High-Level Design Course

Welcome to High-Level Design (HLD) on TechLearningPRO — a structured course for backend engineers, architects, and interview candidates who need to design systems that survive r…

High-Level Design (HLD) and Low-Level Design (LLD) are complementary lenses on the same product.

Every successful HLD starts with requirements — not with Redis or Kubernetes.

Capacity estimation (back-of-the-envelope math) turns vague scale statements into concrete resource needs.

A monolith is a single deployable application containing all business modules — user management, catalog, checkout, notifications — sharing one process space and typically one p…

Microservices decompose an application into independently deployable services, each owning a bounded context, data store, and release cadence.

Service-Oriented Architecture (SOA) organizes enterprise systems as coarse-grained, reusable services exposed through standardized interfaces — historically SOAP over an Enterpr…

Event-driven architecture (EDA) centers on producing, routing, and consuming events — immutable records that something happened (OrderPlaced, PaymentCaptured, DriverLocationUpda…

Layered architecture organizes code into horizontal tiers — typically presentation, business logic, persistence — where each layer depends only on the layer directly below.

Scaling increases system capacity to meet load.

A load balancer (LB) distributes incoming traffic across multiple backend servers to improve capacity, fault tolerance, and maintenance flexibility.

A reverse proxy sits in front of backend servers and acts on their behalf — clients don't know the origin servers exist.

An API Gateway is the single entry point for client applications (mobile, web, partners) into your microservice ecosystem.

A Content Delivery Network (CDN) caches static and cacheable dynamic content at edge PoPs (points of presence) close to users, reducing latency and origin load.

Choosing between SQL (relational) and NoSQL databases is one of the highest-impact HLD decisions.

Sharding (horizontal partitioning) splits data across multiple database instances so no single node holds the entire dataset or write load.

Database replication copies data from a primary (leader) to one or more replicas (followers) for read scaling, high availability, and geographic proximity.

Partitioning divides one logical table into smaller physical pieces (partitions) within a single database instance — by range (date), list (region), or hash.

The CAP theorem states that a distributed data store cannot simultaneously provide all three: Consistency (every read sees latest write), Availability (every request gets a resp…

Redis is an in-memory data structure store used as cache, session store, rate limiter, pub/sub bus, and lightweight queue.

Caching stores copies of expensive computation or data closer to consumers — in-process, Redis, or CDN.

Message queues decouple producers and consumers — producers publish messages; consumers process asynchronously at their pace.

Apache Kafka is a distributed commit log for high-throughput event streaming.

Event streaming treats data as continuous flows of events processed in near real-time — beyond simple queue consumption.

Consistency models define what readers observe after writes in distributed systems — from strong linearizability to eventual consistency.

A distributed transaction spans multiple services or databases with atomicity guarantees — all succeed or all roll back.

The circuit breaker pattern stops calling a failing downstream service after errors exceed a threshold — failing fast instead of hanging threads and cascading outages.

The retry pattern re-executes failed operations when errors are likely transient — network blips, 503 from overloaded servers, deadlock timeouts.

An operation is idempotent if performing it multiple times has the same effect as once — critical for retries, webhooks, and distributed sagas.

Authentication (AuthN) verifies identity — who is calling.

Authorization (AuthZ) determines what an authenticated identity may do — read orders, approve refunds, access tenant data.

Rate limiting caps requests per client, user, IP, or API key within a time window — protecting services from abuse, accidental loops, and DDoS.

Observability — metrics, logs, traces — makes distributed HLD operable.

Disaster recovery (DR) restores systems after region-wide failures, data corruption, or ransomware.

Design a URL shortener like bit.ly — map long URLs to short slugs, redirect with low latency at global scale.

Design a chat application like WhatsApp or Slack — 1:1 and group messaging, online presence, delivery receipts, and message history.

Design food delivery like DoorDash or Swiggy — customers browse restaurants, place orders, restaurants prepare, drivers deliver.

Design a video streaming platform like YouTube or Netflix — upload, transcode, store, and deliver video globally with adaptive bitrate playback.

Design ride booking like Uber — riders request trips, drivers accept, real-time tracking, fare calculation, and payment settlement.

Design an online payment system like Stripe or PayPal — merchants accept payments, funds settle to bank accounts, with PCI compliance, idempotency, fraud detection, and ledger a…

Design a notification system delivering email, SMS, push, and in-app alerts at scale — order shipped, OTP login, marketing campaigns.

This capstone lesson walks a complete HLD interview for a fictional TechLearningPRO Learning Platform — courses, video lessons, payments, notifications, search, and analytics. Includes interactive HLD interview question bank.