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.

You're implementing an LRU cache. Which data structure combination gives you O(1) for both get and put?

HashMap + Doubly Linked List, HashMap provides O(1) key lookup. Doubly Linked List provides O(1) removal and insertion at both ends, which you need to move a node to the front on access and remove from the back on eviction. A singly linked list can't remove arbitrary nodes in O(1) because you need the previous node's pointer.

Your cache holds 100 entries. Entry A was accessed 10,000 times but not in the last hour. Entry B was accessed once, 5 minutes ago. Which policy evicts A first?

LRU. A was least recently accessed, LRU only cares about recency, not frequency. Since A hasn't been accessed in an hour and B was accessed 5 minutes ago, A is the 'least recently used.' LFU would protect A because its access count (10,000) is far higher than B's (1). This is the core trade-off between LRU and LFU.

Why does Redis use 'sampled LRU' instead of true LRU?

True LRU requires a linked list across all keys, which is memory-expensive at scale, A true LRU implementation requires a doubly linked list node for every key in the cache. With millions of keys, that's significant memory overhead (two pointers per node). Redis's sampled approach randomly picks a few keys, evicts the worst one, and gets within a few percent of true LRU accuracy at a fraction of the memory cost.

A nightly batch job scans 5 million rarely-used keys, flushing your popular keys from the LRU cache. This is called:

Cache pollution, Cache pollution occurs when a burst of low-value accesses evicts high-value entries from the cache. LRU is vulnerable because it treats all recent accesses equally, a scan of 5 million one-time keys pushes them all to the 'most recently used' position, evicting the genuinely popular entries. LFU and hybrid policies resist this.

For which eviction policy is 'frequency aging' (stale popularity) a known weakness?

LFU, LFU tracks total access count. An entry that was popular last week but irrelevant today still has a high counter, protecting it from eviction even though it's wasting a cache slot. This is 'frequency aging', the counter reflects historical popularity, not current relevance. Solutions include windowed counting and logarithmic decay.

Cache Eviction Policies (LRU, LFU, FIFO)

Name: System Design Masterclass
Availability: InStock

in Caching Strategies

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