This section describes the phases that a statement goes through starting with the annotation phase and ending with its execution. It also describes the assumptions that underlie the design of the optimizer, and discusses selectivity estimation, cost estimation, and the steps of query processing.
To learn more about SQL Anywhere query processing, see the Query Processing Based on SQL Anywhere 12.0.1 Architecture white paper at http://www.sybase.com/detail?id=1096047.
Statements that have no result sets, such as UPDATE or DELETE statements, go through the query processing phases.
Annotation phase When the database server receives a query, it uses a parser to parse the statement and transform it into an algebraic representation of the query, also known as a parse tree. At this stage the parse tree is used for semantic and syntactic checking (for example, validating that objects referenced in the query exist in the catalog), privilege checking, KEY JOINs and NATURAL JOINs transformation using defined referential constraints, and non-materialized view expansion. The output of this phase is a rewritten query, in the form of a parse tree, which contains annotation to all the objects referenced in the original query.
Semantic transformation phase During this phase, the query undergoes iterative semantic transformations. While the query is still represented as an annotated parse tree, rewrite optimizations, such as join elimination, DISTINCT elimination, and predicate normalization, are applied in this phase. The semantic transformations in this phase are performed based on semantic transformation rules that are applied heuristically to the parse tree representation.
Queries with plans already cached by the database server skip this phase of query processing. Simple statements may also skip this phase of query processing. For example, many statements that use heuristic plan selection in the optimizer bypass are not processed by the semantic transformation phase. The complexity of the SQL statement determines if this phase is applied to a statement.
Queries with plans already cached by the database server skip this phase of query processing. As well, simple statements may also skip this phase of query processing.
This phase is broken into two sub-phases:
Pre-optimization phase The pre-optimization phase completes the optimization structure with the information needed later in the enumeration phase. During this phase the query is analyzed to find all relevant indexes and materialized views that may be used in the query access plan. For example, in this phase, the View Matching algorithm determines all the materialized views that may be used to satisfy all, or part of the query. In addition, based on query predicate analysis, the optimizer builds alternative join methods that may be used in the enumeration phase to join the query's tables. During this phase, no decision is made regarding the best access plan for the query; the goal of this phase is to prepare for the enumeration phase.
Enumeration phase During this phase, the optimizer enumerates possible access plans for the query using the building blocks generated in the pre-optimization phase. The search space is very large and the optimizer uses a proprietary enumeration algorithm to generate and prune the generated access plans. For each plan, cost estimation is computed, which is used to compare the current plan with the best plan found so far. Expensive plans are discarded during these comparisons. Cost estimation takes into account resource utilization such as disk and CPU operations, the estimated number of rows of the intermediate results, optimization goal, cache size, and so on. The output of the enumeration phase is the best access plan for the query.
Plan building phase The plan building phase takes the best access plan and builds the corresponding final representation of the query execution plan used to execute the query. You can see a graphical version of the plan in the Plan Viewer in Interactive SQL. The graphical plan has a tree structure where each node is a physical operator implementing a specific relational algebraic operation, for example, Hash Join and Ordered Group By are physical operators implementing a join and a group by operation, respectively.
Queries with plans already cached by the database server skip this phase of query processing.
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