Awesome ! That would be great !!
On Mon, Jan 26, 2015 at 3:18 PM, Michael Armbrust mich...@databricks.com
wrote:
I'm aiming for 1.3.
On Mon, Jan 26, 2015 at 3:05 PM, Manoj Samel manojsamelt...@gmail.com
wrote:
Thanks Michael. I am sure there have been many requests for this support.
Any
I'm aiming for 1.3.
On Mon, Jan 26, 2015 at 3:05 PM, Manoj Samel manojsamelt...@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 mich...@databricks.com
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 mich...@databricks.com
wrote:
Those annotations actually don't work because the timestamp is SQL has
optional nano-second
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 manojsamelt...@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