Choosing an Order for Joins (16.6) Neha Saxena (214) Instructor: T.Y.Lin

Topics Covered Introduction Significance of Left and Right Join Arguments Join Trees Left-Deep Join Trees Dynamic Programming to Select a Join Order & Grouping Dynamic Programming with More Detailed Cost Functions A Greedy Algorithm for Selecting a Join Order

Introduction This section focuses on critical problem in cost- based optimization: – Selecting order for natural join of three or more relations Compared to other binary operations, joins take more time and therefore need effective optimization techniques

Significance of Left and Right Join Arguments The argument relations in joins determine the cost of the join The left argument of the join is – Called the build relation – Assumed to be smaller – Stored in main-memory

The right argument of the join is – Called the probe relation – Read a block at a time – Its tuples are matched with those of build relation The join algorithms which distinguish between the arguments are: – One-pass join – Nested-loop join – Index join

Join Trees Order of arguments is important for joining two relations Left argument, since stored in main-memory, should be smaller With two relations only two choices of join tree With more than two relations, there are n! ways to order the arguments and therefore n! join trees, where n is the no. of relations

Left-Deep Join Trees Consider 4 relations. Different ways to join them are as follows

In fig (a) all the right children are leaves. This is a left-deep tree In fig (c) all the left children are leaves. This is a right-deep tree Fig (b) is a bushy tree Considering left-deep trees is advantageous for deciding join orders

Dynamic Programming to Select a Join Order and Grouping Three choices to pick an order for the join of many relations are: – Consider all of the relations – Consider a subset – Use a heuristic o pick one Dynamic programming is used either to consider all or a subset – Construct a table of costs based on relation size – Remember only the minimum entry which will required to proceed

Dynamic Programming with More Detailed Cost Functions Disadvantage of dynamic programming is that it does not involve the actual costs of the joins in the calculations Can be improved by considering – Use disk’s I/O for evaluating cost – When computing cost of R1 join R2, since we sum cost of R1 and R2, we must also compute estimates for there sizes

A Greedy Algorithm for Selecting a Join Order It is expensive to use an exhaustive method like dynamic programming Better approach is to use a join-order heuristic for the query optimization Greedy algorithm is an example of that – Make one decision at a time and never backtrack on the decisions once made

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