Indexing is really important for making SQL queries run faster. This is especially true in university databases, where there is a lot of different data to manage.

Think of an index like a roadmap for the SQL engine. It helps find information quickly and makes queries work better. This is super helpful when dealing with large sets of data, like student records or course lists.

When you run a query on a database that has good indexing, the system can quickly find the right information without looking at every single entry. For example, if you're trying to find students in a specific department, the system can use an index on that department listing. This means it doesn’t have to go through the entire database.

To put it simply, if you have a database with $N$ records, looking at every record takes $O(N)$ time. But if you use an indexed search, it could reduce that time to $O(\log N)$, which is much faster.

When we think about how to make queries even quicker, choosing the right indexing method is key. There are single-column indexes and multi-column indexes. Each choice can really change how fast complex queries run, especially when filtering, sorting, or combining different tables.

In a university setting, this is especially useful. During busy times like registration or exam scheduling, lots of queries run at once. Good indexing can help reduce waiting times and improve the experience for everyone.

But there’s a catch! While indexes help with reading data faster, they can slow things down when we change data, like adding or updating records. That’s because the system has to keep the indexes up to date.

So, using indexes wisely in university databases is super important. It helps keep everything running smoothly, even when lots of work is happening at the same time.