I agree it would be a clean approach if data source is only responsible to write into an already-configured table. However, without catalog federation, Spark doesn't have an API to ask an external system(like Cassandra) to create a table. Currently it's all done by data source write API. Data source implementations are responsible to create or insert a table according to the save mode.
As a workaround, I think it's acceptable to pass partitioning/bucketing information via data source options, and data sources should decide to take these informations and create the table, or throw exception if these informations don't match the already-configured table. On Fri, Sep 22, 2017 at 9:35 AM, Ryan Blue <rb...@netflix.com> wrote: > > input data requirement > > Clustering and sorting within partitions are a good start. We can always > add more later when they are needed. > > The primary use case I'm thinking of for this is partitioning and > bucketing. If I'm implementing a partitioned table format, I need to tell > Spark to cluster by my partition columns. Should there also be a way to > pass those columns separately, since they may not be stored in the same way > like partitions are in the current format? > > On Wed, Sep 20, 2017 at 3:10 AM, Wenchen Fan <cloud0...@gmail.com> wrote: > >> Hi all, >> >> I want to have some discussion about Data Source V2 write path before >> starting a voting. >> >> 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 a data source writing framework >> which is very similar to the reading framework, i.e., WriteSupport -> >> DataSourceV2Writer -> WriteTask -> DataWriter. You can take a look at my >> prototype to see what it looks like: https://github.com/apach >> e/spark/pull/19269 >> >> There are some other details need further discussion: >> 1. *partitioning/bucketing* >> Currently only the built-in file-based data sources support them, but >> there is nothing stopping us from exposing them to all data sources. One >> question is, shall we make them as mix-in interfaces for data source v2 >> reader/writer, or just encode them into data source options(a >> string-to-string map)? Ideally it's more like options, Spark just transfers >> these user-given informations to data sources, and doesn't do anything for >> it. >> >> 2. *input data requirement* >> Data sources should be able to ask Spark to preprocess the input data, >> and this can be a mix-in interface for DataSourceV2Writer. I think we need >> to add clustering request and sorting within partitions request, any more? >> >> 3. *transaction* >> I think we can just follow `FileCommitProtocol`, which is the internal >> framework Spark uses to guarantee transaction for built-in file-based data >> sources. Generally speaking, we need task level and job level commit/abort. >> Again you can see more details in my prototype about it: >> https://github.com/apache/spark/pull/19269 >> >> 4. *data source table* >> This is the trickiest one. In Spark you can create a table which points >> to a data source, so you can read/write this data source easily by >> referencing the table name. Ideally data source table is just a pointer >> which points to a data source with a list of predefined options, to save >> users from typing these options again and again for each query. >> If that's all, then everything is good, we don't need to add more >> interfaces to Data Source V2. However, data source tables provide special >> operators like ALTER TABLE SCHEMA, ADD PARTITION, etc., which requires data >> sources to have some extra ability. >> Currently these special operators only work for built-in file-based data >> sources, and I don't think we will extend it in the near future, I propose >> to mark them as out of the scope. >> >> >> Any comments are welcome! >> Thanks, >> Wenchen >> > > > > -- > Ryan Blue > Software Engineer > Netflix >
