Getting back to your window query example:
> Consider the Window function:
> SELECT
> AGG1 over (partition by a),
> AGG2 over (partition by b),
> AGG3 over (partition by c),
> ...
> FROM input
Window is quite special because the logical vs physical operator count is not 1
to 1, generally we generate a physical window operator for each window function
with different partition column. That determines that once the physical
operators are created, their order can't be changed. Hence your proposal of
passing required traits to physical rule can mitigate the problem.
But things would be much easier if we define a different physical window
operator.
For the above query, we can generate the *Single* physical window operator like
this:
PhysicalWindow[AGG1 over (partition by a), AGG2 over (partition by b), AGG3
over (partition by c)]
or PhysicalWindow(a, b, c) for brevity.
How do we define the physical properties for it?
The operator delivers hash distribution on first window partition column a, but
requires its child input to be hash distributed by its last window partition
column c.
If the parent operator request hash distribution on b, or c, the window
operator will be called on "passthrough" method and generate PhysicalWindow(b,
a, c), or PhysicalWindow(c, a, b). After final plan is generated, during post
processing, we can replace the window operator with multiple layer nested
window operators, and insert Exchange operators if necessary. But frankly
speaking, I haven't seen any use cases of this kind in production.
Regarding the rule alternative you proposed;
> class PhysicalAggregateRule extends PhysicalRule {
> void onMatch(RelOptRuleCall call, *RelTraitSet requiredTraits*) {...
Consider the following plan:
InnerJoin (on a)
+-- Agg (on b)
+-- Scan
For the inner join, we can generate sort merge join and hash join.
The sort merge join can request the following traits to Agg:
1) Singleton
2) hash distribution on a, sorted by a
The hash join can request the following traits to Agg:
1) Singleton
2) hash distribution on a
3) any distribution
4) broadcast distribution
The PhysicalAggregateRule will be called and executed 5 times, while generating
the same physical aggregate candidates, unless we pass a whole list of required
traits to the physical rule, which I have prototyped some time ago with the
exact idea.
Regards,
Haisheng Yuan
On 2021/05/27 17:44:23, Haisheng Yuan <hy...@apache.org> wrote:
> > In distributed systems, an implementation rule may produce different
> > physical operators depending on the input traits. Examples are Aggregate,
> > Sort, Window.
>
> No, in most cases, physical operators are generated regardless the input,
> because the input traits are not know yet. Window might be an exception.
>
> > Since input traits are not known when the rule is fired, we must
> > generate *all possible combinations* of physical operators that we may
> > need. For LogicalAggregate, we must generate 1-phase and 2-phase
> > alternatives. For LogicalSort, we also have 1-phase and 2-phase
> > alternatives. Etc.
>
> IMHO, 1 phase and 2 phase are just different logical alternatives, that is
> also why I call it a logical rule to split the aggregate into a 2 phase
> aggregate. But HashAggregate and StreamAggregate are indeed the different
> physical alternatives for a LogicalAggregate.
>
>
> > Unlike Aggregate or Sort, which may have only 1 or 2 phases, certain
> > logical operators may have many physical alternatives. Consider the Window
> > function:......
>
> In window implementation rule, when building physical operator for Window
> that has multiple window functions but with different partition columns, we
> can infer the possible traits that can be delivered by input operators by
> creating your own RelMetaData, hence multiple window combination with certain
> order, but not exhausted enumeration. In fact, the window ordering problem
> exists in every different kind of optimizer.
>
> > As input traits are not known when the rule is fired, the nodes emitted
> > from the implementation rules most likely would not be used in the final
> > plan.
>
> That is quite normal, any operator generated by implementation rule might not
> be used in the final plan, because there may be tens of thousands of
> alternatives, we only choose the one with lowest cost.
>
> > For example, I can create a physical aggregate that demands
> > non-strict distribution {a,b} from its input, meaning that both [a,b] and
> > [b,a] is ok. However, in the final plan, we are obligated to have a strict
> > distribution - either [a, b] in that order, or [b, a] in that order -
> > otherwise, physical operators like Join and Union will not work.
>
> It depends on your own satisfaction model and how do you coordinate property
> requirement among child operators. Unlike Orca optimizer, where there is
> exact match, partial satisfying, orderless match etc, Calcite's default
> implementation always require exact satisfying. But we can still make use of
> "passThrough" and "derive" to achieve our goal. i.e. the aggregate generated
> by implementation rule requires itself and its child to delivered
> distribution on [a,b], but the "derive" method tells Aggregate that [b,a] is
> available, it can generate another option to require [b,a] instead.
>
> > In distributed engines, the nodes emitted from rules are basically
> > "templates"
> > that must be replaced with normal nodes.
>
> There is no difference between distributed and non-distributed engines when
> dealing with this. In Orca and CockroachDB optimizer, the nodes emitted from
> rules are operators without physical properties, the optimizer then request
> physical properties in top-down manner, either recursively or stack, or state
> machine. Calcite is quite different. when the physical operator is generated
> by implementation rule, the physical operator must has its own traits, at the
> same time, the traits that it expects its child operators to deliver. So in
> Calcite, they are not "templates". The difference is there since Calcite's
> inception.
>
> Regards,
> Haisheng Yuan
>
> On 2021/05/27 08:59:33, Vladimir Ozerov <ppoze...@gmail.com> wrote:
> > Hi Haisheng,
> >
> > Thank you for your inputs. They are really helpful. Let me summarize your
> > feedback in my own words to verify that I understand it correctly.
> >
> > 1. In distributed systems, an implementation rule may produce different
> > physical operators depending on the input traits. Examples are Aggregate,
> > Sort, Window.
> > 2. Since input traits are not known when the rule is fired, we must
> > generate *all possible combinations* of physical operators that we may
> > need. For LogicalAggregate, we must generate 1-phase and 2-phase
> > alternatives. For LogicalSort, we also have 1-phase and 2-phase
> > alternatives. Etc.
> > 3. If all combinations are generated, it is expected that "passThrough"
> > and "derive" would be just trivial replacements of traits for most cases.
> > This is why "passThroughTraits" and "deriveTraits" are recommended. A
> > notable exception is TableScan that may emit alternative indexes in
> > response to the pass-through requests.
> >
> > If my understanding is correct, then there are several issues with this
> > approach still.
> >
> > 1. Unlike Aggregate or Sort, which may have only 1 or 2 phases, certain
> > logical operators may have many physical alternatives. Consider the Window
> > function:
> > SELECT
> > AGG1 over (partition by a),
> > AGG2 over (partition by b),
> > AGG3 over (partition by c),
> > ...
> > FROM input
> >
> > To calculate each aggregate, we need to re-shuffle the input based on the
> > partition key. The key question is the order of reshuffling. If the input
> > is shared by [a], I want to calculate AGG1 locally and then re-shuffle the
> > input to calculate other aggregates. For the remaining AGG2 and AGG3, the
> > order is also important. If the parent demands sharding by [b], then the
> > proper sequence is b-c-a:
> > 1: Window[AGG2 over (partition by b)] // SHARDED[b]
> > 2: Window[AGG3 over (partition by c)] // SHARDED[c]
> > 3: Window[AGG1 over (partition by a)] // SHARDED[a]
> > 4: Input // SHARDED[a]
> >
> > But if the parent demands [c], the proper sequence is c-b-a. Since we do
> > not know real distributions when the rule is fired, we must emit all the
> > permutations to ensure that no optimization opportunity is missed. But with
> > complex window aggregate, this might be impractical because we will emit
> > lots of unnecessary nodes.
> >
> > 2. As input traits are not known when the rule is fired, the nodes emitted
> > from the implementation rules most likely would not be used in the final
> > plan. For example, I can create a physical aggregate that demands
> > non-strict distribution {a,b} from its input, meaning that both [a,b] and
> > [b,a] is ok. However, in the final plan, we are obligated to have a strict
> > distribution - either [a, b] in that order, or [b, a] in that order -
> > otherwise, physical operators like Join and Union will not work. In
> > distributed engines, the nodes emitted from rules are basically "templates"
> > that must be replaced with normal nodes.
> >
> > Does this reasoning make any sense? If yes, it means that the current
> > approach forces us to produce many unnecessary nodes to explore the full
> > search space. The question is whether alternative approaches could better
> > fit the requirements of the distributed engine? This is a purely
> > theoretical question. I am currently looking deeper at CockroachDB. They
> > have very different architecture: no separation between logical and
> > physical nodes, physical properties are completely decoupled from nodes,
> > usage of recursion instead of the stack, etc.
> >
> > Regards,
> > Vladimir.
> >
> > чт, 27 мая 2021 г. в 03:19, Haisheng Yuan <hy...@apache.org>:
> >
> > > 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
> > > > >
> > > >
> > >
> >
>
