The Most Overlooked Database Indexing Strategies

let’s get into it: database indexing strategies. Specifically, the ones that often get overlooked but can make a huge difference when you’re chasing better performance. Everyone knows about basic indexing, like throwing an index on your primary keys or foreign keys. That’s database 101. But there’s so much more to indexing than that, and these strategies can take your queries from “acceptable” to “blazing fast.”

First, let’s talk about composite indexes. This is one of the most underutilized tools in the indexing toolbox. A composite index is just an index on multiple columns, which might sound fancy, but it’s really just common sense in the right scenarios. Say you’ve got a table for orders with columns for user_id and created_at. If your app often queries something like “all orders by a user, sorted by date,” a composite index on user_id and created_at can be a game-changer. Without it, the database might use the user_id index but then still sort through all the dates manually. A composite index lets it narrow things down instantly.

But here’s the trick: order matters. If you put created_at first in the index and then user_id, your query might not benefit at all. Think of it like a phone book—if it’s sorted by first name instead of last name, finding “Smith, John” becomes way harder. Always design composite indexes based on how your queries filter and sort data.

Next up: partial indexes. These are amazing for datasets where a lot of rows have the same value in a certain column, but you only query a subset of those rows. For example, let’s say you’ve got a status column in your orders table with values like “completed,” “pending,” and “cancelled.” If most of your queries only care about “completed” orders, you can create an index that only applies to rows where status = 'completed'. It’s smaller, faster, and your queries benefit without bloating the database with unnecessary overhead.

Another overlooked gem: covering indexes. A covering index isn’t a specific type of index—it’s just an index that includes all the columns a query needs to return. Why does that matter? Because if the database can satisfy a query entirely from the index, it doesn’t even have to touch the main table. For instance, let’s say you’ve got a query that grabs user_id and total_price from the orders table. If you create an index on those two columns, the database can serve up the result entirely from the index, skipping the table scan altogether.

Alright, let’s move on to indexing on expressions. A lot of developers don’t even realize this is possible. For example, if you’re always querying something like “find users where LOWER(email) equals this value,” you can index the LOWER(email) expression directly instead of indexing the raw email column. That way, the database doesn’t have to compute LOWER(email) for every row in your table every time you run the query.

And here’s one that’s especially useful for large tables: indexing on JSON or array data. Modern databases like PostgreSQL let you index specific keys or elements within a JSON or array column. So, if you’ve got a JSON column storing user preferences and you’re often filtering by, say, a specific setting, you can create a GIN index on that key. It’s not magic, but it’s pretty close when you see how much faster your queries get.

One last thing I’ll mention is index maintenance. This isn’t a strategy per se, but it’s critical. Indexes don’t clean themselves up. If you’ve got indexes on columns that aren’t queried anymore, they’re just dead weight. And worse, they slow down writes because the database still has to update them every time the data changes. Regularly audit your indexes to make sure they’re still pulling their weight.

So, there you have it: composite indexes, partial indexes, covering indexes, expression-based indexes, and smart indexing for JSON and array data. None of these are particularly hard to implement, but they’re often overlooked because we get stuck in the basics. But once you start using these, you’ll wonder why you ever let your database struggle without them.

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