[ https://issues.apache.org/jira/browse/ARROW-9464?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Andy Grove resolved ARROW-9464. ------------------------------- Fix Version/s: 2.0.0 Resolution: Fixed Issue resolved by pull request 8029 [https://github.com/apache/arrow/pull/8029] > [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 > Labels: pull-request-available > Fix For: 2.0.0 > > Time Spent: 1h 20m > Remaining Estimate: 0h > > 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. > > -- This message was sent by Atlassian Jira (v8.3.4#803005)