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

Databases don't inherently store data in a random order. To simulate randomness, we use functions that generate pseudo-random numbers. These functions create a sequence of numbers that appear random, but are actually deterministic (reproducible with a specific seed value).

Common Functions for Random Selection:

• RAND() (MySQL, PostgreSQL): This function generates a random floating-point number between 0 and 1.
• NEWID() (SQL Server): This function generates a uniqueidentifier value which can be used for random sorting.
• DBMS_RANDOM.VALUE (Oracle): This function generates a pseudo-random number.

Steps to Select a Random Row:

1. Order By Random Function: We use the ORDER BY clause along with the random function mentioned above. This instructs the database to sort the table rows based on the random numbers generated for each row.
2. Limit to One Row: Since we only want one random row, we use the LIMIT clause set to 1. This ensures only the first row (after the random sorting) is returned.

Example (MySQL):

SELECT *
FROM your_table
ORDER BY RAND()
LIMIT 1;

This query selects all columns (*) from the table your_table, orders the results based on a random number generated by RAND(), and then limits the output to only the first row (LIMIT 1).

Important Note:

While the ORDER BY RAND() approach is common, it might not be the most performant option for very large tables. The database needs to scan through all rows to assign a random value before sorting.

SELECT *
FROM your_table
ORDER BY RAND()
LIMIT 1;

PostgreSQL:

SELECT *
FROM your_table
ORDER BY RANDOM()
LIMIT 1;

SQL Server:

SELECT TOP 1 *
FROM your_table
ORDER BY NEWID();

Oracle:

SELECT *
FROM (
  SELECT *
  FROM your_table
  ORDER BY DBMS_RANDOM.VALUE
)
WHERE rownum = 1;

• Concept: This method uses the built-in CHECKSUM() function to generate a pseudo-random value for each row. You can then filter based on a random threshold derived from another RAND() call.
• Pros:
  • Can be efficient for most databases.
  • Doesn't require full table scans.
• Cons:

SELECT *
FROM your_table
WHERE CHECKSUM(your_table.*) > RAND() * (SELECT MAX(CHECKSUM(your_table.*)) FROM your_table);

OFFSET with approximate row count (Limited support):

• Concept: This method relies on the OFFSET clause (available in some databases like SQL Server 2012+) to skip a random number of rows after getting an approximate count. It's not truly random but can be good for estimates.
• Pros:
• Cons:
  • Not all databases support OFFSET.
  • Selection might not be perfectly random.

Example (SQL Server):

DECLARE @random_offset INT;
SET @random_offset = FLOOR(RAND() * (SELECT COUNT(*) FROM your_table));

SELECT TOP 1 *
FROM your_table
ORDER BY your_column  -- Order by an indexed column for efficiency
OFFSET @random_offset ROWS;

TABLESAMPLE with NEWID() (SQL Server):

• Concept: This method combines TABLESAMPLE (introduced in SQL Server 2005) to fetch a small random sample of rows and then sorts them with NEWID() for final selection.
• Pros:
• Cons:

SELECT TOP 1 *
FROM your_table
TABLESAMPLE (100 ROWS)  -- Adjust sample size as needed
ORDER BY NEWID();

Choosing the right method:

• For most cases, the CHECKSUM-based filtering is a good balance of performance and randomness.
• If you're dealing with massive tables in SQL Server and need speed over perfect randomness, consider OFFSET with an indexed column.
• TABLESAMPLE with NEWID() is a good option for SQL Server when you only need a single random row from a large table.