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 > > >
