Section 1.1: Caching

Caching involves temporarily storing frequently accessed data in a high-speed storage system, known as a cache, to minimize latency and improve system performance. The cache acts as an intermediary layer between the application and the original data source, such as a database or a remote web service. It can be implemented at various levels, including the browser, content delivery networks (CDNs), application servers, and databases.

Different caching strategies exist, such as Least Recently Used (LRU), Most Recently Used (MRU), and Time-To-Live (TTL). Grasping these strategies and knowing how to implement them in your system design is crucial for optimizing performance and alleviating unnecessary server loads. Caching can occur in various locations, including clients, DNS, CDNs, load balancers, API gateways, servers, and databases.

For a deeper understanding of caching, check out the following resource:

This video explains the basics of caching and its importance in system design.

Section 1.2: Load Balancing

Load balancing is the practice of distributing incoming network traffic across multiple servers to prevent any single server from being overwhelmed. This technique helps ensure high availability, fault tolerance, and optimal performance.

When designing a system, selecting the appropriate load-balancing method is essential, taking into account factors like latency, throughput, and the nature of the workload. Common methods include Round Robin, Least Connections, and IP Hash.

• Round Robin: Distributes requests evenly across all servers in a cyclical manner.
• Least Connections: Directs requests to the server with the fewest active connections.
• IP Hash: Routes requests based on a hashed value of the client's IP address to maintain session persistence.

For an in-depth exploration of load balancing, refer to this guide:

This video provides five essential tips for succeeding in system design interviews.

Section 1.3: Microservices Architecture

Microservices architecture is a design paradigm that organizes an application into a collection of small, independent services, each responsible for a specific function. These services communicate via APIs and can be developed, deployed, and scaled independently. This architecture offers numerous advantages, including enhanced flexibility, scalability, and fault isolation.

However, transitioning to a microservices architecture can introduce challenges, such as increased operational complexity and the need for effective API management. Key characteristics of microservices include:

• Communication: Utilizing lightweight protocols like HTTP/REST, gRPC, or message queues.
• Single Responsibility: Each service adheres to a specific functionality, making it easier to manage.
• Independence: Services can be scaled and deployed independently, allowing for agile development.
• Fault Tolerance: Failures in one service do not necessarily impact the entire system.
• Decentralization: Each microservice manages its own data and logic, promoting a clear separation of concerns.

Section 1.4: Data Partitioning

Data partitioning is a vital concept in system design, especially for distributed systems, as it involves dividing a large dataset into smaller, manageable segments, also known as partitions or shards. This strategy enhances scalability, performance, and maintainability by distributing data across multiple nodes or storage devices.

There are three main types of data partitioning:

• Horizontal Partitioning (Sharding): Divides data into subsets based on specific attributes, allowing for parallel processing.
• Vertical Partitioning: Splits data based on columns, optimizing access patterns for frequently queried attributes.
• Functional Partitioning: Segregates data according to its function, promoting modularity and independent scaling of services.

Section 1.5: API Gateway

An API Gateway serves as an intermediary between external clients and internal microservices, simplifying the management of multiple APIs. It functions as a reverse proxy, aggregating services needed to fulfill client requests. Key functions include:

• Request Routing: Directs API requests to the appropriate microservices and compiles the responses.
• Authentication and Authorization: Validates client credentials to ensure secure access to services.
• Rate Limiting and Throttling: Protects the system from overload by enforcing limits on incoming requests.
• Request and Response Transformation: Modifies requests and responses as needed.
• Logging and Monitoring: Provides insights into system performance by tracking API traffic.