Grokking System Design Fundamentals
Last updated
Last updated
To excel in system design, one of the most crucial aspects is to develop a deep understanding of fundamental system design concepts such as Load Balancing, Caching, Partitioning, Replication, Databases, and Proxies.
This course will go through key concepts that can make a significant difference in your ability to tackle system design problems. These concepts range from understanding the intricacies of API Gateway and mastering Load Balancing techniques to grasping the importance of CDNs and appreciating the role of Caching in modern distributed systems. By the end of this course, you’ll have a comprehensive understanding of these essential ideas and the confidence to apply them in your next interview.
When discussing system design interviews, it's important to note that they are unstructured by nature. During the interview, it can be challenging to maintain focus and ensure all crucial elements of the design are addressed. This course should guide you in answering any system design interview question by making sure that you have not missed discussing any critical aspect.
Let's get started.
ChatGPT:
Load balancing is a crucial component of System Design, as it helps distribute incoming requests and traffic evenly across multiple servers. The main goal of load balancing is to ensure high availability, reliability, and performance by avoiding overloading a single server and avoiding downtime.
Typically a load balancer sits between the client and the server accepting incoming network and application traffic and distributing the traffic across multiple backend servers using various algorithms. By balancing application requests across multiple servers, a load balancer reduces the load on individual servers and prevents any one server from becoming a single point of failure, thus improving overall application availability and responsiveness.
To utilize full scalability and redundancy, we can try to balance the load at each layer of the system. We can add LBs at three places:
Between the user and the web server
Between web servers and an internal platform layer, like application servers or cache servers
Between internal platform layer and database.
Load Balancer: A device or software that distributes network traffic across multiple servers based on predefined rules or algorithms.
Backend Servers: The servers that receive and process requests forwarded by the load balancer. Also referred to as the server pool or server farm.
Load Balancing Algorithm: The method used by the load balancer to determine how to distribute incoming traffic among the backend servers.
Health Checks: Periodic tests performed by the load balancer to determine the availability and performance of backend servers. Unhealthy servers are removed from the server pool until they recover.
Session Persistence: A technique used to ensure that subsequent requests from the same client are directed to the same backend server, maintaining session state and providing a consistent user experience.
SSL/TLS Termination: The process of decrypting SSL/TLS-encrypted traffic at the load balancer level, offloading the decryption burden from backend servers and allowing for centralized SSL/TLS management.
Load balancers work by distributing incoming network traffic across multiple servers or resources to ensure efficient utilization of computing resources and prevent overload. Here are the general steps that a load balancer follows to distribute traffic:
The load balancer receives a request from a client or user.
The load balancer evaluates the incoming request and determines which server or resource should handle the request. This is done based on a predefined load-balancing algorithm that takes into account factors such as server capacity, server response time, number of active connections, and geographic location.
The load balancer forwards the incoming traffic to the selected server or resource.
The server or resource processes the request and sends a response back to the load balancer.
The load balancer receives the response from the server or resource and sends it to the client or user who made the request.
ChatGPT:
Load balancing is a technique used to distribute workloads evenly across multiple computing resources, such as servers, network links, or other devices, in order to optimize resource utilization, minimize response time, and maximize throughput. This technique helps ensure that no single resource is overwhelmed, thus maintaining a high level of performance and reliability. Here are some common uses of load balancing:
Load balancing can distribute incoming web traffic among multiple servers, reducing the load on individual servers and ensuring faster response times for end users.
Example: An e-commerce website experiences a sudden surge in traffic during a holiday sale. A load balancer distributes incoming requests among multiple web servers, ensuring that each server handles a manageable number of requests, resulting in faster page load times for users
By distributing the workload among multiple servers, load balancing helps prevent single points of failure. If one server fails or experiences an issue, the load balancer can redirect traffic to other available servers, maintaining uptime and minimizing service disruptions.
Example: A banking application relies on several servers to handle user transactions. The load balancer monitors the health of each server and, in the event of a server failure, redirects traffic to the remaining healthy servers, minimizing downtime and maintaining user access to the application.
Load balancing allows organizations to easily scale their infrastructure as traffic and demand increase. Additional servers can be added to the load balancing pool to accommodate increased demand, without the need for significant infrastructure changes.
Example: A video streaming platform sees a steady increase in users as it gains popularity. To handle the growing demand, the platform adds new servers to the load balancing pool, allowing it to scale seamlessly without overloading existing infrastructure.
Load balancing can be used to maintain redundant copies of data and services across multiple servers, reducing the risk of data loss or service outages due to hardware failure or other issues.
Example: An online file storage service uses load balancing to maintain multiple copies of user data across different servers. If one server experiences a hardware failure, users can still access their data from the redundant copies stored on other servers.
Load balancing can help optimize network traffic by distributing it across multiple paths or links, reducing congestion and improving overall network performance.
Example: A large organization has multiple internet connections to handle its network traffic. A load balancer distributes the incoming and outgoing traffic across these connections, reducing congestion and improving overall network performance.
For global organizations, load balancing can be used to distribute traffic across data centers in different geographic locations. This ensures that users are directed to the nearest or best-performing data center, reducing latency and improving user experience.
Example: A multinational company has data centers in North America, Europe, and Asia. A load balancer directs users to the nearest data center based on their geographic location, reducing latency and improving the user experience.
Load balancing can be used to distribute requests for specific applications or services among dedicated servers or resources, ensuring that each application or service receives the necessary resources to perform optimally.
Example: An enterprise uses a suite of applications, including email, file storage, and collaboration tools. A load balancer assigns dedicated resources to each application, ensuring that each service performs optimally without affecting the performance of other applications.
Load balancers can help protect against distributed denial-of-service (DDoS) attacks by distributing incoming traffic across multiple servers, making it more difficult for attackers to overwhelm a single target.
Example: A news website faces a distributed denial-of-service (DDoS) attack, with a large number of malicious requests targeting its servers. The load balancer distributes the traffic among multiple servers, making it more difficult for the attackers to overwhelm a single target and mitigating the impact of the attack.
By distributing workloads across available resources more efficiently, load balancing can help organizations save money on hardware and infrastructure costs, as well as reduce energy consumption.
Example: A small business utilizes cloud-based infrastructure for its web applications. By using load balancing to optimize resource usage, the business can minimize the number of servers needed, resulting in lower infrastructure and energy costs.
Some load balancers can cache static content, such as images and videos. This cached content is then served directly from the load balancer, reducing the demand on the servers and providing faster response times for users.
Example: In a streaming service like Netflix, users access a wide variety of content like TV shows, movies, etc. Now, consider a very popular TV show that millions of users might want to watch. If each request for this show was routed to the servers, it would result in a huge load on the servers, potentially slowing down response times or even leading to server failure. By caching such popular content on the load balancer, the streaming service can drastically reduce the load on its main servers.
ChatGPT:
A load balancing algorithm is a method used by a load balancer to distribute incoming traffic and requests among multiple servers or resources. The primary purpose of a load balancing algorithm is to ensure efficient utilization of available resources, improve overall system performance, and maintain high availability and reliability.
Load balancing algorithms help to prevent any single server or resource from becoming overwhelmed, which could lead to performance degradation or failure. By distributing the workload, load balancing algorithms can optimize response times, maximize throughput, and enhance user experience. These algorithms can consider factors such as server capacity, active connections, response times, and server health, among others, to make informed decisions on how to best distribute incoming requests.
Here are the most famous load balancing algorithms:
This algorithm distributes incoming requests to servers in a cyclic order. It assigns a request to the first server, then moves to the second, third, and so on, and after reaching the last server, it starts again at the first.
Pros:
Ensures an equal distribution of requests among the servers, as each server gets a turn in a fixed order.
Easy to implement and understand.
Works well when servers have similar capacities.
Cons:
May not perform optimally when servers have different capacities or varying workloads.
No consideration for server health or response time.
Round Robin is predictable in its request distribution pattern, which could potentially be exploited by attackers who can observe traffic patterns and might find vulnerabilities in specific servers by predicting which server will handle their requests.
Example: A website with three web servers receives requests in the order A, B, C, A, B, C, and so on, distributing the load evenly among the servers.
The Least Connections algorithm directs incoming requests to the server with the lowest number of active connections. This approach accounts for the varying workloads of servers.
Pros:
Adapts to differing server capacities and workloads.
Balances load more effectively when dealing with requests that take a variable amount of time to process.
Cons:
Requires tracking the number of active connections for each server, which can increase complexity.
May not factor in server response time or health.
Example: An email service receives requests from users. The load balancer directs new requests to the server with the fewest active connections, ensuring that servers with heavier workloads are not overwhelmed.
The Weighted Round Robin algorithm is an extension of the Round Robin algorithm that assigns different weights to servers based on their capacities. The load balancer distributes requests proportionally to these weights.
Pros:
Accounts for different server capacities, balancing load more effectively.
Simple to understand and implement.
Cons:
Weights must be assigned and maintained manually.
No consideration for server health or response time.
Example: A content delivery network has three servers with varying capacities. The load balancer assigns weights of 3, 2, and 1 to these servers, respectively, distributing requests in a 3:2:1 ratio.
The Weighted Least Connections algorithm combines the Least Connections and Weighted Round Robin algorithms. It directs incoming requests to the server with the lowest ratio of active connections to assigned weight.
Pros:
Balances load effectively, accounting for both server capacities and active connections.
Adapts to varying server workloads and capacities.
Cons:
Requires tracking active connections and maintaining server weights.
May not factor in server response time or health.
Example: An e-commerce website uses three servers with different capacities and assigned weights. The load balancer directs new requests to the server with the lowest ratio of active connections to weight, ensuring an efficient distribution of load.
The IP Hash algorithm determines the server to which a request should be sent based on the source and/or destination IP address. This method maintains session persistence, ensuring that requests from a specific user are directed to the same server.
Pros:
Maintains session persistence, which can be useful for applications requiring a continuous connection with a specific server.
Can distribute load evenly when using a well-designed hash function.
Cons:
May not balance load effectively when dealing with a small number of clients with many requests.
No consideration for server health, response time, or varying capacities.
Example: An online multiplayer game uses the IP Hash algorithm to ensure that all requests from a specific player are directed to the same server, maintaining a continuous connection for a smooth gaming experience.
The Least Response Time algorithm directs incoming requests to the server with the lowest response time and the fewest active connections. This method helps to optimize the user experience by prioritizing faster-performing servers.
Pros:
Accounts for server response times, improving user experience.
Considers both active connections and response times, providing effective load balancing.
Cons:
Requires monitoring and tracking server response times and active connections, adding complexity.
May not factor in server health or varying capacities.
Example: A video streaming service uses the Least Response Time algorithm to direct users to the server with the fastest response time, ensuring that videos start quickly and minimize buffering times.
The Custom Load algorithm allows administrators to create their own load balancing algorithm based on specific requirements or conditions. This can include factors such as server health, location, capacity, and more.
Pros:
Highly customizable, allowing for tailored load balancing to suit specific use cases.
Can consider multiple factors, including server health, response times, and capacity.
Cons:
Requires custom development and maintenance, which can be time-consuming and complex.
May require extensive testing to ensure optimal performance.
Example: An organization with multiple data centers around the world develops a custom load balancing algorithm that factors in server health, capacity, and geographic location. This ensures that users are directed to the nearest healthy server with sufficient capacity, optimizing user experience and resource utilization.
The Random algorithm directs incoming requests to a randomly selected server from the available pool. This method can be useful when all servers have similar capacities and no session persistence is required.
Pros:
Simple to implement and understand.
Can provide effective load distribution when servers have similar capacities.
Security systems that rely on detecting anomalies or implementing rate limiting (e.g., to mitigate DDoS attacks) might find it slightly more challenging to identify malicious patterns if a Random algorithm is used, due to the inherent unpredictability in request distribution. This could potentially dilute the visibility of attack patterns.
Cons:
No consideration for server health, response times, or varying capacities.
May not be suitable for applications requiring session persistence.
Example: A static content delivery network uses the Random algorithm to distribute requests for images, JavaScript files, and CSS stylesheets among multiple servers. This ensures an even distribution of load and reduces the chances of overloading any single server.
The Least Bandwidth algorithm directs incoming requests to the server currently utilizing the least amount of bandwidth. This approach helps to ensure that servers are not overwhelmed by network traffic.
Pros:
Considers network bandwidth usage, which can be helpful in managing network resources.
Can provide effective load balancing when servers have varying bandwidth capacities.
Cons:
Requires monitoring and tracking server bandwidth usage, adding complexity.
May not factor in server health, response times, or active connections.
Example: A file hosting service uses the Least Bandwidth algorithm to direct users to the server with the lowest bandwidth usage, ensuring that servers with high traffic are not overwhelmed and that file downloads are fast and reliable.
ChatGPT:
A load balancing type refers to the method or approach used to distribute incoming network traffic across multiple servers or resources to ensure efficient utilization, improve overall system performance, and maintain high availability and reliability. Different load balancing types are designed to meet various requirements and can be implemented using hardware, software, or cloud-based solutions.
Each load balancing type has its own set of advantages and disadvantages, making it suitable for specific scenarios and use cases. Some common load balancing types include hardware load balancing, software load balancing, cloud-based load balancing, DNS load balancing, and Layer 4 and Layer 7 load balancing. By understanding the different load balancing types and their characteristics, you can select the most appropriate solution for your specific needs and infrastructure.
Hardware load balancers are physical devices designed specifically for load balancing tasks. They use specialized hardware components, such as Application-Specific Integrated Circuits (ASICs) or Field-Programmable Gate Arrays (FPGAs), to efficiently distribute network traffic.
Pros:
High performance and throughput, as they are optimized for load balancing tasks.
Often include built-in features for network security, monitoring, and management.
Can handle large volumes of traffic and multiple protocols.
Cons:
Can be expensive, especially for high-performance models.
May require specialized knowledge to configure and maintain.
Limited scalability, as adding capacity may require purchasing additional hardware.
Example: A large e-commerce company uses a hardware load balancer to distribute incoming web traffic among multiple web servers, ensuring fast response times and a smooth shopping experience for customers.
Software load balancers are applications that run on general-purpose servers or virtual machines. They use software algorithms to distribute incoming traffic among multiple servers or resources.
Pros:
Generally more affordable than hardware load balancers.
Can be easily scaled by adding more resources or upgrading the underlying hardware.
Provides flexibility, as they can be deployed on a variety of platforms and environments, including cloud-based infrastructure.
Cons:
May have lower performance compared to hardware load balancers, especially under heavy loads.
Can consume resources on the host system, potentially affecting other applications or services.
May require ongoing software updates and maintenance.
Example: A startup with a growing user base deploys a software load balancer on a cloud-based virtual machine, distributing incoming requests among multiple application servers to handle increased traffic.
Cloud-based load balancers are provided as a service by cloud providers. They offer load balancing capabilities as part of their infrastructure, allowing users to easily distribute traffic among resources within the cloud environment.
Pros:
Highly scalable, as they can easily accommodate changes in traffic and resource demands.
Simplified management, as the cloud provider takes care of maintenance, updates, and security.
Can be more cost-effective, as users only pay for the resources they use.
Cons:
Reliance on the cloud provider for performance, reliability, and security.
May have less control over configuration and customization compared to self-managed solutions.
Potential vendor lock-in, as switching to another cloud provider or platform may require significant changes.
Example: A mobile app developer uses a cloud-based load balancer provided by their cloud provider to distribute incoming API requests among multiple backend servers, ensuring smooth app performance and quick response times.
DNS (Domain Name System) load balancing relies on the DNS infrastructure to distribute incoming traffic among multiple servers or resources. It works by resolving a domain name to multiple IP addresses, effectively directing clients to different servers based on various policies.
Pros:
Relatively simple to implement, as it doesn't require specialized hardware or software.
Provides basic load balancing and failover capabilities.
Can distribute traffic across geographically distributed servers, improving performance for users in different regions.
Cons:
Limited to DNS resolution time, which can be slow to update when compared to other load balancing techniques.
No consideration for server health, response time, or resource utilization.
May not be suitable for applications requiring session persistence or fine-grained load distribution.
Example: A content delivery network (CDN) uses DNS load balancing to direct users to the closest edge server based on their geographical location, ensuring faster content delivery and reduced latency.
Global Server Load Balancing (GSLB) is a technique used to distribute traffic across geographically dispersed data centers. It combines DNS load balancing with health checks and other advanced features to provide a more intelligent and efficient traffic distribution method.
Pros:
Provides load balancing and failover capabilities across multiple data centers or geographic locations.
Can improve performance and reduce latency for users by directing them to the closest or best-performing data center.
Supports advanced features, such as server health checks, session persistence, and custom routing policies.
Cons:
Can be more complex to set up and manage than other load balancing techniques.
May require specialized hardware or software, increasing costs.
Can be subject to the limitations of DNS, such as slow updates and caching issues.
Example: A multinational corporation uses GSLB to distribute incoming requests for its web applications among several data centers around the world, ensuring high availability and optimal performance for users in different regions.
Hybrid load balancing combines the features and capabilities of multiple load balancing techniques to achieve the best possible performance, scalability, and reliability. It typically involves a mix of hardware, software, and cloud-based solutions to provide the most effective and flexible load balancing strategy for a given scenario.
Pros:
Offers a high degree of flexibility, as it can be tailored to specific requirements and infrastructure.
Can provide the best combination of performance, scalability, and reliability by leveraging the strengths of different load balancing techniques.
Allows organizations to adapt and evolve their load balancing strategy as their needs change over time.
Cons:
Can be more complex to set up, configure, and manage than single-technique solutions.
May require a higher level of expertise and understanding of multiple load balancing techniques.
Potentially higher costs, as it may involve a combination of hardware, software, and cloud-based services.
Example: A large-scale online streaming platform uses a hybrid load balancing strategy, combining hardware load balancers in their data centers for high-performance traffic distribution, cloud-based load balancers for scalable content delivery, and DNS load balancing for global traffic management. This approach ensures optimal performance, scalability, and reliability for their millions of users worldwide.
Layer 4 load balancing, also known as transport layer load balancing, operates at the transport layer of the OSI model (the fourth layer). It distributes incoming traffic based on information from the TCP or UDP header, such as source and destination IP addresses and port numbers.
Pros:
Fast and efficient, as it makes decisions based on limited information from the transport layer.
Can handle a wide variety of protocols and traffic types.
Relatively simple to implement and manage.
Cons:
Lacks awareness of application-level information, which may limit its effectiveness in some scenarios.
No consideration for server health, response time, or resource utilization.
May not be suitable for applications requiring session persistence or fine-grained load distribution.
Example: An online gaming platform uses Layer 4 load balancing to distribute game server traffic based on IP addresses and port numbers, ensuring that players are evenly distributed among available game servers for smooth gameplay.
Layer 7 load balancing, also known as application layer load balancing, operates at the application layer of the OSI model (the seventh layer). It takes into account application-specific information, such as HTTP headers, cookies, and URL paths, to make more informed decisions about how to distribute incoming traffic.
Pros:
Provides more intelligent and fine-grained load balancing, as it considers application-level information.
Can support advanced features, such as session persistence, content-based routing, and SSL offloading.
Can be tailored to specific application requirements and protocols.
Cons:
Can be slower and more resource-intensive compared to Layer 4 load balancing, as it requires deeper inspection of incoming traffic.
May require specialized software or hardware to handle application-level traffic inspection and processing.
Potentially more complex to set up and manage compared to other load balancing techniques.
Example: A web application with multiple microservices uses Layer 7 load balancing to route incoming API requests based on the URL path, ensuring that each microservice receives only the requests it is responsible for handling.
