Core Concepts of System Design in Software Engineering

Jul 20, 2022 · 15 min read · system design software engineering ·

Author: Sadman Kabir Soumik

What is System Design in Software Engineering?

System design in software engineering is defining the structure, components, connections, and information for a system that meets specific needs. It is a vital step in software development as it outlines how the system will work and be structured. A team of developers usually performs this process with input from business analysts and end-users.

Process of Designing a System

To create a good software system, we need to follow these steps:

Identify and understand what the system needs to do, and what it can and cannot do. For example, performance, reliability, and security requirements.
Figure out how the system will be structured, including what parts it will have and how they will interact.
Design each part/component of the system, including what it does, how it stores data, and how it processes information.
Make sure the system can handle various amounts of loads, is easy to maintain, and can be improved later if needed.
Pick the right tools to build the system with.
Write down exactly what the system needs to do, and use this as a guide while building the software.

The point of all this is to make a software system that works well and does what everyone needs it to do.

Core Concepts of System Design

Client-Server Model

The client-server model is a software architecture where a client is a computer or device that asks for something from a server, and a server is a computer or device that responds to the request. This model is often used in networked systems, where a client asks for something from a server over a network, and the server sends back the requested thing or service.

One of the main benefits of the client-server model is that it separates the parts of the system. The client handles what the user interacts with, and the server manages the data and how the system works. This makes it easier to make changes to the system, as updates to the client or server don't affect the other side. The client-server model's capacity to manage numerous clients concurrently is an additional advantage. This can be helpful in systems with lots of users, as the server can easily deal with the extra work.

Network Protocols

Network protocols are rules and standards that control how computers and devices communicate over a network. In system design, network protocols are important for enabling different components of a system to exchange information and coordinate their actions.

There are many types of network protocols, each designed for specific purposes and operating at different layers of the network stack. At the lowest layer, there are protocols like Ethernet and Wi-Fi, which define how devices physically connect to the network and exchange data. At the next layer, there are protocols like TCP and UDP, which provide reliable and efficient data transmission between devices. At the top layer, there are protocols like HTTP and FTP, which define the formats and rules for exchanging specific types of data, like web pages and files.

Choosing the right network protocols is important for ensuring that a system can work correctly and efficiently. For example, if a system needs to transfer a lot of data in real-time, a protocol like UDP may be better than TCP, as UDP is faster but doesn't provide the same level of error checking and reliability.

Storage

In system design, storage refers to how data is saved and accessed. This includes temporary storage, like your computer's memory (RAM), and permanent storage, like hard drives and databases. Storage is critical in a system because it enables data to be saved and retrieved. This data can be user-generated content, such as text, images, and videos, or system-generated data, like logs and records.

When designing a system's storage, several key factors must be considered. First, the system must have enough storage capacity to handle its expected data. This may involve using different storage devices, like hard drives and cloud storage.

Second, the system must have efficient algorithms and data structures for storing and accessing data. This includes choosing the right data formats and structures, like tables and indexes, to optimize data retrieval and update performance.

Third, the system must have mechanisms for protecting and backing up data to prevent data loss and ensure data integrity. This may involve redundant storage systems, backup procedures, and error-correction algorithms.

Latency and Throughput

When designing a system, we need to make sure it can handle its workload well. To do this, we look at two important metrics: latency and throughput. Latency refers to the time it takes for a system to respond to a request, while throughput refers to the amount of data that the system can process within a given period of time. If a system has high latency, users may get frustrated waiting for the system to respond. If it has low throughput, it may not be able to handle a lot of requests.

To improve these metrics, we can take a few steps. We can optimize the way the system processes data, by using better algorithms and data structures. We can also distribute the workload among multiple machines or processors, using techniques like parallelization and distributed computing.

We can reduce the time it takes to access data from storage by using caching and pre-fetching techniques. Load balancing and other techniques also help distribute the workload across multiple resources. All of these steps can improve both latency and throughput, making the system faster and more efficient.

Caching

Caching is a way to make computer systems faster at finding data. It stores data that is frequently-used in a spot (For example, Memory) that is easy to find. This spot is called cache. When the system needs the data again, it can find it faster because it's already in the cache.

There are three types of caching:

Memory caching: This puts data in the computer's memory (RAM). This is faster than other types of storage.
Disk caching: This puts data on a fast disk, like an SSD. This is faster than a normal hard drive.
Network caching: This puts data on another nearby device, like a router or server. Other devices on the network can find it faster.

Caching can help a system work better because it makes finding data faster. It can also make storage devices work less, which makes them last longer and work better by increasing the lifespan.

Proxy

A proxy is a device or service that acts as a middleman between a client and a server. The proxy receives client queries, which then transmits them to the server and returns the server's response to the client.

Proxies are often used in system design to do things like:

Provide security: A proxy can block harmful traffic and protect the server from attacks.
Enhance privacy: A proxy can hide the client's IP address, making it hard for others to track their online activity.
Improve performance: A proxy can save frequently-used data, reducing what the server has to do and making it faster for the client to get data.
Load balance: A proxy can divide incoming requests across multiple servers, making the system more reliable and faster.

Load Balancers

A load balancer is a tool that shares incoming requests across different servers or resources. Load balancing aims to enhance the performance and dependability of a system by spreading the workload evenly across multiple resources.

Benefits of using load balancers in system design include:

Better performance: By evenly distributing the workload, load balancers can ensure that each server or resource has enough capacity to manage its share of requests. This can improve the system's overall performance, as it can handle a greater number of requests without getting overloaded.
Better reliability: By distributing the workload, load balancers can help ensure the system remains available and responsive, even if one or more servers or resources fail. This can enhance the system's reliability, as it can continue functioning despite failures.
Improved scalability: Load balancers can simplify adding additional servers or resources to a system, as they can automatically distribute incoming requests across the available resources. This can make it easier to scale a system up or down, depending on its workload.

Hashing

In system design, hashing is a technique used to efficiently store and retrieve data. Hashing involves applying a mathematical function, called a hash function, to a data item to generate a fixed-size value, called a hash code or hash value. The hash code is then used as an index or key to store and retrieve the data item in a data structure, such as a hash table or hash map.

Hashing has several benefits in system design, including:

Efficiency: Hashing allows for efficient data storage and retrieval, as it reduces the amount of data that must be stored and compared in order to find a specific item. This can improve the performance of a system, as it can access and manipulate data more quickly.
Uniqueness: A well-designed hash function will generate unique hash codes for each data item, making it unlikely that two items will have the same hash code. This can help ensure the integrity and correctness of data in a system.
Security: Hashing can be used to securely store sensitive data, such as passwords, as the hash code cannot be easily reversed to reveal the original data. This can help protect the security of a system and its users.

Replication and Sharding

In system design, replication and sharding are two techniques used to improve the performance, reliability, and scalability of a system. Replication involves creating multiple copies of data and storing them on different servers or devices, while sharding involves dividing a large dataset into smaller partitions and storing each partition on a different server or device.

Replication and sharding can be used together or independently in system design, depending on the specific requirements of the system. Some of the benefits of using replication and sharding include:

Improved performance: By storing multiple copies of data or partitioning a large dataset, a system can access and manipulate data more quickly, as it can read from or write to multiple servers or devices in parallel. This can improve the overall performance of the system.
Improved reliability: By storing multiple copies of data, a system can continue to function even if one or more servers or devices fail. This can improve the reliability of the system, as it can continue to serve users and maintain data integrity.
Enhanced scalability: By dividing a large dataset into smaller partitions, a system can more easily scale up or down, as it can add or remove servers or devices without having to move or redistribute the entire dataset.

P2P Network

In system design, a peer-to-peer (P2P) network is a type of network in which each device, or peer, has the same capabilities and functions as every other device in the network. In a P2P network, there is no central server or authority, and each peer can communicate and exchange data directly with any other peer in the network.

P2P networks have several benefits in system design, including:

Decentralization: P2P networks are decentralized, meaning that there is no central server or authority controlling the network. This can make P2P networks more resilient and flexible, as they can continue to function even if some peers fail or leave the network.
Efficiency: In a P2P network, each peer can act as both a client and a server, allowing for more efficient data exchange. This can reduce the workload on any one peer and improve the overall performance of the network.
Scalability: P2P networks can easily scale up or down, as new peers can join or leave the network without requiring any changes to the network infrastructure. This can make P2P networks well-suited to applications with a large number of users or devices.

One example of a P2P network is BitTorrent, a popular file-sharing application. In BitTorrent, users can share files with each other directly, without the need for a central server. Each user's computer acts as a peer in the network, and can download and upload pieces of a file from and to other peers.

In BitTorrent, each peer maintains a list of other peers that it is connected to, and can exchange data directly with these peers. As a result, the network can function even if some peers are offline or leave the network, and it can easily scale up or down as new users join or leave.

API Design

Application programming interface (API) design is the process of establishing the interfaces and requirements for APIs in a system. An API is a set of standards and programming guidelines that specify how various system parts, or systems themselves, can communicate and share data.

There are several common architectures for designing APIs in software engineering, each with its own set of advantages and disadvantages. Here are some common API design architectures:

REST (Representational State Transfer): This is a popular architectural style for designing web APIs that is based on the principles of HTTP and the RESTful web. REST APIs frequently employ HTTP methods (such GET, POST, and DELETE) to indicate the operations that can be carried out on a resource. They are created to be scalable, modular, and simple to use.
SOAP (Simple Object Access Protocol): This is an older architectural style for designing web APIs that is based on the principles of XML and Web Services. SOAP APIs are typically more complex and difficult to use than REST APIs, but they can support a wider range of messaging formats and protocols, such as HTTP, SMTP, and JMS.
GraphQL: This is a relatively new architectural style for designing APIs that is based on the principles of graph theory and the querying language of the same name. GraphQL APIs are designed to be flexible and efficient, and they allow clients to specify exactly the data they need, in a single request. This can make GraphQL APIs more efficient and scalable than other types of APIs.
gRPC (Google Remote Procedure Call): This is an open-source framework for designing APIs that is based on the principles of RPC and Protocol Buffers. gRPC APIs are designed to be fast, efficient, and low-latency, and they use a binary encoding format to transmit data, which can make them more efficient than APIs that use text-based formats.
Webhooks: This is a simple architectural style for designing APIs that is based on the principles of webhooks and real-time notifications. Webhook APIs are designed to be lightweight and easy to use, and they allow clients to register a URL to which the API can send notifications when certain events occur. This can make Webhook APIs ideal for applications that need to be notified of events in real-time.

Leader Election

Leader election is a common problem in distributed systems, where a group of nodes (or "processes") need to agree on which node should be the leader. This is typically done by having each node send a message to the other nodes, announcing its intention to become the leader. The other nodes then decide which node to elect as the leader based on some predetermined criteria, such as the node's rank or its availability.

There are several different algorithms and strategies for implementing leader election in a distributed system. Here are some common approaches:

Bully algorithm: This is a simple algorithm where each node sends a "request to be leader" message to all of the other nodes. The other nodes reply with an "acknowledge" message if they are not themselves trying to be leader. If a node does not receive any "acknowledge" messages, it assumes that it has won the election and becomes the leader. This algorithm is easy to implement, but it can be slow and inefficient, especially in large distributed systems.
Ranking algorithm: This is a more sophisticated algorithm where each node is assigned a rank, and the node with the highest rank wins the election. The rank can be determined based on factors such as the node's availability, its processing power, or its connectivity to the other nodes. This algorithm is more efficient than the bully algorithm, but it can be difficult to determine the rank of each node in a fair and unbiased way.
Virtual synchrony algorithm: This is an algorithm that relies on the concept of virtual synchrony, where the nodes in a distributed system are treated as if they were executing in a synchronized manner. The leader is elected by having each node send a "request to be leader" message to a designated "coordinator" node, which then decides which node to elect as the leader based on some predetermined criteria. This algorithm is more complex than the bully or ranking algorithms, but it can be more efficient and reliable in large distributed systems.

Messages and Pub-Sub

In distributed systems, messages and the pub-sub (publish-subscribe) pattern can be used to facilitate communication between different components. Messages are units of data that can be sent to share information, request services, or trigger actions between different parts of a distributed system. In a pub-sub system, a "publisher" sends messages to one or more "subscriber" nodes. The publisher does not need to know which nodes are subscribed to its messages, and the subscribers do not need to know where the messages come from.

The pub-sub pattern has several benefits in distributed systems. It allows nodes to work together without being closely connected, and it can scale easily. Furthermore, it enables nodes to share any number of messages, regardless of the types of messages or the number of other nodes in the system.

However, the pub-sub pattern also presents some challenges. One issue is ensuring that messages are delivered to the correct nodes quickly and securely. Another issue is protecting messages from being intercepted or compromised by attackers. A third issue is managing a large volume of messages, which can slow down the network and consume resources.