Cost estimation for Query Optimization
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The document discusses cost estimation in query optimization. It explains that the query optimizer should estimate the cost of different execution strategies and choose the strategy with the minimum estimated cost. The cost functions used are estimates and depend on factors like selectivity. The main cost components include access cost to storage, storage cost, computation cost, memory use cost, and communication cost. For different types and sizes of databases, the emphasis may be on minimizing different cost components, such as access cost for large databases. The document provides examples of cost functions for select and join operations that consider factors like index levels, block sizes, and selectivity.
![Information in system Catalogue
• The number of tuples in relation as R [nTuples(R)].
• The average record size in relation R.
• The number of blocks required to store relation R as
[nBlocks(R)].
• The blocking factors in relation R (that is the number of
tuples of R that fit into one block) as [bFactor(R)].
• Primary access method for each file.
• Primary access attributes for each file.
• The number of level of each multilevel index I (primary,
secondary or clustering) as [nLevelsA(I)].](https://image.slidesharecdn.com/costestimation-170307145609/85/Cost-estimation-for-Query-Optimization-8-320.jpg)
![• The number of first level index blocks as [nBlocksA (I)].
• The number of distinct values that are appear for
attribute A in relation R as [nDistinctA(R)].
• The minimum and maximum possible values for
attribute A in relation R as [minA(R), maxA(R)].
• The selectivity of an attribute, which is the fraction of
records satisfying an equality condition on the
attribute.
• The selection cardinality of given attribute A in relation
R as [SCA(R)].
• The selection cardinality is the average number of
tuples that satisfied an equality condition on attribute
A.](https://image.slidesharecdn.com/costestimation-170307145609/85/Cost-estimation-for-Query-Optimization-9-320.jpg)
![Cost functions for SELECT Operation
• Linear Search:
– [nBlocks(R)/2], if the record is found.
– [nBlocks(R)], if no record satisfied the condition.
• Binary Search :
o [log2(nBlocks(R))], if equality condition is on key attribute,
because SCA(R) = 1 in this case.
o[log2(nBlocks(R))] + [SCA(R)/bFactor(R)] – 1, otherwise.](https://image.slidesharecdn.com/costestimation-170307145609/85/Cost-estimation-for-Query-Optimization-10-320.jpg)
![• Equity condition on Primary key
– [nLevelA(I) + 1]
• Equity condition on Non-Primary key :-
– [nLevelA(I) + 1] + [nBlocks(R)/2]](https://image.slidesharecdn.com/costestimation-170307145609/85/Cost-estimation-for-Query-Optimization-11-320.jpg)
Cost estimation for Query Optimization
- 2. Cost Estimation in Query Optimization • The main aim of query optimization is to choose the most efficient way of implementing the relational algebra operations at the lowest possible cost. • The query optimizer should not depend solely on heuristic rules, but, it should also estimate the cost of executing the different strategies and find out the strategy with the minimum cost estimate.
- 3. • The cost functions used in query optimization are estimates and not exact cost functions. • The cost of an operation is heavily dependent on its selectivity, that is, the proportion of select operation(s) that forms the output. • In general the different algorithms are suitable for low or high selectivity queries. • In order for query optimizer to choose suitable algorithm for an operation an estimate of the cost of executing that algorithm must be provided
- 4. • The cost of an algorithm is depend of a cardinality of its input. • To estimate the cost of different query execution strategies, the query tree is viewed as containing a series of basic operations which are linked in order to perform the query. • It is also important to know the expected cardinality of an operation’s output because this forms the input to the next operation.
- 5. Cost Components of Query Execution • The cost of executing the query includes the following components: – Access cost to secondary storage. – Storage cost. – Computation cost. – Memory uses cost. – Communication cost.
- 6. Importance of Access cost • Out of the above five cost components, the most important is the secondary storage access cost. • The emphasis of the cost minimization depends on the size and type of database applications. • For example in smaller database the emphasis is on the minimizing computing cost as because most of the data in the files involve in the query can be completely store in the main memory. • For large database, the main emphasis is on minimizing the access cost to secondary device.
- 7. • For distributed database, the communication cost is minimized as because many sites are involved for the data transfer. • To estimate the cost of various execution strategies, we must keep track of any information that is needed for the cost function. This information may be stored in database catalog, where it is accessed by the query optimizer.
- 8. Information in system Catalogue • The number of tuples in relation as R [nTuples(R)]. • The average record size in relation R. • The number of blocks required to store relation R as [nBlocks(R)]. • The blocking factors in relation R (that is the number of tuples of R that fit into one block) as [bFactor(R)]. • Primary access method for each file. • Primary access attributes for each file. • The number of level of each multilevel index I (primary, secondary or clustering) as [nLevelsA(I)].
- 9. • The number of first level index blocks as [nBlocksA (I)]. • The number of distinct values that are appear for attribute A in relation R as [nDistinctA(R)]. • The minimum and maximum possible values for attribute A in relation R as [minA(R), maxA(R)]. • The selectivity of an attribute, which is the fraction of records satisfying an equality condition on the attribute. • The selection cardinality of given attribute A in relation R as [SCA(R)]. • The selection cardinality is the average number of tuples that satisfied an equality condition on attribute A.
- 10. Cost functions for SELECT Operation • Linear Search: – [nBlocks(R)/2], if the record is found. – [nBlocks(R)], if no record satisfied the condition. • Binary Search : o [log2(nBlocks(R))], if equality condition is on key attribute, because SCA(R) = 1 in this case. o[log2(nBlocks(R))] + [SCA(R)/bFactor(R)] – 1, otherwise.
- 11. • Equity condition on Primary key – [nLevelA(I) + 1] • Equity condition on Non-Primary key :- – [nLevelA(I) + 1] + [nBlocks(R)/2]
- 12. Cost functions for JOIN Operation • Join operation is the most time consuming operation to process. • An estimate for the size (number of tuples) of the file that results after the JOIN operation is required to develop reasonably accurate cost functions for JOIN operations. • The JOIN operations define the relation containing tuples that satisfy a specific predicate F from the Cartesian product of two relations R and S.
- 13. Different strategies for JOIN operations
- 14. Different strategies for JOIN operations