Hi Jinpeng,
Thank you, I would try this approach with aggregates. But as I mentioned in
the previous email, it is not ideal, because we may generate wasteful
alternatives. This is so because we generate the physical nodes outside of
the optimization context: (1) we do not know what the parent needs, and (2)
we do not know which physical children are available. For example, if there
is only a single Aggregate on top of the Scan, I can generate only a single
good alternative after the input is optimized instead of pessimistically
emitting both 1-phase and 2-phase aggregates.
Speaking of imaginary alternative implementation, consider that we have a
slightly different API. First, we pass the optimization request to the
"RelOptRule.onMatch". Second, the "RelOptRule.convert" triggers the
synchronous optimization of the child group wrt the adjusted optimization
request, and returns the List<RelTraitSet> for produced physical child
nodes. That is, we basically converge "passThrough" and "derive" to a
single point - "RelOptRule.onMatch". If this would be the case, there is a
chance that we would not need to generate some physical nodes if there is
either no demand from the parent, or there are no matching inputs.
class PhysicalAggregateRule extends PhysicalRule {
void onMatch(RelOptRuleCall call, *RelTraitSet requiredTraits*) {
Aggregate logicalAgg = call.get(0);
RelNode input = logicalAgg.getInput();
// 1. Pass-through: calculate required input properties
// based on the current optimization request. There
// could be several such properties in the general
// case.
RelTraitSet requiredInputTraits =
calculateInputTraitSet(logicalAgg, requiredTraits);
// 2. Derive: *optimize input* wrt the required properties.
// Return collection of available physical input traits.
List<RelTraitSet> derivedInputTraits =
optimize(input, requiredInputTraits);
// 3. Create physical implementations based on (a) parent request,
// (b) available inputs.
List<PhysicalAggregate> physicalAggs =
createPhysicalAggregates(requiredTraits, derivedInputTraits, ...);
}
}
I wonder if this idea might be viable from a theoretical standpoint. AFAIU
the original Columbia paper doesn't address this: their APPLY_RULE task
receives the optimization context, but it is not propagated to the rule,
and the equivalent nodes are generated from a rule before O_INPUTS. In the
code snippet above, the optimization context is passed to the rule, and the
O_INPUTS is invoked from the rule before the equivalent node is emitted.
Regards,
Vladimir.
чт, 27 мая 2021 г. в 11:20, Jinpeng Wu <wjpabc...@gmail.com>:
> Hi，Vladimir. This could be a picture of how calcite optimize the two
> aggregates problem:
>
> step 1:
> Without any hints for pruning, BOTH implementation of aggregations should
> be built and held in memo.
> For the top aggregation, the one-pass implementation requests a
> HASH_DISTRIBUTED [a] distribution to its input and the two-pass
> implementation requests an "ANY" distribution to its input.
> When bottom aggregation gets built, it also builds two implementations. So
> we get 4 valids candidates:
>
> // Candidate 1. top agg is one-pass and bottom agg is one-pass
> PhysicalAggregate[group=[a], F2_phase2(c)]
> Exchange[dist[a]]
> PhysicalAggregate[group=[a,b], F1_phase2(c)]
> Exchange[dist[a,b]]
> ...
>
> // Candidate 2. top agg is one-pass and bottom agg is two-pass
> PhysicalAggregate[group=[a], F2_phase2(c)]
> Exchange[dist[a]]
> PhysicalAggregate[group=[a,b], F1_phase2(c)]
> Exchange[dist[a,b]]
> PhysicalAggregate[group=[a,b], F1_phase1(c)]
> ...
>
> // Candidate 3. top agg is two-pass, bottom agg is one-pass
> PhysicalAggregate[group=[a], F2_phase2(c)]
> Exchange[dist[a]]
> PhysicalAggregate[group=[a], F2_phase1(c)]
> PhysicalAggregate[group=[a,b], F1_phase2(c)]
> Exchange[dist[a,b]]
> ...
>
> // Candidate 4. top agg is two-pass, bottom agg is two-pass
> PhysicalAggregate[group=[a], F2_phase2(c)]
> Exchange[dist[a]]
> PhysicalAggregate[group=[a], F2_phase1(c)]
> PhysicalAggregate[group=[a,b], F1_phase2(c)]
> Exchange[dist[a,b]]
> PhysicalAggregate[group=[a,b], F1_phase1(c)]
> ...
>
> step 2:
> No matter which aggregation is built first. The calcite framework passes
> the HASH_DISTRIBUTED[a] trait requirement through bottom aggregation, both
> implementations. Note that a concrete physical node only needs to consider
> its own implementation. And we get two more valid candidates:
>
> // Candidate 5. passThrough called on candidate1
> PhysicalAggregate[group=[a], F2_phase2(c)]
> PhysicalAggregate[group=[a,b], F1_phase2(c)] // deliver dist[a]
> Exchange[dist[a]]
> ...
>
> // Candidate 6. passThrough called on candidate2
> PhysicalAggregate[group=[a], F2_phase2(c)]
> PhysicalAggregate[group=[a,b], F1_phase2(c)] // deliver dist[a]
> Exchange[dist[a]]
> PhysicalAggregate[group=[a,b], F1_phase1(c)]
> ...
>
> step 3:
> The cost model chooses the best candidate.
> Note that Candidate 5 is not always the best. For example, when it is
> detected, from stats or other, that data is skewed on key [a], Candidate 2
> may be better. When it is detected that NDV(a, b) = 0.99 * ROWCOUNT() ,
> Candidate 6 is preferred, as partial aggregate can reduce little data. So
> it is not wasty to build all those candidates.
>
> Most of the above works are done by calcite frameworks. Users only need to:
> 1. Fire both implementations during aggregation builds.
> 2. Overwrite the passThroughTraits method.
>
> Thanks,
> Jinpeng Wu
>
>
> On Thu, May 27, 2021 at 8:19 AM Haisheng Yuan <hy...@apache.org> wrote:
>
> > Another point I would like to mention is that it is not recommended to
> > override method "passThrough" and "derive" directly, override
> > "passThroughTraits" and "deriveTraits" instead, so that we can make sure
> > only the same type of physical node is created and no nested relnodes or
> > additional RelSets are created, unless you know you have to create
> > different type of nodes. For example, if the table foo has an btree index
> > on column a, and the parent relnode is requesting ordering on column a,
> > then we may consider to override "passThrough" of TableScan to return an
> > IndexScan instead of a TableScan.
> >
> > Regards,
> > Haisheng Yuan
> > On 2021/05/26 22:45:20, Haisheng Yuan <hy...@apache.org> wrote:
> > > Hi Vladimir,
> > >
> > > 1. You need a logical rule to split the aggregate into a local
> aggregate
> > and global aggregate, for example:
> > >
> >
> https://github.com/greenplum-db/gporca/blob/master/libgpopt/src/xforms/CXformSplitGbAgg.cpp
> > > Only implementation rules can convert a logical node to a physical node
> > or multiple physical nodes.
> > > After physical implementation, you have 2 physical alternatives:
> > > 1) single phase global physical aggregate,
> > > 2) 2 phase physical aggregate with local and global aggregate.
> > > It should be up to the cost to decide which one to choose.
> > >
> > > 2. Given a desired traitset from parent node, the current relnode only
> > needs to generate a single relnode after passing down the traitset.
> Given a
> > traitset delivered by child node, the current relnode only derive a
> single
> > relnode. Quite unlike other optimizer, in Calcite's top-down optimizer,
> you
> > don't need to worry about issuing multiple optimization requests to
> inputs,
> > which is handled by Calcite framework secretly. i.e.
> > > SELECT a, b, min(c) from foo group by a, b;
> > > In many other optimizer, we probably need ask the aggregate to issue 3
> > distribution requests for tablescan on foo, which are
> > > 1) hash distributed by a,
> > > 2) hash distributed by b,
> > > 3) hash distributed by a, b
> > > However in Calcite top-down optimizer, your physical implementation
> rule
> > for global aggregate only need generate a single physical node with hash
> > distribution by a, b. In case the table foo happens to be distributed by
> a,
> > or b, the derive() method will tell you there is an opportunity. This is
> > the feature that Calcite's top-down optimizer excels over other
> optimizers,
> > because this can dramatically reduce the search space while keeping the
> > optimal optimization opportunity.
> > >
> > > 3. This is by design. Nodes produced from "passThrough" and "derive"
> and
> > just sibling physical node with different traitset, we only need the
> > initial physical nodes after implementation to avoid unnecessary
> > operations. The fundamental reason is, unlike Orca optimizer where
> physical
> > node and physical property are separate things, Calcite's
> logical/physical
> > nodes contains traitset. With regard to the latter question, can you give
> > an example?
> > >
> > > Regards,
> > > Haisheng Yuan
> > >
> > >
> > > On 2021/05/26 20:11:57, Vladimir Ozerov <ppoze...@gmail.com> wrote:
> > > > Hi,
> > > >
> > > > I tried to optimize a certain combination of operators for the
> > distributed
> > > > engine and got stuck with the trait propagation in the top-down
> > engine. I
> > > > want to ask the community for advice on whether the problem is
> solvable
> > > > with the current Apache Calcite implementation or not.
> > > >
> > > > Consider the following logical tree:
> > > > 3: LogicalAggregate[group=[a], F2(c)]
> > > > 2: LogicalAggregate[group=[a,b], F1(c)]
> > > > 1: LogicalScan[t]
> > > >
> > > > Consider that these two aggregates cannot be merged or simplified for
> > > > whatever reason. We have only a set of physical rules to translate
> this
> > > > logical tree to a physical tree. Also, there could be any number of
> > > > other operators between these two aggregates. We omit them for
> clarity,
> > > > assuming that the distribution is not destroyed.
> > > >
> > > > In the distributed environment, non-collocated aggregates are often
> > > > implemented in two phases: local pre-aggregation and final
> aggregation,
> > > > with an exchange in between. Consider that the Scan operator is hash
> > > > distributed by some key other than [a] or [b]. If we optimize
> operators
> > > > without considering the whole plan, we may optimize each operator
> > > > independently, which would give us the following plan:
> > > > 3: PhysicalAggregate[group=[a], F2_phase2(c)] //
> > > > HASH_DISTRIBUTED [a]
> > > > 3: Exchange[a] //
> > > > HASH_DISTRIBUTED [a]
> > > > 3: PhysicalAggregate[group=[a], F2_phase1(c)] //
> > > > HASH_DISTRIBUTED [a,b]
> > > > 2: PhysicalAggregate[group=[a,b], F1_phase2(c)] //
> > > > HASH_DISTRIBUTED [a,b]
> > > > 2: Exchange[a, b] //
> > > > HASH_DISTRIBUTED [a,b]
> > > > 2: PhysicalAggregate[group=[a,b], F1_phase1(c)] //
> > > > HASH_DISTRIBUTED [d]
> > > > 1: PhysicalScan[t] //
> > > > HASH_DISTRIBUTED [d]
> > > >
> > > > This plan is not optimal, because we re-hash inputs twice. A better
> > plan
> > > > that we want to get:
> > > > 3: PhysicalAggregate[group=[a], F2(c)] //
> > HASH_DISTRIBUTED
> > > > [a]
> > > > 2: PhysicalAggregate[group=[a,b], F1_phase2(c)] //
> > HASH_DISTRIBUTED
> > > > [a]
> > > > 2: Exchange[a] //
> > HASH_DISTRIBUTED
> > > > [a]
> > > > 2: PhysicalAggregate[group=[a,b], F1_phase1(c)] //
> > HASH_DISTRIBUTED
> > > > [d]
> > > > 1: PhysicalScan[t] //
> > HASH_DISTRIBUTED
> > > > [d]
> > > >
> > > > In this case, we take advantage of the fact that the distribution [a]
> > is
> > > > compatible with [a,b]. Therefore we may enforce only [a], instead of
> > doing
> > > > [a,b] and then [a]. Since exchange operators are very expensive, this
> > > > optimization may bring a significant boost to the query engine. Now
> the
> > > > question - how do we reach that state? Intuitively, a pass-through is
> > > > exactly what we need. We may pass the optimization request from top
> > > > aggregate to bottom aggregate to find physical implementations shared
> > by
> > > > [a]. But the devil is in the details - when and how exactly to pass
> > this
> > > > request?
> > > >
> > > > Typically, we have a conversion rule that converts a logical
> aggregate
> > to a
> > > > physical aggregate. We may invoke "convert" on the input to initiate
> > the
> > > > pass-through:
> > > >
> > > > RelNode convert(...) {
> > > > return new PhysicalAggregate(
> > > > convert(input, HASH_DISTRIBUTED[a])
> > > > )
> > > > }
> > > >
> > > > The first problem - we cannot create the normal physical aggregate
> here
> > > > because we do not know input traits yet. The final decision whether
> to
> > do a
> > > > one-phase or two-phase aggregate can be made only in the
> > > > "PhysicalNode.derive" method when concrete input traits are resolved.
> > > > Therefore the converter rule should create a kind of "template"
> > physical
> > > > operator, which would be used to construct the final operator(s) when
> > input
> > > > traits are resolved. AFAIU Enumerable works similarly: we create
> > operators
> > > > with virtually arbitrary traits taken from logical nodes in the
> > conversion
> > > > rules. We only later do create normal nodes in the derive() methods.
> > > >
> > > > The second problem - our top aggregate doesn't actually need the
> > > > HASH_DISTRIBUTED[a] input. Instead, it may accept inputs with any
> > > > distribution. What we really need is to inform the input (bottom
> > aggregate)
> > > > that it should look for additional implementations that satisfy
> > > > HASH_DISTRIBUTED[a]. Therefore, enforcing a specific distribution on
> > the
> > > > input using the "convert" method is not what we need because this
> > > > conversion might enforce unnecessary exchanges.
> > > >
> > > > The third problem - derivation. Consider that we delivered the
> > optimization
> > > > request to the bottom aggregate. As an implementor, what am I
> supposed
> > to
> > > > do in this method? I cannot return the final aggregate from here
> > because
> > > > the real input traits are not derived yet. Therefore, I can only
> return
> > > > another template, hoping that the "derive" method will be called on
> it.
> > > > However, this will not happen because trait derivation is skipped on
> > the
> > > > nodes emitted from pass-through. See "DeriveTrait.perform" [1].
> > > >
> > > > BottomAggregate {
> > > > RelNode passThrough(distribution=HASH_DISTRIBUTED[a]) {
> > > > // ???
> > > > }
> > > > }
> > > >
> > > > I feel that I am either going in the wrong direction, or some gaps in
> > the
> > > > product disallow such optimization. So I would like to ask the
> > community to
> > > > assist with the following questions:
> > > > 1. In the top-down optimizer, how should we convert a logical node
> to a
> > > > physical node, provided that "derive" is not called yet? I have a gut
> > > > feeling that the trait propagation is currently not implemented to
> the
> > full
> > > > extent because based on Cascades paper I would expect that parent
> > physical
> > > > nodes are produced after the child physical nodes. But in our rules,
> > this
> > > > is not the case - some physical nodes are produced before the trait
> > > > derivation.
> > > > 2. How to propagate several optimization requests to inputs? We need
> > either
> > > > inputs with a specific distribution or inputs with an arbitrary
> > > > distribution in the example above. It seems that to achieve that, I
> > need to
> > > > emit several alternative nodes with different requirements to inputs.
> > Does
> > > > it make sense?
> > > > 3. Why are nodes produced from the "passThrough" method excluded from
> > trait
> > > > derivation? If this is by design, how can I preserve the optimization
> > > > request to satisfy it on the derivation stage when input traits are
> > > > available?
> > > >
> > > > Regards,
> > > > Vladimir.
> > > >
> > > > [1]
> > > >
> >
> https://github.com/apache/calcite/blob/calcite-1.26.0/core/src/main/java/org/apache/calcite/plan/volcano/TopDownRuleDriver.java#L828
> > > >
> > >
> >
>
