Why Doesn't SQL Have a Multiplication Aggregate Function?

SQL's Missing Multiplication Aggregate: Why "MUL" Doesn't Exist

SQL's standard aggregation functions omit a dedicated multiplication operator (like "MUL"). This design choice stems from the significant risk of data overflow and the relatively infrequent need for such an operation. Unlike averaging (AVG), summing (SUM), and counting (COUNT), multiplication rapidly generates enormous numbers, even with modestly sized datasets.

The potential for overflow is inherent to the multiplicative process. The cumulative nature of multiplication means that even a hundred relatively small numbers can quickly produce results exceeding the capacity of standard data types.

Due to this high overflow risk and the limited practical applications of multiplicative aggregation, a dedicated "MUL" function was excluded from the SQL standard. However, workarounds exist to achieve a similar outcome.

Alternatives to Direct Multiplication Aggregation

While a direct "MUL" function is absent, mathematical formulas using logarithms and exponentials provide approximations. These methods leverage the properties of logarithms to transform multiplication into addition, reducing the risk of overflow:

• Oracle: EXP(SUM(LN(column))) or POWER(N,SUM(LOG(column, N)))
• MSSQL: EXP(SUM(LOG(column))) or POWER(N,SUM(LOG(column)/LOG(N)))
• MySQL: EXP(SUM(LOG(column))) or POW(N,SUM(LOG(N,column)))

It's crucial to remember that these methods have limitations. For instance, logarithmic functions can encounter errors when dealing with negative numbers or zero values.

Illustrative Example and Implementation

Let's say we need the product of values within a column named "data." The following query provides a solution using the logarithmic/exponential approach, handling potential negative numbers and zeros:

SELECT EXP(SUM(LOG(ABS(nullif(data, 0))))) * ROUND(0.5 - COUNT(nullif(SIGN(SIGN(data) + 0.5), 1)) % 2, 0)
FROM MUL

This query effectively calculates the product of the "data" column, addressing potential issues with negative or zero entries. The result is a numeric value, functionally mirroring a hypothetical "MUL" aggregate.

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