[jira] [Commented] (ARROW-9464) [Rust] [DataFusion] Physical plan refactor to support optimization rules and more efficient use of threads

["Andy Grove (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-9464?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17176414#comment-17176414 ] 

Andy Grove commented on ARROW-9464:
-----------------------------------

Based on recent experience testing query execution with async, I no longer feel that async makes sense for DataFusion. Async is good for network io but not for file io. It is better to have a dedicated thread per partition when executing queries. 

Also, we can't use async with Parquet currently without launching a dedicated thread per partition which pretty much defeats the point of using async in the first place.

I believe that we do need the concept of executors and a scheduler in DataFusion, where each executor would run on a dedicated thread. Other projects would then be able to extend this for distributed execution for example.

> [Rust] [DataFusion] Physical plan refactor to support optimization rules and more efficient use of threads
> ----------------------------------------------------------------------------------------------------------
>
>                 Key: ARROW-9464
>                 URL: https://issues.apache.org/jira/browse/ARROW-9464
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: Rust, Rust - DataFusion
>            Reporter: Andy Grove
>            Assignee: Andy Grove
>            Priority: Major
>
> I would like to propose a refactor of the physical/execution planning based on the experience I have had in implementing distributed execution in Ballista.
> This will likely need subtasks but here is an overview of the changes I am proposing.
> h3. *Introduce enum to represent physical plan.*
> By wrapping the execution plan structs in an enum, we make it possible to build a tree representing the physical plan just like we do with the logical plan. This makes it easy to print physical plans and also to apply transformations to it.
> {code:java}
>  pub enum PhysicalPlan {
>     /// Projection.
>     Projection(Arc<ProjectionExec>),
>     /// Filter a.k.a predicate.
>     Filter(Arc<FilterExec>),
>     /// Hash aggregate
>     HashAggregate(Arc<HashAggregateExec>),
>     /// Performs a hash join of two child relations by first shuffling the data using the join keys.
>     ShuffledHashJoin(ShuffledHashJoinExec),
>     /// Performs a shuffle that will result in the desired partitioning.
>     ShuffleExchange(Arc<ShuffleExchangeExec>),
>     /// Reads results from a ShuffleExchange
>     ShuffleReader(Arc<ShuffleReaderExec>),
>     /// Scans a partitioned data source
>     ParquetScan(Arc<ParquetScanExec>),
>     /// Scans an in-memory table
>     InMemoryTableScan(Arc<InMemoryTableScanExec>),
> }{code}
> h3. *Introduce physical plan optimization rule to insert "shuffle" operators*
> We should extend the ExecutionPlan trait so that each operator can specify its input and output partitioning needs, and then have an optimization rule that can insert any repartioning or reordering steps required.
> For example, these are the methods to be added to ExecutionPlan. This design is based on Apache Spark.
>  
> {code:java}
> /// Specifies how data is partitioned across different nodes in the cluster
> fn output_partitioning(&self) -> Partitioning {
>     Partitioning::UnknownPartitioning(0)
> }
> /// Specifies the data distribution requirements of all the children for this operator
> fn required_child_distribution(&self) -> Distribution {
>     Distribution::UnspecifiedDistribution
> }
> /// Specifies how data is ordered in each partition
> fn output_ordering(&self) -> Option<Vec<SortOrder>> {
>     None
> }
> /// Specifies the data distribution requirements of all the children for this operator
> fn required_child_ordering(&self) -> Option<Vec<Vec<SortOrder>>> {
>     None
> }
>  {code}
> A good example of applying this rule would be in the case of hash aggregates where we perform a partial aggregate in parallel across partitions and then coalesce the results and apply a final hash aggregate.
> Another example would be a SortMergeExec specifying the sort order required for its children.
>  
>  



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