Random Sampling in SQL Server: Exploring Techniques and Best Practices


Here's how it works:

Here's an example:

FROM Customers

This query selects the top 10 rows from the Customers table in a seemingly random order.

Important Considerations:

  • This method isn't perfect for very large tables. Sorting the entire table with NEWID() can be slow.
  • The randomness isn't truly cryptographically secure, but it's sufficient for most use cases.

For very large tables, you might explore alternative methods like TABLESAMPLE, but these have their own limitations for selecting small random samples.

Example 1: Selecting Top N Rows with NEWID()

This code uses the NEWID() function and TOP clause as explained before:

-- Replace 'YourTable' with the actual table name
-- Replace 'n' with the desired number of random rows
FROM YourTable

This query selects the top n rows from your table named YourTable in a seemingly random order based on the NEWID() function.

This code modifies the first example to select a random percentage of rows:

-- Replace 'YourTable' with the actual table name
-- Replace 'percentage' with the desired percentage (e.g., 10 for 10%)
SELECT TOP (SELECT COUNT(*) * percentage / 100 FROM YourTable) *
FROM YourTable

This query retrieves a random selection of rows that represents the specified percentage of the total rows in YourTable. The inner SELECT statement calculates the number of rows to select based on the total count and the desired percentage.


  • This method uses a dedicated function called TABLESAMPLE which allows for statistically representative random sampling.
  • It's generally faster than NEWID() for large tables as it avoids full table scans and sorting.
  • However, TABLESAMPLE might not be suitable for selecting a small number of rows (e.g., top 10) because it can clump results together.
-- Replace 'YourTable' with the actual table name
-- Replace 'n' with the desired number of random rows
FROM YourTable TABLESAMPLE (n ROWS) WITH (SEED = some_seed_value);

Important Note: TABLESAMPLE requires specifying a seed value to ensure some level of repeatability in the random selection process. You can use a random number or another unpredictable value for the seed.

CHECKSUM-based Filtering (For specific scenarios):

  • This method utilizes the table's existing clustered index or a calculated checksum column.
  • It involves filtering rows based on a mathematical operation on the checksum value and a random number.
  • This approach can be efficient if you already have a clustered index, but it might not be suitable for all table structures.

Picking from Indexed Columns (For specific scenarios):

  • This method leverages existing indexes on specific columns.
  • You can filter rows based on a range of indexed column values chosen randomly.
  • This approach is efficient if the chosen column has a good index and the desired number of random rows is relatively small compared to the total table size.

Choosing the Right Method:

The best method depends on your specific needs. Here's a general guideline:

  • For small numbers of rows, NEWID() with TOP can be sufficient.
  • For large tables and statistically representative samples, consider TABLESAMPLE.
  • For scenarios where you already have a clustered index or specific column indexes, explore CHECKSUM-based filtering or picking from indexed columns (consult resources for specific implementations).

Remember, it's always recommended to research and test the chosen method to ensure optimal performance for your specific scenario.

sql sql-server random

Network Restore vs. Local Transfer: Choosing the Right Method for Your SQL Server

SQL Server: The relational database management system you're using.Database: The specific database you want to restore.SQL Server 2005: The specific version of SQL Server you're using (considerations may differ for other versions)...

Beyond SHA1: Using HASHBYTES for Secure Hashing in MS SQL (with Caution)

Deprecated: HASHBYTES with algorithms like SHA1 and MD5 is deprecated since SQL Server 2016. It's recommended to use the stronger and more secure alternatives like SHA2_256 or SHA2_512 whenever possible...

Readability vs. Consistency: Choosing the Right Naming Convention for Your SQL Tables

Arguments for Plural Names:Readability: It feels more natural because a table typically stores many records, like a drawer full of "Socks"...

Ensuring Data Integrity with Unicode: When to Use the 'N' Prefix in T-SQL

What it Does:The "N" prefix in T-SQL indicates that a string literal is in Unicode format, also known as the National Language Character Set (NLCS)...

Why Can't I Select a Column Directly in a SQL GROUP BY? Fixing the 'Invalid Column' Error

Understanding the ErrorThis error arises when you're using a GROUP BY clause in your SQL query and you try to include a non-grouped column in the SELECT list without applying an aggregate function to it...

sql server random

Demystifying Randomness in SQL: From Functions to Full-fledged Selection

Finding Randomness:Databases don't inherently store data in a random order. To simulate randomness, we use functions that generate pseudo-random numbers

Unsticking the Sticky Seed: Overcoming RAND() Limitations in SQL Server 2000

RAND() function: While SQL Server offers the RAND() function to generate random numbers, it has a limitation. RAND() calculates a random number based on a single seed value