+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! >>> >> >> >
