust <michael@databricks.com>
wrote:

> Those annotations actually don't work because the timestamp is SQL has
> optional nano-second precision.
>
> However, there is a PR to add support using parquets INT96 type:
> https://github.com/apache/spark/pull/3820
>
> On Fri, Jan 23, 2015 at 12:08 PM, Manoj Samel <manojsameltech@gmail.com>
> wrote:
>
>> Looking further at the trace and ParquetTypes.scala, it seems there is no
>> support for Timestamp and Date in fromPrimitiveDataType(ctype: DataType):
>> Option[ParquetTypeInfo]. Since Parquet supports these type with some
>> decoration over Int (
>> https://github.com/Parquet/parquet-format/blob/master/LogicalTypes.md),
>> any reason why Date / Timestamp are not supported right now ?
>>
>> Thanks,
>>
>> Manoj
>>
>>
>> On Fri, Jan 23, 2015 at 11:40 AM, Manoj Samel <manojsameltech@gmail.com>
>> wrote:
>>
>>> Using Spark 1.2
>>>
>>> Read a CSV file, apply schema to convert to SchemaRDD and then
>>> schemaRdd.saveAsParquetFile
>>>
>>> If the schema includes Timestamptype, it gives following trace when
>>> doing the save
>>>
>>> Exception in thread "main" java.lang.RuntimeException: Unsupported
>>> datatype TimestampType
>>>
>>> at scala.sys.package$.error(package.scala:27)
>>>
>>> at
>>> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$fromDataType$2.apply(
>>> ParquetTypes.scala:343)
>>>
>>> at
>>> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$fromDataType$2.apply(
>>> ParquetTypes.scala:292)
>>>
>>> at scala.Option.getOrElse(Option.scala:120)
>>>
>>> at org.apache.spark.sql.parquet.ParquetTypesConverter$.fromDataType(
>>> ParquetTypes.scala:291)
>>>
>>> at org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$4.apply(
>>> ParquetTypes.scala:363)
>>>
>>> at org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$4.apply(
>>> ParquetTypes.scala:362)
>>>
>>> at scala.collection.TraversableLike$$anonfun$map$1.apply(
>>> TraversableLike.scala:244)
>>>
>>> at scala.collection.TraversableLike$$anonfun$map$1.apply(
>>> TraversableLike.scala:244)
>>>
>>> at scala.collection.mutable.ResizableArray$class.foreach(
>>> ResizableArray.scala:59)
>>>
>>> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>>>
>>> at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>>>
>>> at scala.collection.AbstractTraversable.map(Traversable.scala:105)
>>>
>>> at
>>> org.apache.spark.sql.parquet.ParquetTypesConverter$.convertFromAttributes(
>>> ParquetTypes.scala:361)
>>>
>>> at org.apache.spark.sql.parquet.ParquetTypesConverter$.writeMetaData(
>>> ParquetTypes.scala:407)
>>>
>>> at org.apache.spark.sql.parquet.ParquetRelation$.createEmpty(
>>> ParquetRelation.scala:166)
>>>
>>> at org.apache.spark.sql.parquet.ParquetRelation$.create(
>>> ParquetRelation.scala:145)
>>>
>>> at
>>> org.apache.spark.sql.execution.SparkStrategies$ParquetOperations$.apply(
>>> SparkStrategies.scala:204)
>>>
>>> at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(
>>> QueryPlanner.scala:58)
>>>
>>> at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(
>>> QueryPlanner.scala:58)
>>>
>>> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
>>>
>>> at org.apache.spark.sql.catalyst.planning.QueryPlanner.apply(
>>> QueryPlanner.scala:59)
>>>
>>> at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan$lzycompute(
>>> SQLContext.scala:418)
>>>
>>> at org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan(
>>> SQLContext.scala:416)
>>>
>>> at
>>> org.apache.spark.sql.SQLContext$QueryExecution.executedPlan$lzycompute(
>>> SQLContext.scala:422)
>>>
>>> at org.apache.spark.sql.SQLContext$QueryExecution.executedPlan(
>>> SQLContext.scala:422)
>>>
>>> at org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(
>>> SQLContext.scala:425)
>>>
>>> at org.apache.spark.sql.SQLContext$QueryExecution.toRdd(
>>> SQLContext.scala:425)
>>>
>>> at org.apache.spark.sql.SchemaRDDLike$class.saveAsParquetFile(
>>> SchemaRDDLike.scala:76)
>>>
>>> at org.apache.spark.sql.SchemaRDD.saveAsParquetFile(SchemaRDD.scala:108)
>>>
>>> at bdrt.MyTest$.createParquetWithDate(MyTest.scala:88)
>>>
>>> at bdrt.MyTest$delayedInit$body.apply(MyTest.scala:54)
>>>
>>> at scala.Function0$class.apply$mcV$sp(Function0.scala:40)
>>>
>>> at scala.runtime.AbstractFunction0.apply$mcV$sp(
>>> AbstractFunction0.scala:12)
>>>
>>> at scala.App$$anonfun$main$1.apply(App.scala:71)
>>>
>>> at scala.App$$anonfun$main$1.apply(App.scala:71)
>>>
>>> at scala.collection.immutable.List.foreach(List.scala:318)
>>>
>>> at scala.collection.generic.TraversableForwarder$class.foreach(
>>> TraversableForwarder.scala:32)
>>>
>>> at scala.App$class.main(App.scala:71)
>>>
>>> at bdrt.MyTest$.main(MyTest.scala:10)
>>>
>>>
>>>
>>
>

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