This PR adds a new strategy, `FileSourceStrategy`, that can be used for planning scans of collections of files that might be partitioned or bucketed.
Compared with the existing planning logic in `DataSourceStrategy` this version has the following desirable properties:
- It removes the need to have `RDD`, `broadcastedHadoopConf` and other distributed concerns in the public API of `org.apache.spark.sql.sources.FileFormat`
- Partition column appending is delegated to the format to avoid an extra copy / devectorization when appending partition columns
- It minimizes the amount of data that is shipped to each executor (i.e. it does not send the whole list of files to every worker in the form of a hadoop conf)
- it natively supports bucketing files into partitions, and thus does not require coalescing / creating a `UnionRDD` with the correct partitioning.
- Small files are automatically coalesced into fewer tasks using an approximate bin-packing algorithm.
Currently only a testing source is planned / tested using this strategy. In follow-up PRs we will port the existing formats to this API.
A stub for `FileScanRDD` is also added, but most methods remain unimplemented.
Other minor cleanups:
- partition pruning is pushed into `FileCatalog` so both the new and old code paths can use this logic. This will also allow future implementations to use indexes or other tricks (i.e. a MySQL metastore)
- The partitions from the `FileCatalog` now propagate information about file sizes all the way up to the planner so we can intelligently spread files out.
- `Array` -> `Seq` in some internal APIs to avoid unnecessary `toArray` calls
- Rename `Partition` to `PartitionDirectory` to differentiate partitions used earlier in pruning from those where we have already enumerated the files and their sizes.
Author: Michael Armbrust <michael@databricks.com>
Closes#11646 from marmbrus/fileStrategy.
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