Adventures in Machine Learning

Maximizing SQL Server Query Performance with Filtered Indexes

Structured Query Language (SQL) Server is a popular database management system used by businesses worldwide. Efficient query performance is a crucial aspect that determines the success of any SQL Server implementation.

To achieve optimal performance, SQL Server offers several indexing options that enable users to search for data quickly and accurately. One such indexing technique is the filtered index, which can significantly improve query performance, depending on the specific use case.

In this article, we will explore filtered indexes in SQL Server, their benefits, costs, and how to use them in creating highly optimized indexes.

Benefits of Nonclustered Indexes

Before diving into filtered indexes, it’s important to understand the benefits of nonclustered indexes. Nonclustered indexes allow you to access and analyze a subset of data from large tables quickly.

They improve the performance of queries that use specific columns in a table, as they reduce the overall number of rows scanned, returning only the relevant information. This type of index is also useful in cases where the table has low insert or update operations.

Nonclustered indexes can help improve query speed by creating a more efficient set-up for the database to access the data.

Costs of Nonclustered Indexes

While nonclustered indexes offer significant benefits to query performance, they do have their costs, namely in storage and maintenance. Nonclustered indexes consume storage space, which can affect the database’s overall size, leading to larger backup times, increased transactional logs, and hard disk space.

Additionally, nonclustered indexes increase database maintenance costs, as they must be maintained and updated whenever database records are added, updated, or deleted. This can have an impact on database performance during bulk changes or periods of high numbers of updates, which can become a bottleneck.

Filtered Indexes Explained

Filtered indexes are a variation of nonclustered indexes designed to offer even more efficiency in large tables that require search operations based on a specific condition or value. They allow you to create an index of only those rows that are required or relevant to your application.

This reduces the total number of rows that need to be scanned during the search, leading to incredible performance improvements. Filtered indexes use a predicate that defines the filter condition for the indexed rows.

Since they exclude the unwanted rows, they take up less storage space and less maintenance effort than non-filtered indexes. Additionally, filtered indexes allow database administrators to create and manage indexes more efficiently, especially when updating or deleting records.

Creating SQL Server Filtered Indexes

When configuring a filtered index, the SQL Server query optimizer can use it to speed up the execution of queries with the same filter condition, regardless of the predicate conditions of the queries. To create a filtered index, you begin by specifying the name of the index, the table on which it needs to be created, and the filter condition you want to use for the index.

The following syntax shows how to create a filtered index on the ‘sales.customers’ table with a predicate condition on the ‘phone’ column:

CREATE NONCLUSTERED INDEX idx_customer_phone

ON sales.customers (phone)

WHERE phone IS NOT NULL;

In the example above, the index name is “idx_customer_phone,” and the condition limits the indexed rows to those with non-null phone values. This will increase search performance for queries that specifically look for customer phone numbers, as it only searches in the indexed rows that match the condition.

Example of using a Filtered Index

As an illustration, let’s use the ‘sales.customers’ table to demonstrate how a filtered index can improve query performance. Consider the following query, which searches for customers with a phone number:

SELECT * FROM sales.customers WHERE phone = ‘555-555-1212’;

When this query is executed without a filtered index, the SQL Server engine will scan the entire ‘sales.customers’ table, looking for all rows that match the condition.

This is time-consuming, especially when dealing with large tables. To improve the search experience, you can create a filtered index on the ‘phone’ column to search only the indexed rows that match the condition.

In this case, the index only contains the rows that have non-null values for the phone column. As a result of using the filtered index, the query only scans the index, significantly reducing the number of rows scanned.

This results in faster query execution and optimized performance. Note that filtered indexes also have associated costs, like bulk insert and update operations, that can affect performance.

Careful planning is necessary to achieve optimal performance benefits from filtered indexes.

Conclusion

In summary, filtered indexes are an efficient tool for optimizing search queries on large tables that require a specific condition or value. They eliminate the need for scanning entire tables and provide significant benefits to query performance.

Filtered indexes do require careful planning to use effectively, as they have associated costs that can impact database operations. However, once implemented, a filtered index can help reduce data storage, improve query performance, and reduce maintenance costs.

By creating filtered indexes, database administrators can maximize the query performance of their SQL Server databases, ultimately leading to a better overall user experience and business outcomes.

Benefits of SQL Server Filtered Indexes

Filtered indexes in SQL Server enable us to create highly efficient and optimized indexes on tables, which help to improve query performance while reducing storage and maintenance costs. In this article’s expansion, we will take a deeper look into how filtered indexes can help save space with sparse columns, and reduce maintenance costs whenever a portion of data rows have been updated.

Saving Space with Sparse Columns

Sparse columns are an incredibly beneficial feature in SQL Server that can help save space if used correctly. They are handy when dealing with tables that have lots of NULL values, which can occur for different reasons, such as variations in the data values or deliberately introduced blanks.

However, the regular implementation of non-clustered indexes on columns that contain NULL values may not help to save storage space in the database, as they will include all rows, including those where the indexed column is NULL. Take a sales order scenario, where a table contains rich and descriptive order details, and some columns like ‘comment’ contain lengthy but infrequently used text.

In such situations, sparse columns can be used to eliminate null-valued data from the table, saving precious space in the database. Creating a filtered index on a sparse column can help eliminate NULL values, thereby reducing the storage costs associated with the column.

This is because filtered indexes only index rows that match the predicate condition of the column’s filter, leaving out those that don’t match. Let’s consider a table named ‘Orders’ with a column containing sparse data.

In this case, we can use a filtered index to save space by only indexing those rows that have non-NULL data as follows:

CREATE NONCLUSTERED INDEX Order_Comment_IDX

ON Orders(Comment)

WHERE Comment IS NOT NULL;

In the example above, the filtered index is created only for rows that are not null in the Comment column, improving storage and query performance.

Reducing Maintenance Costs

Whenever a portion of data rows are updated in a table, there may be a need to update the associated index columns, which can be costly in terms of time and system resources. In scenarios where large data sets are frequently updated or inserted, the maintenance overhead can slow down the performance of the entire database.

However, the use of filtered indexes can help alleviate such costs by enabling the creation of a filtered, smaller index that is easier to maintain.

Let’s take an example where the ‘Products’ table in a sales database is being updated frequently, and only a specific portion of the rows have been modified.

In this case, we can utilize a filtered index on the product’s price column to minimize the work needed to update the index whenever a product price value changes:

CREATE NONCLUSTERED INDEX Products_Price_IDX

ON Products(Price)

WHERE Price BETWEEN 1 AND 100;

In the above illustration, the filtered index only indexes products within a specific price range. This means that when a change is made to the price of products within that range, only a portion of the index will be updated, thereby reducing maintenance costs.

Conclusion

Filtered indexes offer a great alternative to regular non-clustered indexes when addressing sparse data storage and maintenance issues. By filtering out null values, filtered indexes enable more effective storage of data, thereby reducing storage space needs and associated costs.

Similarly, their ability to index only portions of a table based on a condition means that their maintenance costs are typically lower than for regular indexes. Which ultimately leads to improved database performance times.

With careful planning, filtered indexes can help significantly enhance database performance and benefit different stakeholders, such as DBAs, developers, and query analysts. In conclusion, SQL Server filtered indexes are a powerful tool that enables efficient search operations on large tables with specific conditions.

Filtered indexes help to improve query performance by reducing the number of rows scanned, while also leading to cost savings in both storage and maintenance costs. Additionally, by using filtered indexes on sparse columns, null value data can be eliminated, reducing storage requirements.

Furthermore, by filtering only a portion of a table, filtered indexes can also help minimize maintenance costs when updating or inserting new data. For efficient SQL server database performance, users should make use of filtered indexes to optimize storage space, query performance, and reduce maintenance costs for more significant gains in operational efficiency.

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