Consider a large join between a sales table and a customer table on the column
cust_id, as shown in Example 3-4. The query "find the records of all customers who bought more than 100 articles in Quarter 3 of 1999" is a typical example of a SQL statement performing such a join.
Such a large join is typical in data warehousing environments. In this case, the entire customer table is joined with one quarter of the sales data. In large data warehouse applications, this might mean joining millions of rows. The join method to use in that case is obviously a hash join. You can reduce the processing time for this hash join even more if both tables are equipartitioned on the
cust_id column. This functionality enables a full partition-wise join.
When you execute a full partition-wise join in parallel, the granule of parallelism is a partition. Consequently, the degree of parallelism is limited to the number of partitions. For example, you require at least 16 partitions to set the degree of parallelism of the query to 16.
Example 3-4 Querying with a full partition-wise join
SELECT c.cust_last_name, COUNT(*) FROM sales s, customers c WHERE s.cust_id = c.cust_id AND s.time_id BETWEEN TO_DATE('01-JUL-1999', 'DD-MON-YYYY') AND (TO_DATE('01-OCT-1999', 'DD-MON-YYYY')) GROUP BY c.cust_last_name HAVING COUNT(*) > 100;