1. Introduction
In my previous article, we explored how SQL views can improve your reports by simplifying data access and automating analysis. Today, we’ll focus on another essential tool for optimizing your databases: SQL indexes. These structures significantly improve query performance, especially when dealing with large amounts of data.
To illustrate these concepts, we’ll use a dataset from Kaggle called Big Mart Sales. You can access the dataset here: Big Mart Sales Dataset from Kaggle Import Flat File into SQL Server
In this article, we’ll see how and why to use indexes to make your databases faster and more efficient.
2. What is an SQL Index?
An SQL index is a data structure that improves the speed of data retrieval in a table. It works like the index of a book: instead of scanning each line, the index allows for quickly locating the sought information.
There are several types of indexes, including:
• Unique Index: Ensures that each value in the indexed column is unique.
• Non-Unique Index: Allows duplicates in the indexed columns.
Indexes are particularly useful for queries that filter, sort, or join large tables of data.
3. Why use indexes?
Indexes improve the speed of SQL queries by facilitating the search and sorting of data. Here are a few reasons why it’s essential to use indexes:
3.1 Speeding up searches
Without an index, a database must scan every row of a table to find the data matching a query. With an index, the search becomes much faster, as the database can use the index to quickly locate the necessary rows, much like using the table of contents in a book.
Example :
If you are searching for a specific product in the Big Mart Sales dataset based on its identifier, using an index on the Item_Identifier column will significantly reduce search time.
3.2 Optimizing sorting
When executing SQL queries that require sorting, an index on the relevant columns can speed up the process. For example, sorting sales by amount (Item_Outlet_Sales) will be faster if an index is created on this column.
Example :
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CREATE INDEX idx_sales_amount ON BigMartSales (Item_Outlet_Sales); |
3.3 Improving joins
Joins between tables can be time-consuming. Using indexes on the join columns can speed up table merging.
Example :
In a query that links stores (Outlet_Identifier) and products, an index on these columns will save time during the join.
Indexes are essential for performance in databases, especially when dealing with large tables, which in real life can contain millions of rows, similar to the Big Mart Sales dataset.
4. How to create and use an index?
Creating an index in SQL is relatively simple. Here are some examples and explanations to understand how to use indexes effectively in your databases.
4.1 Creating a simple index
The basic syntax for creating an index is as follows:
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CREATE INDEX index_name ON table_name (column_name); |
Example: To speed up searches on the Item_Identifier column in the Big Mart Sales dataset, you can create an index like this:
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CREATE INDEX idx_item_identifier ON BigMartSales (Item_Identifier); |
This optimizes queries such as:
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SELECT * FROM BigMartSales WHERE Item_Identifier = 'FDA15'; |
4.2 Creating a multi-column index
In some cases, you may want to optimize queries that filter or sort on multiple columns. You can create a multi-column index to improve performance.
Example: If you frequently perform searches combining the product identifier (Item_Identifier) and the store identifier (Outlet_Identifier), you can create an index on these two columns:
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CREATE INDEX idx_item_outlet ON BigMartSales (Item_Identifier, Outlet_Identifier); |
This index is particularly useful for queries like:
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SELECT * FROM BigMartSales WHERE Item_Identifier = 'FDA15' AND Outlet_Identifier = 'OUT049'; |
4.3 Dropping an index
If you no longer need an index or if it slows down insertions and updates, you can drop it with the following command:
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DROP INDEX index_name ON table_name; |
Example :
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DROP INDEX idx_item_identifier ON BigMartSales; |
5. Best Practices for Using Indexes
While indexes are very useful for improving query performance, it’s essential to use them wisely to avoid negative effects. Here are some best practices to follow:
5.1 Don’t index every column
It can be tempting to index multiple columns to speed up searches, but this can negatively impact the performance of insertions, updates, and deletions because each modification to the table requires an index update.
Tip: Limit the use of indexes to columns frequently queried in WHERE filters or ORDER BY sorts.
5.2 Use indexes on the most selective columns
An index is most effective when the column contains many unique values. For example, an index on a column like Item_Identifier (product identifier) will perform better than on a column with few unique values, like Outlet_Size.
Example :
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CREATE INDEX idx_item_sales ON BigMartSales (Item_Identifier, Item_Outlet_Sales); |
5.3 Monitor index size
Indexes themselves take up space on the disk, and the larger they are, the more they slow down write performance. Avoid creating indexes on columns containing large data, like long text fields.
5.4 Avoid duplicate indexes
There’s no need to create multiple indexes on similar columns. For example, creating an index on Item_Identifier and another on Item_Identifier, Outlet_Identifier could be redundant if the first column is already well covered.
5.5 Use covering indexes
A covering index is one that contains all the columns necessary for a query, which avoids having to access the table to retrieve additional columns.
Example: If you frequently query to filter and display both identifiers and sales amounts, a covering index could include all relevant columns:
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CREATE INDEX idx_covering ON BigMartSales (Item_Identifier, Outlet_Identifier, Item_Outlet_Sales); |
6. Conclusion
SQL indexes are a powerful tool for improving query performance in your databases, especially when working with large volumes of data. By creating indexes on columns frequently used for searches and sorts, you can significantly speed up query processing. However, it’s important to follow best practices to avoid negative impacts on insert and update performance.
In the next article, we’ll explore stored procedures in SQL. These powerful tools allow you to automate tasks in your databases and further optimize your analysis processes.