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.

How does HNSW achieve fast approximate nearest neighbor search?

It builds a multi-layer graph where nearby vectors are connected, and navigates from coarse top layers to fine bottom layers, like taking a highway then side streets, HNSW constructs a hierarchical graph. Top layers have sparse, long-range connections for quickly narrowing down the search area. Bottom layers have dense, short-range connections for precise local search. This achieves sub-millisecond queries with 95-99% recall.

Why is IVF+PQ preferred over HNSW for datasets with billions of vectors?

HNSW's graph structure requires too much memory at billion-scale, while IVF+PQ uses clustering and compression to dramatically reduce memory usage, HNSW needs to store the graph structure in memory, which can be as large as the vectors themselves. At 1 billion vectors, that's hundreds of GB just for the graph. IVF+PQ uses k-means clustering (compact centroid list) plus product quantization (10-50x vector compression) to fit in far less memory.

What does '95% recall@10' mean for an ANN algorithm?

95% of the true 10 nearest neighbors are included in the algorithm's top-10 results, Recall@10 measures what fraction of the true 10 nearest neighbors appear in the approximate algorithm's top-10 results. 95% recall@10 means, on average, 9.5 out of the true 10 nearest neighbors are found. The missed ones are borderline cases at similar distances.

Approximate Nearest Neighbor (ANN)

Name: System Design Masterclass
Availability: InStock

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This lesson is part of the full System Design Masterclass. Unlock it along with 765 other lessons for a one-time payment.

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“The interactive diagrams alone are worth way more than the price.”

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