Types of System Design

System design varies based on the type of application, the architecture chosen, the system’s functionality, and the use cases it needs to address. Understanding the differences between these types helps in crafting the most suitable architecture and ensures scalability, reliability, and maintainability for specific needs. The major types of system design include:

1. Designing Web Applications vs Distributed Systems
2. Real-time Systems vs Batch Systems
3. Offline-first Systems vs Always-online Systems

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1. Designing Web Applications vs Distributed Systems

Web Applications:

Web applications are typically designed to be run on a single server or a set of closely linked servers. These systems focus on handling user requests, providing responses via a user interface, and managing backend services in a relatively centralized environment.

• Architecture:

  • Client-server model: A web application usually follows a request-response architecture, where a client (browser) sends a request to the server, and the server processes the request and returns a response.
  • Monolithic vs Microservices: Web applications can be monolithic (where everything runs in a single application) or use microservices (where different functionalities are handled by separate services).

• Key Considerations:

  • Scalability: Scaling a web app often involves adding more servers or using load balancing strategies to distribute incoming traffic.
  • Session Management: Since web apps involve stateful user interactions, proper session management (e.g., using cookies or tokens) is crucial.
  • Reliability: Web apps must ensure high availability, especially when dealing with high traffic.

• Example:

  • An e-commerce website where users browse products, make purchases, and check order statuses. The design focuses on ensuring quick response times, easy navigation, and stable operations.

Distributed Systems:

Distributed systems involve multiple independent machines or nodes working together to achieve a common goal. They are designed to handle large-scale applications and are often more complex than web applications, as they need to manage communication, synchronization, and data consistency across multiple nodes.

• Architecture:
  • Microservices: A common architecture pattern where each component is a standalone service communicating over a network. Examples include REST APIs or message queues.
  • Data Consistency: Ensuring data consistency across multiple nodes in distributed databases (using CAP theorem principles).
• Key Considerations:
  • Fault Tolerance: Distributed systems must be designed to handle node failures, network partitions, and delays.
  • Scalability: These systems must support horizontal scaling where new nodes can be added to meet increased demand.
  • Latency: Minimizing latency in communication between nodes is essential for maintaining performance.
• Example:
  • A cloud storage platform like Dropbox, which needs to distribute files across multiple data centers, ensuring data is accessible reliably and with low latency from any part of the world.

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2. Real-time Systems vs Batch Systems

Real-time Systems:

Real-time systems are designed to handle tasks that require immediate processing and responses. The system processes inputs and generates outputs within a predictable time frame, often used in scenarios where timing is critical.

• Types:
  • Hard Real-time: Systems that must meet deadlines with no tolerance for failure (e.g., controlling medical devices or flight control systems).
  • Soft Real-time: Systems where meeting deadlines is important, but occasional delays can be tolerated (e.g., video streaming, online gaming).
• Key Considerations:
  • Latency: Real-time systems must process requests with minimal delay, ensuring that response times are consistent.
  • Concurrency: Handling multiple simultaneous operations without blocking.
  • Failure Recovery: Ensuring the system can recover quickly from failures without impacting time-sensitive operations.
• Example:
  • Online multiplayer games: These need to process player actions (e.g., movement, attack) in real-time with minimal latency to ensure a smooth gameplay experience.

Batch Systems:

Batch systems, on the other hand, process large volumes of data at scheduled times or in bulk. These systems are designed to execute processes that do not need immediate interaction or feedback and are often used for tasks like data analysis, report generation, or batch updates.

• Architecture:

  • Scheduled Jobs: Batch systems often work by running jobs at specific intervals or during off-peak hours to process data in bulk.
  • Queueing Mechanisms: Data is usually queued and processed asynchronously.

• Key Considerations:

  • Throughput: The ability to handle large volumes of data efficiently is a primary concern.
  • Failure Handling: Batch jobs can be retried or reprocessed if they fail during execution.
  • System Load: Ensuring the system does not become overwhelmed during heavy processing periods.

• Example:

  • Payroll processing: A batch system that runs monthly to calculate and process employee salaries, tax deductions, and other payments.

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3. Offline-first Systems vs Always-online Systems

Offline-first Systems:

Offline-first systems are designed to function fully or partially without an internet connection. These systems are optimized to allow users to interact with the application even when there is no connectivity and sync data when the connection is restored.

• Architecture:
  • Local Storage: Use of local databases (e.g., SQLite, IndexedDB) to store data while offline.
  • Data Synchronization: When the device reconnects to the internet, data is synchronized with a central server.
• Key Considerations:
  • Conflict Resolution: Handling data conflicts when syncing changes made offline with data stored on the server.
  • User Experience: Ensuring the app remains functional and intuitive even when offline.
  • Sync Mechanism: Efficiently handling the transfer of data once the device reconnects to the internet.
• Example:
  • Google Docs: You can work on documents offline, and when the internet connection is restored, your changes are synchronized with the cloud.

Always-online Systems:

Always-online systems depend on a constant internet connection to function. These systems require real-time communication between the client and the server for processing requests, fetching data, and providing services.

• Architecture:

  • Centralized Server: The system relies on the server to process requests and fetch data, making it essential to maintain internet connectivity.

• Key Considerations:

  • Connectivity Dependency: Users must have an active internet connection to use the system.
  • Latency and Performance: The system must be optimized to handle network delays effectively, as user experience relies on real-time interactions.
  • Data Integrity: Since the system relies on central servers, ensuring that data is consistent and always available is important.

• Example:

  • Online banking apps: These require an active internet connection to process transactions, check balances, and perform other tasks.