Return rows in random order

sql

randomization

database-performance

data-analysis

byAlex Kataev·Nov 20, 2024

To shuffle your SQL table rows instantly, use ORDER BY NEWID() in SQL Server or ORDER BY RAND() in MySQL:

-- SQL Server
SELECT * FROM YourTable 
-- Let's play bingo!
ORDER BY NEWID();

-- MySQL
SELECT * FROM YourTable 
-- Shake it up, baby!
ORDER BY RAND();

This generates a random sequence of your database records, ideal for applications such as unbiased sampling or shuffle-like operations.

Understanding random draws in SQL

Basic random functions: NEWID() versus RAND()

The function NEWID() generates a unique identifier for each record, ensuring a fully randomized output. Unlike RAND(), which computes a single random value that stays the same for the whole query execution, NEWID() assigns a random ID to each row, providing a real shuffle.

Handling large datasets: Ingenious solutions

Using ORDER BY RAND() for large datasets can be resource taxing. SQL Server provides TABLESAMPLE, that randomly selects a percentage of rows, offering an optimized approach.

-- SQL Server
SELECT * FROM YourTable 
-- Let's keep it light and breezy!
TABLESAMPLE (10 PERCENT);

Fair is fair: Unbiased randomness

Guaranteeing unbiased selection when sampling can be tricky. It’s important to understand that using random functions like NEWID() and RAND() assures that each row has an equal chance of being selected - a fair game.

Advanced tips: Turbocharge your random queries

Bypassing indexes: Watch for performance

An ORDER BY clause with non-deterministic functions, like NEWID(), ignores the use of indexes, and can hit performance. To boost speed in such cases, you might want to use indexed random values, or pseudo-random algorithms.

Stay in order: Paging through randomness

Implementing pagination on a randomized result set can be challenging. To maintain a consistent order across pages, you can store NEWID() results in a temporary table or use a consistent seed with RAND().

Fun with SQL syntax: Random selection in RDBMS

Diversity is fun, but can be tricky! PostgreSQL and Oracle offer different methods for row randomization, requiring mastery of unique functions such as SETSEED() and platform-specific sampling syntax.

Pragmatic applications and critical considerations

Test Drive: Quality assurance, the fun way

Random datasets are perfect for testing new applications or fleshing out development environments. By exposing your applications to diverse data scenarios, you ensure they can handle whatever live operations throw at them.

Data wizardry: Unleash your inner analyst

Random row selection is a powerful tool for data analysis initiatives like statistical sampling or anonymizing datasets. You can pull a quick sample without sweating over data integrity.