+1 Regards, Vaquar khan
On Oct 11, 2017 10:14 PM, "Weichen Xu" <weichen...@databricks.com> wrote: +1 On Thu, Oct 12, 2017 at 10:36 AM, Xiao Li <gatorsm...@gmail.com> wrote: > +1 > > Xiao > > On Mon, 9 Oct 2017 at 7:31 PM Reynold Xin <r...@databricks.com> wrote: > >> +1 >> >> One thing with MetadataSupport - It's a bad idea to call it that unless >> adding new functions in that trait wouldn't break source/binary >> compatibility in the future. >> >> >> On Mon, Oct 9, 2017 at 6:07 PM, Wenchen Fan <cloud0...@gmail.com> wrote: >> >>> I'm adding my own +1 (binding). >>> >>> On Tue, Oct 10, 2017 at 9:07 AM, Wenchen Fan <cloud0...@gmail.com> >>> wrote: >>> >>>> I'm going to update the proposal: for the last point, although the >>>> user-facing API (`df.write.format(...).option(...).mode(...).save()`) >>>> mixes data and metadata operations, we are still able to separate them in >>>> the data source write API. We can have a mix-in trait `MetadataSupport` >>>> which has a method `create(options)`, so that data sources can mix in this >>>> trait and provide metadata creation support. Spark will call this `create` >>>> method inside `DataFrameWriter.save` if the specified data source has it. >>>> >>>> Note that file format data sources can ignore this new trait and still >>>> write data without metadata(it doesn't have metadata anyway). >>>> >>>> With this updated proposal, I'm calling a new vote for the data source >>>> v2 write path. >>>> >>>> The vote will be up for the next 72 hours. Please reply with your vote: >>>> >>>> +1: Yeah, let's go forward and implement the SPIP. >>>> +0: Don't really care. >>>> -1: I don't think this is a good idea because of the following >>>> technical reasons. >>>> >>>> Thanks! >>>> >>>> On Tue, Oct 3, 2017 at 12:03 AM, Wenchen Fan <cloud0...@gmail.com> >>>> wrote: >>>> >>>>> Hi all, >>>>> >>>>> After we merge the infrastructure of data source v2 read path, and >>>>> have some discussion for the write path, now I'm sending this email to >>>>> call >>>>> a vote for Data Source v2 write path. >>>>> >>>>> The full document of the Data Source API V2 is: >>>>> https://docs.google.com/document/d/1n_vUVbF4KD3gxTmkNEon5qdQ >>>>> -Z8qU5Frf6WMQZ6jJVM/edit >>>>> >>>>> The ready-for-review PR that implements the basic infrastructure for >>>>> the write path: >>>>> https://github.com/apache/spark/pull/19269 >>>>> >>>>> >>>>> The Data Source V1 write path asks implementations to write a >>>>> DataFrame directly, which is painful: >>>>> 1. Exposing upper-level API like DataFrame to Data Source API is not >>>>> good for maintenance. >>>>> 2. Data sources may need to preprocess the input data before writing, >>>>> like cluster/sort the input by some columns. It's better to do the >>>>> preprocessing in Spark instead of in the data source. >>>>> 3. Data sources need to take care of transaction themselves, which is >>>>> hard. And different data sources may come up with a very similar approach >>>>> for the transaction, which leads to many duplicated codes. >>>>> >>>>> To solve these pain points, I'm proposing the data source v2 writing >>>>> framework which is very similar to the reading framework, i.e., >>>>> WriteSupport -> DataSourceV2Writer -> DataWriterFactory -> DataWriter. >>>>> >>>>> Data Source V2 write path follows the existing FileCommitProtocol, and >>>>> have task/job level commit/abort, so that data sources can implement >>>>> transaction easier. >>>>> >>>>> We can create a mix-in trait for DataSourceV2Writer to specify the >>>>> requirement for input data, like clustering and ordering. >>>>> >>>>> Spark provides a very simple protocol for uses to connect to data >>>>> sources. A common way to write a dataframe to data sources: >>>>> `df.write.format(...).option(...).mode(...).save()`. >>>>> Spark passes the options and save mode to data sources, and schedules >>>>> the write job on the input data. And the data source should take care of >>>>> the metadata, e.g., the JDBC data source can create the table if it >>>>> doesn't >>>>> exist, or fail the job and ask users to create the table in the >>>>> corresponding database first. Data sources can define some options for >>>>> users to carry some metadata information like partitioning/bucketing. >>>>> >>>>> >>>>> The vote will be up for the next 72 hours. Please reply with your vote: >>>>> >>>>> +1: Yeah, let's go forward and implement the SPIP. >>>>> +0: Don't really care. >>>>> -1: I don't think this is a good idea because of the following >>>>> technical reasons. >>>>> >>>>> Thanks! >>>>> >>>> >>>> >>> >>
