Hi Igor, Your comment - "because actual cost may be calculated correctly using physical operators only. So won't be able to implement Branch and Bound Space Pruning.“ is actually not true. In Cascade’s lower bound / upper bound pruning algorithm, you can get cost lower bound of input RelNode using cardinality * unit_copy_cost. The unit_copy_cost is a constant, which stands for the minimal cost for processing a tuple from the input. So this will be the minimal cost the input RelNode can achieve, and if this is indeed larger than the current best cost, this input node can be pruned.
In this proposal, we are not trying to split logical and physical planning entirely. But for any given equivalent set, we would need to finish exploration before implementation. But for the entire memo tree, each set could be in different planning stage, and an equivalent set can be pruned even before it’s implemented. A text book example of aforementioned “lower bound / upper bound pruning” would be the join ordering case. Regarding #3, I think we can still achieve that (partially) through this proposal. Remember every time when we start the optimization, we pass down an upper bound limit. Initially this upper bound for root is infinite - meaning that no feasible plan is available. Every time when we find a physical plan we update this upper bound, then start the search again. We could stop the search when the cost is less than a pre-defined threshold - which gives you a “good enough” plan with early termination. Still this wouldn't avoid the logical exploration. For that, you would probably archive through rule configurations, and avoid some expansive transformation to keep the cost down. > On Apr 19, 2020, at 7:30 PM, Haisheng Yuan <hy...@apache.org> wrote: > > Igor, > > a) Given current Calcite's stats derivation strategy, mixing logical and > physical planning won't make it better. I hope you went through my email to > the end, currently operators inside a memo group don't share stats info, each > operator's stats may differ with the other one, and the RelSubset's best node > and stats may vary during optimization. So logical and physical rule pruning > are not safe at the moment, otherwise it almost implies changing downstream > project's cost model silently. > > On the other hand, ensuring child group exploration task finish first will > make rule mutual exclusivity check possible, like the result of > ReduceExpressionRule won't need trigger the same rule again, The join result > of JoinCommuteRule won't need trigger JoinCommuteRule and > ReduceExpressionRule again. > > More importantly, most if not all the the long planning queries in Calcite > are not caused by too many alternatives, but mutual triggering, or cyclic > triggering, which can be avoided by customizing the rules instead of using > the default one. Unless you use dynamic programming (Calcite use heuristic) > on join reordering (left-deep, right-deep, bushy), space pruning won't help > make long / infinite running query faster. > > b) No evidence shows current version of Calcite will return the most > promising plan in first planning iteration. Instead of praying for getting > good enough plan in the first iteration, why not focus on fixing rules that > causes the issue? > > c) That is not the goal. > > On 2020/04/19 15:14:57, Seliverstov Igor <gvvinbl...@gmail.com> wrote: >> Haisheng, >> >> From my point of view splitting logical and physical planning parts isn’t a >> good idea. >> >> I think so because actual cost may be calculated correctly using physical >> operators only. So that we: >> a) won't be able to implement Branch and Bound Space Pruning (as far as I >> understand, at exploring time there are no physical operators, no correct >> costs, but assumptions only, I don’t think we should rely on them) >> b) won’t be able to get good enough plan (without Branch and Bound Space >> Pruning it’s non-trivial task to get right search direction and most >> promising plans in first planning iterations) >> c) won’t be able to tune the good enough plan during several similar queries >> are executed >> >> Regards, >> Igor >> >> >>> 19 апр. 2020 г., в 17:37, Haisheng Yuan <hy...@apache.org> написал(а): >>> >>> Hi Igor, >>> >>> You can't have your cake and eat it. >>> But one feasible work item definitely we can do is that once timeout, stop >>> exploring, use the first available physical operator in each group and >>> optimize it. >>> >>> Because most, if not all, of the long / infinite running optimizations are >>> caused by project related transpose, join reordering (join commute + >>> associative), constant reduction for large expression (see CALCITE-3460), >>> all of which are logical transformations rules and many of which have >>> corresponding JIRAs. So another alternative is, let's fix these bugs to >>> improve Calcite to make timeout option less usable. >>> >>> Another thing worth noting is that sql server optimizer timeout mechanism >>> is not based on clock time, but the number of optimization tasks it has >>> done [1]. >>> >>> [1] >>> https://techcommunity.microsoft.com/t5/sql-server-support/understanding-optimizer-timeout-and-how-complex-queries-can-be/ba-p/319188 >>> >>> Regards, >>> Haisheng >>> >>> On 2020/04/19 11:31:27, Seliverstov Igor <gvvinbl...@gmail.com> wrote: >>>> Haisheng, >>>> >>>> Ok, then such notification isn't needed >>>> >>>> But in this case we don't have any control over how long planning takes >>>> >>>> For some systems it's necessary to get good enough plan right now instead >>>> of best one after while >>>> >>>> For example we've been considering a case when a query is optimised several >>>> times in short iterations in case it's impossible to get the best plan in >>>> reasonable period of time (for example there is an SLA for response time) >>>> >>>> This mean we need all needed physical implementations after each logical >>>> transformation is applied. >>>> >>>> Regards, >>>> Igor >>>> >>>> >>>> вс, 19 апр. 2020 г., 13:55 Haisheng Yuan <hy...@apache.org>: >>>> >>>>> Hi Igor, >>>>> >>>>> There will be no rule queue anymore. Y will be fully explored (logical >>>>> rules are matched and applied) before it can be implemented and optimized. >>>>> >>>>> Thanks, >>>>> Haisheng >>>>> >>>>> On 2020/04/19 10:11:45, Seliverstov Igor <gvvinbl...@gmail.com> wrote: >>>>>> Hi Haisheng, >>>>>> >>>>>> Great explanation, thanks. >>>>>> >>>>>> One thing I'd like to cover in advance is trait propagation process (I >>>>> need >>>>>> it for Apache Ignite SQL engine implementation). >>>>>> >>>>>> For example: >>>>>> >>>>>> There are two nodes: Rel X and its child node Rel Y >>>>>> >>>>>> Both nodes are in Optimized state, and there is a Logical rule for Rel Y >>>>> in >>>>>> a rules queue matched, >>>>>> >>>>>> After logical rule is applied, there is a logical rel Rel Y' and >>>>> containing >>>>>> it set moves into Exploring state (since there is a logical node which >>>>> has >>>>>> to be implemented) >>>>>> >>>>>> After whole process Exploring -> ... -> Optimized we need to force parent >>>>>> Rel X set to switch its state from Optimized to Optimizing to peek the >>>>>> newly implemented child and (possible) produce a new Rel X' physical rel >>>>> on >>>>>> the basis of previously requested from the Rel X set traits. >>>>>> >>>>>> If a new Rel X' is produced, a Rel X' parent should move its state >>>>>> Optimized -> Optimizing and repeat described above operations. >>>>>> >>>>>> Does it look like true? >>>>>> >>>>>> Regards, >>>>>> Igor >>>>>> >>>>>> >>>>>> вс, 19 апр. 2020 г., 6:52 Haisheng Yuan <h.y...@alibaba-inc.com>: >>>>>> >>>>>>> Hi, >>>>>>> >>>>>>> In the past few months, we have discussed a lot about Cascades style >>>>>>> top-down optimization, including on-demand trait derivation/request, >>>>> rule >>>>>>> apply, branch and bound space pruning. Now we think it is time to move >>>>>>> towards these targets. >>>>>>> >>>>>>> We will separate it into several small issues, and each one can be >>>>>>> integrated as a standalone, independent feature, and most importantly, >>>>>>> meanwhile keep backward compatibility. >>>>>>> >>>>>>> 1. Top-down trait request >>>>>>> In other words, pass traits requirements from parent nodes to child >>>>> nodes. >>>>>>> The trait requests happens after all the logical transformation rules >>>>> and >>>>>>> physical implementation rules are done, in a top-down manner, driven >>>>> from >>>>>>> root set. e.g.: >>>>>>> SELECT a, sum(c) FROM >>>>>>> (SELECT * FROM R JOIN S USING (a, b)) t >>>>>>> GROUP BY a; >>>>>>> >>>>>>> Suppose we have the following plan tree in the MEMO, and let's only >>>>>>> consider distribution for simplicity, each group represents a RelSet >>>>> in the >>>>>>> MEMO. >>>>>>> >>>>>>> Group 1: Agg on [a] >>>>>>> Group 2: +-- MergeJoin on [a, b] >>>>>>> Group 3: |--- TableScan R >>>>>>> Group 4: +--- TableScan S >>>>>>> | Group No | Operator | Derived Traits | Required Traits | >>>>>>> | ----------- | ------------- | --------------- | --------------- | >>>>>>> | Group 1 | Aggregate | Hash[a] | N/A | >>>>>>> | Group 2 | MergeJoin | Hash[a,b] | Hash[a] | >>>>>>> | Group 3 | TableScan R | None | Hash[a,b] | >>>>>>> | Group 4 | TableScan S | None | Hash[a,b] | >>>>>>> >>>>>>> We will add new interface PhysicalNode (or extending RelNode) with >>>>>>> methods: >>>>>>> >>>>>>> - Pair<RelTraitSet,List<RelTraitSet>> requireTraits(RelTraitSet >>>>> required); >>>>>>> pair.left is the current operator's new traitset, pair.right is the >>>>> list >>>>>>> of children's required traitset. >>>>>>> >>>>>>> - RelNode passThrough(RelTraitSet required); >>>>>>> Default implementation will call above method requireTraits() and >>>>>>> RelNode.copy() to create new RelNode. Available to be overriden for >>>>>>> physical operators to customize the logic. >>>>>>> >>>>>>> The planner will call above method on MergeJoin operator to pass the >>>>>>> required traits (Hash[a]) to Mergejoin's child operators. >>>>>>> >>>>>>> We will get a new MergeJoin: >>>>>>> MergeJoin hash[a] >>>>>>> |---- TableScan R hash[a] (RelSubset) >>>>>>> +---- TableScan S hash[a] (RelSubset) >>>>>>> >>>>>>> Now the MEMO group looks like: >>>>>>> | Group No | Operator | Derived Traits | Required Traits >>>>> | >>>>>>> | ---------- | -------- ----- | -------------------- | >>>>>>> --------------------- | >>>>>>> | Group1 | Aggregate | Hash[a] | N/A >>>>>>> | >>>>>>> | Group2 | MergeJoin | Hash[a,b], Hash[a]| Hash[a] >>>>>>> | >>>>>>> | Group3 | TableScan R | None | Hash[a,b], >>>>> Hash[a] >>>>>>> | >>>>>>> | Group4 | TableScan S | None | Hash[a,b], >>>>> Hash[a] >>>>>>> | >>>>>>> >>>>>>> Calcite user may choose to ignore / not implement the interface to keep >>>>>>> the original behavior. Each physical operator, according to its own >>>>> logic, >>>>>>> decides whether passThrough() should pass traits down or not by >>>>> returning: >>>>>>> - a non-null RelNode, which means it can pass down >>>>>>> - null object, which means can't pass down >>>>>>> >>>>>>> 2. Provide option to disable AbstractConverter >>>>>>> Once the plan can request traits in top-down way in the framework, many >>>>>>> system don't need AbstractConverter anymore, since it is just a >>>>>>> intermediate operator to generate physical sort / exchange. For those, >>>>> we >>>>>>> can provide option to disable AbstractConverter, generate physical >>>>> enforcer >>>>>>> directly by adding a method to interface Convention: >>>>>>> - RelNode enforce(RelNode node, RelTraitSet traits); >>>>>>> >>>>>>> The default implementation may just calling >>>>> changeTraitsUsingConverters(), >>>>>>> but people may choose to override it if the system has special needs, >>>>> like >>>>>>> several traits must implement together, or the position of collation in >>>>>>> RelTraitSet is before distribution. >>>>>>> >>>>>>> 3. Top-down driven, on-demand rule apply >>>>>>> For every RelNode in a RelSet, rule is matched and applied >>>>> sequentially, >>>>>>> newly generated RelNodes are added to the end of RelNode list in the >>>>> RelSet >>>>>>> waiting for rule apply. RuleQueue and DeferringRuleCall is not needed >>>>>>> anymore. This will make space pruning and rule mutual exclusivity check >>>>>>> possible. >>>>>>> >>>>>>> There are 3 stages for each RelSet: >>>>>>> a). Exploration: logical transformation, yields logical nodes >>>>>>> b). Implementation: physical transformation, yields physical nodes >>>>>>> c). Optimization: trait request, enforcement >>>>>>> >>>>>>> The general process looks like: >>>>>>> - optimize RelSet X: >>>>>>> implement RelSet X >>>>>>> for each physical relnode in RelSet X: >>>>>>> // pass down trait requests to child RelSets >>>>>>> for each child RelSet Y of current relnode: >>>>>>> optimize RelSet Y >>>>>>> >>>>>>> - implement RelSet X: >>>>>>> if X has been implemented: >>>>>>> return >>>>>>> explore RelSet X >>>>>>> for each relnode in RelSet X: >>>>>>> apply physical rules >>>>>>> - explore RelSet X: >>>>>>> if X has been explored >>>>>>> return >>>>>>> for each relnode in RelSet X: >>>>>>> // ensure each child RelSet of current relnode is explored >>>>>>> for each child RelSet Y of current relnode: >>>>>>> explore RelSet Y >>>>>>> apply logical rules on current relnode >>>>>>> >>>>>>> Basically it is a state machine with several states: Initialized, >>>>>>> Explored, Exploring, Implemented, Implementing, Optimized, Optimizing >>>>> and >>>>>>> several transition methods: exploreRelSet, exploreRelNode, >>>>> implementRelSet, >>>>>>> implementRelNode, optimizeRelSet, optimizeRelNode... >>>>>>> >>>>>>> To achieve this, we need to mark the rules either logical rule or >>>>> physical >>>>>>> rule. >>>>>>> To keep backward compatibility, all the un-marked rules will be >>>>> treated as >>>>>>> logical rules, except rules that uses AbstractConverter as rule >>>>> operand, >>>>>>> these rules still need to applied top-down, or random order. >>>>>>> >>>>>>> >>>>>>> 4. On-demand, bottom-up trait derivation >>>>>>> It is called bottom-up, but actually driven by top-down, happens same >>>>> time >>>>>>> as top-down trait request, in optimization stage mentioned above. Many >>>>>>> Calcite based bigdata system only propagate traits on Project and >>>>> Filter by >>>>>>> writing rules, which is very limited. In fact, we can extend trait >>>>>>> propagation/derivation to all the operators, without rules, by adding >>>>>>> interface PhysicalNode (or extending RelNode) with method: >>>>>>> - RelNode derive(RelTraitSet traits, int childId); >>>>>>> >>>>>>> Given the following plan (only consider distribution for simplicity): >>>>>>> Agg [a,b] >>>>>>> +-- MergeJoin [a] >>>>>>> |---- TableScan R >>>>>>> +--- TableScan S >>>>>>> >>>>>>> Hash[a] won't satisfy Hash[a,b] without special treatment, because >>>>> there >>>>>>> isn't a mechanism to coordinate traits between children. >>>>>>> >>>>>>> Now we call derive method on Agg [a,b] node, derive(Hash[a], 0), we get >>>>>>> the new node: >>>>>>> Agg [a] >>>>>>> +-- MergeJoin [a] (RelSubset) >>>>>>> >>>>>>> We will provide different matching type, so each operator can specify >>>>> what >>>>>>> kind of matching type it requires its children: >>>>>>> - MatchType getMatchType(RelTrait trait, int childId); >>>>>>> >>>>>>> a) Exact: Hash[a,b] exact match Hash[a,b], aka, satisfy >>>>>>> b) Partial: Hash[a] partial match Hash[a,b] >>>>>>> c) Permuted: Sort[a,b,c] permuted match Sort[c,b,a] >>>>>>> >>>>>>> In addition, optimization order is provided for each opertor to >>>>> specify: >>>>>>> a) left to right >>>>>>> b) right to left >>>>>>> c) both >>>>>>> >>>>>>> For example, for query SELECT * FROM R join S on R.a=S.a and R.b=S.b >>>>> and >>>>>>> R.c=S.c: >>>>>>> Suppose R is distributed by a,b, and S is distributed by c. >>>>>>> MergeJoin [a,b,c] >>>>>>> |--- TableScan R [a,b] >>>>>>> +-- TableScan S [c] >>>>>>> a) left to right, call derive(Hash[a,b], 0), we get MergeJoin [a,b] >>>>>>> b) right to left, call derive(Hash[c], 1), we get MergeJoin [c], most >>>>>>> likely a bad plan >>>>>>> c) both, get above 2 plans. >>>>>>> >>>>>>> For system that doesn't have a fine-tuned stats and cost model, it may >>>>> not >>>>>>> be able to make a right decision based purely on cost. Probably we >>>>> need to >>>>>>> provide the MergeJoin with both children's derived traitset list. >>>>>>> - List<RelNode> derive(List<List<RelTraitSet>>); >>>>>>> >>>>>>> Of course, all above methods are optional to implement for those who >>>>>>> doesn't need this feature. >>>>>>> >>>>>>> 5. Branch and Bound Space Pruning >>>>>>> After we implement on-demand, top-down trait enforcement and >>>>> rule-apply, >>>>>>> we can pass the cost limit at the time of passing down required >>>>> traits, as >>>>>>> described in the classical Cascades paper. Right now, Calcite doesn't >>>>>>> provide group level logical properties, including stats info, each >>>>> operator >>>>>>> in the same group has its own logical property and the stats may vary, >>>>> so >>>>>>> we can only do limited space pruning for trait enforcement, still >>>>> good. But >>>>>>> if we agree to add option to share group level stats between relnodes >>>>> in a >>>>>>> RelSet, we will be able to do more aggresive space pruning, which will >>>>> help >>>>>>> boost the performance of join reorder planning. >>>>>>> >>>>>>> >>>>>>> With all that being said, how do we move forward? >>>>>>> >>>>>>> There are 2 ways: >>>>>>> a) Modify on current VolcanoPlanner. >>>>>>> Pros: code reuse, existing numerous test cases and infrastructure, >>>>> fast >>>>>>> integration >>>>>>> Cons: changing code always brings risk >>>>>>> >>>>>>> b) Add a new XXXXPlanner >>>>>>> Pros: no risk, no tech debt, no need to worry about backward >>>>>>> compatability >>>>>>> Cons: separate test cases for new planner, one more planner to >>>>> maintain >>>>>>> >>>>>>> We'd like to hear the community's thoughts and advices. >>>>>>> >>>>>>> Thanks. >>>>>>> - Haisheng >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>> >>>> >> >>
