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The essential steps of a typical relational database life cycle are as follows: 1. Requirements gathering and analysis 2. Logical database design 3. Physical database design 4. Production implementation I will examine each of these stages in detail in the rest of this chapter. You could, of course, forget about using any methodology, and just design your database any way you want, create the structures, load the data, and be in business. However, improper database design has serious long-term performance implications, and you risk ending up with an inadequate database or simply with one that is wrong for your company s information and analysis needs. One thing to bear in mind is that databases tend to grow, and the better the database, the bigger it tends to get as more and more users rely on it. In addition, it won t take long for your application developers to begin to expand upon the core data, especially with today s requirements to make as much data as possible available on the Web.

Partitioned tables usually lead to tremendous improvements in performance, and they re easy to administer. By partitioning a table into several subpartitions, you re in essence limiting the amount of data that needs to be examined to satisfy your queries. If you have large tables, running into tens of millions of rows, consider partitioning them. Five table partitioning schemes are available to you in Oracle Database 11g, and I explain them in 7. You can index partitioned tables in a variety of ways, depending on the needs of the application. Partition maintenance is also easy, and it s well worth the additional effort when you consider the tremendous gains partitioned tables provide.

The relationship between event sources and event handlers may be one-to-one, one-to-many, many-to-one, or many-to-many. In all cases, it is important that event notifications reach the intended subscribers. The one-to-one relationship is the simplest arrangement, so I ll start my discussion with that one.

The Oracle database lets you use table compression to compress tables, table partitions, and materialized views. Table compression helps reduce space requirements for the tables and enhances query performance. Oracle compresses the tables by eliminating the duplicate values in a data block and replacing those values with algorithms to re-create the data when necessary. The table compression technique is especially suitable for data warehouse and OLAP databases, but OLTP databases can also use the technique fruitfully. The larger the table that is compressed, the more benefits you ll achieve with this technique. Here s a simple table compression statement: SQL> CREATE table sales_compress 2 COMPRESS 3 AS SELECT * FROM sh.sales; Table created. SQL> You can also use index key compression to compress the primary key columns of IOTs. This compression not only saves you storage space, but also enhances query performance. Index compression works by removing duplicate column values from the index. To compress an index, all you have to do is add the keyword COMPRESS after the index-creation statement, as shown here: SQL> CREATE INDEX item_product_x 2 ON order_items(product_id) 3 TABLESPACE order_items_indx_01 4 COMPRESS; Index created. SQL> Perform some tests to confirm the space savings and the time savings during the creation statements. Later, you can test query performance to measure the improvement.

If you re dealing with large amounts of data, you should seriously consider using materialized views to improve response time. Materialized views are objects with data in them usually summary data from the underlying tables. Expensive joins can be done beforehand and saved in the materialized view. When users query the underlying table, Oracle automatically rewrites the query to access the materialized view instead of the tables. Materialized views reduce the need for several complex queries because you can precalculate aggregates with them. Joins between large tables and data aggregation are expensive in terms of resource usage, and materialized views significantly reduce the response time for complex queries on large tables. If you aren t sure which materialized views to create, not to worry you can use the DBMS_OLAP package supplied by Oracle to get recommendations on ideal materialized views. 7 discusses materialized views in more detail, and also shows you how to use the SQL Access Advisor tool to get recommendations for creating materialized views and materialized view logs.