mingmwang opened a new issue, #284:
URL: https://github.com/apache/arrow-ballista/issues/284
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(This section helps Arrow developers understand the context and *why* for
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In current Ballista code base, when it generates the distributed plan, it
will remove any non-hash repartition from the distributed plan.
And In DataFusion, when it does the physical planning, it added
RepartitionExec node blindly when it sees the hash join or aggregation without
considering the children's output partitioning.
````
match repart.output_partitioning() {
Partitioning::Hash(_, _) => {
let shuffle_writer = create_shuffle_writer(
job_id,
self.next_stage_id(),
children[0].clone(),
Some(repart.partitioning().to_owned()),
)?;
let unresolved_shuffle =
Arc::new(UnresolvedShuffleExec::new(
shuffle_writer.stage_id(),
shuffle_writer.schema(),
shuffle_writer.output_partitioning().partition_count(),
shuffle_writer
.shuffle_output_partitioning()
.map(|p| p.partition_count())
.unwrap_or_else(|| {
shuffle_writer.output_partitioning().partition_count()
}),
));
stages.push(shuffle_writer);
Ok((unresolved_shuffle, stages))
}
_ => {
// remove any non-hash repartition from the distributed
plan
Ok((children[0].clone(), stages))
}
}
````
When I look into Presto's source code, presto's distributed plan can
includes both remote exchanges and local exchanges.
Local exchange can benefit the inner Stage parallelism. Presto can add the
remote exchanges and local exchanges only when necessary. I think it is time
to introduce more advanced methods to reason the partitioning in a distributed
plan, something more powerful than Spark SQL EnsureRequirements rule
Incorporating Partitioning and Parallel Plans into the SCOPE Optimizer
http://www.cs.albany.edu/~jhh/courses/readings/zhou10.pdf
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you've considered.
**Additional context**
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