Design
Step 1: Scope the Problem
- Define Core Requirements:
- Clarify the problem and confirm what you’re being asked to design.
- Identify Key Features:
- Break down major use cases or functions (e.g., for a TinyURL service: URL shortening, retrieval, analytics).
- Clarify Optional Details:
- Ask about customization (e.g., user-defined URLs), data persistence (e.g., expiration), and extra features (e.g., user accounts).
- List Key Use Cases:
- Outline user actions to structure the design around real-world usage.
Step 2: Make Reasonable Assumptions
- It’s Okay to Assume:
- Make educated guesses about scale, usage, and constraints (e.g., supporting 1 million URLs per day).
- Discuss and Confirm:
- Share your thought process and verify assumptions with the interviewer.
Step 3: Draw the Major Components
- Utilize the Whiteboard:
- Sketch a diagram showing the system’s components.
- Walk Through the Flow:
- Explain end-to-end actions using user scenarios (e.g., what happens when a user submits a new URL?).
Step 4: Identify the Key Issues
- Analyze Bottlenecks:
- Consider potential scalability, latency, or reliability challenges.
- Take Interviewer Feedback:
- Listen for hints or guidance during this step.
Step 5: Redesign for Key Issues
- Address Constraints:
- Adjust your design to handle the bottlenecks or challenges identified.
- Be Open About Limitations:
- Acknowledge known issues and communicate possible tradeoffs.
Key Concepts
Horizontal vs. Vertical Scaling
- Vertical Scaling: Add resources to a single node (e.g., more memory).
- Horizontal Scaling: Add more nodes and distribute the load.
Load Balancer
- Distributes traffic across servers to prevent overload.
- Requires a network of cloned servers with identical code and data access.
Database Optimization
- Denormalization: Add redundant data to speed up reads (e.g., avoid joins).
- NoSQL Databases: Scale better for unstructured data, no table joins.
- Partitioning (Sharding):
- Vertical: Split by feature (e.g., profiles vs. messages).
- Key-Based: Partition using a hash of the data.
- Directory-Based: Use a lookup table to locate data.
Caching
- In-Memory Cache: Store frequently accessed data for quick retrieval.
- Cache First: Application checks the cache before querying the data store.
Asynchronous Processing
- Execute slow operations asynchronously using queues to improve performance.
Network Metrics
- Bandwidth: Maximum data transfer rate (e.g., bits per second).
- Throughput: Actual data transferred.
- Latency: Time taken for data to travel from source to destination.
MapReduce
- Map: Emits
<key, value> pairs from data.
- Reduce: Combines values for a given key recursively.
Design Considerations
- Failures: Design for handling component failures (e.g., fallback mechanisms).
- Availability and Reliability:
- Availability: Percent of time the system is operational.
- Reliability: Probability the system stays operational over time.
- Read-Heavy vs. Write-Heavy:
- Read-heavy: Consider caching.
- Write-heavy: Consider queuing writes (handle potential failure).
- Security: Anticipate and mitigate security risks.