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.

In the cache-aside pattern, who is responsible for populating the cache after a miss?

The application, it fetches from the database and writes to the cache, In cache-aside, the application is the orchestrator. It checks the cache, handles misses by querying the database, and writes the result back to the cache. The cache is passive, it just stores and retrieves data. This is what 'aside' means: the cache is a side resource, not the primary path.

Your application writes to the database, then tries to delete the cache entry, but the delete fails due to a network issue. What happens?

The cache serves stale data until the TTL expires, The database has the new data, but the cache still has the old version. Readers will get stale data from the cache until either the TTL expires (triggering a fresh fetch) or a retry successfully deletes the cache entry. This is the bounded staleness trade-off of cache-aside.

Why does cache-aside delete the cache entry on writes instead of updating it with the new value?

Updating risks caching stale data if the DB write fails, and the cached value may involve complex derivations, Two reasons: (1) If you update the cache first and the database write fails, the cache has data that doesn't exist in the database, a dangerous inconsistency. Deleting is safe because the worst case is a cache miss. (2) The cached value might be a complex aggregation (joins, computed fields) that's expensive and error-prone to recompute inline during a write.

Redis goes down in your cache-aside setup. What happens to your application?

The application degrades gracefully, all reads go to the database, slower but functional, This is cache-aside's biggest strength: resilience. The cache is optional. When Redis is down, every request is a cache miss, and the application falls back to the database. Response times increase, but the application keeps working. Compare this to write-through, where cache failure can block writes entirely.

Cache-Aside Pattern

Name: System Design Masterclass
Availability: InStock

in Caching Strategies

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