Use Query Patterns to Suggest Indexes Dynamically

[Brahmareddy]

Hey folks,

Ever notice how a query that used to run super fast suddenly starts dragging? We’ve all been there. As data grows, those little inefficiencies in your SQL start showing up — and they show up hard. That’s where something cool comes in: using query patterns to suggest indexes dynamically.

Let me break it down in a simple way?

Let’s say your team constantly runs queries like this:

SELECT * FROM orders WHERE customer_id = 'C101' AND order_date >= '2024-01-01';

You might see those same columns — customer_id, order_date — being used in dozens of queries every day. That’s a pattern. Now imagine if your system could say:

“Hey, everyone seems to be filtering on customer_id and order_date. Maybe we should throw an index on that combo?”

Boom. You’ve just prevented a future performance issue.

How to Make This Happen

Here’s the general idea:

1. Monitor queries running in your workspace.
2. Look for repeat usage of columns in WHERE, JOIN, and GROUP BY.
3. Track frequency — like how often customer_id shows up.
4. If a column shows up a lot, flag it as an index candidate.
5. Suggest something like:
   CREATE INDEX idx_customer_date ON orders(customer_id, order_date)

You don’t need to auto-create the index — just recommending it is a huge help. Even better if you can surface it in a dashboard for your data team.

A Few Friendly Warnings

Before you suggest indexes left and right, here are some quick don’ts:

• Don’t suggest indexes for columns that rarely get filtered.
• Don’t suggest duplicates — check what indexes already exist.
• Don’t go wild with composite indexes unless the pattern truly needs it.
• Always test in staging before changing prod (please!).

Why This Works So Well in Databricks

Databricks makes it super easy to build something like this:

• You can tap into query logs from SQL endpoints or notebooks.
• Use Delta’s DESCRIBE HISTORY or SparkListener to monitor usage.
• Build a small ML model or rule-based system to recommend indexes.
• Show those recommendations on a dashboard or Slack alert.

No magic, no huge effort — just small automation for a big win.

Final Thoughts

This kind of smart indexing system saves time, boosts performance, and honestly makes life easier for everyone on the team. You’re not just fixing problems — you’re preventing them.

If you're building something like this or have ideas to make it even smarter, drop them in the comments! Let’s keep sharing and learning from each other

Cheers,
Brahma
Just a data guy who loves clean, fast queries.