Master the Basics of System Design

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Master the Basics of System Design

Table of Contents

  1. Introduction to System Design Process
  2. API Design
  3. Low Level Design (LLD)
  4. High Level Design (HLD)
  5. Scalability and Load Balancing
  6. Stateful Services and Databases
  7. CAP Theorem and Database Replication
  8. Sharding for Scaling
  9. Blob Storage and Message Queues
  10. Caching for Improved Performance

Introduction to System Design Process

System design can often seem daunting, especially with the abundance of technical terms and concepts that are associated with it. In this article, we aim to break down the system design process into various stages, making it more approachable and easier to understand. We will provide a brief introduction to each stage and recommend essential reading material to help You kickstart your system design Journey.

API Design

The first stage in the system design process is API design. In this stage, we take a vague problem statement and come up with the API design of the system. The API design defines how the system interacts with users and what actions it can perform. We will explore the importance of functional requirements and discuss how to convert them into APIs. Additionally, we will provide examples and recommend essential resources for further reading on HTTP methods and REST API architecture.

Low Level Design (LLD)

Once we have the API design, the next step is to Create the Low Level Design (LLD) of the system. LLD involves creating entities and establishing relationships between them. We will dive into the process of identifying entities, assigning attributes, and drawing entity relationship diagrams (ERDs). Furthermore, we will provide practice examples and suggest resources for understanding relational databases and entity-relationship concepts.

High Level Design (HLD)

The High Level Design (HLD) is the overarching plan that gives us a high-level overview of how the system will look and its various components. This stage focuses on understanding the constraints of the system, known as non-functional requirements (NFRs). We will discuss the importance of NFRs and their impact on system architecture. Key components such as load balancing layer, stateless services, and stateful services (databases) will be explained in Detail, along with their roles and relationships within the system. Resources for understanding the CAP theorem, master-slave architecture, database replication, sharding, and other scaling techniques will be provided.

Scalability and Load Balancing

Scalability is a crucial aspect when designing a system. As user demand increases, the system should be able to handle the load efficiently without becoming slow or unresponsive. In this section, we will explore strategies for managing scalability and load balancing. We will discuss load balancer layers and their role in distributing network load among instances. Additionally, we will Delve into the concept of stateless services, which handle user requests and transform them Based on the system's logic. Resources for understanding load balancing strategies and practicing load balancing techniques will be recommended.

Stateful Services and Databases

Stateful services, also known as databases, play a crucial role in storing and persisting data within the system. We will explore the different types of databases and their functions. A simple trick to convert entities into tables will be explained, along with practice examples. Concepts related to relational databases, such as ACID properties, keys, joins, and indexes, will be covered. Moreover, we will recommend resources for understanding the access times of various storage media, including hard disk drives (HDDs), solid-state drives (SSDs), and RAM.

CAP Theorem and Database Replication

To ensure the system remains functional and available, it is essential to understand the CAP theorem and database replication. We will dive into the CAP theorem, which states that Consistency, Availability, and Partition Tolerance cannot be achieved simultaneously in a distributed computer system. We will provide recommended resources for further reading on the CAP theorem and its implications. Moreover, we will discuss database replication, a technique used to increase system availability and improve fault tolerance.

Sharding for Scaling

Sharding is a technique used to Scale databases horizontally by distributing data across multiple machines or nodes. This technique allows for better performance and handles increased user demand. We will explain the concept of sharding in detail, along with its advantages and strategies. Recommended resources for understanding sharding and practicing sharding techniques will be provided.

Blob Storage and Message Queues

In this section, we will explore blob storage and message queues, which are crucial components for managing large amounts of data and communication between different components. We will discuss the concept of blob storage, focusing on Amazon S3 as an example. Additionally, we will delve into message queues and, in particular, Kafka as a widely used messaging system. Resources for understanding blob storage and message queuing systems will be recommended.

Caching for Improved Performance

Caching is a powerful technique used to improve system performance by storing pre-computed information for faster access. We will explain the concept of caching using simple real-life examples and discuss its importance in system design. The placement of the caching layer within the system and its benefits will be explained. We will recommend resources for understanding caching strategies and learning about popular caching tools and technologies.

Conclusion

By breaking down the system design process into stages and providing essential reading material, we aim to make system design more accessible and manageable. Each stage focuses on a specific aspect of system design, including API design, low-level design, high-level design, scalability, databases, CAP theorem, sharding, blob storage, message queues, and caching. These stages provide a foundation for understanding and implementing system design principles effectively. Explore each stage, read the recommended resources, and practice the concepts to enhance your system design skills.


Article:

Introduction to System Design Process

System design can often seem daunting, especially with the abundance of technical terms and concepts that are associated with it. In this article, we aim to break down the system design process into various stages, making it more approachable and easier to understand. We will provide a brief introduction to each stage and recommend essential reading material to help you kickstart your system design journey.

API Design

The first stage in the system design process is API design. In this stage, we take a vague problem statement and come up with the API design of the system. The API design defines how the system interacts with users and what actions it can perform. We will explore the importance of functional requirements and discuss how to convert them into APIs. Additionally, we will provide examples and recommend essential resources for further reading on HTTP methods and REST API architecture.

Low Level Design (LLD)

Once we have the API design, the next step is to create the Low Level Design (LLD) of the system. LLD involves creating entities and establishing relationships between them. We will dive into the process of identifying entities, assigning attributes, and drawing entity relationship diagrams (ERDs). Furthermore, we will provide practice examples and suggest resources for understanding relational databases and entity-relationship concepts.

High Level Design (HLD)

The High Level Design (HLD) is the overarching plan that gives us a high-level overview of how the system will look and its various components. This stage focuses on understanding the constraints of the system, known as non-functional requirements (NFRs). We will discuss the importance of NFRs and their impact on system architecture. Key components such as load balancing layer, stateless services, and stateful services (databases) will be explained in detail, along with their roles and relationships within the system. Resources for understanding the CAP theorem, master-slave architecture, database replication, sharding, and other scaling techniques will be provided.

Scalability and Load Balancing

Scalability is a crucial aspect when designing a system. As user demand increases, the system should be able to handle the load efficiently without becoming slow or unresponsive. In this section, we will explore strategies for managing scalability and load balancing. We will discuss load balancer layers and their role in distributing network load among instances. Additionally, we will delve into the concept of stateless services, which handle user requests and transform them based on the system's logic. Resources for understanding load balancing strategies and practicing load balancing techniques will be recommended.

Stateful Services and Databases

Stateful services, also known as databases, play a crucial role in storing and persisting data within the system. We will explore the different types of databases and their functions. A simple trick to convert entities into tables will be explained, along with practice examples. Concepts related to relational databases, such as ACID properties, keys, joins, and indexes, will be covered. Moreover, we will recommend resources for understanding the access times of various storage media, including hard disk drives (HDDs), solid-state drives (SSDs), and RAM.

CAP Theorem and Database Replication

To ensure the system remains functional and available, it is essential to understand the CAP theorem and database replication. We will dive into the CAP theorem, which states that Consistency, Availability, and Partition Tolerance cannot be achieved simultaneously in a distributed computer system. We will provide recommended resources for further reading on the CAP theorem and its implications. Moreover, we will discuss database replication, a technique used to increase system availability and improve fault tolerance.

Sharding for Scaling

Sharding is a technique used to scale databases horizontally by distributing data across multiple machines or nodes. This technique allows for better performance and handles increased user demand. We will explain the concept of sharding in detail, along with its advantages and strategies. Recommended resources for understanding sharding and practicing sharding techniques will be provided.

Blob Storage and Message Queues

In this section, we will explore blob storage and message queues, which are crucial components for managing large amounts of data and communication between different components. We will discuss the concept of blob storage, focusing on Amazon S3 as an example. Additionally, we will delve into message queues and, in particular, Kafka as a widely used messaging system. Resources for understanding blob storage and message queuing systems will be recommended.

Caching for Improved Performance

Caching is a powerful technique used to improve system performance by storing pre-computed information for faster access. We will explain the concept of caching using simple real-life examples and discuss its importance in system design. The placement of the caching layer within the system and its benefits will be explained. We will recommend resources for understanding caching strategies and learning about popular caching tools and technologies.

By breaking down the system design process into stages and providing essential reading material, we aim to make system design more accessible and manageable. Each stage focuses on a specific aspect of system design, including API design, low-level design, high-level design, scalability, databases, CAP theorem, sharding, blob storage, message queues, and caching. These stages provide a foundation for understanding and implementing system design principles effectively. Explore each stage, read the recommended resources, and practice the concepts to enhance your system design skills.

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