In Oracle SQL, an index is a schema object that enhances the speed of data retrieval. Similar to an index in a book, an SQL index provides a way to locate data more efficiently without scanning the entire table. Indexes are essential for optimizing query performance, especially when working with large datasets.

Understanding Indexes in Oracle SQL

An index is created on one or more columns of a table and maintains an ordered structure based on the values in these columns. By organizing data in a way that Oracle can quickly search through, indexes drastically reduce the time it takes to locate records. The most common type of index in Oracle is the B-tree index, which is balanced, making searches efficient.

How Indexes Improve Performance

When a query searches for data, it generally goes through two paths:

1. Full Table Scan: Oracle reads each row in the table to find matching records. This can be slow for large tables.
2. Index Scan: Oracle uses the index to locate only relevant rows, skipping non-matching data.

For instance, if you create an index on a frequently searched column, such as employee_id in an employees table, Oracle can use the index to find specific employee_id values much faster than scanning every row in the table.

Types of Indexes in Oracle SQL

Oracle SQL offers several types of indexes, each suited to different situations:

1. B-tree Index: This is the default and most common index type. It is optimal for equality and range queries.

   • Example: If you frequently search for employees by their employee_id, a B-tree index on this column improves search speed.
   • Syntax:
     sql

      

     CREATE INDEX emp_id_idx ON employees (employee_id);


2. Bitmap Index: Useful for columns with low cardinality, meaning columns with fewer unique values (e.g., gender or department).

   • Example: If a status column has values like ‘Active’ or ‘Inactive,’ a bitmap index works efficiently.
   • Syntax:
     sql

      

     CREATE BITMAP INDEX status_idx ON employees (status);


3. Unique Index: Created on columns that must contain unique values. It prevents duplicate entries in the indexed column.

   • Example: Unique indexes are useful for columns like email or SSN in employee tables.
   • Syntax:
     sql

     CREATE UNIQUE INDEX email_idx ON employees (email);

4. Function-based Index: Created on expressions or functions rather than simple columns. This index is helpful for cases where queries use functions on indexed columns.

   • Example:
     sql

      

     CREATE INDEX upper_name_idx ON employees (UPPER(name));


     This allows faster search results when queries use UPPER(name).

5. Composite Index: Created on multiple columns and is helpful when queries commonly use multiple columns in the WHERE clause.

   • Example:
     sql

      

     CREATE INDEX emp_dept_idx ON employees (department_id, job_id);


Practical Example of Index Use

Suppose we have an employees table with millions of rows. Searching by employee_id without an index requires a full table scan, which takes time. By creating an index on employee_id, we make searches efficient:

After creating this index, a query such as:

will use the index, making data retrieval faster and reducing the system load.

Managing and Maintaining Indexes

While indexes improve performance, they also consume storage and can slow down DML operations (like INSERT, UPDATE, and DELETE) since each modification must also update the index. Regular maintenance is key to ensuring indexes stay efficient. This includes:

• Rebuilding Indexes: Over time, indexes can become fragmented, leading to inefficiencies. Use ALTER INDEX ... REBUILD; to reorganize and optimize them.
• Dropping Unused Indexes: If an index is not used frequently, it might consume space without providing performance benefits. You can drop it with:
  sql

   

  DROP INDEX emp_id_idx;


When Not to Use an Index

While indexes are beneficial, they aren’t suitable for every situation. Avoid using indexes for:

• Columns with low selectivity: Columns with very few unique values (e.g., boolean flags) don’t benefit much from indexing.
• Small tables: If a table has only a few rows, a full table scan is often faster than using an index.
• Frequent DML Operations: If a table undergoes constant updates, inserts, or deletes, maintaining indexes can become overhead.

Example Scenario

Imagine a retail application where customers search by customer_id and order status. For customer_id, a B-tree index will speed up searches, while a bitmap index on status (e.g., 'Processed,' 'Pending') will enhance performance because there are limited unique statuses. Together, these indexes enable the system to respond to user searches rapidly.