Problems of Excessive Indexing

Indexes are, no doubt, an awesome way to make your databases faster, but as the saying goes, "the best is the enemy of the good." Not every index is helpful, and having too many can do more harm than good. Sounds weird, but that's how it is. Let's break it down.

Imagine a huge library where you have several catalogs to find books — like by author, by genre, by year of publication. Each catalog helps you quickly find the book you need. But if there are too many catalogs — like for every word in the title or every little detail — instead of helping, you'll just get confused: searching takes longer, the catalogs eat up space, and the librarian has to constantly update all those lists.

In a database, indexes work pretty much the same way: they help you quickly find the data you need, but if there are too many, updating them when you add or change records becomes a pain. Disk space also takes a hit. Plus, when there are too many indexes, the system can get confused about which one to use.

So, just like with library catalogs, you don't want to go overboard with indexes — it's better to have a few useful and efficient ones than dozens of pointless ones.

Let's play "PostgreSQL detectives." Imagine you added three indexes for a single column. You did it thinking it might boost performance. But just think about it:

• If your table is a big list of students and you have three indexes, every time you add a new student, you get three index update operations. Doesn't sound like "speeding things up," right?
• And if you have 10 such tables, each overloaded with indexes? The performance of your whole database will tank.

How do you know if you have an excessive indexing problem?

The first thing to do to figure out if you have a problem is to check your existing indexes. In PostgreSQL, you can do this with the command:

\d table_name

This command will show you the table, its columns, and related indexes. If you see a ton of indexes tied to one table, that's a red flag.

Another handy tool is the system view pg_stat_user_indexes. It shows how actively indexes are used, which helps you spot "dead weight":

SELECT
    relname AS table_name,
    indexrelname AS index_name,
    idx_scan AS index_scans
FROM
    pg_stat_user_indexes
WHERE
    idx_scan = 0;

If idx_scan is 0, it means the index has never been used in queries. That's a clear candidate for deletion.

Example of Excessive Indexing

Let's imagine a table with users:

CREATE TABLE users (
    user_id SERIAL PRIMARY KEY,
    email VARCHAR(255) UNIQUE,
    username VARCHAR(50),
    created_at TIMESTAMP DEFAULT NOW()
);

And we have three indexes:

-- Index on email
CREATE INDEX idx_users_email ON users (email);

-- Index on username
CREATE INDEX idx_users_username ON users (username);

-- Index on created_at
CREATE INDEX idx_users_created_at ON users (created_at);

Now let's look at the typical queries we run:

1. Find a user by email.
2. Find a user by username.
3. Sort users by created_at.

Seems like the indexes are useful. But here's the catch: if these queries are rare (say, once a week), creating indexes isn't worth it. Even more, if some of these indexes are never used, they're just slowing down inserts and updates.

For example: let's say we have the following data in the users table:

If queries by username almost never happen, the idx_users_username index is never used (idx_scan = 0) and can be dropped for optimization.

So, an index is a great tool, but you gotta use it wisely. It's better to have a few well-used and needed indexes than a bunch of extras.

How to Avoid Excessive Indexing

1. Analyze your index usage. Like we mentioned, check index usage stats with pg_stat_user_indexes. If an index is barely used, you can probably drop it:

DROP INDEX IF EXISTS index_name;

2. Only create indexes for frequently used queries. Before adding an index, ask yourself:

• Does this column often show up in WHERE, ORDER BY, GROUP BY?
• Is the table big?
• Is the query really too slow without the index?

If the answer is "no" to any of these, you probably don't need the index.

3. Use composite indexes. If you often use several columns in one query, instead of making separate indexes for each, create a composite index:

CREATE INDEX idx_users_email_username ON users (email, username);

This will speed up queries that filter by email and username at the same time.

4. Regularly review your existing indexes. As your database grows, your queries might change. What was useful a year ago might be useless now. Check your indexes from time to time and drop the ones you don't use anymore.

Minimizing Indexes: An Example

Let's go back to our users table. Instead of three separate indexes, we can optimize like this:

• Drop the separate index on created_at if sorting by this column is rare.
• Instead of two separate indexes on email and username, make a composite index:

CREATE INDEX idx_users_email_username ON users (email, username);

Summary: What's the Secret to Balance?

Like in many parts of programming, minimalism is key: "Less is more." Don't index every column just because you can. Think about why you need the index and how much it will actually help your queries. Be pragmatic and remember, a good developer isn't the one who adds indexes everywhere, but the one who understands their impact and uses them smartly.

Now, with this tool in your hands, you can avoid the disaster of excessive indexing and make your database as fast as a cheetah, not as slow as a turtle weighed down with useless indexes.