The Hidden Problem in ClickHouse Streaming Pipelines

⚠️ The Hidden Trap in ClickHouse Streaming

Why Your Real-Time Analytics Might Be Completely Wrong

ClickHouse adoption is growing rapidly for good reason — blazing-fast queries, columnar architecture, massive dataset processing 🚀

But there's a critical issue:

❗ If your streaming pipeline isn't designed correctly, your data gets silently corrupted and dashboards show wrong numbers — without any errors or warnings.

🧩 The Common Pattern

Many teams build pipelines like this:

Kafka → ReplacingMergeTree → Materialized View → Aggregation Tables

Looks logical: deduplication, aggregation, all automated.
But this is where the problem hides.

🧠 The Root Cause

1️⃣ ReplacingMergeTree doesn't deduplicate on insert
Only during background merges
Duplicates exist for a while (sometimes long)

2️⃣ Materialized Views execute on raw data
Before deduplication happens

Result:
Duplicate arrives → View fires → Aggregation updates → Source deduplicates later
But aggregated stats? Corrupted forever ❌

3️⃣ No automatic fix
Once wrong, stays wrong

🌍 When Does This Happen?

More often than you think:
Network failures
Kafka rebalancing
Consumer restarts
At-least-once delivery (Kafka default)
Backfills and testing mistakes

Result:
Wrong revenue, user counts, conversion rates
No errors in logs — just silent corruption 🚨

🛠️ Solutions

✅ Prevent duplicates from entering
✅ Don't rely only on ClickHouse deduplication
✅ Design idempotent summary tables
✅ FINAL is not production-ready (too expensive)
✅ Use real streaming engines for critical systems

Flink, RisingWave, Materialize provide:
Exactly-once semantics
Proper updates and retracts
True stream-level deduplication

ClickHouse becomes the serving layer (where it shines) ⚡

🏗️ Mature Architecture

Kafka → Streaming Engine → ClickHouse
(Correct Processing) (Fast Queries)

🎥 Hands-On Workshop

Watch me demonstrate this problem live:
Healthy pipeline → Duplicate data arrives → Silent corruption
Why FINAL shows different numbers
How to fix the architecture

Includes:
Complete setup (Redpanda, ClickHouse, Python)
Live corruption demonstration
Verification scripts
All source code and configs
Solutions and best practices

💡 Who Should Watch:
Data engineers with streaming pipelines
ClickHouse users doing real-time analytics
Teams facing data reliability issues

🔗 Resources:
Code: https://github.com/sepahram-school/wo...

📌 Key Takeaways:
-= ReplacingMergeTree doesn't prevent duplicate inserts
-= Materialized Views fire before deduplication
-= Aggregations can be permanently wrong
-= For critical real-time work, use proper streaming engines

#ClickHouse #DataEngineering #StreamProcessing #RealTimeAnalytics #Kafka #datareliability
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