🧠 From Hive and Elastic to ClickHouse: What Surprised Me

Over the past few weeks, I’ve been diving into ClickHouse — and it’s been full of surprises.

Coming from Hive (for batch jobs) and Elasticsearch (for log analytics), switching to ClickHouse made me rethink a lot of assumptions about how queries should work — and how OLAP engines operate under the hood.

So here’s a breakdown of:

• 🔄 What changed when I moved from HiveQL
• 🧩 What was different from Elastic’s JSON query DSL
• 💡 Why ClickHouse feels faster, simpler, and cheaper

Let’s go 🚀

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🔁 HiveQL vs ClickHouse SQL

I started by migrating some old Hive queries — and quickly ran into ClickHouse’s strict SQL rules.

🟡 GROUP BY is strict (but fair)

In Hive, you can get away with being chill:

-- Hive lets this slide
SELECT name, COUNT(*) FROM users GROUP BY age;

ClickHouse? Not having it.

-- ClickHouse needs name in GROUP BY
SELECT name, COUNT(*) FROM users GROUP BY name;

At first it feels annoying. But over time, it forces you to be explicit — and that helps the engine optimize better.

Feels like strict school rules. Annoying at first, but you end up learning discipline 😂

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🟡 map() > struct()

In Hive I used to rely on named structs for semi-structured data. ClickHouse’s map() just made life easier.

SELECT map('country', 'India', 'device', 'mobile') AS details;
-- Access like: details['country']

Much smoother than playing with tuple() or nested fields. Helped a lot during migrations.

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🟡 The performance mindset shift

Hive taught me:

• “Run it and go grab coffee”
• “Batch is normal”
• “Just get the job done eventually”

ClickHouse flipped that:

• Reads are blazing fast
• Design for reads
• Aggregations on billions? Real-time vibes

It’s not just faster. It makes you think differently.

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🆚 Elasticsearch Query DSL vs ClickHouse SQL

I’ve used Elasticsearch for log-heavy dashboards — but ClickHouse honestly made me question that.

🔍 Filtering is so much simpler

Elasticsearch:

{
  "query": {
    "bool": {
      "must": [
        { "match": { "status": "success" }},
        { "range": { "timestamp": { "gte": "now-1d/d" }}}
      ]
    }
  }
}

ClickHouse:

SELECT * FROM logs
WHERE status = 'success'
AND timestamp >= now() - INTERVAL 1 DAY;

Less DSL, more SQL. Easier to read, debug, and write — plus way less infra cost.

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📊 Aggregations just… fly

Elastic gets heavy when you scale up. ClickHouse handles:

• Billions of rows 💪
• Materialized views and projections like a pro
• Native SQL logic — no gymnastics

It’s just built for speed at scale.

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🧠 What I Took Away

• OLAP engines work differently — and that’s fine once you get used to it
• ClickHouse being strict forces you to be more intentional with SQL
• Migrating from Hive or Elastic isn’t 1:1 — expect some adjustments
• Understanding how queries actually run helps more than just tweaking syntax

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💭 Final Thoughts

Hive and Elastic still have solid use cases — but for real-time analytics, log filtering, or dashboards, ClickHouse is worth checking out.

If you’re migrating or just exploring OLAP tools, I hope this gives you a good head start.

Let me know — what was your biggest surprise when switching to ClickHouse?

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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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