Complete System Design Interview Preparation Roadmap

Your Journey to Mastery

Mastering System Design is about building an Architectural Intuition. This roadmap takes you from a single server to global-scale distributed systems.

graph TD
    Start((Start Here)) --> P1[Phase 1: Foundations]
    P1 --> P2[Phase 2: Data & Storage]
    P2 --> P3[Phase 3: Distributed Theory]
    P3 --> P4[Phase 4: Deep Dives]
    P4 --> P5[Phase 5: Master Case Studies]
    P5 --> Finish((MANG Ready))
    
    subgraph "Phase 1"
        P1 --- LB[Load Balancing]
        P1 --- C[Caching / CDN]
        P1 --- API[API Design]
    end
    
    subgraph "Phase 2"
        P2 --- SQL[SQL vs NoSQL]
        P2 --- SH[Sharding]
        P2 --- IDX[Indexing Internals]
    end

6-8 Week Learning Roadmap

Visual Architecture Roadmap

The Interview Blueprint (PEDAL)

We use the PEDAL framework covered in Module 2:

1. Parameters: Clarify requirements (Functional & Non-Functional).
2. Estimates: Back-of-the-envelope capacity planning.
3. Diagrams: High-level architecture.
4. APIs & Data: Define contracts and schemas.
5. Logic: Deep dive into bottlenecks and scaling.

Final Takeaway

This course is not about memorizing components. It's about developing the intuition to see why one database is better than another for a specific load.

Engineering Standard: The "Staff" Perspective

In high-throughput distributed systems, the code we write is often the easiest part. The difficulty lies in how that code interacts with other components in the stack.

1. Data Integrity and The "P" in CAP

Whenever you are dealing with state (Databases, Caches, or In-memory stores), you must account for Network Partitions. In a standard Java microservice, we often choose Availability (AP) by using Eventual Consistency patterns. However, for financial ledgers, we must enforce Strong Consistency (CP), which usually involves distributed locks (Redis Redlock or Zookeeper) or a strictly linearizable sequence.

2. The Observability Pillar

Writing logic without observability is like flying a plane without a dashboard. Every production service must implement:

• Tracing (OpenTelemetry): Track a single request across 50 microservices.
• Metrics (Prometheus): Monitor Heap usage, Thread saturation, and P99 latencies.
• Structured Logging (ELK/Splunk): Never log raw strings; use JSON so you can query logs like a database.

3. Production Incident Prevention

To survive a 3:00 AM incident, we use:

• Circuit Breakers: Stop the bleeding if a downstream service is down.
• Bulkheads: Isolate thread pools so one failing endpoint doesn't crash the entire app.
• Retries with Exponential Backoff: Avoid the "Thundering Herd" problem when a service comes back online.

Critical Interview Nuance

When an interviewer asks you about this topic, don't just explain the code. Explain the Trade-offs. A Staff Engineer is someone who knows that every architectural decision is a choice between two "bad" outcomes. You are picking the one that aligns with the business goal.

Performance Checklist for High-Load Systems:

1. Minimize Object Creation: Use primitive arrays and reusable buffers.
2. Batching: Group 1,000 small writes into 1 large batch to save I/O cycles.
3. Async Processing: If the user doesn't need the result immediately, move it to a Message Queue (Kafka/SQS).

Read Next

• Module 1: Introduction to System Design
• Module 2: The Interview Framework (PEDAL) 埋