Why HLD Matters

Bonus Module

The Missing Link

You’ve mastered classes. You know your design patterns. Your code is clean, testable, and follows SOLID principles. But here’s the uncomfortable truth…

Reality Check

Your beautifully designed PaymentProcessor class will eventually need to:

• Handle 10,000 transactions per second
• Work when the database is down for maintenance
• Process payments even if one payment gateway fails
• Stay consistent when running on 50 different servers

That’s where HLD knowledge becomes your superpower.

Why This Module Exists

You might be wondering: “This is lowleveldesignmastery.com—why are we talking about system design?”

Great question. Here’s the thing:

LLD and HLD aren’t separate skills—they’re two halves of the same coin.

When you understand why certain architectural decisions are made, you write better classes. When you know how distributed systems fail, you design better interfaces.

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This Module is Your Secret Weapon

🎯 Better LLD Decisions

Understand why certain patterns exist—they solve distributed problems at the code level!

💼 Interview Ready

“How would this scale?” won’t catch you off guard anymore. You’ll answer with confidence.

🔧 Practical Implementations

Not just theory—implement rate limiters, circuit breakers, consistent hashing, and more.

🌉 Bridge the Gap

Connect your class designs to real-world system architecture seamlessly.

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The LLD ↔ HLD Connection

Every HLD concept has a direct impact on how you write classes. Let’s look at some examples:

Design Patterns Meet Distributed Systems

LLD Pattern	HLD Concept	The Connection
Observer Pattern	Message Queues	Observers on different servers? Use Kafka/RabbitMQ!
Factory Pattern	Service Discovery	Creating objects across service boundaries requires discovery
Strategy Pattern	Load Balancing	Different algorithms, same interface—at infrastructure level
State Pattern	Circuit Breaker	Managing state transitions for failure handling
Decorator Pattern	Caching Layer	Adding behavior (caching) without modifying core logic

Key Insight

When you implement the Observer pattern in a monolith, it’s straightforward. But what happens when observers are on different servers? Suddenly, you need message queues, event serialization, and retry logic. Understanding HLD helps you anticipate these needs from the start.

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Real-World Example: The Singleton Problem

Let’s see how a “perfect” LLD design falls apart without HLD knowledge:

The “Perfect” Singleton

Python

singleton_counter.py

class RequestCounter:
    """A singleton to track API request counts"""
    _instance = None

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
            cls._instance.count = 0
        return cls._instance

    def increment(self):
        self.count += 1
        return self.count

    def get_count(self):
        return self.count

# Usage
counter = RequestCounter()
counter.increment()  # Returns 1
counter.increment()  # Returns 2

Java

RequestCounter.java

public class RequestCounter {
    // A singleton to track API request counts
    private static RequestCounter instance;
    private int count = 0;

    private RequestCounter() {}

    public static synchronized RequestCounter getInstance() {
        if (instance == null) {
            instance = new RequestCounter();
        }
        return instance;
    }

    public synchronized int increment() {
        count++;
        return count;
    }

    public int getCount() {
        return count;
    }
}

// Usage
public class Main {
    public static void main(String[] args) {
        RequestCounter counter = RequestCounter.getInstance();
        counter.increment();  // Returns 1
        counter.increment();  // Returns 2
    }
}

Looks good, right? Clean, follows the pattern, works perfectly… on one server.

The Distributed Reality

The Singleton Trap

Each server has its own Singleton instance! Your “single source of truth” just became three conflicting sources. Now you need:

• Redis for distributed state
• Distributed locks for thread safety
• Eventual consistency handling

None of this is obvious from the LLD alone.

The HLD-Informed Solution

Python

distributed_counter.py

import redis

class DistributedRequestCounter:
    """A counter that works across multiple servers"""

    def __init__(self, redis_client: redis.Redis):
        self.redis = redis_client
        self.key = "api:request_count"

    def increment(self) -> int:
        # INCR is atomic in Redis - no race conditions!
        return self.redis.incr(self.key)

    def get_count(self) -> int:
        count = self.redis.get(self.key)
        return int(count) if count else 0

# Usage
redis_client = redis.Redis(host='redis-cluster', port=6379)
counter = DistributedRequestCounter(redis_client)
counter.increment()  # Works across all servers!

Java

DistributedRequestCounter.java

import redis.clients.jedis.Jedis;

public class DistributedRequestCounter {
    // A counter that works across multiple servers
    private final Jedis redis;
    private final String key = "api:request_count";

    public DistributedRequestCounter(Jedis redisClient) {
        this.redis = redisClient;
    }

    public long increment() {
        // INCR is atomic in Redis - no race conditions!
        return redis.incr(key);
    }

    public long getCount() {
        String count = redis.get(key);
        return count != null ? Long.parseLong(count) : 0;
    }
}

// Usage
public class Main {
    public static void main(String[] args) {
        Jedis redisClient = new Jedis("redis-cluster", 6379);
        DistributedRequestCounter counter = new DistributedRequestCounter(redisClient);
        counter.increment();  // Works across all servers!
    }
}

This design came from understanding:

• Why Singletons fail in distributed systems
• How Redis provides atomic operations
• The CAP theorem trade-offs

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What You’ll Learn

This module covers essential HLD concepts that directly impact your LLD work:

mindmap
  root((HLD Concepts))
    Foundations
      Scalability
      Latency & Throughput
      CAP Theorem
      PACELC Theorem
    Databases
      SQL vs NoSQL
      Sharding
      Replication
      Isolation Levels
    Communication
      REST & GraphQL
      gRPC
      WebSockets
      Message Queues
    Resiliency
      Circuit Breaker
      Load Balancing
      Retry Patterns
      Bulkhead
    Architecture
      Monolith vs Microservices
      Event-Driven
      API Gateway
      Caching

Section Overview

Section	What You’ll Learn	LLD Impact
Foundations	Scalability, CAP theorem, consistency models	Know when strong consistency matters in your classes
Databases	Sharding, replication, isolation levels	Design repositories that handle distributed data
Caching	Strategies, eviction, invalidation	Implement cache decorators and managers
Communication	APIs, queues, real-time	Design service interfaces and event handlers
Resiliency	Circuit breakers, retries, bulkheads	Build fault-tolerant components
Architecture	Microservices, event-driven	Structure code for distributed deployment

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The Interview Advantage

In senior engineering interviews, LLD questions often evolve into HLD discussions:

Interview Reality

Interviewers at top companies (FAANG, startups, etc.) expect you to answer:

• “What happens if this runs on multiple servers?”
• “How would you handle 1 million users?”
• “What if the database goes down?”

This module prepares you to answer with confidence.

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How to Use This Module

Recommended Learning Path

If you’re new to HLD:

1. Start with Foundations (Scalability, CAP Theorem)
2. Then Databases (understand your persistence layer)
3. Then Caching (your first performance optimization)
4. Continue with remaining sections

If you’re preparing for interviews:

1. Focus on Resiliency (Circuit Breaker, Load Balancing)
2. Then Caching and Communication
3. Review classic implementations (Rate Limiter, Consistent Hashing)

If you want to connect LLD ↔ HLD:

1. Read each topic’s “LLD Connection” section
2. Review the code examples
3. Try implementing the patterns yourself

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What Makes This Module Different

Our Unique Approach

Unlike typical HLD resources, this module:

1. Connects to LLD - Every topic shows how it affects your class design
2. Includes Code - Not just boxes and arrows, but actual implementations
3. Stays Practical - Real trade-offs, not theoretical purity
4. Interview-Focused - Prepares you for “how would this scale?” questions

You Won’t Just Learn Concepts

You’ll implement:

• Rate Limiter with Token Bucket and Sliding Window
• Circuit Breaker using the State pattern
• Consistent Hashing ring for distributed systems
• LRU Cache with thread-safe operations
• Message Queue consumer patterns

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Key Takeaways

Remember

• LLD and HLD are connected - Great code considers both levels
• Design patterns evolve - Observer becomes message queues at scale
• Singleton is dangerous - Distributed systems need different patterns
• Interviews test both - Be ready for “how would this scale?”
• This is a bonus - Enhance your LLD mastery with system-level thinking

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Ready to Level Up?

You’ve mastered the fundamentals. Now let’s see how your designs perform in the real world.

Next up: Scalability Fundamentals - Understanding how systems grow

Pro Tip

As you go through these topics, keep asking: “How does this affect the classes I design?” That connection is the whole point of this module.

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Welcome to the bonus module that bridges the gap between beautiful code and production-ready systems. 🚀