Key Components for a Successful System Design Interview

Importance of Preparation and Knowledge

• Knowledge: Familiarity with system design concepts and combining them is crucial.

Stages of a System Design Interview

Problem Understanding

• Example Problem: Counting likes/views on Youtube, Instagram, etc.
• Clarification: Define metrics, questions to ask the interviewer to clarify requirements.

Problem Scope Clarification

Reasons for Asking Questions

1. For Interviewer: To assess your problem-solving approach in real life.
2. For Interviewee: Understanding requirements fully helps in determining the right technologies and building blocks.

Key Categories to Focus On

• Users: Who will use the system and how.
• Scale: How much data will be handled, expected spikes in traffic.
• Performance: How fast must the system be.
• Cost: Budget constraints and cost-effective solutions.

Specific Questions to Ask

• Users: Who uses total counts? What granularity of data is required?
• Scale: Expected queries per second, data per request, traffic spikes.
• Performance: Real-time vs. delayed processing capabilities.
• Cost: Development vs. maintenance costs.

Functional Requirements

• APIs Definition: Define actions and parameters (e.g., countVideoViews(videoId)).
• Generalization: Account for event types (views, likes, shares) and functions (sum, average).

Non-Functional Requirements

• Attributes: Scalability, performance, availability, eventual consistency, cost minimization.
• Writing Down Requirements: Helps in the decision-making process for technology stacks.

High-Level Architecture

• Initial Components: Database, web services for data processing and retrieval.
• Gradual Development: Start simple, then expand to full architecture.

Data Model Definition

• Options: Storing raw events vs. aggregated data.
• Trade-offs: Pros and cons of each method (speed vs. storage cost).
• Combining Approaches: Benefits of storing both raw and secondary data.

Data Storage Technologies

• SQL vs. NoSQL: Evaluation against non-functional requirements.
• Scaling SQL Databases: Sharding, leader-follower setup, cluster proxy.
• Scaling NoSQL Databases: Apache Cassandra, consistent hashing, gossip protocol.
• Data Modeling in SQL: Normalization and relationships among tables.
• Data Modeling in NoSQL: Query-based design and denormalization.

Data Processing Overview

• Real-Time Requirements: Pre-aggregation vs. in-memory processing.
• Push vs Pull Approach: Reliability and scalability benefits.
• Partitioning: Distributing data across multiple queues.
• System Components: Consumer, aggregator, internal queue, database writer, dead letter queue, embedded databases.

Full Data Ingestion Path

• Overview: Sequence from API Gateway through partitioner service to database.
• Partition Management: Strategies to handle hot partitions, service discovery.
• Fault Tolerance: Leader-follower replication, quorum writes.

Data Retrieval Path

• Query Service: Real-time counts vs. stored metrics, data roll-up strategies.
• Cache: Distributed cache for improved performance.

Technology Stack

• Frameworks & Load Balancers: Netty, Hystrix, NGINX.
• Database Solutions: Vitess, Cassandra, InfluxDB, Hadoop.
• Event Processing Frameworks: Apache Kafka, Apache Spark.
• Embedded Databases: RocksDB.
• Service Registry Solutions: Zookeeper, Eureka.

Additional Considerations

• Performance Testing: Load testing, stress testing, soak testing.
• Health Monitoring: Metrics, dashboards, alerts.
• Audit Systems: Weak vs. strong audit systems.
• Handling Popular Events: Scaling partitions, fallback strategies.

Summary of Design Steps

1. Clarify Requirements: Define APIs and non-functional attributes.
2. High-Level Design: Outline data paths and architecture.
3. Focus on Data: Address ingestion, storage, processing, retrieval.
4. Discuss Bottlenecks: Performance tests and monitoring.
5. Technology Choices: Align solutions with requirements and constraints.