Top 50 Must-Know System Design Interview Terminologies: A Comprehensive Guide

Introduction

System design interviews are a critical part of the hiring process for software engineers, especially those working on large-scale applications. These interviews assess your ability to design and architect robust, scalable, and performant systems. To excel in these interviews, a deep understanding of fundamental system design concepts and terminology is essential.

This comprehensive guide will equip you with the top 50 must-know terminologies used in system design interviews, providing a solid foundation for tackling any system design challenge.

Historical Context and Evolution:

System design as a field has evolved alongside the growth of computing technology. Initially, systems were designed for single users or small groups, with limited processing power and storage. As technology progressed, the need for larger, more complex systems arose.

The internet boom in the late 20th century further accelerated the evolution of system design, demanding solutions for handling massive user bases and ever-growing data volumes. This led to the development of distributed systems, cloud computing, and the widespread adoption of microservices, all of which require a deep understanding of system design principles.

Problem Solved and Opportunities Created:

System design enables developers to build complex applications that are reliable, scalable, and meet the needs of ever-growing user bases. It addresses the challenges of:

Scalability: Designing systems capable of handling increasing traffic and data volumes.
Availability: Ensuring systems remain operational and accessible even during failures or peak load.
Performance: Optimizing system speed and response times to provide a seamless user experience.
Security: Protecting user data and system integrity against threats.

Opportunities:

The demand for skilled system designers is high across various industries, from tech giants to startups and traditional businesses. This knowledge opens doors to exciting roles with high growth potential and attractive compensation.

Key Concepts, Techniques, and Tools

1. Scalability:

Horizontal Scaling: Adding more servers or instances to handle increased load.
Vertical Scaling: Increasing the resources of existing servers (e.g., CPU, RAM).
Load Balancing: Distributing traffic evenly across multiple servers to prevent overload.

2. Availability:

High Availability (HA): Ensuring continuous service availability, even in the presence of failures.
Redundancy: Duplicating components to provide backups in case of failure.
Failover: Automatically switching to a backup system when the primary system fails.
Fault Tolerance: Designing systems that can tolerate failures without impacting functionality.

3. Performance:

Latency: Time taken for a request to be processed and a response returned.
Throughput: Number of requests a system can process per unit of time.
Caching: Storing frequently accessed data in memory for faster retrieval.
Data Compression: Reducing data size to improve transmission and storage efficiency.

4. Security:

Authentication: Verifying user identity.
Authorization: Controlling user access to resources.
Encryption: Encoding data to protect it from unauthorized access.
Firewall: Filtering network traffic to block malicious connections.
Vulnerability Assessment: Identifying security weaknesses in systems.

5. Databases:

Relational Database Management System (RDBMS): Stores data in tables with relationships between them (e.g., MySQL, PostgreSQL).
NoSQL Database: Stores data in various formats, offering flexibility and scalability (e.g., MongoDB, Cassandra).
Data Modeling: Representing data structures and relationships using diagrams.
Query Optimization: Improving query performance by choosing efficient execution plans.
Transaction Management: Ensuring data consistency during multiple operations.

6. Caching:

Cache: Temporary storage for frequently accessed data, improving performance.
Cache Hit: When data is found in the cache.
Cache Miss: When data is not found in the cache and needs to be retrieved from the main storage.
Cache Eviction: Removing data from the cache to make space for new data.
Cache Invalidation: Removing outdated or incorrect data from the cache.

7. Messaging Systems:

Message Queue: A temporary storage system that allows components to communicate asynchronously.
Message Broker: A centralized service for managing message delivery and routing.
Publish/Subscribe: A messaging pattern where publishers send messages to subscribers.
Queue: A FIFO (First In, First Out) data structure used for message delivery.
Topic: A category or subject for messages in a publish/subscribe system.

8. Load Balancing:

Round Robin: Distributing requests to servers in a circular fashion.
Least Connections: Sending requests to the server with the fewest active connections.
Random: Distributing requests randomly across servers.
Sticky Sessions: Keeping a user's requests routed to the same server throughout a session.

9. Microservices:

Microservice Architecture: Building an application as a collection of small, independent services.
Service Discovery: Discovering the location and availability of services.
API Gateway: A single entry point for all external requests to microservices.
Service Mesh: A layer of infrastructure that simplifies communication between microservices.

10. Cloud Computing:

Infrastructure as a Service (IaaS): Provides access to virtualized computing resources (e.g., AWS EC2, Azure VMs).
Platform as a Service (PaaS): Offers a platform for developing and deploying applications (e.g., Heroku, Google App Engine).
Software as a Service (SaaS): Delivers applications over the internet (e.g., Google Docs, Salesforce).
Cloud Storage: Storing data on remote servers managed by a cloud provider (e.g., AWS S3, Google Cloud Storage).

11. Content Delivery Network (CDN):

CDN: A distributed network of servers that caches and delivers content closer to users.
Edge Server: A server located at the edge of the network, closer to users.
Origin Server: The main server where the original content is stored.
Caching: Storing copies of content on edge servers for faster delivery.

12. Data Structures:

Array: A contiguous block of memory that stores a collection of elements of the same data type.
Linked List: A data structure where elements are linked together using pointers.
Stack: A data structure that follows the Last In, First Out (LIFO) principle.
Queue: A data structure that follows the First In, First Out (FIFO) principle.
Hash Table: A data structure that uses a hash function to map keys to values.

13. Algorithms:

Sorting Algorithms: Algorithms that arrange data in a specific order (e.g., Bubble Sort, Merge Sort).
Searching Algorithms: Algorithms that find a specific element in a data set (e.g., Linear Search, Binary Search).
Graph Algorithms: Algorithms for working with graph data structures (e.g., Dijkstra's Algorithm, Depth-First Search).
Dynamic Programming: A technique for solving problems by breaking them down into smaller subproblems.

14. Design Patterns:

Singleton: Ensuring only one instance of a class is created.
Factory: Creating objects without specifying their exact class.
Observer: Defining a one-to-many dependency between objects.
Decorator: Dynamically adding responsibilities to an object.

15. Other Key Terminologies:

API (Application Programming Interface): A set of rules that define how software components interact.
REST (Representational State Transfer): A set of architectural constraints for designing web services.
HTTP (Hypertext Transfer Protocol): The protocol used for communication between web browsers and web servers.
DNS (Domain Name System): Translates domain names to IP addresses.
SSL/TLS (Secure Sockets Layer/Transport Layer Security): Protocols for encrypting communication over the internet.
Load Testing: Simulating real-world load on a system to assess its performance.
Stress Testing: Pushing a system beyond its normal capacity to identify its breaking point.
Performance Monitoring: Observing system performance metrics to identify potential issues.
DevOps: A set of practices that combine software development and IT operations to deliver software faster.
Agile Methodology: An iterative approach to software development that focuses on flexibility and collaboration.

Practical Use Cases and Benefits

1. E-commerce Website:

Scalability: Handling peak traffic during sales events or holidays.
Availability: Ensuring uninterrupted service for online shoppers.
Performance: Providing fast page loading times and checkout processes.
Security: Protecting customer data and payment information.

2. Social Media Platform:

Scalability: Supporting millions of active users and massive data volumes.
Availability: Ensuring constant access for users worldwide.
Performance: Delivering real-time updates and interactive experiences.
Security: Preventing data breaches and protecting user privacy.

3. Online Gaming Platform:

Scalability: Handling large numbers of concurrent players and intensive game logic.
Availability: Ensuring smooth gameplay without interruptions.
Performance: Delivering low latency and high frame rates.
Security: Protecting user accounts and game assets.

4. Financial Trading Platform:

Performance: Ensuring fast order execution and data processing for high-frequency trading.
Availability: Providing 24/7 access for traders worldwide.
Security: Protecting sensitive financial information and preventing fraud.

Benefits:

Improved User Experience: Fast, reliable, and secure systems lead to satisfied users.
Increased Business Efficiency: Optimized systems enable organizations to operate more efficiently.
Reduced Costs: Scalable and resilient systems minimize downtime and operational expenses.
Enhanced Competitiveness: Well-designed systems can provide a competitive edge in today's digital world.

Step-by-Step Guide: Designing a Simple Chat Application

1. Define Requirements:

Functionality: Users should be able to send and receive messages in real-time.
Scalability: The system should handle a large number of users and messages.
Availability: The service should be available 24/7.
Security: User messages should be protected from unauthorized access.

2. Choose Architecture:

Client-Server: Clients (users) connect to a central server that manages message routing.
Microservices: Break down the application into independent services (e.g., user management, message storage, real-time communication).

3. Design Components:

Client: A web or mobile application that allows users to interact with the chat service.
Server: Hosts the application logic and manages communication between clients.
Message Queue: A temporary storage system for messages, enabling asynchronous communication between clients and servers.
Database: Stores user information and messages.

4. Choose Technologies:

Programming Language: JavaScript, Python, Java.
Database: MySQL, MongoDB.
Message Queue: Redis, RabbitMQ.
Web Framework: React, Angular, Vue.js.

5. Implement Security Measures:

Authentication: Require users to log in with secure credentials.
Authorization: Control access to specific features and messages.
Encryption: Encrypt messages to protect user privacy.

6. Test and Deploy:

Load Testing: Simulate real-world load to ensure scalability.
Stress Testing: Identify the system's breaking point.
Performance Monitoring: Monitor key metrics to detect performance issues.
Deploy the application to a cloud platform or on-premises infrastructure.

Code Snippet (Python):

from flask import Flask, render_template, request, jsonify

app = Flask(__name__)

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/send_message', methods=['POST'])
def send_message():
    message = request.form['message']
    # Process and store the message
    return jsonify({'success': True})

if __name__ == '__main__':
    app.run(debug=True)

Example Frontend Code (React):

import React, { useState, useEffect } from 'react';

function ChatApp() {
  const [messages, setMessages] = useState([]);
  const [newMessage, setNewMessage] = useState('');

  useEffect(() =&gt; {
    // Fetch messages from the server
  }, []);

  const handleSendMessage = () =&gt; {
    // Send the message to the server
  };

  return (
<div>
 <ul>
  {messages.map((message, index) =&gt; (
  <li key="{index}">
   {message}
  </li>
  ))}
 </ul>
 <input =="" onchange="{(e)" type="text" value="{newMessage}"/>
 setNewMessage(e.target.value)}
      /&gt;
 <button onclick="{handleSendMessage}">
  Send
 </button>
</div>
);
}

export default ChatApp;

Tips and Best Practices:

Start with a simple design and iterate: Don't try to build everything at once.
Focus on scalability from the beginning: Choose technologies that can handle future growth.
Implement security measures throughout the development process: Don't wait until the end to address security concerns.
Use industry-standard tools and frameworks: Leverage existing solutions to save time and effort.
Test thoroughly: Ensure your system is robust and performs as expected.

Resources:

GitHub Repository: [Link to relevant repository]
Documentation: [Link to relevant documentation]

Challenges and Limitations

1. Complexity: Designing and implementing large-scale systems can be complex and time-consuming.
2. Cost: Building and maintaining robust systems can be expensive, especially for startups and smaller organizations.
3. Security Threats: Systems are vulnerable to various security threats, requiring ongoing monitoring and updates.
4. Legacy Systems: Modernizing or integrating legacy systems can pose challenges.

Overcoming Challenges:

Modular Design: Break down systems into smaller, manageable components.
Cloud Computing: Utilize cloud services to reduce infrastructure costs and complexity.
Security Best Practices: Implement industry-standard security practices and tools.
Phased Approach: Modernize legacy systems gradually, minimizing disruption.

Comparison with Alternatives

Traditional Monolithic Architecture vs. Microservices Architecture:

Monolithic Architecture: A single, large application that handles all functionalities.
Microservices Architecture: A collection of small, independent services that communicate with each other.

Advantages of Microservices:

Scalability: Easier to scale individual services independently.
Flexibility: Enables faster development and deployment of new features.
Resilience: Failures in one service do not impact other services.

Advantages of Monolithic Architecture:

Simpler to develop and deploy: Easier to manage in smaller projects.
Potentially better performance: Reduced communication overhead.

Choose Microservices when:

Building complex applications with multiple functionalities.
Need to support rapid development and deployment cycles.
Require high scalability and resilience.

Choose Monolithic Architecture when:

Building simpler applications with limited functionality.
Resources and time are limited for development and deployment.

Conclusion

Understanding key system design terminologies is crucial for success in system design interviews and building robust, scalable, and performant applications. This guide has covered 50 essential terms, providing a solid foundation for navigating the world of system design.

Key Takeaways:

System design is a critical aspect of building large-scale applications.
Understanding fundamental concepts and terminologies is essential.
Scalability, availability, performance, and security are critical design considerations.
Choosing the right architecture and technologies is crucial for success.
Testing and monitoring are essential steps in the development process.

Next Steps:

Continue learning about advanced system design concepts and techniques.
Practice designing and implementing systems using different technologies.
Explore open-source projects and contribute to the development of real-world applications.

The future of system design is exciting, with emerging technologies like edge computing, serverless computing, and AI driving innovation. By staying informed and continuously learning, you can position yourself for success in this rapidly evolving field.

Call to Action

Take your system design knowledge to the next level!

Explore online resources: Find courses, tutorials, and articles to deepen your understanding.
Join online communities: Connect with other system designers and learn from their experience.
Practice designing and implementing systems: Apply your knowledge to real-world projects.

Ready to conquer your next system design interview?

Keep learning and keep building!