TL;DR

  • Slow queries? Check your indexes first.
  • Use EXPLAIN and the slow query log to guide indexing decisions.
  • Composite indexes only help if you use leading columns.
  • Covering indexes = 🔥 for performance.
  • Too many indexes hurt write performance.
  • Indexes can bloat your storage—monitor carefully.
  • Tools like pt-online-schema-change or gh-ost are lifesavers for zero-downtime changes.

Working with large datasets is when theory meets the cold hard reality of production. I recently worked on a MySQL table with over 200 million rows, and learned a ton about indexes—the hard way.

Here’s what that experience taught me about performance, tradeoffs, and keeping MySQL happy at scale.

🧱 The Setup

The table was part of a data processing pipeline, getting non-stop inserts and being queried for analytics. It started fine. Then the row count hit 50M. Then 100M. Then 200M.

Query performance? Yeah… it fell off a cliff.

🔍 The First Signs of Trouble

Queries were taking 30 seconds or more. Some just never returned. I started checking indexes and realized—some of the most commonly filtered and sorted columns had no indexes at all.

I added a single composite index and one query dropped from 30s to 300ms. That was my wake-up call.

🛠 Key Lessons on Indexing Large Tables

1. Start with the slow queries

Use:

EXPLAIN SELECT ...
SHOW INDEX FROM your_table

…and MySQL’s slow_query_log. These tools show you what the optimizer is doing (or not doing).

2. Composite indexes are powerful—but specific

Order matters! An index on (user_id, created_at) helps:

WHERE user_id = ? AND created_at > ?

But not:

WHERE created_at > ?

3. Balance reads vs. writes

Indexes speed up reads, but slow down inserts and updates. We dropped low-value indexes to reduce write latency, especially for high-throughput tables.

4. Covering indexes are gold

If all columns used in the query are part of the index, MySQL can skip reading the base table. Huge speedups.

CREATE INDEX idx_status_created_at ON orders (status, created_at);
SELECT status, created_at
FROM orders
WHERE status = 'pending'
ORDER BY created_at DESC
LIMIT 50;

Blazing fast.

⚠️ Gotchas

  • Creating indexes on huge tables is slow and can block operations. Use ALGORITHM=INPLACE if supported.
  • MySQL’s query planner sometimes picks bad indexes. You might need to use FORCE INDEX (with care).
  • Index size bloat: In our case, indexes used more disk than the data itself—300GB+.
  • Always test schema/index changes on real data volumes.

✅ My Go-To Tools and Practices

  • EXPLAIN ANALYZE for query insight
  • pt-online-schema-change for online index operations
  • ANALYZE TABLE to keep optimizer stats fresh
  • Avoid indexing every column “just in case”
  • Always test performance after changes

💡 Final Thoughts

Taming a massive table isn’t about throwing indexes at it. It’s about understanding your access patterns, crafting intentional indexes, and constantly observing behavior as your data grows.

Indexing isn’t just a technical task—it’s part of designing for scale. And when you get it right, the payoff is immediate.

If you’ve gone through similar experiences or have some indexing horror stories, I’d love to hear them! Drop a comment or reach out.