System Design 101 - Concepts
Learn the basics of system design
System design is a critical skill for software engineers and architects, enabling them to build efficient, scalable, and reliable applications. However, designing a complex system requires a deep understanding of various concepts and principles, from database design and caching strategies to network protocols and load-balancing techniques.
In this article, we'll explore some key system design concepts that every engineer should know, along with some examples and best practices to help you design better systems.
Scalability is the ability of your program to gracefully meet the demand of stress caused by increased usage. In short, ensuring that your program doesn’t slow or bust when pounded by more users than you originally anticipated. Having good scalability means that your system can work reliably amid the increased load.
What is your current peak load that you can handle?
How many database records can create until critical operations slow down?
Is the primary scaling strategy to “scale up” or to “scale out” — that is, to upgrade the nodes in a fixed topology, or to add nodes?
The two main ways to scale are:
Vertical - Vertical scaling is increasing your machine's capacity by upgrading the hardware. If you need more storage, you can get a hard drive with a higher storage capacity. If you require more RAM, you can get a higher RAM.
Horizontal - Horizontal scale is when you're adding more machines to your system to distribute the load. If you require more server bandwidth, you don't upgrade your server, you add more servers into the load balancer pool.
Availability (or Reliability)
Availability defines the system's ability to continue to function even with faults and failures. A fault is usually defined as one component of the system deviating from its spec, whereas a failure is when the system as a whole, stops providing the required service to the user.
How long the system is up and running and the Mean Time Between Failure (MTBF) is known as the availability of a program.
How long does the system need to run without failure?
What is the acceptable length of time for the system to be down?
Can downtimes be scheduled?
Maintainability implies how brittle the code is to change. Or how easy it is for future developers to make changes to the system. The majority cost of software is in ongoing maintenance, not in its initial development. It is very, very important to write maintainable software.
Does the entire team understand the code base, or do knowledge islands exist?
Is the code thoroughly regression tested?
Can modifications to the project be done on time?
A maintainable software must adhere to these 3 principles:
Simple (low complexity)
Are there points in the system where changes can be made with (or without) program changes?
Can the database schema flex to accommodate change?
Does the system allow Inversion of Control (IoC)?
Can end users extend the system (scripts, user-defined fields, etc.)?
Can 3rd party developers leverage your system?
Bandwidth refers to the maximum amount of data that can be transmitted and received during a specific period. For instance, if a network has high bandwidth, this means a higher amount of data can be transmitted and received. Examples of bandwidth optimizations include compressing data and using content delivery networks (CDNs). For example, a video streaming service can use compression to reduce the size of video files and a CDN to distribute the content to users from servers located closer to them.
Throughput is the rate at which requests can be processed by the system. Examples of throughput optimizations include optimizing the system for speed and scalability. For example, a web application can use a distributed architecture and load balancing to handle high volumes of traffic and improve throughput.
Latency is the time it takes for a request to be processed by the system. Examples of latency optimizations include using caching, load balancing, and minimizing network hops. For example, a web application can use caching to store frequently accessed data in memory, and load balancing can distribute incoming requests across multiple servers to minimize latency.
Consistency ensures that data is consistent across different parts of the system. Examples of consistency mechanisms include distributed transactions and consensus protocols. For example, a distributed database can use a consensus protocol like Paxos or Raft to ensure that multiple nodes agree on the state of the data.
Redundancy ensures that the system can handle failures without downtime. Examples of redundancy mechanisms include using multiple servers or nodes that can take over if one fails. For example, a web application can use redundant servers and load balancing to handle increased load and failover mechanisms to switch to backup servers in the event of a failure.
Caching involves storing frequently accessed data in memory or on disk to improve performance. Examples of caching techniques include in-memory caching and CDNs. For example, a web application can use in-memory caching to store frequently accessed data in memory, reducing the number of times the data needs to be retrieved from the database.
Load balancing involves distributing incoming network traffic across multiple servers to ensure that no single server becomes overwhelmed. Examples of load-balancing techniques include round-robin and least connections. For example, a web application can use a load balancer to distribute incoming requests across multiple servers, improving performance and scalability.
Sharding partitions data across multiple servers to improve scalability. Examples of sharding algorithms include consistent hashing and range partitioning. For example, a distributed database can use consistent hashing to distribute data across multiple nodes.
Replication keeps multiple copies of data in sync to improve availability and reliability. Examples of replication mechanisms include master-slave replication and multi-master replication. For example, a database can use master-slave replication to keep a primary copy of the data and multiple slave copies that are kept in sync.
Fault tolerance refers to the ability of a system to continue functioning in the event of a failure or error. Examples of fault tolerance techniques include redundancy, failover, and replication. For example, a web application can use database replication to ensure that data is stored in multiple locations and can be accessed even if one of the servers fails.
Rate limiting is a technique used to control the rate at which requests are sent to a system or API. Examples of rate-limiting techniques include the token bucket and leaky bucket algorithms. For example, a web application can use rate limiting to limit the number of requests a user can make in a given period, preventing abuse and ensuring fair usage.
Network layers refer to the different layers of the network protocol stack, which are a way to divide the network communication process into smaller, more manageable parts. The most commonly referenced network model is the OSI model, which consists of seven layers: physical, data link, network, transport, session, presentation, and application. Each layer is responsible for a specific aspect of network communication, such as data transmission, error detection and correction, routing, and application-level protocols. Understanding the network layers is important for designing and troubleshooting network architectures, as it helps identify where issues may be occurring and which protocols are involved. For example, a web application developer may need to understand how HTTP messages are encapsulated in TCP packets at the transport layer and routed through the network layer using IP addresses.
Network protocols are a set of rules and standards that govern how devices communicate with each other over a network. Some common network protocols include TCP, UDP, IP, HTTP, FTP, SMTP, DNS, DHCP, ARP, and ICMP. Understanding network protocols is important for designing and troubleshooting network architectures and developing web-based applications. For example, developers may need to understand HTTP and HTTPS protocols to design RESTful APIs or secure web applications with SSL/TLS encryption.
Proxies are intermediaries between clients and servers that can be used to improve performance, security, and privacy. Examples of proxies include reverse proxies and forward proxies. For example, a web application can use a reverse proxy to handle incoming requests and distribute them to multiple servers, improving scalability and load balancing.
The CAP theorem is a concept in distributed systems that states that it is impossible to achieve all three of consistency, availability, and partition tolerance in a distributed system. Examples of distributed systems include databases and messaging systems. For example, a distributed database can choose to prioritize consistency and partition tolerance over availability in the event of a network partition.
Consistent hashing is a technique used to distribute data across multiple servers in a way that minimizes the number of keys that need to be reassigned when a server is added or removed. Examples of consistent hashing algorithms include the Rendezvous Hashing algorithm. For example, a web application can use consistent hashing to distribute user sessions across multiple servers, ensuring that sessions remain available even if one of the servers fails.
Designing a scalable and reliable system requires a comprehensive understanding of various concepts and techniques, from database design and caching strategies to network protocols and load-balancing techniques.
By mastering these key system design concepts, you can build systems that can handle large amounts of traffic, minimize downtime, and provide a seamless user experience. Whether you're designing a small application or a large-scale distributed system, these concepts and best practices can help you build better, more efficient, and more reliable systems.