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See:
Description
| Interface Summary | |
|---|---|
| VolcanoPlannerPhaseRuleMappingInitializer | VolcanoPlannerPhaseRuleMappingInitializer describes an inteface for
initializing the mapping of VolcanoPlannerPhases to sets of rule
descriptions. |
| Enum Summary | |
|---|---|
| VolcanoPlannerPhase | VolcanoPlannerPhase represents the phases of operation that the VolcanoPlanner passes through during optimization of a tree of RelNode objects. |
Optimizes relational expressions.
| Revision | $Id: //open/dev/farrago/src/org/eigenbase/relopt/volcano/package.html#1 $ |
|---|---|
| Copyright | Copyright (C) 2005-2009 The Eigenbase Project
Copyright (C) 2003-2009 SQLstream, Inc. Copyright (C) 2009-2009 LucidEra, Inc. |
| Author | Julian Hyde, Stephan Zuercher |
A planner (also known as an optimizer) finds the most
efficient implementation of a relational expression.
Interface RelOptPlanner defines a planner, and class VolcanoPlanner is an implementation which uses a dynamic
programming technique. It is based upon the Volcano optimizer [1].
Interface RelOptCost defines a cost model; class VolcanoCost is the implementation for a VolcanoPlanner.
A RelSet is a set of equivalent relational expressions.
They are equivalent because they will produce the same result for any set of
input data. It is an equivalence class: two expressions are in the same set if
and only if they are in the same RelSet.
One of the unique features of the optimizer is that expressions can take on a variety of
physical traits. Each relational expression has a set of traits. Each trait is described by
an implementation of RelTraitDef. Manifestations of the trait
implement RelTrait. The most common example of a trait is calling
convention: the protocol used to receive and transmit data. CallingConventionTraitDef defines the trait and CallingConvention enumerates the protocols. Every relational expression
has a single calling convention by which it returns its results. Some examples:
CallingConvention.RESULT_SET is a fairly conventional
calling convention; the results are rows from a JDBC
result set.CallingConvention.NONE means that a relational
expression cannot be implemented; typically there are rules which can
transform it to equivalent, implementable expressions.CallingConvention.JAVA implements the expression by
generating Java code. The code places the current row in a Java variable, then
calls the piece of code which implements the consuming relational expression.
For example, a Java array reader of the names array would
generate the following code:
String[] names;
for (int i = 0; i < names.length; i++) {
String name = names[i];
// ... code for consuming relational expression ...
}
New traits are added to the planner in one of two ways:
RelNode should include its manifestation of the trait as part of the RelTraitSet passed to AbstractRelNode's
constructor. It may be useful to provide alternate AbstractRelNode constructors
if most relational expressions use a single manifestation of the trait.VolcanoPlanner.setRoot(org.eigenbase.rel.RelNode)
should have their trait sets expanded before the setRoot(RelNode) call.The second trait extension mechanism requires that implementations of AbstractRelNode.clone() must not assume the type and quantity of traits in
their trait set. In either case, the new RelTraitDef implementation must be
VolcanoPlanner.addRelTraitDef(org.eigenbase.relopt.RelTraitDef)
registered with the planner.
A RelSubset is a subset of a RelSet
containing expressions which are equivalent and which have the same
CallingConvention. Like RelSet, it is an equivalence class.
org.eigenbase.rel defines relational expressions.openjava.ptree
defines an object model for Java expressions....
Sets merge when the result of a rule already exists in another set. This implies that all of the expressions are equivalent. The RelSets are merged, and so are the contained RelSubsets.
Expression registration.
Algorithm
To optimize a relational expression R:
1. Register R.
2. Create rule-calls for all applicable rules.
3. Rank the rule calls by importance.
4. Call the most important rule
5. Repeat.
Importance. A rule-call is important if it is likely to produce better implementation of a relexp on the plan's critical path. Hence (a) it produces a member of an important RelSubset, (b) its children are cheap.
Conversion. Conversions are difficult because we have to work backwards from the goal.
Rule triggering
The rules are:
PushFilterThroughProjectRule. Operands:Filter Project
CombineProjectsRule. Operands:Project Project
A rule can be triggered by a change to any of its operands. Consider the rule to combine two filters into one. It would have operands [Filter [Filter]]. If I register a new Filter, it will trigger the rule in 2 places. Consider:
Project (deptno) [exp 1, subset A]
Filter (gender='F') [exp 2, subset B]
Project (deptno, gender, empno) [exp 3, subset C]
Project (deptno, gender, empno, salary) [exp 4, subset D]
TableScan (emp) [exp 0, subset X]
Apply PushFilterThroughProjectRule to [exp 2, exp 3]:
Project (deptno) [exp 1, subset A]
Project (deptno, gender, empno) [exp 5, subset B]
Filter (gender='F') [exp 6, subset E]
Project (deptno, gender, empno, salary) [exp 4, subset D]
TableScan (emp) [exp 0, subset X]
Two new expressions are created. Expression 5 is in subset B (because it is equivalent to expression 2), and expression 6 is in a new equivalence class, subset E.
The products of a applying a rule can trigger a cascade of rules. Even in this simple system (2 rules and 4 initial expressions), two more rules are triggered:
CombineProjectsRule(exp 1, exp 5),
which creates
Project (deptno) [exp 7, subset A]
Filter (gender='F') [exp 6, subset E]
Project (deptno, gender, empno, salary) [exp 4, subset D]
TableScan (emp) [exp 0, subset X]
PushFilterThroughProjectRule(exp
6, exp 4), which creates
Project (deptno) [exp 1, subset A]
Project (deptno, gender, empno) [exp 5, subset B]
Project (deptno, gender, empno, salary) [exp 8, subset E]
Filter (gender='F') [exp 9, subset F]
TableScan (emp) [exp 0, subset X]
Each rule application adds additional members to existing subsets. The
non-singleton subsets are now A {1, 7}, B {2, 5} and E {6, 8}, and new
combinations are possible. For example, CombineProjectsRule(exp 7,
exp 8) further reduces the depth of the tree to:
Project (deptno) [exp 10, subset A]
Filter (gender='F') [exp 9, subset F]
TableScan (emp) [exp 0, subset X]
Todo: show how rules can cause subsets to merge.
Conclusion:
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