⚡ 3 ClickHouse Query Optimization Lessons I Learned the Hard Way

When I first started using ClickHouse, my initial reaction was:

“This thing is blazing fast… do I really need to optimize queries?”

It sounded logical — after all, ClickHouse is known for its lightning speed and analytical performance.
But then I wrote a few real-world queries and… reality hit hard: speed isn’t automatic, it’s intentional.

What I realized is that ClickHouse can feel instant when used well, but just like any other database, you need to understand how it works under the hood.
In this post, I’ll share 3 mistakes I made early on, how I fixed them, and what I learned about writing better ClickHouse queries.

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🔀 Put the Smaller Table on the LEFT when Joining

This one completely caught me off guard. In ClickHouse, joins are not symmetric.

ClickHouse loads the left table fully into memory before joining with the right table.
That means:

• If your bigger table is on the left, memory usage spikes.
• If you swap them, it often runs way faster.

What I Did Wrong

I had a 1 billion row event table and a small 10K lookup table.
I wrote something like this:

-- Wrong (big table on left)
SELECT *
FROM events AS e
JOIN country_lookup AS c 
    ON e.country_code = c.country_code;

ClickHouse had to load all events into memory first. Ouch.

The Fix

I swapped the join order:

-- Right (small table on left)
SELECT *
FROM country_lookup AS c
JOIN events AS e 
    ON e.country_code = c.country_code;

Boom — query execution time dropped significantly.

Lesson: Always be mindful of join order. Put the smaller table on the LEFT.

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🚫 Don’t Blindly Use FINAL

When I discovered the FINAL keyword, it felt like a safety net.
FINAL forces ClickHouse to resolve parts of the MergeTree table that are still merging or mutating.

So, naturally, I slapped FINAL on everything:

SELECT * FROM events FINAL WHERE status = 'success';

Why This Was a Bad Idea

FINAL forces an extra merge for every query, and that’s expensive.
On large datasets, it slowed queries down drastically.

What I Learned Instead

• Rethink table design if you often feel the need for FINAL.
• Use materialized views or proper deduplication strategies if duplicates are an issue.
• Treat FINAL like a debugging tool, not a default.

Lesson: If your queries always need FINAL, the problem is your data model, not ClickHouse.

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⏩ Use LIMIT When Exploring

This one’s simple but powerful.
When debugging or exploring, I often ran queries like:

SELECT *
FROM events
WHERE user_id = 12345;

No LIMIT.
No thought.
Just raw full table scans.

ClickHouse is fast, but why scan billions of rows when I only need a sample?

The Fix

I now add LIMIT by default when exploring:

SELECT *
FROM events
WHERE user_id = 12345
LIMIT 100;

This saves time, compute, and frustration when iterating.

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🧠 What I Took Away

1. ClickHouse is fast, but not magical → You still need to design queries smartly.
2. Understand how joins work → The left table choice can make or break performance.
3. Don’t abuse FINAL → Fix schema issues instead of paying a runtime penalty.
4. Use LIMIT while exploring → Saves resources and speeds up feedback loops.

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Why These Mistakes Happen (And Why That’s Okay)

Most of these mistakes come from assumptions we carry from traditional databases:

• We assume join order doesn’t matter (like in PostgreSQL/MySQL).
• We assume consistency always comes for free.
• We forget to limit results because in OLAP we “expect speed”.

ClickHouse is built differently, and these small differences change how you write queries.
It’s part of the learning curve — and honestly, I’m glad I made these mistakes because I now understand how ClickHouse thinks under the hood.

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📬 Need help with ClickHouse, SQL, or data pipelines?
I'm open to short-term gigs, collaborations, or mentoring.
Message me on LinkedIn or drop a mail: [e-mail]

👋 About Me
Hey, I’m Mohamed Hussain — Associate Data Engineer Intern, learning in public one pipeline at a time.

Thanks for reading — and if you're exploring OLAP land too, follow me for more ClickHouse insights!

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Final Thoughts

These aren’t “expert tips”.
They’re battle scars from early mistakes — and those lessons stick with you forever.

If you’re new to ClickHouse, I hope this helps you avoid some of those early pitfalls and think more intentionally about how you write queries.

Your Turn →

What’s one query mistake you made early on and never repeated?

Drop it in the comments 👇

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