Can there be an explicit create function?
On Sun, Sep 24, 2017 at 7:17 PM, Wenchen Fan <cloud0...@gmail.com> wrote: > 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/apache/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 >> > >
