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In this chapter, we explored redo and undo and took a look at what they mean to the developer. I ve mostly presented here situations or conditions you should be on the lookout for, since it is actually the DBAs or SAs who must correct these issues. The key point to take away from this chapter is the importance of redo and undo, and the fact that they are not overhead they are integral components of the database and are necessary and mandatory. Once you have a good understanding of how they work and what they do, you ll be able to make better use of them. Understanding that you are not saving anything by committing more frequently than you should (you are actually wasting resources, as it takes more CPU, more disk, and more programming) is probably the most important point of all. Be aware of what the database needs to do, and then let the database do it.

Note The copyright information here must be preserved based on the license agreement. The license is available in the Source Code section of the Apress website (www.apress.com) or www.lhotka.net/cslanet/ license.aspx.

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In this chapter, we will discuss the various types of database tables and cover when you might want to use each type (i.e., when one type of table is more appropriate than another). We will concentrate on the physical storage characteristics of the tables: how the data is organized and stored. Once upon a time, there was only one type of table, really: a normal table. It was managed in the same way a heap of stuff is managed (the definition of which appears in the next section). Over time, Oracle added more sophisticated types of tables. Now, in addition to the heap organized table, there are clustered tables (three types of those), index organized tables, nested tables, temporary tables, external tables, and object tables. Each type of table has different characteristics that make it suitable for use in different application areas.

We will define each type of table before getting into the details. There are nine major types of tables in Oracle, as follows: Heap organized tables: These are normal, standard database tables. Data is managed in a heap-like fashion. As data is added, the first free space found in the segment that can fit the data will be used. As data is removed from the table, it allows space to become available for reuse by subsequent INSERTs and UPDATEs. This is the origin of the name heap as it refers to this type of table. A heap is a bunch of space, and it is used in a somewhat random fashion. Index organized tables: These tables are stored in an index structure. This imposes physical order on the rows themselves. Whereas in a heap the data is stuffed wherever it might fit, in index-organized tables (IOTs) the data is stored in sorted order, according to the primary key. Index clustered tables: Clusters are groups of one or more tables, physically stored on the same database blocks, with all rows that share a common cluster key value being stored physically near each other. Two goals are achieved in this structure. First, many tables may be stored physically joined together. Normally, you would expect data from only one table to be found on a database block, but with clustered tables, data from many tables may be stored on the same block. Second, all data that contains the same cluster key value, such as DEPTNO = 10, will be physically stored together. The data is clustered around the cluster key value. A cluster key is built using a B*Tree index.

Hash clustered tables: These tables are similar to index clustered tables, but instead of using a B*Tree index to locate the data by cluster key, the hash cluster hashes the key to the cluster to arrive at the database block the data should be on In a hash cluster, the data is the index (metaphorically speaking) These tables are appropriate for data that is read frequently via an equality comparison on the key Sorted hash clustered tables: This table type is new in Oracle 10g and combines some aspects of a hash-clustered table with those of an IOT The concept is as follows: you have some key value that rows will be hashed by (say, CUSTOMER_ID), and then a series of records related to that key that arrive in sorted order (timestamp-based records) and are processed in that sorted order.

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