SQL Server

Understanding Sparse Columns

Sparse columns in SQL Server are an intriguing feature designed to optimize storage for columns that contain a significant amount of NULL values. Introduced in SQL Server 2008, sparse columns offer a way to store nulls efficiently, consuming no storage space for NULL values in a column. This feature can be particularly useful in scenarios where the table has many columns that are not frequently used or that often contain null values. Sparse columns are ideal for situations where NULL values can constitute 20% to 40% or more of the dataset in a column. Beyond storage optimization, sparse columns also have implications for the performance of data retrieval and manipulation operations.

Internal Implementation and Metadata

Internally, SQL Server implements sparse columns by not physically storing NULL values. Non-NULL values in sparse columns consume more storage space than their non-sparse counterparts due to the overhead associated with maintaining sparsity. This trade-off means that while sparse columns can significantly reduce the storage footprint of highly null columns, they might increase the size of columns with a low proportion of NULL values.

To manage sparse columns, SQL Server uses a special kind of row structure that includes a column set, which is an XML representation of all the sparse columns in a table. This column set is not stored physically in the table but can be queried to retrieve data from sparse columns as if they were part of a single XML document.

To explore metadata related to sparse columns in the current database, you can use the following query, which leverages the sys.columns catalog view:

    OBJECT_NAME([object_id]) AS [TableName], [name] AS [ColumnName], [is_sparse]
FROM [sys].[columns]
WHERE [is_sparse] = 1

This query lists all sparse columns in the database, providing insights into which tables and columns are utilizing this feature.

Test it

This script is designed to demonstrate the storage efficiency of sparse columns in SQL Server compared to standard nullable columns when dealing with a large volume of NULL values:


DROP TABLE IF EXISTS [dbo].[StandardNulls]
DROP TABLE IF EXISTS [dbo].[SparseNulls]

CREATE TABLE [dbo].[StandardNulls] (
    [StandardNull] INT NULL

CREATE TABLE [dbo].[SparseNulls] (
    [SparseNull] INT SPARSE

WHILE @i < 100000
	INSERT INTO [StandardNulls] ([StandardNull])
	INSERT INTO [SparseNulls] ([SparseNull])

	SET @i = @i + 1

EXEC [sp_spaceused] @objname = '[dbo].[StandardNulls]'
EXEC [sp_spaceused] @objname = '[dbo].[SparseNulls]'

The script creates two new tables, [dbo].[StandardNulls] and [dbo].[SparseNulls]. The first table has a standard nullable integer column named [StandardNull], while the second table has a sparse column named [SparseNull].

Then Inside the loop, it inserts into both tables. For every 1000th iteration (i.e., when @i % 1000 = 0), it inserts the value of @i into the tables; otherwise, it inserts a NULL. This results in tables where only 1 out of every 1000 rows has a non-NULL value, simulating a high proportion of NULL values which is ideal for demonstrating the efficiency of sparse columns.

After the loop completes and both tables are populated, the script uses the stored procedure sp_spaceused to measure the amount of storage space used by each table. This comparison is crucial as it highlights the storage savings achieved by using a sparse column in scenarios where NULL values dominate. Sparse columns do not use storage space for NULL values, unlike standard columns which allocate minimal storage even for NULLs.

Space saved using the SPARSE column is about 200 KB in our example. In real-world applications, this can translate into significant storage savings, especially for large databases with many such columns. However, it’s also important to remember the overhead associated with sparse columns for non-NULL values, which means they are not universally the best choice for all scenarios.

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