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If you re joining small subsets of data, the nested loop (NL) method is ideal. If you re returning fewer than, say, 10,000 rows, the NL join may be the right join method. If the Optimizer is using hash joins or full table scans, force it to use the NL join method by using the following hint: SELECT /*+ USE_NL (TableA, TableB) */
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If the join will produce large subsets of data or a substantial proportion of a table is going to be joined, use the hash join hint if the Optimizer indicates it isn t going to use it: SELECT /* USE_HASH */
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If the tables in the join are being joined with an inequality condition (not an equi join), the merge join method is ideal: SELECT /*+ USE_MERGE (TableA, TableB) */
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Bitmap join indexes (BJIs) prestore the results of a join between two tables in an index, and thus do away with the need for an expensive runtime join operation. BJIs are specially designed for data warehouse star schemas, but any application can use them as long as there is a primary key/foreign key relationship between the two tables. Typically, in a data warehouse setting, the primary key is in a dimension table and the fact table has the foreign key. For example, customer_id in the customer dimension table is the primary key, and customer_id in the fact table is the foreign key. Using a BJI, you can avoid a join between these
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CHAPTER 21 IMPROVING DATABASE PERFORMANCE: SQL QUERY OPTIMIZATION
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two tables because the rows that would result from the join are already stored in the BJI. Let s look at a simple example of a BJI here. Say you expect to use the following SQL statement frequently in your application: SQL> SELECT SUM((s.quantity) FROM sales s, customers c WHERE s.customer_id = c.customer_id AND c.city = 'DALLAS'; In this example, the sales table is the fact table with all the details about product sales, and the customers table is a dimension table with information about your customers. The column customer_id acts as the primary key for the customers table and as the foreign key for the sales table, so the table meets the requirement for creating a BJI. The following statement creates the BJI. Notice line 2, where you re specifying the index on sales (c.cust_id). This is how you get the join information to place in the new BJI. Because the sales table is partitioned, you use the clause LOCAL in line 5 to create a locally partitioned index: SQL> CREATE BITMAP INDEX cust_orders_BJI 2 ON sales (c.cust_id) 3 FROM sales s, customers c 4 WHERE c.cust_id = s.cust_id 5 LOCAL 6*TABLESPACE users; Index created. SQL> You can confirm that the intended index has been created with the help of the following query. The first index is the new BJI index you just created: SQL> SELECT index_name, index_type, join_index 2 FROM dba_indexes 3*WHERE table_name='SALES'; INDEX_NAME INDEX_TYPE JOIN_INDEX ------------------------------ ---------------CUST_ORDERS_BJI BITMAP YES SALES_CHANNEL_BIX BITMAP NO SALES_CUST_BIX BITMAP NO 3 rows selected. SQL> Being a bitmap index, the new BJI uses space extremely efficiently. However, the real benefit of using this index is that when you need to find out the sales for a given city, you don t need to join the sales and customers tables. You only need to use the sales table and the new BJI that holds the join information already.
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Selecting the Best Join Order
When your SQL statement includes a join between two or more tables, the order in which you join the tables is extremely important. The driving table in a join is the first table that comes after the WHERE clause. The driving table in the join should contain the filter that will eliminate the most rows. Choose the join order that gives you the least number of rows to be joined to the other tables. That is, if you re joining three tables, the one with the more restrictive filter should be joined first to one of the other two tables. Compare various join orders and pick the best one after you consider the number of rows returned by each join order.
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