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From Michael Armbrust <mich...@databricks.com>
Subject Re: spark 1.2 - Writing parque fails for timestamp with "Unsupported datatype TimestampType"
Date Mon, 26 Jan 2015 23:18:11 GMT
I'm aiming for 1.3.

On Mon, Jan 26, 2015 at 3:05 PM, Manoj Samel <manojsameltech@gmail.com>
wrote:

> 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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