You can code. But can your system survive production traffic?
This is a channel about real world architecture.
Cinematic breakdowns of how tech systems survive at scale — explained in simple Hinglish for Indian engineers.
I’m a Software Architect (12+ years India + Europe).
Here we don’t grind tutorials. We dissect production systems.
Instagram. Payments. Distributed systems. AI in production.
The engineering decisions that prevent crashes.
🎯 Mission: Turn coders into architects.
You’ll learn how to:
• Think like a system designer
• Build AI that survives production
• Scale from 1K to 10M users
• Avoid real-world failure patterns
• Make architecture decisions with confidence
❌ Not coding classes
❌ Not interview tricks
✅ Real engineering stories
🌐 Portfolio & Resources: desiarchitect.com
📧 Business/Inquiries: hello@desiarchitect.com
Subscribe if you want to design systems that don’t break.
The Desi Architect
The Desi Architect
🚨 What actually breaks when your system goes from 1M → 10M users?
It’s NOT always your application servers.
At 10M users, you start running into very different problems:
→ Database bottlenecks
→ Read vs. write scaling
→ Hot keys & cache stampedes
→ Read replicas & sharding
→ Async processing
→ And the biggest mistake: adding microservices too early
The real architect question isn’t:
“How do I handle 10M users?”
It’s:
“What are those 10M users actually doing?”
I’ve broken down the complete scaling journey — from 1M → 10M users with the architecture decisions, bottlenecks, and trade-offs you need to understand.
🎥 Watch the full video:
[Scaling 1M to 10M Users: What Breaks First | System Design](https://youtu.be/u36KhE2dgz4)
And a huge shoutout to our Pro Architect community members for supporting the channel ❤️
🙏 @debasishchakraborty4196
🙏 @vikramgandhi79
🙏 @MsDivik
🙏 @deepaar9171
Your support helps me keep creating deep, practical System Design content for engineers.
Scale the bottleneck. Not the architecture.
What do you think breaks first at 10M users? 👇
4 hours ago (edited) | [YT] | 11
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The Desi Architect
🚀 Next System Design video is coming soon!
Before it drops, this is a good time to revise our previous architecture journey:
Scaling from 1 User to 1M Users: Real Architecture Journey
👉 https://youtu.be/sXohJ3pYAfI
The next video picks up from where this one left off and this time, we’re taking Tadka from 1M to 10M users.
But here’s the catch:
At 10M users, simply adding more servers is not enough.
Sharding? Redis? Microservices?
We’ll make each decision only when the system actually needs it.
Problem first. Technology later.
Coming soon. 🔥
2 days ago | [YT] | 4
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The Desi Architect
Redis isn’t just about caching.
Choosing between String, Hash, List, Set and Sorted Set is an architectural decision.
In this video, I break down Redis Data Structures with practical scenarios, code demos and the trade offs behind each choice.
🎥 https://youtu.be/CIQCAy_jKzk
Think access patterns first. Data structures second.
6 days ago | [YT] | 42
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The Desi Architect
Most of us learn Redis as "just a cache."
That mental model is incomplete.
In this video, I break Redis down from first principles and explain why it's really a shared in-memory data structure server. You'll finally understand why it's so fast, why it's mostly single-threaded, and why companies use it for much more than caching.
Watch here: https://youtu.be/G9oAVXjO7aw
After watching, tell me what changed your mental model the most.
2 weeks ago | [YT] | 46
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The Desi Architect
Redis isn't just about speed. It's about tradeoffs.
Most engineers learn Redis as a cache. Few learn when Redis becomes the wrong choice.
Every architecture decision comes with tradeoffs:
⚡ Speed vs Persistence
💾 Memory vs Capacity
⚖️ Consistency vs Availability
🧩 Flexibility vs Simplicity
🚀 Cache vs Primary Data Store
🔄 Replication vs Cost
🗑️ Eviction Policy vs Data Safety
🧵 Single Thread vs Parallelism
There is no universally correct configuration.
The best Redis architecture depends on:
Your workload
Failure tolerance
Latency requirements
Budget
Scale
👇 Let's discuss.
Which Redis tradeoff has hurt you the most in production?
Some examples:
Cache invalidation nightmares?
OOM because of a bad eviction policy?
Async replication causing unexpected data loss?
Cluster cross slot issues?
AOF making writes slower than expected?
Using Redis as a primary database?
Share your story. what happened and what did you learn?
(Attached is a cheat sheet summarizing the major Redis tradeoffs.)
#Redis #SystemDesign #SoftwareArchitecture #BackendEngineering #DistributedSystems #TechDiscussion #Engineering
3 weeks ago | [YT] | 30
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