Random Thoughts | The System Design Interview Process

February 29th, 2024

My System Design Interview Process

I’ve decided that it could be helpful to myself, current and future, to document my current system design process for interview purposes. This process will be iterated upon as I continue to get more practice with system design interviews throughout my career. My goal with this post is to capture a cheatsheet of sorts, that also attempts to go into more depth than a normal cheatsheet.

Table of Contents

The Process

1. Ask clarifying questions and agree on the scope of the system:

Define System Boundaries: Clearly establish the boundaries of the system, identifying its inputs, outputs, and the interactions it will have with external components.

  • Examples of System Boundaries:

    1. E-commerce Platform:

    • Inbound Data Sources: User-generated input, including product searches, reviews, and purchase transactions.
    • Outbound Data Consumers: Order fulfillment systems, payment gateways, and customer notification services.
    • External Interfaces: Mobile and web applications, third-party integrations for payment processing, and shipping services.

    2. Social Media Platform:

    • Inbound Data Sources: User-generated content, friend requests, and media uploads.
    • Outbound Data Consumers: User feeds, notifications, and content recommendation engines.
    • External Interfaces: Mobile and web applications, third-party authentication services, and advertising platforms.

    3. Banking System:

    • Inbound Data Sources: User account transactions, fund transfers, and account updates.
    • Outbound Data Consumers: Core banking systems, payment networks, and regulatory reporting services.
    • External Interfaces: ATMs, online banking portals, and external financial institutions.

    4. Healthcare Information System:

    • Inbound Data Sources: Patient records, diagnostic results, and appointment scheduling.
    • Outbound Data Consumers: Electronic health record systems, billing platforms, and prescription management systems.
    • External Interfaces: Patient portals, medical devices, and insurance providers.

    5. Ride-Sharing Application:

    • Inbound Data Sources: Ride requests, driver locations, and user feedback.
    • Outbound Data Consumers: Matching algorithms, payment processors, and driver navigation systems.
    • External Interfaces: Mobile applications for users and drivers, third-party mapping services, and payment gateways.

    6. Cloud Storage Service:

    • Inbound Data Sources: File uploads, data synchronization requests, and access control configurations.
    • Outbound Data Consumers: Data retrieval requests, synchronization acknowledgments, and billing systems.
    • External Interfaces: Web and mobile applications for file management, third-party integrations, and data transfer protocols.

Functional Requirements: Elaborate on the core functionalities the system should perform, ensuring a shared understanding of the expected behavior.

  • Examples of Functional Requirements:

    1. E-commerce Platform:

    • Product Catalog and Inventory Management: The system should maintain an up-to-date product catalog with details such as product descriptions, prices, and availability. It must support inventory management to reflect real-time stock levels.
    • User Authentication and Authorization: Users should be able to create accounts, log in securely, and manage their profiles. The system should enforce role-based access control to ensure that users have appropriate permissions.

    2. Social Media Platform:

    • User Profiles and Connections: Users should have profiles with customizable information and privacy settings. The system must enable users to connect with others, send friend requests, and manage their social connections.
    • Content Sharing and Interaction: Users should be able to create, share, and interact with various types of content, including text posts, images, and videos. The system must support features such as liking, commenting, and sharing.

    3. Banking System:

    • Account Management: Users should be able to create and manage their bank accounts, including checking and savings accounts. The system should handle account transactions, balance inquiries, and account statements.
    • Transaction Security and Fraud Prevention: The system must implement robust security measures for transactions, including encryption and multi-factor authentication. It should also include mechanisms for detecting and preventing fraudulent activities.

    4. Healthcare Information System:

    • Electronic Health Records (EHR): The system should maintain electronic health records for patients, including medical history, diagnoses, medications, and treatment plans. Healthcare professionals should have secure access to these records.
    • Appointment Scheduling and Notifications: Patients should be able to schedule appointments with healthcare providers through the system. The system must send reminders and notifications for upcoming appointments.

    5. Ride-Sharing Application:

    • Ride Booking and Tracking: Users should be able to request rides, view available drivers, and track the location of their assigned vehicle. The system should provide estimated arrival times and route information.
    • Payment Processing and Invoicing: The system must securely handle payment transactions for rides, including fare calculation, invoicing, and digital receipts.

    6. Cloud Storage Service:

    • File Upload and Download: Users should be able to upload files to the cloud storage service and download them as needed. The system must support various file formats and sizes.
    • Data Security and Encryption: The system should implement robust security measures to protect user data, including encryption during data transmission and storage. It should also provide access controls and permissions.

Non-functional Requirements: Discuss non-functional aspects like performance, scalability, reliability, and maintainability to set expectations for system characteristics.

  • Examples of Non-Functional Requirements:

    1. E-commerce Platform:

    • Performance: The system must respond to user interactions within a maximum of 2 seconds to provide a seamless shopping experience.
    • Scalability: The platform should handle a 50% increase in simultaneous user activity during peak hours without significant performance degradation.
    • Reliability: The system should achieve 99.9% uptime, ensuring that users can access and use the platform reliably.

    2. Social Media Platform:

    • Availability: The platform must be available 24/7, with scheduled downtime for maintenance communicated in advance to users.
    • Data Privacy: The system should comply with data protection regulations, ensuring the secure handling of user data and providing robust privacy controls.
    • Usability: The user interface must be intuitive, supporting users of all demographics and providing accessibility features.

    3. Banking System:

    • Security: The system must adhere to industry standards for data security, including encryption for sensitive information and regular security audits.
    • Compliance: The system should comply with financial regulations and standards, ensuring transparency and accountability in financial transactions.
    • Auditability: The system must log all financial transactions, providing an auditable trail for compliance and investigative purposes.

    4. Healthcare Information System:

    • HIPAA Compliance: The system should adhere to the Health Insurance Portability and Accountability Act (HIPAA) standards for the protection of patient health information.
    • Interoperability: The system should support interoperability standards, allowing seamless integration with other healthcare systems for data exchange.
    • Reliability: Healthcare professionals should experience minimal system downtime to ensure continuous access to patient records.

    5. Ride-Sharing Application:

    • Geographical Accuracy: The system must accurately determine the location of users and drivers for precise matching and navigation.
    • Payment Security: Payment transactions must be secure, utilizing encryption and tokenization to protect sensitive financial information.
    • Reliability: The application should maintain high availability, ensuring users can book rides and access the service consistently.

    6. Cloud Storage Service:

    • Data Redundancy: The system should implement data redundancy and backups to prevent data loss in case of hardware failures or other incidents.
    • Scalability: The service must scale horizontally to accommodate increased storage demands without compromising performance.
    • Data Retrieval Speed: Users should experience fast data retrieval times, with the system optimizing access speed for various file sizes.

User Base Identification: Determine the target user base and understand their needs, preferences, and expectations from the system.

Usage Scenarios: Describe sequences of events that demonstrate how users will interact with the system to achieve specific tasks, ensuring a comprehensive understanding of user journeys.

Traffic Handling Constraints: Identify and quantify the expected traffic, specifying factors such as peak requests per second, concurrent users, and traffic patterns.

  • Examples of Traffic Handling Constraints:

    1. E-commerce Platform:

      • Peak Requests: The system should handle up to 10,000 requests per second during high-traffic events, such as flash sales or holiday promotions.
      • Concurrent Users: The platform should support a minimum of 100,000 concurrent users during peak shopping hours.

    2. Social Media Platform:

      • Peak Requests: The system needs to handle spikes of up to 50,000 requests per second during viral content sharing or major events.
      • Concurrent Users: The platform should accommodate a minimum of 500,000 concurrent users during popular live video streaming sessions.

    3. Banking System:

      • Transaction Rate: The system should process a minimum of 50 transactions per second during regular business hours.
      • Concurrent Users: The platform must support at least 10,000 concurrent users accessing online banking services simultaneously.

    4. Healthcare Information System:

      • Peak Requests: The system should handle a peak load of 1,000 data queries per second during times of heavy clinical activity.
      • Concurrent Users: The platform should support 5,000 healthcare professionals accessing patient records simultaneously.

    5. Ride-Sharing Application:

      • Booking Requests: The system needs to process up to 20,000 ride booking requests per minute during peak commuting hours.
      • Concurrent Users: The application should accommodate a minimum of 200,000 concurrent users during weekend evenings.

    6. Cloud Storage Service:

      • Data Uploads: The system should support a peak rate of 5,000 file uploads per second during periods of heavy user-generated content creation.
      • Concurrent Users: The service must handle at least 1 million concurrent users accessing stored data and files.

Data Handling Constraints: Define the scale of data operations, including rates of data writes, reads, and storage requirements.

  • Examples of Data Handling Constraints:

    1. E-commerce Platform:

      • Data Writes: The system should support a rate of at least 2,000 product updates per second during inventory changes or new product launches.
      • Data Reads: The platform must efficiently handle up to 15,000 product catalog queries per second during peak shopping periods.
      • Storage Requirements: The system should accommodate a minimum of 10 TB of product images and descriptions.

    2. Social Media Platform:

      • Data Writes: The platform needs to handle a rate of 5,000 posts or comments per second during moments of high user engagement.
      • Data Reads: The system should support up to 30,000 content retrieval requests per second during peak browsing times.
      • Storage Requirements: The platform must provide scalable storage for user-generated multimedia content, aiming for a total capacity of 100 TB.

    3. Banking System:

      • Data Writes: The system should process a minimum of 100 financial transactions per second during peak transaction periods.
      • Data Reads: The platform must efficiently retrieve customer account information, supporting up to 50,000 queries per second.
      • Storage Requirements: The system should securely store transaction records and customer data, aiming for a storage capacity of 5 PB.

    4. Healthcare Information System:

      • Data Writes: The system should handle a rate of 500 patient record updates per second during periods of intensive care activities.
      • Data Reads: The platform must support up to 5,000 real-time queries for patient data during emergency situations.
      • Storage Requirements: The system should provide secure storage for electronic health records, with a target capacity of 20 TB.

    5. Ride-Sharing Application:

      • Data Writes: The system should process a minimum of 10,000 ride booking updates per minute during high-demand periods.
      • Data Reads: The application must efficiently retrieve location and route information, supporting up to 50,000 queries per second.
      • Storage Requirements: The platform should store historical trip data and user preferences, aiming for a storage capacity of 2 TB.

    6. Cloud Storage Service:

      • Data Writes: The system should handle a peak rate of 1,000 file uploads per second during periods of increased user activity.
      • Data Reads: The service must efficiently retrieve stored files, supporting up to 20,000 retrieval requests per second.
      • Storage Requirements: The platform should provide scalable storage for user data, aiming for a total capacity of 50 PB.

Rules of Thumb for Server Capacity Estimation

  • Estimating requests bound by the CPU involves determining the Requests Per Second (RPS) based on the computational capabilities of the server. The formula used is:

      RPSCPU=NumCPU/Tasktime

    Where:

      RPSCPU​ is the CPU-bound Requests Per Second.
  NumCPU​ is the number of CPU threads or hardware threads.
  Tasktime​ is the time each CPU-bound task takes to complete.

For example, if a server has 72 CPU threads and each CPU-bound task takes 200 milliseconds, the calculation would be RPSCPU=72/0.2=360 RPS.

  • Estimating requests bound by memory involves considering the server’s RAM size and the time it takes for memory-bound tasks to complete. The formula used is:

      RPSmemory=(RAMsize/Workermemory)Ă—(1/Tasktime)

    Where:

      RPSmemory​ is the Memory-bound Requests Per Second.
  RAMsize​ is the total size of RAM.
  Workermemory​ is the amount of RAM consumed by a worker managing a request.
  Tasktime​ is the time each memory-bound task takes to complete.

    For instance, if a server has 240 GB of RAM, each worker consumes 300 MB of RAM, and each memory-bound task takes 50 milliseconds, the calculation would be RPSmemory=(240)/(.3Ă—0.05)=16,000 RPS.

2. Implement the High-Level Architecture Design

1. Application Service Layer:

  • Description: This layer serves as the primary entry point for user requests and orchestrates the application’s business logic.
  • Components:
    • Web Server (Load Balancer):
      • Responsible for distributing incoming requests across multiple application servers.
      • Ensures efficient load balancing to handle varying request volumes.
    • Application Service (Service Partition):
      • Implements the core business logic and handles specific functionalities.
      • May be partitioned into multiple services to manage specific features independently.
  • Connections:
    • The load balancer directs user requests to appropriate service partitions based on load and availability.

2. Data Storage Layer:

  • Description: Manages the storage, retrieval, and manipulation of data necessary for the application.
  • Components:
    • Database (Master/Slave Database Cluster):
      • Stores and retrieves application data with a master node for write operations and slave nodes for read operations.
      • Ensures data consistency and availability through replication.
    • Caching Systems:
      • Enhances performance by storing frequently accessed data in-memory.
      • Reduces the load on the database for read-heavy operations.
  • Connections:
    • The application service layer communicates with the database for persistent data storage.
    • Caching systems help optimize read operations by serving cached data when applicable.

3. Other Important Components:

  • Description: Additional components essential for the system’s functionality.
  • Components:
    • External Services:
      • Integration points with third-party services or APIs that provide supplementary functionalities.
    • Logging and Monitoring:
      • Tracks system behavior, performance metrics, and user interactions for analysis and debugging.
  • Connections:
    • External services may be accessed to fetch data or perform specific tasks.
    • Logging and monitoring components observe and record system activities for analysis.

4. Communication Channels:

  • Description: Defines how different components communicate within the architecture.
  • Components:
    • Internal Communication Channels:
      • Enable communication between components within the same layer or across layers.
      • Examples include message queues, RPC (Remote Procedure Call) mechanisms, or direct API calls.
    • External Communication Channels:
      • Facilitate communication with external services, clients, or third-party APIs.
  • Connections:
    • Internal communication ensures seamless interaction between application and data layers.
    • External communication channels handle interactions with external entities.

5. Security Considerations:

  • Description: Outlines security measures implemented at the architectural level.
  • Components:
    • Authentication and Authorization Systems:
      • Verify user identities and control access to resources.
    • Encryption Mechanisms:
      • Secure data transmission and storage through encryption protocols.
  • Connections:
    • Authentication and authorization systems validate user access.
    • Encryption mechanisms safeguard sensitive data during transmission and storage.

6. Scalability and Redundancy:

  • Description: Addresses the scalability and redundancy aspects of the architecture.
  • Components:
    • Scalability Mechanisms:
      • Horizontal scaling through load balancing and partitioning.
    • Redundancy Measures:
      • Replication of critical components for fault tolerance and high availability.
  • Connections:
    • Scalability mechanisms enable the system to handle increased loads.
    • Redundancy measures prevent single points of failure and ensure system resilience.

7. Abstract Design Principles:

  • Description: Guiding principles shaping the overall architecture.
  • Components:
    • Loose Coupling:
      • Minimizes dependencies between components for flexibility and ease of modification.
    • High Cohesion:
      • Ensures related functionalities are grouped together for maintainability and clarity.
  • Connections:
    • Loose coupling allows components to evolve independently.
    • High cohesion enhances the clarity and maintainability of the system.

3. Implement Component Level Design

1. Identify Components and APIs:

  • Description: Break down the application service layer into individual components, each responsible for specific functionalities.
  • Example Components:
    • User Authentication Component:
      • Manages user authentication, registration, and login.
      • APIs: registerUser(), authenticateUser().
    • Order Processing Component:
      • Handles user orders, including validation, processing, and fulfillment.
      • APIs: placeOrder(), processOrder().
  • Object-Oriented Design:
    • Each component encapsulates related functionalities and exposes APIs for interaction.

2. Database Schema Design:

1. Define Database Entities:
  • Description: Identify key entities in the system and represent them as tables in the database.
  • Example Entities:
    • User Entity:
      • Fields: user_id, username, password_hash, email.
    • Order Entity:
      • Fields: order_id, user_id, items, status, total_price.
  • Considerations:
    • Establish relationships (foreign keys) between entities to maintain data integrity.
2. Normalize Database Tables:
  • Description: Apply normalization techniques to minimize redundancy and improve data consistency.
  • Examples of Normalization:
    • User Table:
      • Split into Users and UserDetails tables to avoid repeating non-essential user information.
    • Order Table:
      • Normalize item details into a separate OrderItems table to handle multiple items per order.
  • Benefits:
    • Reduces data duplication and enhances database efficiency.
3. Indexing Strategy:
  • Description: Define an indexing strategy to optimize query performance.
  • Example Indexes:
    • User Table:
      • Index on user_id for fast user lookups.
    • Order Table:
      • Index on user_id and order_id for efficient order retrieval.
  • Considerations:
    • Balance the number and type of indexes to speed up queries without compromising on write performance.
4. Data Constraints and Validations:
  • Description: Enforce data constraints and validations at the database level.
  • Example Constraints:
    • User Table:
      • Unique constraint on username to ensure unique usernames.
    • Order Table:
      • Check constraint on total_price to ensure non-negative values.
  • Benefits:
    • Maintains data accuracy and consistency by preventing invalid or inconsistent entries.
5. Database Security Measures:
  • Description: Implement security measures to protect sensitive data.
  • Security Measures:
    • Encryption:
      • Encrypt sensitive fields like passwords.
    • Access Control:
      • Define strict access controls to limit database access.
  • Considerations:
    • Prioritize security to safeguard user information and maintain compliance with privacy standards.
6. Database Backup and Recovery:
  • Description: Establish a robust backup and recovery strategy for data protection.
  • Strategies:
    • Regular Backups:
      • Schedule regular backups of the database.
    • Point-in-Time Recovery:
      • Enable features for point-in-time recovery for data restoration.
  • Benefits:
    • Mitigates data loss risks and ensures system resilience in case of unforeseen events.
7. Scalability Considerations:
  • Description: Anticipate scalability requirements and design the database schema accordingly.
  • Scalability Features:
    • Sharding:
      • Consider sharding strategies for distributing data across multiple database instances.
    • Caching:
      • Implement caching mechanisms to reduce database load for frequently accessed data.
  • Considerations:
    • Design the schema to support horizontal scalability and accommodate growing data volumes.
8. Optimizing Queries:
  • Description: Optimize frequently executed queries for improved performance.
  • Query Optimization:
    • Use of Indexes:
      • Ensure queries leverage appropriate indexes.
    • Avoiding Costly Joins:
      • Design schema to minimize the need for complex joins in queries.
  • Considerations:
    • Balance query optimization with the overall schema design for optimal system performance.

2. Object-Oriented Design for Functionality:

  • Description: Apply object-oriented principles to map features to modules and establish relationships among them.
  • Example Modules:
    • User Module:
      • Includes classes for user-related functionalities like registration and authentication.
    • Order Module:
      • Contains classes for order processing, validation, and fulfillment.
  • Example Relationships:
    • Singletons:
      • User Authentication Component may use a Singleton pattern for global user authentication state.
    • Composition:
      • Order Processing Component may compose multiple sub-modules for payment processing, inventory management, etc.
    • Inheritance:
      • Common functionalities shared among modules can be implemented in a base class, promoting code reuse.

3. Scenario Mapping:

  • Description: Map system scenarios to individual modules, ensuring clarity and separation of concerns.
  • Example Scenarios:
    • User Registration Scenario:
      • Mapped to the User Module, involving the registerUser() API.
    • Order Fulfillment Scenario:
      • Mapped to the Order Module, involving the processOrder() API.
  • Benefits:
    • Modules handle specific scenarios independently, promoting maintainability and modularity.

4. Relationships Among Modules:

  • Description: Define relationships based on the nature of functionalities and dependencies.
  • Singletons:
    • Global User Authentication State:
      • Ensures a single instance for managing user authentication across the system.
  • Composition:
    • Order Processing Sub-Modules:
      • Composes payment processing, inventory management, and shipping as independent modules.
  • Inheritance:
    • Base Class for Common Functionality:
      • Inherits shared functionalities like logging and error handling for consistent implementation.

5. Consideration of Design Patterns:

  • Description: Evaluate the need for design patterns to address recurring design challenges.
  • Patterns:
    • Observer Pattern:
      • User Module observes changes in user authentication status.
    • Strategy Pattern:
      • Order Processing Component may use different strategies for order validation based on payment methods.
  • Benefits:
    • Design patterns enhance flexibility, maintainability, and adaptability in the system.

6. API Documentation:

  • Description: Document the specifics of each API, including input parameters, expected outputs, and error handling.
  • Example Documentation:
    • User Authentication API:
      • registerUser(username: string, password: string): Registers a new user.
      • authenticateUser(username: string, password: string): Authenticates a user.
    • Order Processing API:
      • placeOrder(items; Item[]): Places a new order.
      • processOrder(orderId: string): Processes and fulfills an order.

7. Testing Strategy:

  • Description: Develop a testing strategy to ensure the correctness and robustness of the component design.
  • Testing:
    • Unit Testing:
      • Validate individual components in isolation using unit tests.
    • Integration Testing:
      • Verify interactions and collaborations between components through integration tests.
  • Benefits:
    • Rigorous testing ensures the reliability of each component and the overall system.

4. Understand and Discuss the Bottlenecks of the Design

1. Load Balancing Considerations:

  • Description: Assess the need for a load balancer and multiple backend machines to handle user requests efficiently.
  • Considerations:
    • Scalability: Evaluate the scalability benefits of load balancing for distributing incoming traffic.
    • Fault Tolerance: Discuss how load balancing enhances fault tolerance by redirecting traffic in case of server failures.
    • Downsides: Address potential downsides such as increased complexity and potential single points of failure.

2. Database Distribution Challenges:

  • Description: Evaluate the necessity of distributing the database across multiple machines.
  • Considerations:
    • Data Sharding: Discuss the concept of data sharding to distribute data across multiple database instances.
    • Consistency and Partitioning: Address challenges related to maintaining consistency and handling partitions in distributed databases.
    • Downsides: Explore downsides like increased complexity in managing distributed data and potential challenges in cross-node transactions.

3. Database Performance Optimization:

  • Description: Examine if the database performance can be enhanced through in-memory caching.
  • Considerations:
    • Caching Strategies: Discuss the use of in-memory caching to store frequently accessed data for faster retrieval.
    • Cache Invalidation: Address challenges related to cache invalidation and ensuring data consistency.
    • Downsides: Consider downsides such as increased memory usage and potential staleness of cached data.

4. Identifying System-Wide Bottlenecks:

  • Description: Identify and analyze potential bottlenecks affecting the overall system performance.
  • Considerations:
    • Monitoring and Profiling: Discuss the importance of monitoring tools and profiling techniques to identify performance bottlenecks.
    • Resource Utilization: Explore potential bottlenecks in CPU, memory, network, or storage resources.
    • Downsides: Acknowledge downsides like increased system complexity in managing and addressing bottlenecks.

5. Scalability Trade-offs:

  • Description: Understand the trade-offs involved in scaling the system horizontally or vertically.
  • Considerations:
    • Horizontal Scaling: Discuss benefits such as improved redundancy and potential downsides like increased inter-node communication.
    • Vertical Scaling: Explore benefits like simplified management and downsides such as scalability limitations.
    • Downsides: Discuss potential downsides associated with the chosen scalability approach.

6. Performance Testing and Optimization:

  • Description: Emphasize the importance of performance testing to identify and address bottlenecks.
  • Considerations:
    • Load Testing: Discuss the role of load testing in simulating real-world scenarios and identifying system limitations.
    • Optimization Strategies: Explore strategies for optimizing code, database queries, and overall system architecture.
    • Downsides: Address challenges like the need for continuous optimization efforts and potential trade-offs between speed and complexity.

7. Iterative Optimization Process:

  • Description: Recognize that addressing bottlenecks is an iterative process requiring continuous improvement.
  • Considerations:
    • Feedback Loops: Discuss the importance of feedback loops from monitoring tools and user feedback.
    • Adaptability: Emphasize the need for an adaptable system design to accommodate evolving requirements and performance challenges.
    • Downsides: Acknowledge downsides like the ongoing effort required for optimization and potential disruptions during optimization phases.

5. Discuss Scaling the Abstract Design

1. Vertical Scaling:

  • Note: Involves adding more power (CPU, RAM) to an existing machine.
  • Considerations:
    • Resource Limitations: Address limitations associated with a single machine’s capacity.
    • Ease of Implementation: Discuss the relatively straightforward process of upgrading hardware for improved performance.
    • Downsides: Acknowledge potential constraints in terms of scalability and cost-effectiveness.

2. Horizontal Scaling:

  • Note: Involves adding more machines to expand the pool of resources.
  • Considerations:
    • Distributed Architecture: Discuss the benefits of distributing the load across multiple machines.
    • Scalability: Highlight the potential for increased scalability by adding more servers.
    • Downsides: Address challenges related to coordination, communication, and system complexity.

3. Caching:

  • Note: Enables efficient resource utilization and facilitates meeting product requirements.
  • Considerations:
    • Application Caching: Discuss the integration of caching mechanisms within the application code.
    • Database Caching: Explore the default caching configurations provided by databases and the need for optimization.
    • In-Memory Caches: Emphasize the performance benefits of storing data in memory using tools like Memcached or Redis.
    • Use Cases: Provide examples of precalculating results, pre-generating indexes, and storing frequently accessed data in a faster backend.
    • Downsides: Address challenges related to cache invalidation and potential memory usage concerns.

4. Load Balancing:

  • Note: Involves distributing user requests evenly across a group/cluster of application servers.
  • Considerations:
    • Role of Load Balancer: Explain how a load balancer acts as a gateway to public servers, distributing user requests.
    • Types of Load Balancers: Differentiate between smart client, hardware load balancers (expensive but reliable), and software load balancers (hybrid and suitable for most systems).
    • Downsides: Acknowledge the complexity in achieving perfect load balancing and potential cost considerations for hardware solutions.

5. Database Replication:

  • Note: Involves copying data from one database to another for shared information access.
  • Considerations:
    • Data Consistency: Highlight the role of database replication in maintaining consistent data across multiple locations.
    • Distributed Database: Discuss the benefits of a distributed database where users can access relevant data without interference.
    • Normalization: Introduce the concept of normalization in the context of eliminating data ambiguity or inconsistency.
    • Downsides: Address potential challenges in managing replicated data and ensuring synchronization.

6. Database Partitioning:

  • Note: Involves decomposing tables either horizontally or vertically for efficient data management.
  • Considerations:
    • Table Decomposition: Discuss the options of decomposing tables row-wise or column-wise.
    • Scalability: Explore how partitioning contributes to improved scalability.
    • Data Organization: Address the impact of partitioning on data organization and retrieval.
    • Downsides: Acknowledge potential complexities in managing partitioned data and potential impact on query performance.

7. Map-Reduce:

  • Note: Adds a layer for performing data and/or processing intensive operations efficiently.
  • Considerations:
    • Data Analysis: Discuss the need for dedicated tools like Hadoop for analyzing large quantities of data.
    • Processing Efficiency: Explore how map-reduce facilitates efficient processing, especially in data-intensive operations.
    • Use Cases: Provide examples such as calculating suggested users in a social graph or generating analytics reports.
    • Downsides: Address potential challenges in integrating and managing map-reduce processes.

8. Platform Layer (Services):

  • Note: Separating the platform and web application layers for independent scaling.
  • Considerations:
    • Independence: Highlight the benefit of scaling platform and web application tiers independently.
    • Infrastructure Reuse: Discuss the reuse of infrastructure for multiple products or interfaces without redundant code.
    • Flexibility: Address how a platform layer enhances flexibility in supporting various interfaces.
    • Downsides: Acknowledge potential complexities in maintaining multiple layers and the need for effective coordination.