object JoinReorderDP extends PredicateHelper with Logging
Reorder the joins using a dynamic programming algorithm. This implementation is based on the paper: Access Path Selection in a Relational Database Management System. http://www.inf.ed.ac.uk/teaching/courses/adbs/AccessPath.pdf
First we put all items (basic joined nodes) into level 0, then we build all two-way joins at level 1 from plans at level 0 (single items), then build all 3-way joins from plans at previous levels (two-way joins and single items), then 4-way joins ... etc, until we build all n-way joins and pick the best plan among them.
When building m-way joins, we only keep the best plan (with the lowest cost) for the same set of m items. E.g., for 3-way joins, we keep only the best plan for items {A, B, C} among plans (A J B) J C, (A J C) J B and (B J C) J A. We also prune cartesian product candidates when building a new plan if there exists no join condition involving references from both left and right. This pruning strategy significantly reduces the search space. E.g., given A J B J C J D with join conditions A.k1 = B.k1 and B.k2 = C.k2 and C.k3 = D.k3, plans maintained for each level are as follows: level 0: p({A}), p({B}), p({C}), p({D}) level 1: p({A, B}), p({B, C}), p({C, D}) level 2: p({A, B, C}), p({B, C, D}) level 3: p({A, B, C, D}) where p({A, B, C, D}) is the final output plan.
For cost evaluation, since physical costs for operators are not available currently, we use cardinalities and sizes to compute costs.
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case class
JoinPlan(itemIds: Set[Int], plan: LogicalPlan, joinConds: Set[Expression], planCost: Cost) extends Product with Serializable
Partial join order in a specific level.
Partial join order in a specific level.
- itemIds
Set of item ids participating in this partial plan.
- plan
The plan tree with the lowest cost for these items found so far.
- joinConds
Join conditions included in the plan.
- planCost
The cost of this plan tree is the sum of costs of all intermediate joins.
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type
JoinPlanMap = Map[Set[Int], JoinPlan]
Map[set of item ids, join plan for these items]
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def
canEvaluate(expr: Expression, plan: LogicalPlan): Boolean
Returns true if
exprcan be evaluated using only the output ofplan.Returns true if
exprcan be evaluated using only the output ofplan. This method can be used to determine when it is acceptable to move expression evaluation within a query plan.For example consider a join between two relations R(a, b) and S(c, d).
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canEvaluate(EqualTo(a,b), R)returnstrue-canEvaluate(EqualTo(a,c), R)returnsfalse-canEvaluate(Literal(1), R)returnstrueas literals CAN be evaluated on any plan- Attributes
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def
canEvaluateWithinJoin(expr: Expression): Boolean
Returns true iff
exprcould be evaluated as a condition within join.Returns true iff
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def
findExpressionAndTrackLineageDown(exp: Expression, plan: LogicalPlan): Option[(Expression, LogicalPlan)]
Find the origin of where the input references of expression exp were scanned in the tree of plan, and if they originate from a single leaf node.
Find the origin of where the input references of expression exp were scanned in the tree of plan, and if they originate from a single leaf node. Returns optional tuple with Expression, undoing any projections and aliasing that has been done along the way from plan to origin, and the origin LeafNode plan from which all the exp
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log: Logger
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def
replaceAlias(condition: Expression, aliases: AttributeMap[Expression]): Expression
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- def search(conf: SQLConf, items: Seq[LogicalPlan], conditions: Set[Expression], output: Seq[Attribute]): LogicalPlan
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def
splitConjunctivePredicates(condition: Expression): Seq[Expression]
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def
splitDisjunctivePredicates(condition: Expression): Seq[Expression]
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