Introduction to High-Level Design

Introduction

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 real traffic, not just whiteboard sketches.

HLD is the art and science of choosing components (APIs, databases, caches, queues, CDNs), defining how they communicate, and reasoning about scale, failure, and cost before anyone writes production code. Whether you are preparing for FAANG-style system design rounds or leading a greenfield microservice, the same principles apply: clarify requirements, estimate capacity, draw boundaries, and defend trade-offs with numbers.

Across 42 lessons you will move from fundamentals (requirements, CAP, scaling) through infrastructure building blocks (load balancers, Kafka, Redis) to full case studies (URL shortener, chat, payments, ride-hailing). Each module is interview-focused, includes practical Spring Boot and YAML snippets, and takes about 10 minutes to read.

Understanding the topic

Key concepts

• HLD defines system topology: services, data stores, async pipelines, and external integrations.
• Interviews test requirement gathering, back-of-envelope math, API design, and failure handling — not memorized diagrams.
• Non-functional requirements (latency, availability, consistency) drive architecture more than feature lists.
• Every design choice is a trade-off: consistency vs availability, cost vs performance, build vs buy.
• Modern stacks combine stateless app tiers, managed databases, Redis caches, and Kafka event backbones.
• TechLearningPRO's HLD track pairs theory with classic case studies asked at Amazon, Google, Uber, and Stripe.

flowchart TB
  Client --> LB[Load Balancer]
  LB --> GW[API Gateway]
  GW --> SvcA[Service A]
  GW --> SvcB[Service B]
  SvcA --> Cache[(Redis)]
  SvcB --> DB[(Database)]
  SvcB --> MQ[Message Queue]

Internal architecture

Architecture overview

flowchart TB
  Client --> LB[Load Balancer]
  LB --> GW[API Gateway]
  GW --> SvcA[Service A]
  GW --> SvcB[Service B]
  SvcA --> Cache[(Redis)]
  SvcB --> DB[(Database)]
  SvcB --> MQ[Message Queue]

Step-by-step explanation

1. Start every design by listing actors, core use cases, and explicit non-functional targets (QPS, p99 latency, SLA).
2. Sketch a logical diagram: clients → edge (CDN/LB) → API layer → services → data tier → async workers.
3. Identify read-heavy vs write-heavy paths; place caches and read replicas on hot read paths first.
4. Define synchronous boundaries (REST/gRPC) vs asynchronous boundaries (events, queues) early.
5. Walk one request end-to-end and one failure scenario (DB down, cache miss storm, partial outage).
6. Close with scaling levers: horizontal pods, sharding keys, CDN offload, and observability hooks.

Informative example

A minimal Spring Boot 3 health-check and readiness pattern — the first step before adding load balancers and Kubernetes probes:

@RestController
@RequestMapping("/api/v1")
public class HealthController {

    private final DataSource dataSource;
    private final RedisConnectionFactory redis;

    public HealthController(DataSource dataSource, RedisConnectionFactory redis) {
        this.dataSource = dataSource;
        this.redis = redis;
    }

    @GetMapping("/health")
    public Map<String, String> health() {
        return Map.of("status", "UP", "service", "catalog-api");
    }

    @GetMapping("/ready")
    public ResponseEntity<Map<String, String>> ready() throws Exception {
        try (var conn = dataSource.getConnection();
             var jedis = redis.getConnection()) {
            conn.isValid(2);
            jedis.ping();
            return ResponseEntity.ok(Map.of("status", "READY"));
        }
    }
}

Load balancers and orchestrators route traffic only to READY instances. Separate liveness (/health) from readiness (/ready) so transient dependency blips do not kill the whole pod.

Real-world use

Real-world use cases

• Staff engineer interviews requiring end-to-end system design (feed, search, payments).
• Writing architecture RFCs before a team commits to microservice boundaries.
• On-call incident response — understanding which component failed in a multi-tier stack.
• Migrating monoliths to services without losing data consistency guarantees.

Best practices

• Always clarify scope: MVP features vs scale targets vs geographic regions.
• Use back-of-envelope estimates (QPS, storage, bandwidth) before picking databases.
• Design APIs as contracts — versioning and idempotency matter from day one.
• Assume failures: single AZ loss, cache stampede, downstream timeout.
• Prefer boring, proven components (PostgreSQL, Redis, Kafka) unless requirements forbid them.
• Document trade-offs explicitly — interviewers reward honest constraints over perfect boxes.
• Practice drawing one diagram in under 3 minutes; spend remaining time on depth.

Common mistakes

• Jumping to microservices before understanding domain boundaries or team size.
• Ignoring non-functional requirements until the interviewer prompts for scale.
• Drawing technology logos without explaining why each component exists.
• Treating caches as source of truth instead of performance accelerators.
• Skipping monitoring, rate limits, and security until the final two minutes.
• Memorizing Netflix/Uber blogs without adapting patterns to the stated problem.

Advanced interview questions

Summary

TechLearningPRO HLD teaches scalable architecture from first principles through case studies. Clarify functional and non-functional requirements before choosing components. Every architecture is a set of trade-offs — state them with numbers. Modern systems combine stateless services, caches, queues, and observability. Interview success comes from structured communication, not memorized diagrams. The next 41 lessons build the vocabulary and patterns you will reuse in every design.