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employeename, employeehire_date, departmentname employee, department employeedept_name = departmentname departmentname <> 'Research';
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Because we know the basics of join queries, we know that it's necessary to read data from both EMPLOYEE and DEPARTMENT and then combine them based on meeting two conditions: that the department IDs are the same and that the department is not Research One possible abstract plan for this is shown in Figure 10-5 Figure 10-5 Abstract query plan
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As the figure shows, the flow of data drives execution For example, when the plan starts to execute, the only two operations that can execute are both GET operations The other operations (FILTER and JOIN) are waiting for input before they can execute After the GET of employee data finishes, the tuples containing employees working in the Research department can be filtered out Finally, after receiving data from its respective tables, the JOIN operation can match employees and departments together based on the join condition Again, the graph in Figure 10-5 is partially ordered in the sense that getting employee data and getting department data are independent operations and, conceptually, can be executed in parallel To illustrate better how this abstract example relates to an actual query plan, consider the real graph generated by the Oracle query optimizer:
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Execution Plan ---------------------------------------------------------0 SELECT STATEMENT Optimizer=CHOOSE 1 0 MERGE JOIN 2 1 SORT (JOIN) 3 2 TABLE ACCESS (FULL) OF 'DEPARTMENT' 4 1 SORT (JOIN) 5 4 TABLE ACCESS (FULL) OF 'EMPLOYEE'
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This graph contains three columns:
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The first column indicates the index of the operator The second column indicates which operator is the target of the current operator The third column indicates the name and options of the chosen operator
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As Figure 10-6 shows, the plan results in a graph very similar to the one for our abstract plan The only real differences between the two are that the Oracle query plan performs a sort prior to the JOIN, which is part of the query optimization phase, and that the additional filtering of nonResearch employees is lumped into the JOIN phase Figure 10-6 Oracle query plan dataflow graph
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The process of optimizing SQL queries involves analyzing these plans, determining where the bottlenecks are, and implementing solutions For example, if both of these tables are extremely large, we can create an index on one of them to speed up the JOIN operation In fact, this can even change the type of join used by the optimizer For example, in Oracle, if we create an index on the DEPARTMENT ID column:
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CREATE INDEX dep_idx ON department(id);
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and then analyze the plan generated, we see that it consists of the following:
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Execution Plan ---------------------------------------------------------0 SELECT STATEMENT Optimizer=CHOOSE 1 0 NESTED LOOPS 2 1 TABLE ACCESS (FULL) OF 'EMPLOYEE' 3 1 TABLE ACCESS (BY INDEX ROWID) OF 'DEPARTMENT' 4 3 INDEX (RANGE SCAN) OF 'DEP_IDX' (NON-UNIQUE)
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which corresponds to the graph in Figure 10-7 Figure 10-7 Query plan using index
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Obviously, a few things have changed Instead of performing a SORT on the full scan of both tables, the optimizer chooses to use the index DEP_IDX and changes its join strategy to use the nested loops algorithm (The details of how a nested loop join differs from a hash join or a merge join is beyond the scope of this text) The point here is to demonstrate that the processing of SQL queries involves an important optimization phase that can be analyzed in detail to identify bottlenecks Based on this analysis, you can find out which SQL queries are the most costly and why
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We now look at various stategies for designing efficient databases Note that none of these has to do with JDBC or SQL we will get to that in 11 Instead, our present focus is on higherlevel issues, such as effective data modeling and the use of stored procedures and triggers
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