Backend With Sushil

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👨‍💻 Backend Engineer | System Design
🚀 Live Weekend Cohorts
💻 Backend Engineering + 🏗️ System Design
🎯 Practical Learning • Real Projects • Interview Prep
🌐 backendwithsushil.com
💬 Enquiries ↓

29/09/2026

Redis fast hai… but kya aapki API bhi fast hai?

Redis khud 2ms mein response de sakta hai, phir bhi API 800ms+ le sakti hai. 😳

Reason? Connection Pool Exhaustion.

Jab saare Redis connections busy ho jaate hain, new requests Redis tak pahunchne se pehle hi wait karne lagti hain. ⏳

Redis slow nahi hai. Requests connections ke liye wait kar rahi hain.

Episode 115 | BackendWithSushil 🚀

29/09/2026

🚀 System Design — from 0 → 1M Users

Tired of learning System Design as a collection of random concepts?

In this cohort, we'll go from fundamentals to real-world scalable systems and HLD interviews.

🔹 25 Live Sessions
🔹 75 Minutes / Session
🔹 12 Weeks
🔹 Weekend Only
🔹 Starts 3 October
🔹 ₹15,000

If you're serious about System Design, this is for you.

28/09/2026

🔥 Redis is fast… but one hot key can become your bottleneck.

Imagine a video suddenly goes viral.
Millions of users request the same data:

video:123

In Redis Cluster, that key maps to a particular shard.
So one shard can become overloaded while others are relatively normal.

🔥 This is the Redis Hot Key problem.

One simple solution?
If the data is mostly read-only, create multiple copies:

video:123:1
video:123:2
video:123:3

Then distribute reads across those copies.

You can also use local cache, CDN, or rethink the caching strategy.

The important lesson:
It’s not just about how much traffic you have.
How that traffic is distributed matters.

Save this if you work with Redis or distributed systems. 🚀

28/09/2026

System Design Series — Episode 08 🚀

Your database is scalable and highly available… but should every request still hit the database?

Imagine CampusHub has a popular event that thousands of users are requesting at the same time.

Instead of repeatedly fetching the same data from the database, can we store frequently accessed data somewhere faster?

That's where Caching comes in.

In this episode, we use our CampusHub Event Management Application to understand caching from a System Design perspective.

📌 What You'll Learn
Why do we need caching?
What is a cache?
Cache vs Database
Cache Hit vs Cache Miss
Cache-Aside Pattern
How the cache read flow works
What happens during a cache miss?
Redis as a distributed cache
Local Cache vs Distributed Cache
What data should we cache?
What data should we avoid caching?
TTL — Time To Live
Cache invalidation basics
Stale data and cache consistency
How caching reduces database load
Where caching fits in a scalable system
🏗️ CampusHub Architecture

We continue building the same CampusHub architecture from previous episodes:

Users → DNS → Load Balancer → App Servers → Cache → Database

By the end of this episode, you'll understand why caching is introduced, where it fits in a system, and how it helps reduce database load.

🔥 Important

This episode focuses on the fundamentals of caching for System Design.

Advanced caching problems like:

Cache Stampede
Hot Key
Cache Pe*******on
Cache Avalanche
Cache Eviction Policies

will be covered separately in the System Design Fundamentals series.

📚 Previous Episodes

EP06 — Database Replication
https://youtu.be/H-UqWeOHlFs?si=P47Uyzj6nYKd8yRw

EP07 — Database Partitioning vs Sharding vs Replication
https://youtu.be/KtDsGx_Zq3g?si=FfZRgF6ZS79gYeHk

🚀 System Design Series Playlist
https://www.youtube.com/watch?v=DWa4M6IxniE&list=PLZvr3zzpE7BM

If you're preparing for System Design interviews and want to learn by building one system step-by-step, subscribe to BackendWithSushil.

Next Episode: Message Queues — How do we handle background and asynchronous processing?

27/09/2026

🔥 Kafka Interview Trap

Interviewer: “Kafka topic mein 3 partitions hain aur 10 consumers. Actually messages process kitne consumers karenge?”

Candidate: “10.”

Aur interview wahi khatam ho gaya. 😅

But why?

Agar 10 consumers same consumer group mein hain aur topic mein sirf 3 partitions hain, toh maximum 3 consumers actively consume kar sakte hain.

Baaki consumers idle rahenge.

👉 Isliye Kafka mein consumers blindly increase karne se throughput nahi badhta.
Pehle check karo: partitions kitne hain?

More Consumers ≠ More Throughput 🚀

Save this for your next Kafka interview.

26/09/2026

🚨 Don’t start with the database schema!

System Design interview mein requirements aur capacity estimation ke baad, next step kya hona chahiye?

Follow this flow 👇

1️⃣ Requirements — System ko exactly kya karna hai?
2️⃣ Access Patterns — Data ko kaise read/write karenge?
3️⃣ APIs — Clients system ke saath kaise interact karenge?
4️⃣ Data Model — Access patterns ke basis par data kaise store hoga?

💡 Database schema se start mat karo. Start with how your system will actually be used.

🔖 Save this before your next System Design interview!

Follow for practical Backend & System Design content.

🎤 AMA — ASK ME ANYTHING!11+ years in Backend Engineering.Staff Engineer.Ask me anything about:🏗️ System Design💻 Backend🧠...
26/09/2026

🎤 AMA — ASK ME ANYTHING!

11+ years in Backend Engineering.
Staff Engineer.

Ask me anything about:
🏗️ System Design
💻 Backend
🧠 DSA & Interviews
💼 Career & Job Switch
☁️ Distributed Systems
🗄️ Databases

Drop your question in the comments 👇
I’ll answer the most interesting questions in an upcoming AMA.

26/09/2026

🚀 System Design Series — Episode 07

In this episode, we use our CampusHub Event Management Application to understand three important database concepts:

Database Partitioning vs Sharding vs Replication.

As CampusHub grows, our `users`, `events`, and `registrations` tables become larger. How do we manage this data? Can we split a large table? What if one database server is not enough?

In this video, we cover everything with practical examples and MySQL syntax.

What You'll Learn

✅ Quick recap of Database Replication
✅ CampusHub database structure — Users, Events & Registrations
✅ What is Database Partitioning?
✅ MySQL Partitioning Syntax
✅ Partitioning `registrations` using `college_id` and registration date
✅ Partitioning advantages and disadvantages
✅ Horizontal Scaling and Database Sharding
✅ What is a Shard Key?
✅ Hashing and its limitations
✅ Consistent Hashing and data redistribution problems
✅ Sharding advantages and disadvantages
✅ Cross-shard queries and hot shard problems
✅ Partitioning vs Sharding vs Replication
✅ How these three concepts can work together

Quick Recap

Replication: Same data ki multiple copies.

Partitioning: Large data ko smaller parts mein divide karna.

Sharding: Data ko multiple database nodes par distribute karna.

Related Videos

📌 Database Replication — EP 06:
https://www.youtube.com/watch?v=H-UqWeOHlFs

📌 Consistent Hashing — Detailed Explanation:
https://www.youtube.com/watch?v=rs7N12RY02I
https://www.youtube.com/watch?v=wJNwCDIBE0w

📚 Watch the complete System Design Series:
https://youtube.com/playlist?list=PLZvr3zzpE7BM

# # # 💬 Question for You

If your database has multiple shards and one shard fails, how will you keep the system available?

Share your answer in the comments! 👇

👍 Like | 💬 Comment | 🔔 Subscribe

Follow BackendWithSushil for practical Backend Engineering, System Design and Distributed Systems content.

25/09/2026

🚨 10 Million Users ≠ 10 Million QPS!

Capacity estimation is one of the most important parts of a System Design interview.

In this episode, we break it down step by step:

👥 Users → Requests
⚡ Requests → QPS
📈 Average → Peak Traffic
🔄 Read vs Write
💾 Storage
📊 Future Growth

The goal isn’t to get the exact number.

It’s about making reasonable assumptions and using them to understand the scale of your system.

🔖 Save this before your next System Design interview!

Follow for practical Backend & System Design content.

24/09/2026

🚨 Instagram Caption — EP 110

System Design interview mein directly architecture banana start mat karo!

Interviewer agar bole — “Design a URL Shortener” — toh Load Balancer, Redis aur Database draw karne se pehle requirements clear karo.

Because the problem statement is usually vague.
Requirements clarify karna is part of the interview.

Start with:

✅ Functional Requirements — System exactly kya karega?
✅ Non-Functional Requirements — Latency, availability, consistency?
✅ Clarify the scope — What is actually expected?

💡 Pehle problem samjho, phir solution design karo.

🔖 Save this before your next System Design interview.

Follow for practical Backend & System Design content.

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