Indexing is a technique used by Relational Database Management Systems (RDBMS) to optimize the performance of database queries. In this blog, we’ll explore indexing in RDBMS, including what indexing is, how it works, and some examples of indexing in practice.
What is indexing?
In RDBMS, indexing refers to the process of creating data structures that allow for faster access to data in a database table. Indexes are used to speed up queries by reducing the number of rows that need to be scanned when searching for data.
Indexes work by creating a separate data structure that contains a subset of the data in a table. This data structure is optimized for searching and can be quickly scanned to find matching rows. When a query is executed that includes a search condition, the RDBMS can use the index to quickly locate the relevant rows.
Importance of Indexing
The primary objective of indexing is to reduce the time taken to execute queries. Without indexing, the RDBMS has to scan through the entire table to retrieve the data that meets the query criteria. This process can be time-consuming and can impact the performance of the application. By creating an index, the database system can locate the required data quickly and efficiently, leading to faster query execution.
How does indexing work?
Indexes work by creating a separate data structure that contains a subset of the data in a table. This data structure is optimized for searching and can be quickly scanned to find matching rows. When a query is executed that includes a search condition, the RDBMS can use the index to quickly locate the relevant rows.
Types of Indexing
RDBMS supports various types of indexing. The choice of indexing technique depends on the nature of the data, the size of the table, and the type of queries executed. Some of the commonly used indexing techniques are:
Primary Index
A primary index is a unique index that is created on the primary key column of a table. The primary key column is a column that uniquely identifies each row in the table. By default, the primary key column is indexed, and the index is used to enforce the uniqueness constraint. The primary index is also used to optimize queries that involve the primary key column.
Example:
Consider a table ‘Customers’ with columns ‘CustomerID’ (primary key), ‘FirstName’, ‘LastName’, ‘Email’, and ‘Phone’. The primary index is created on the ‘CustomerID’ column. The query “SELECT * FROM Customers WHERE CustomerID=1001;” can be optimized using the primary index.
Unique Index
A unique index is created on a column or set of columns that have unique values. The unique index is used to enforce the uniqueness constraint and optimize queries that involve the unique column(s). Unlike the primary index, a table can have multiple unique indexes.
Example:
Consider a table ‘Orders’ with columns ‘OrderID’ (primary key), ‘CustomerID’, ‘OrderDate’, and ‘OrderTotal’. The unique index is created on the ‘OrderID’ column to enforce uniqueness. The query “SELECT * FROM Orders WHERE OrderID=5001;” can be optimized using the unique index.
Non-Unique Index
A non-unique index is created on a column or set of columns that may have duplicate values. The non-unique index is used to optimize queries that involve the indexed column(s).
Example:
Consider a table ‘Products’ with columns ‘ProductID’ (primary key), ‘ProductName’, ‘CategoryID’, ‘UnitPrice’, and ‘UnitsInStock’. The non-unique index is created on the ‘CategoryID’ column. The query “SELECT * FROM Products WHERE CategoryID=2;” can be optimized using the non-unique index.
Clustered Index
A clustered index is created on a column or set of columns that determines the physical order of the data in the table. The clustered index is used to optimize queries that involve range-based queries and sorting.
Example:
Consider a table ‘Sales’ with columns ‘SalesID’ (primary key), ‘CustomerID’, ‘ProductID’, ‘SaleDate’, and ‘SaleAmount’. The clustered index is created on the ‘SaleDate’ column to optimize queries that involve range-based queries and sorting.
Non-Clustered Index
A non-clustered index is created on a column or set of columns that do not determine the physical order of the data in the table. The non-clustered index is used to optimize queries that involve searching and filtering.
Example:
Consider a table ‘Employees’ with columns ‘EmployeeID’ (primary key), ‘FirstName’, ‘LastName’, ‘DepartmentID’, ‘Salary’, and ‘HireDate’. The non-clustered index is created on the ‘DepartmentID’ column. The query “SELECT * FROM Employees WHERE DepartmentID=10;” can be optimized using the non-clustered index.
Check Related Articles: Query planning in RDBMS, Partitioning in RDBMS, Query optimization in RDBMS
Scenarios for Indexing
Indexing is a powerful tool for optimizing query performance, but it is important to use it wisely. Here are some scenarios where indexing can be beneficial:
Large Tables
If a table contains a large number of rows, indexing can significantly improve query performance by reducing the amount of time required to search the table. Without indexing, the RDBMS has to scan through the entire table to retrieve the data that meets the query criteria. This process can be time-consuming and can impact the performance of the application.
For example, consider a table ‘Sales’ with millions of records. If a query needs to retrieve all sales transactions for a particular date range, without indexing, the RDBMS has to scan through the entire table to retrieve the data, which can be slow and inefficient. However, if an index is created on the ‘SaleDate’ column, the RDBMS can quickly locate the required data, leading to faster query execution.
Frequent Searches
If a column is frequently used in search queries, indexing can improve query performance by providing quick access to the required data. Without indexing, the RDBMS has to scan through the entire table to retrieve the data that meets the query criteria, which can be time-consuming and inefficient.
For example, consider a table ‘Employees’ with thousands of records. If a query needs to retrieve all employees who work in a particular department, without indexing, the RDBMS has to scan through the entire table to retrieve the data, which can be slow and inefficient. However, if an index is created on the ‘DepartmentID’ column, the RDBMS can quickly locate the required data, leading to faster query execution.
Range-Based Queries
If a query involves range-based searches, such as searching for all records within a specific date range or a range of values, clustering index can significantly improve performance. A clustering index is created on a column or set of columns that determines the physical order of the data in the table.
For example, consider a table ‘Sales’ with millions of records. If a query needs to retrieve all sales transactions for a particular date range, a clustering index can significantly improve performance. If a clustering index is created on the ‘SaleDate’ column, the RDBMS can quickly locate the required data, leading to faster query execution.
Sorting
If a query involves sorting, indexing can improve performance by providing quick access to the sorted data. Without indexing, the RDBMS has to scan through the entire table to retrieve the data and sort it, which can be time-consuming and inefficient.
For example, consider a table ‘Employees’ with thousands of records. If a query needs to retrieve all employees sorted by salary, without indexing, the RDBMS has to scan through the entire table to retrieve the data and sort it, which can be slow and inefficient. However, if an index is created on the ‘Salary’ column, the RDBMS can quickly retrieve the data and sort it, leading to faster query execution.
Conclusion
Indexing is an essential aspect of RDBMS that helps improve query performance by providing quick access to the required data. The choice of indexing technique depends on the nature of the data, the size of the table, and the type of queries executed. It is important to use indexing wisely and avoid over-indexing, which can negatively impact performance. By carefully selecting the appropriate indexing strategy, RDBMS can deliver excellent query performance, making applications more responsive and user-friendly.
