The quest for randomness in SQL Server leads us to two prominent players: RAND() and NEWID(). This blog post shows examples of both functions, exploring their capabilities, use cases, and the subtle distinctions that set them apart when generating random values.

RAND() Function

The RAND() is a built-in SQL Server function designed to produce pseudo-random float values within a specified range.

-- Example using RAND() to generate random float values between 0 and 1
SELECT RAND() AS RandomFloat;

-- Generate random integers between 1 and 100
SELECT CAST(RAND() * 100 AS INT) AS RandomInteger;

-- Generate random dates within a date range
SELECT DATEADD(DAY, CAST(RAND() * 365 AS INT), '2023-01-01') AS RandomDate;

-- Using RAND() with a seed for reproducibility
SELECT RAND(42) AS RandomWithSeed;

RAND() has a deterministic nature, generating the same sequence of numbers if invoked with the same seed. This is valuable for scenarios involving simulation, testing, and generating diverse datasets for analysis.

In the context of pseudo-random number generation, a “seed” is an initial value used to start the random number generator algorithm. When a pseudo-random number generator is initialized with a specific seed, it will produce a sequence of numbers that appears to be random but is, in fact, deterministic. This means that if you use the same seed to initialize the generator multiple times, you will get the same sequence of random numbers each time.

The Role of NEWID() in True Randomness

NEWID(): Ideal for situations demanding true randomness, especially when uniqueness is a priority. It introduces true randomness by generating unique identifiers, ensuring each row’s unpredictability. This function generates a unique identifier for each row, ensuring unpredictability and uniqueness in the generated values.

Creating the JupiterHarvest Table with RAND() and NEWID()

Let’s embark on a journey into the practical application of these functions by creating the JupiterHarvest table. The script below showcases the interplay between RAND() and NEWID() to populate the table with 100,000 rows of test data.

-- Example using NEWID() to generate unique identifiers
SELECT NEWID() AS UniqueIdentifier;

-- Use NEWID() to order rows randomly
SELECT *
FROM YourTable
ORDER BY NEWID();

In this example above with ORDER BY NEWID(), it’s a common technique to shuffle rows randomly when selecting from a table. Each row gets assigned a new unique identifier (NEWID()), and then the result set is ordered by these identifiers, effectively randomizing the order of rows.

Navigating the Distinctions

Consistency vs. Unpredictability

• RAND(): Offers consistency and predictability as it produces the same sequence with the same seed.
• NEWID(): Introduces true randomness by generating unique identifiers, ensuring each row’s unpredictability.

Use Cases

• RAND(): Valuable for scenarios involving simulation, testing, and generating diverse datasets for analysis.
• NEWID(): Ideal for situations demanding true randomness, especially when uniqueness is a priority.

Conclusion

Understanding the dynamics between RAND() and NEWID() empowers you to choose the right tool for the right job. May your SQL adventures be guided by the precision of RAND() and the unpredictability of NEWID() going forward.