Is this a video course?

No. This is an interactive, slide-based learning platform. Each lesson has rich text, animated diagrams, live code editors, and quizzes. You learn by reading, interacting, and doing, not by watching videos passively.

How long do I have access?

Forever. Both pricing tiers are one-time payments with lifetime access. This includes all current 766 lessons and any future content we add.

What level of experience do I need?

None. We start from absolute basics like 'What is latency?' and build up to distributed consensus protocols. The Foundation level assumes zero prior knowledge of system design.

How much does the system design course cost?

7.99 US dollars for lifetime access globally, or 499 Indian rupees for lifetime access in India. One-time payment, no subscription, no hidden fees. 11 lessons are free with no signup required.

What technologies are covered?

Everything from DNS and load balancers to Kubernetes, Kafka, distributed databases, consensus protocols, stream processing, security architecture, and observability. We cover principles and real-world implementations used at Netflix, Google, Amazon, Uber, Stripe, and more.

Is this useful for system design interview preparation?

Yes. The lessons are structured around the exact topics asked in system design interviews at FAANG and top-tier companies. Interactive diagrams help you practice whiteboard-style explanations. Covers everything from URL shortener design to distributed payment systems.

How is this different from ByteByteGo or Educative?

766 interactive lessons (4x more than most competitors), 16 different diagram types that build step by step, real production examples from Netflix, Google, Amazon, Uber, and Stripe, and lifetime access for a one-time payment of $7.99 instead of annual subscriptions costing 100 to 200 dollars per year.

Your cache has a TTL of 5 minutes. A product price changes in the database. What's the worst-case scenario for how long users see the stale price?

Up to 5 minutes, until the cached entry expires, With TTL-based invalidation, the worst case is the full TTL duration. If the price changed 1 second after the cache was populated, users see the stale price for 4 minutes and 59 seconds. This is why critical data (prices, inventory, auth) often uses shorter TTLs or active invalidation in addition to TTL.

A popular cache key expires at exactly 12:00:00. 5,000 requests arrive simultaneously, all requesting that key. What problem might occur?

Cache stampede, all 5,000 requests hit the database at once, This is the classic cache stampede (thundering herd) problem. All 5,000 requests check the cache, all find it empty, and all query the database simultaneously. The database might buckle under the sudden load. Solutions include cache locking (only one request fetches from DB while others wait), stale-while-revalidate (serve old data while refreshing), or pre-warming (refresh the cache before it expires).

You're choosing between local application cache (in-memory HashMap) and a distributed cache (Redis). Which factor would make you choose Redis?

You have multiple application servers and need them to see consistent cached data, With multiple servers, local caches become inconsistent, each server has its own version of cached data. If you update a user's name, some servers cache the old name and some the new one. Redis provides a single shared cache that all servers read from and write to, ensuring consistency. The trade-off is a small network hop (~0.1ms) that local caches don't have.

Which caching pattern would you choose for data that is read 1000x per second but updated once per hour?

Cache-Aside with a 5-minute TTL, the data barely changes, so a 5-minute staleness window is fine, This data is read 1000x/sec but only changes once per hour. Cache-Aside with a 5-minute TTL means the data is stale for at most 5 minutes out of every 60, more than acceptable. Write-Through is overkill since the data barely changes. No caching at 1000 reads/sec would hammer your database unnecessarily. And caching forever with no TTL is dangerous, when the data does change, you'd never see the update.

Caching

Name: System Design Masterclass
Availability: InStock

in Core Fundamentals

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