Lesson 8

Index-organized tables can be a nice performance enhancer for the right situation. In this module, you learned about index-organized tables, including:

1. The benefits of index-organized tables
2. How to create an index-organized table
3. How to create secondary indexes on an index-organized table
4. How to handle large rows in an index-organized table
5. How to delete and modify index-organized tables
6. How to reorganize an index-organized table

The simplest explanation of an index-organized table is that it is accessed like any other Oracle table (typically a heap-organized table^[1]) but is physically stored like an Oracle B-tree index. Index-organized tables are typically created on "thin" tables (tables without too many columns). Typically, multiple columns of the table make up the primary key of the index-organized table. The non-key columns can also be stored as part of the B-tree index. The proper configuration and use of index-organized tables is fairly specific and does not meet all application needs. From a user or developer perspective, an index-organized table (IOT) appears like a normal table. IOTs are stored in a B-tree structure. There must be a primary key on an index-organized table, as the data is stored in primary key order. Since there is no data segment, there is no physical ROWID values for indexorganized tables. See Figure 4 for an example of an IOT.

IOTs support many of the same features found in heap-organized tables, such as

1. Constraints
2. Partitioning
3. Triggers
4. LOB columns
5. Parallelism
6. Indexes (e.g. secondary indexes on IOTs)
7. Global hash-partitioned indexes
8. Online reorganization

Because all of the data within an index-organized table is stored within the index itself, there are physical differences in the way an index-organized table is stored, as compared to a normal B-tree index that supports a normal heap-organized table. Some of the unique aspects of IOT's are as follows:

1. Secondary indexes use logical ROWIDs rather than physical ROWIDs.
2. They require a primary key.
3. Primary key compression can be used to save storage and reduce size of an IOT.
4. An overflow segment can be used for non-key column data.
5. Secondary bitmap indexes require a defined mapping table.
6. Non-key column data is stored in the leaf blocks of an IOT.

There are limitations on index-organized tables, although many of the limitations will not affect their use in the majority of applications. Some of these limitations include:

1. Rows exceeding 50% of a block must use an overflow segment.
2. IOTs cannot use virtual columns.
3. Tables with more than 255 columns must have an overflow segment.
4. Tables cannot have more than 1,000 total columns.
5. The primary key cannot be more than 32 columns.

Oracle OLAP is embedded within Oracle Database 12c, offering a centralized platform for managing data and business rules securely and efficiently. It utilizes a star schema design, where dimension views form a constellation around the fact view (or cube), facilitating comprehensive analysis. Key features of Oracle OLAP include:

1. Multidimensional Analysis: Enables users to analyze data across multiple dimensions, such as product, time, and location, to identify patterns and trends.
2. Fast Query Response Times: Leverages Oracle Database's powerful indexing and caching mechanisms to deliver rapid responses to complex analytical queries.
3. Ease of Use: Provides user-friendly tools and interfaces for data exploration and analysis, making it accessible to a wide range of users.
4. Scalability: Supports large and growing datasets, ensuring scalability to meet increasing data needs.
5. Integration with Oracle Applications: Integrates seamlessly with Oracle's enterprise applications, providing a unified platform for business intelligence and decision-making.

Oracle OLAP is a valuable tool for businesses that need to extract meaningful insights from their data to make informed decisions. It empowers users to analyze trends, identify patterns, and uncover hidden relationships within their data, enabling them to optimize business processes, improve customer satisfaction, and gain a competitive edge. In the next module, you will learn about Oracle's auditing capabilities.

In this module, you were introduced to the following glossary term:

1. online analytical processing: Online analytical processing (OLAP) refers to a technology that enables users to quickly and easily analyze large datasets from multiple dimensions. It provides a multidimensional view of data, allowing users to drill down into specific details and slice and dice data in various ways to gain insights and trends.

Click the Quiz link below to test your knowledge of using index-organized tables.
Index Organized Tables - Quiz

[1]heap-organized table: A heap-organized table in Oracle is a table where data rows are stored in no particular order, unlike the usual sorted format of Oracle tables. Think of it like a pile of unsorted items. This contrasts with index-organized tables, where data is arranged based on the primary key index.