1. Prepared by : Ankit Patel (226)
The Query Compiler
Prepared by :
Ankit Patel (226)

2. References
H. Garcia-Molina, J. Ullman, and J. Widom, “Database System: The Complete Book,” second edition: p , Prentice Hall, New Jersey, 2008

3. Compilation of Queries
Compilation means turning a query into a physical query plan, which can be implemented by query engine.
Steps of query compilation :
Parsing
Semantic checking
Selection of the preferred logical query plan
Generating the best physical plan

4. The Parser The first step of SQL query processing.
Generates a parse tree
Nodes in the parse tree corresponds to the SQL constructs
Similar to the compiler of a programming language

5. View Expansion A very critical part of query compilation.
Expands the view references in the query tree to the actual view.
Provides opportunities for the query optimization.

6. Semantic Checking Checks the semantics of a SQL query.
Examines a parse tree.
Checks :
Attributes
Relation names
Types
Resolves attribute references.

7. Conversion to a logical query plan
Converts a semantically parsed tree to a algebraic expression.
Conversion is straightforward but subqueries need to be optimized.
Two argument selection approach can be used.

8. Algebraic transformation
Many different ways to transform a logical query plan to an actual plan using algebraic transformations.
The laws used for this transformation :
Commutative and associative laws
Laws involving selection
Pushing selection
Laws involving projection
Laws about joins and products
Laws involving duplicate eliminations
Laws involving grouping and aggregation

9. Estimating sizes of relations
True running time is taken into consideration when selecting the best logical plan.
Two factors the affects the most in estimating the sizes of relation :
Size of relations ( No. of tuples )
No. of distinct values for each attribute of each relation
Histograms are used by some systems.

10. Cost based optimizing
Best physical query plan represents the least costly plan.
Factors that decide the cost of a query plan :
Order and grouping operations like joins,unions and intersections.
Nested loop and the hash loop joins used.
Scanning and sorting operations.
Storing intermediate results.

11. Plan enumeration strategies
Common approaches for searching the space for best physical plan .
Dynamic programming : Tabularizing the best plan for each sub expression
Selinger style programming : sort-order the results as a part of table
Greedy approaches : Making a series of locally optimal decisions
Branch-and-bound : Starts with enumerating the worst plans and reach the best plan

12. Left-Deep join trees
Left – Deep Join Trees are the binary trees with a single spine down the left edge and with leaves as right children.
This strategy reduces the number of plans to be considered for the best physical plan.
Restrict the search to Left – Deep Join Trees when picking a grouping and order for the join of several relations.

13. Physical Plans for Selection
Breaking a selection into an index-scan of relation, followed by a filter operation.
The filter then examines the tuples retrieved by the index-scan.
Allows only those to pass which meet the portions of selection condition.

14. Pipelining versus Materializing
An operator always consumes the result of other operator and is passed through the main memory.
This flow of data between the operators can be controlled to implement “ Pipelining “ .
The intermediate results should be removed from main memory to save space for other operators.
This techniques can implemented using “ materialization “ .
Both the pipelining and the materialization should be considered by the physical query plan generator.