It's easy to think of comparisons as straightforward: is this number bigger than that one? Is this word the same as another? But when we delve into the world of data and logic, especially in systems like databases, things can get a little more interesting. We're talking about boolean comparisons, and while they ultimately boil down to TRUE or FALSE, the journey there can involve a third, rather elusive state: NULL.
Think of it like this: when you ask a question, you expect a clear 'yes' or 'no' answer. But what if the person you're asking doesn't know the answer? Or what if the question itself doesn't make sense in the context? That's where NULL comes in. In the realm of boolean comparisons, if one of the things you're comparing is NULL, the result of that comparison isn't TRUE or FALSE; it's also NULL. It’s a way of saying, 'I can't give you a definitive answer because some of the information is missing or invalid.'
This is particularly important when you're working with data. For instance, if you're checking if a counter variable is NULL, you can't just assume it's zero. As one example shows, even if you try to add 1 to a NULL value, it remains NULL. So, to properly check for its absence of value, you'd use an IS NULL operator. It’s a direct way to ask, 'Is this empty?'
Beyond the NULL state, we have the familiar relational operators. These are your everyday comparison tools: equals (=), not equals (<>, !=, ~=, ^=), less than (<), greater than (>), less than or equal to (<=), and greater than or equal to (>=). These work on numbers, characters, and even dates. For numbers, it's pretty intuitive. For characters, it's based on their underlying binary values, so 'y' is indeed greater than 'r'. Dates are compared by recency – a later date is considered 'greater'.
But what about comparing TRUE and FALSE themselves? Interestingly, TRUE is considered greater than FALSE. This might seem a bit abstract, but it has practical implications in how logical operations are evaluated. When you combine boolean values using operators like AND, the presence of NULL can lead to a NULL result, as seen when comparing TRUE and NULL. It’s not FALSE, but it’s not definitively TRUE either.
Then there's the LIKE operator, which is fantastic for pattern matching in text. It uses wildcards: the underscore (_) for a single character and the percent sign (%) for zero or more characters. So, 'J%s_n' would match 'Johnson' because 'J' is the start, '%s' matches 'ohnso', and '_n' matches 'n'. Case sensitivity matters here, so 'J%s_n' won't match 'J%S_N' if the system is case-sensitive.
Finally, the BETWEEN operator is a neat way to check if a value falls within a range, inclusive of the boundaries. It’s like asking, 'Is this number between 10 and 20?'
Understanding these comparison operators, especially how they handle NULL, is fundamental to writing accurate and robust logic, whether you're building complex database queries or simple conditional statements. It’s about more than just getting a TRUE or FALSE; it’s about understanding the full spectrum of possible outcomes and how to interpret them correctly.
