1. COST ESTIMATION FOR THE RELATIONAL ALGEBRA OPERATIONS MIT 813 GROUP 15 PRESENTATION

2. 1.0 COST ESTIMATION IN QUERY OPTIMIZATION  A DBMS may have many different ways of implementing the relational algebra operations.  The aim of query optimization is to choose the most efficient one.  Query optimization uses formulae that estimate the costs for a number of options and selects the one with the lowest cost.

3. 1.1 DATABASE STATISTICS  nTuples(R) – the number of tuples (records) in relation R (that is, its cardinality).  bFactor(R) – the blocking factor of R (that is, the number of tuples of R that fit into one block).  nBlocks(R) – the number of blocks required to store R. If the tuples of R are stored physically together, then: nBlocks(R) = [nTuples(R)/bFactor(R)].

4. 1.2 DATABASE STATISTICS  nDistinct A (R) – the number of distinct values that appear for attribute A in relation R.  Min A (R),Max A (R) – the minimum and maximum possible values for the attribute A in relation R.  SC A (R) – the selection cardinality of attribute A in relation R.  nLevels A (I) – the number of levels in I.  nLfBlocks A (I) – the number of leaf blocks in I.

5. 1.3 SELECTION OPERATION (S =  P (R)) The main strategies that we consider are:  Linear search (unordered file, no index);  Binary search (ordered file, no index);  Equality on hash key;  Equality condition on primary key;  Inequality condition on primary key;  Equality condition on clustering (secondary) index;  Equality condition on a non-clustering (secondary) index;  Inequality condition on a secondary B+-tree index.

6. 1.4 SUMMARY OF ESTIMATED I/O COST OF STRATEGIES FOR SELECTION OPERATION.

7. LINEAR SEARCH (UNORDERED FILE, NO INDEX)  With this approach, it may be necessary to scan each tuple in each block to determine whether it satisfies the predicate.  This is sometimes referred to as a full table scan. In the case of an equality condition on a key attribute, assuming tuples are uniformly distributed about the file, then on average only half the blocks would be searched before the specific tuple is found, so the cost estimate is: [nBlocks(R)/2]  For any other condition, the entire file may need to be searched, so the more general cost estimate is: nBlocks(R)

8. BINARY SEARCH (ORDERED fiLE, NO INDEX)  If the predicate is of the form (A = x) and the file is ordered on attribute A, which is also the key attribute of relation R, then the cost estimate for the search is: [log2(nBlocks(R))]  More generally, the cost estimate is: [log2(nBlocks(R))] + [SCA(R)/bFactor(R)] − 1  The first term represents the cost of finding the first tuple using a binary search method. We expect there to be SC A (R) tuples satisfying the predicate, which will occupy [SC A (R)/bFactor(R)] blocks, of which one has been retrieved in finding the first tuple.

9. EQUALITY ON HASH KEY  If attribute A is the hash key, then we apply the hashing algorithm to calculate the target address for the tuple. If there is no overflow, the expected cost is 1.  If there is overflow, additional accesses may be necessary, depending on the amount of overflow and the method for handling overflow.

10. EQUALITY CONDITION ON PRIMARY KEY  If the predicate involves an equality condition on the primary key field (A = x), then we can use the primary index to retrieve the single tuple that satisfies this condition. In this case, we need to read one more block than the number of index accesses, equivalent to the number of levels in the index, and so the estimated cost is: nLevels A (I) + 1

11. INEQUALITY CONDITION ON PRIMARY KEY  If the predicate involves an inequality condition on the primary key field A (A x, A >= x), then we can first use the index to locate the tuple satisfying the predicate A = x. Provided the index is sorted, then the required tuples can be found by accessing all tuples before or after this one. Assuming uniform distribution, then we would expect half the tuples to satisfy the inequality, so the estimated cost is: nLevels A (I) + [nBlocks(R)/2]

12. EQUALITY CONDITION ON CLUSTERING (SECONDARY) INDEX  If the predicate involves an equality condition on attribute A, which is not the primary key but does provide a clustering secondary index, then we can use the index to retrieve the required tuples. The estimated cost is: nLevels A (I) + [SC A (R)/bFactor(R)]  The second term is an estimate of the number of blocks that will be required to store the number of tuples that satisfy the equality condition, which we have estimated as SC A (R).

13. EQUALITY CONDITION ON A NON-CLUSTERING (SECONDARY) INDEX  If the predicate involves an equality condition on attribute A, which is not the primary key but does provide a non-clustering secondary index, then we can use the index to retrieve the required tuples. In this case, we have to assume that the tuples are on different blocks (the index is not clustered this time), so the estimated cost becomes: nLevels A (I) + [SC A (R)]

14. INEQUALITY CONDITION ON A SECONDARY B+-TREE INDEX  If the predicate involves an inequality condition on attribute A (A x, A >= x), which provides a secondary B+-tree index, then from the leaf nodes of the tree we can scan the keys from the smallest value up to x (for or >= conditions). Assuming uniform distribution, we would expect half the leaf node blocks to be accessed and, via the index, half the tuples to be accessed. The estimated cost is then: nLevels A (I) + [nLfBlocks A (I)/2 + nTuples(R)/2]

15. Example: we make the following assumptions about the Staff relation:  There is a hash index with no overflow on the primary key attribute staff_No.  There is a clustering index on the foreign key attribute branch_No.  There is a B  -tree index on the salary attribute.  The Staff relation has the following statistics stored in the system catalog:

16. n Tuples(Staff)  3000 b Factor(Staff)  30  n Blocks(Staff)  100 n Distinct branch_No (Staff)  500  Sc branch_N o (Staff)  6 n Distinct position (Staff)  10  SC position (Staff)  300 nDistinct salary (Staff) = 500 SC salary (Staff) = 6 Min salary (Staff) = 10,000 Max salary (Staff) = 50,000 nLevels branch_No (I) = 2 nLevels salary (I) = 2 nLfBlocks salary (I) = 50

17. The estimated cost of a linear search on the key attribute staff_No is 50 blocks, and the cost of a linear search on a non-key attribute is 100 blocks. Now we consider the following Selection operations, and use the above strategies to improve on these two costs: S1:  staff_No  ‘SG5’ (Staff) S2:  position  ‘Manager’ (Staff) S3:  branch_No  ‘B003’ (Staff) S4:  salary  20000 (Staff) S5:  position  ‘Manager’ ˄  branch_No  ‘B003’ (Staff)

18. JOIN OPERATION (T = (R F S)) Join operation is the most time-consuming operation to process, and one we have to ensure is performed as efficiently as possible. The predicate F is of the form R. a  S. b. If the predicate contains only equality (=), the join is an Equijoin but If the join involves all common attributes of R and S, the join is called a Natural join. The main strategies for implementing the Join operation:

19. THE MAIN STRATEGIES FOR IMPLEMENTING THE JOIN OPERATION:  Block nested loop join  Indexed nested loop join  Sort–merge join  Hash join

20. BLOCK NESTED LOOP JOIN  The simplest join algorithm is a nested loop that joins the two relations together a tuple at a time.  The outer loop iterates over each tuple in one relation R, and the inner loop iterates over each tuple in the second relation S.  Since each block of R has to be read, and each block of S has to be read for each block of R, the estimated cost of this approach is: nBlocks(R) + (nBlocks(R) * nBlocks(S)) if buffer has only one block for R and S

21. INDEXED NESTED LOOP JOIN  If there is an index (or hash function) on the join attributes of the inner relation, then we can replace the inefficient file scan with an index lookup.  For each tuple in R, we use the index to retrieve the matching tuples of S.  The cost of retrieving the matching tuples in S depends on the type of index and the number of matching tuples.  For example, if the join attribute A in S is the primary key, the cost estimate is: nBlocks(R) + nTuples(R)*(nLevelsA(I) + 1), if join attribute A in S is the primary key  If the join attribute A in S is a clustering index, the cost estimate is: nBlocks(R) + nTuples(R)*(nLevelsA(I) + [SCA(R)/bFactor(R)])

22. SORT–MERGE JOIN  For Equijoins, the most efficient join is achieved when both relations are sorted on the join attributes.  Here, we look into qualifying tuples of R and S by merging the two relations.  Since the relations are in sorted order, tuples with the same join attribute value are guaranteed to be in consecutive order.  Therefore, the cost estimate for the sort–merge join is: nBlocks(R) + nBlocks(S)

23. HASH JOIN  For a Natural join (or Equijoin), a hash join algorithm may also be used to compute the join of two relations R and S on join attribute set A.  We can estimate the cost of the hash join as: 3(nBlocks(R) + nBlocks(S))

24. ESTIMATING THE CARDINALITY OF THE JOIN OPERATION The cardinality of the Cartesian product of R and S, R × S, is simply: nTuples(R) * nTuples(S) Lets take for example, we make the following assumptions:  There are separate hash indexes with no overflow on the primary key attributes staff_No of Staff and branchNo of Branch.  There are 100 database buffer blocks.  The system catalog holds the following statistics:

25. nTuples(Staff) = 6000 bFactor(Staff) = 30 == nBlocks(Staff) = 200 nTuples(Branch) = 500 bFactor(Branch) = 50 == nBlocks(Branch) = 10 nTuples(PropertyForRent) = 100,000 bFactor(PropertyForRent) = 50 == nBlocks(PropertyForRent) = 2000 Using the above strategies, find the join operation for the following: J1: Staff staffNo PropertyForRent J2: Branch branchNo PropertyForRent

26. CONCLUSION  Cost estimation for the relation algebra uses query optimization to estimate cost and determine the most efficient with the lowest price.  Join operation is the most time-consuming operation to process and an accurate cost function depends on the estimate of the size of file (number of records) after the join operation.

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