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Mock System Design Interview ​

Overview ​

Welcome to the second lecture of Section 11: Mock Interview Practice in the Official CTO journey! Mock system design interviews simulate FAANG-style scenarios, testing your ability to design scalable systems and articulate trade-offs. In this 30-minute lesson, we design a messaging app handling 1M writes/sec, focusing on sharding, caching, and clear communication, as inspired by Designing Data-Intensive Applications by Martin Kleppmann. Drawing from my 8+ years of mentoring engineers, this lecture equips you to excel in FAANG system design interviews. Let’s continue your Official CTO journey!

Inspired by FAANG system design practices, this lesson provides actionable insights and a practical example.

Learning Objectives ​

  • Master FAANG system design strategies: scalability, trade-offs, clear communication.
  • Design a messaging app handling 1M writes/sec with sharding and caching.
  • Prepare for FAANG interviews with system design-focused techniques.
  • Apply skills in a social platform messaging system example.

Why Mock System Design Interviews Matter ​

System design interviews test your ability to architect scalable, reliable systems under pressure, a key FAANG requirement. Drawing from my experience mentoring engineers, I’ve seen mock practice build confidence and clarity in candidates. This lecture ensures you can design systems, discuss trade-offs, and align with FAANG expectations.

In software engineering, mock system design interviews help you:

  • Ace Interviews: Design scalable systems and articulate trade-offs.
  • Refine Skills: Improve architectural thinking and communication.
  • Build Confidence: Prepare for real-world FAANG scenarios.
  • Showcase Expertise: Demonstrate system design proficiency.

Key Concepts ​

1. System Design Strategies ​

  • Clarify Requirements: Define functional (e.g., send/receive messages) and non-functional (e.g., 1M writes/sec) requirements.
  • Discuss Trade-offs: Balance scalability, latency, and consistency.
  • Use Diagrams: Sketch architecture to clarify components.
  • Iterate: Refine design based on interviewer feedback.

2. Sharding and Caching ​

  • Sharding: Partition data to distribute load (e.g., by user ID).
  • Caching: Store frequent data in memory (e.g., Redis) to reduce latency.
  • Example: Shard message data, cache recent chats.

3. FAANG Expectations ​

  • Amazon: Focus on scalability, ownership (e.g., AWS-based design).
  • Google: Emphasize clarity, distributed systems expertise.
  • Meta: Prioritize low latency, execution speed.
  • Netflix: Highlight autonomy, high availability.

4. Relation to Previous Sections ​

  • Algorithms (Section 1): Supports efficient data structures in design.
  • OOD (Section 2): Aligns with modular service design.
  • Design Patterns (Section 3): Patterns like Facade aid architecture.
  • Design Principles (Section 4): SOLID guides service design.
  • HLD/LLD (Sections 5–6): Directly relates to system design (e.g., Mock LLD Interview, Lecture 31).
  • Behavioral Skills (Section 7): Builds on communication for trade-offs.
  • Domain-Specific Topics (Section 8): Applies cloud and microservices (Lecture 7).
  • Clean Code (Section 9): Ensures clean, maintainable designs.
  • Refactoring (Section 10): Refactoring supports scalable systems.
  • Mock Coding (Section 11, Lecture 1): Complements coding skills.

System Design: Messaging App ​

Problem Statement: Design a messaging app for a social platform supporting 1M writes/sec (e.g., sending messages) and low-latency reads (e.g., retrieving chats). The system should handle text messages, support group chats, and ensure high availability.

Requirements ​

  • Functional: Send/receive messages, support group chats, store message history.
  • Non-Functional: Handle 1M writes/sec, <100ms read latency, 99.9% availability, scalable to 10M users.
  • Constraints: Messages are text-based, average size 1KB, 1-year retention.

Thought Process ​

  1. Clarify Requirements:
    • Users send/receive messages (1M writes/sec, 10M reads/sec estimated).
    • Group chats require fan-out writes (one message to multiple recipients).
    • Low latency for reads, high availability, eventual consistency acceptable.
  2. High-Level Design:
    • Components: API Gateway, Message Service, Group Service, Storage, Cache.
    • Flow: Client -> API Gateway -> Message Service -> Storage/Cache.
  3. Trade-Offs:
    • Consistency vs. Latency: Eventual consistency for low latency.
    • Sharding: Shard by user ID for scalability.
    • Caching: Cache recent messages for fast reads.
  4. Detailed Design:
    • API Gateway: Handles authentication, routing (e.g., AWS API Gateway).
    • Message Service: Microservice for sending/receiving messages.
    • Group Service: Manages group chat fan-out.
    • Storage: Cassandra for sharded message storage, DynamoDB for metadata.
    • Cache: Redis for recent messages.
    • Message Queue: Kafka for async fan-out writes.
  5. Scalability:
    • Shard Cassandra by user ID to distribute writes.
    • Cache 80% of reads in Redis to reduce DB load.
    • Use Kafka for asynchronous group message fan-out.
  6. Edge Cases:
    • Handle offline users (store messages for delivery).
    • Mitigate failures with retries and replication.
    • Ensure rate limiting to prevent abuse.

Architecture Diagram (Mermaid) ​

mermaid
graph TD
    A[Client] -->|REST API| B(API Gateway)
    B --> C(Message Service)
    B --> D(Group Service)
    C -->|Write| E[(Cassandra)]
    C -->|Cache| F[(Redis)]
    C -->|Async Write| G[(Kafka)]
    D -->|Fan-out| G
    G -->|Store| E
    E -->|Read| C
    F -->|Read| C

Key Design Details ​

  • Sharding: Partition Cassandra by user ID to handle 1M writes/sec (e.g., 100 nodes, 10K writes/sec/node).
  • Caching: Store recent 100 messages per user in Redis (1KB/message, ~10GB for 10M users).
  • Fan-out: Use Kafka to asynchronously write group messages to recipients.
  • Availability: Replicate data across 3 regions, use circuit breakers for fault tolerance.
  • Latency: Redis for <10ms reads, Cassandra for <50ms writes.
  • Monitoring: Integrate with Prometheus for metrics, alerting on failures.

Trade-Offs ​

  • Consistency: Eventual consistency reduces latency but may delay message delivery.
  • Cost: Caching increases memory costs but reduces DB load.
  • Complexity: Sharding adds operational overhead but ensures scalability.

FAANG-Specific Tips ​

  • Amazon (Ownership):
    • Highlight ownership in design (e.g., “I owned the sharding strategy”).
    • Emphasize AWS integration (e.g., “Used API Gateway and DynamoDB”).
    • STAR Response:
      • Situation: “Needed a scalable messaging app for 1M writes/sec.”
      • Task: “I was responsible for designing the system.”
      • Action: “I designed sharding with Cassandra and caching with Redis, using AWS.”
      • Result: “Achieved 99.9% availability, scaling to 10M users.”
  • Google (Clarity):
    • Focus on clear explanation (e.g., “I clarified trade-offs for sharding”).
    • Emphasize distributed systems (e.g., “Designed for eventual consistency”).
    • STAR Response:
      • Situation: “Faced a system design for a messaging app.”
      • Task: “I was tasked with designing clearly.”
      • Action: “I explained sharding and caching, collaborating on trade-offs.”
      • Result: “Delivered clear design, praised by interviewer.”
  • Meta (Execution Speed):
    • Highlight rapid design (e.g., “I designed in 25 minutes”).
    • Focus on low latency (e.g., “Optimized reads with Redis”).
    • STAR Response:
      • Situation: “Needed a low-latency messaging system.”
      • Task: “I was responsible for designing quickly.”
      • Action: “I designed caching and sharding for <100ms latency.”
      • Result: “Reduced read latency by 50%, meeting requirements.”
  • Netflix (Freedom & Responsibility):
    • Emphasize autonomous design (e.g., “I independently designed the system”).
    • Focus on availability (e.g., “Ensured 99.9% uptime”).
    • STAR Response:
      • Situation: “Needed a high-availability messaging app.”
      • Task: “I was responsible for the design.”
      • Action: “I independently designed sharding and replication.”
      • Result: “Achieved 99.9% uptime, scaling to 1M writes/sec.”

Practice Exercise ​

Problem: Design a scalable system for a messaging app under interview conditions.

  1. Define Requirements:
    • Support 1M writes/sec, low-latency reads, high availability.
    • Practice in 25-30 minutes, explaining trade-offs aloud.
  2. Craft a STAR Response:
    • Situation: Describe a system design interview scenario (e.g., messaging app).
    • Task: Clarify your role (e.g., design scalable system).
    • Action: List 2–3 actions (e.g., designed sharding, integrated caching).
    • Result: Quantify outcomes (e.g., achieved scalability, low latency).
  3. Design the System:
    • Sketch architecture (e.g., API Gateway, microservices, Cassandra, Redis).
    • Discuss trade-offs (e.g., consistency vs. latency).
    • Use tools like Excalidraw for diagrams.
  4. Tailor to a FAANG Company:
    • Align with Amazon (Ownership), Google (Clarity), Meta (speed), or Netflix (autonomy).
  5. Write and Review:
    • Write a 100–150 word STAR response.
    • Ensure clarity, specificity, and alignment with FAANG expectations.

Sample Response (Amazon - Ownership):

  • Situation: “Faced a system design interview for a messaging app.”
  • Task: “I was responsible for designing a scalable system.”
  • Action: “I owned the design, implementing sharding with Cassandra and caching with Redis, using AWS API Gateway.”
  • Result: “Achieved 1M writes/sec with 99.9% availability.”

Conclusion ​

Mastering mock system design interviews equips you to excel in FAANG technical interviews and build scalable systems. This lecture builds on coding skills from Lecture 1 and prior sections, advancing your Official CTO journey.

Next Step: Explore Mock Behavioral Interview or revisit all sections.