What are the components of System Design?

System Design involves looking at the system's requirements, determining its assumptions and limitations, and defining its high-level structure and components. The primary elements of system design, including databases, load balancers, and messaging systems, will be discussed.

1. Load Balancer

Incoming requests or workloads are divided across several distinct resources or servers using a load balancer, a component of system design. When a system has many servers and has to divide requests evenly among them, or when a system receives a large number of requests and wants to split them up across multiple servers to prevent overloading any one of them, this can be useful.

Some common types of load balancers include:

• Layer 4 load balancers: It functions at the OSI model's Transport layer, allocating requests according to their TCP/UDP port numbers and source and destination IP addresses.
• Layer 7 load balancers: Distributing requests according to their content, including the URL or HTTP method type, it functions at the OSI model's application layer.
• Global load balancers are used in distributed systems to distribute requests among multiple servers located in different geographic regions.
• Application load balancers are specialized load balancers that are designed to work with specific types of applications or protocols, such as HTTP or HTTPS.

2. Caching

A technique for temporarily storing frequently requested data that speeds up its retrieval when needed again called caching. The main database or data source is less burdened when caching is included in system architecture, which enhances performance and efficiency.

Here’s how caching works and why it’s beneficial:

• Quick Access: When data is cached, it’s stored in a faster, temporary storage (like memory) instead of being fetched repeatedly from a slower storage source. This makes retrieving information faster.
• Reduces Database Load: The system can process more requests without experiencing any lag by keeping frequently used or popular data in the cache instead of constantly querying the main database.
• Improves User Experience: Faster data access means faster response times for users, which improves the overall experience on websites or apps.

3. Content Delivery Network (CDN)

A Content Delivery Network (CDN) is a network of servers spread across different regions that enables faster delivery of content to users, such as webpages, movies, and photos. A CDN is utilized in most system designs to increase the speed and dependability of content delivery to consumers, particularly when they are dispersed across many geographical locations.

How a CDN works?

when a user requests content (like a video or an image), instead of retrieving it from the main server, the request is handled by a nearby CDN server, which has a cached copy of the content. This reduces the distance the data has to travel, making it load faster for the user.

4. API Gateways

An API Gateway is like a central doorway or "traffic controller" for requests coming into a system. In system design, it acts as a single entry point for clients (such as apps or websites) to access multiple backend services in an organized and secure way.

Let's see how it works:

• Request Routing: A client submits a request to the API Gateway when it wants information or actions from several services. After forwarding the request to the appropriate services, the gateway compiles their answers and provides the client with a single, unified response.
• Simplifies Client Interaction: Without an API Gateway, clients might need to connect directly to each service individually, which can be complex. The API Gateway simplifies this by hiding the backend details from clients.
• Security and Monitoring: The gateway can handle security features like authentication (checking user identities) and authorization (checking what they’re allowed to do). It also helps monitor traffic, so you can track and log usage, spot issues, or prevent attacks.
• Load Management: In busy systems, the gateway can help balance and control the flow of requests to prevent overload, ensuring smoother performance.

5. Key-value stores

One kind of NoSQL database that is meant for storing data as a collection of key-value pairs is called a key-value store. Every piece of data is kept in a key-value store under a distinct key, and the data itself serves as the value. Since key-value stores allow for quick access to data by key, they are typically used to store data that is accessed frequently.

• Key-value stores come in a variety of forms, such as persistent key-value stores, which store data on disk or in a distributed file system for durability, and in-memory key-value stores, which store data in memory for quick access.
• Key-value stores are generally simpler to use and more scalable than other types of databases, such as RDBMS. However, they are not as well-suited for storing complex structured data that requires advanced querying capabilities.

6. Blob storage & Databases

Blob storage and database systems are two different types of storage systems that can be used to store and manage data.

Large volumes of unstructured data, including documents, photos, videos, and audio files, can be stored in blob storage, sometimes referred to as object storage. In general, blob storage systems are very scalable and capable of managing several requests at once. They are widely used to store frequently accessible material, such user-generated content or media files.

On the other hand, database systems are made to hold structured data that has been arranged in a particular manner. RDBMSs, NoSQL databases, and in-memory databases are among the several kinds of database systems. Database systems are typically used to store data that needs to be queried and accessed in a structured way, such as customer records or financial transactions.

7. Rate limiters

System design components known as rate limiters are used to restrict the rate at which a system or application responds to requests or carries out specific tasks. This can be helpful in a variety of situations, such as when a system has to guard against receiving too many requests or when a company want to stop a particular user or group of users from submitting excessive requests that can affect the system's performance.

Some common types of rate limiters include:

• Request rate limiters are used to limit the number of requests that a system or application processes within a given time period.
• User rate limiters are used to limit the rate at which a specific user or group of users can make requests to a system or application.
• Token bucket rate limiters are used to limit the rate at which requests are processed by a system by allowing a certain number of requests to be processed in each time period, with any excess requests being held in a "bucket" until the next time period.

8. Monitoring System

A monitoring system is a system design component that is used to collect, analyze, and report on various metrics and performance data related to a system or application. This can be useful in a number of different scenarios, such as when a system needs to track its own performance and availability, or when an organization needs to monitor the performance of its systems and applications to ensure that they are meeting their desired service levels.

Some common types of monitoring systems include:

• Network monitoring systems, are used to monitor the performance of a network and its various components, such as routers, switches, and servers.
• System monitoring systems, are used to monitor the performance of a computer system and its various components, such as the CPU, memory, and disk usage.
• Application monitoring systems, are used to monitor the performance of specific applications or services, such as web servers or databases.

9. Distributes system messaging queue

A distributed system messaging queue is a system that enables the exchange of messages between different nodes in a distributed system. Messaging queues allow nodes to communicate asynchronously, decoupling the sender and receiver of a message and enabling each node to operate independently.

There are several different types of messaging queues, including:

• Point-to-point queues: In this type of queue, messages are delivered to a specific recipient.
• Publish-subscribe queues: In this type of queue, messages are published to a topic and are delivered to all subscribers to that topic.
• Hybrid queues: Hybrid queues combine elements of both point-to-point and publish-subscribe queues, allowing messages to be delivered to specific recipients or to all subscribers to a topic.

Distributed system messaging queues can be used to enable communication between different components of a distributed system, such as microservices or distributed applications. They can also be used to decouple different parts of the system, allowing each component to operate independently and improving the system's resilience and scalability.

10. Distributed unique id generator

A distributed unique ID generator is a system that generates unique identifiers (IDs) that can be used to identify objects or entities in a distributed system. These IDs are typically used to uniquely identify items in a database or to provide a stable identifier for a resource that is accessed over the network.

There are several approaches to generating distributed unique IDs:

• Using a centralized service
• Using a distributed consensus algorithm
• Using timestamps

11. Distributes search

Distributed search refers to the practice of using multiple nodes or servers to index and search large datasets in a distributed system. Distributed search can be used to improve the performance and scalability of search operations, as it allows for parallel processing of search queries and the distribution of data across multiple nodes.

There are several approaches to implementing distributed search, including:

• Using a distributed search engine: A distributed search engine is a search platform that is designed to scale horizontally across multiple nodes. These systems typically use a distributed index to store the data being searched, allowing for parallel processing of search queries.
• Using a database with search capabilities: Some databases, such as MongoDB and Cassandra, have built-in search capabilities that allow for the indexing and searching of data stored in the database.
• Using a cloud-based search service: Cloud-based search services, such as Amazon Elastic search Service and Google Cloud Search, can be used to implement distributed search in a distributed system. These services are typically highly scalable and fault-tolerant.

12. Distributed logging services

Distributed logging refers to the practice of collecting, storing, and analyzing log data from multiple sources in a distributed system. This can be useful for tracking the health and performance of a distributed system, as well as for debugging issues that may arise.

There are several approaches to implementing distributed logging, including:

• Using a centralized logging service
• Using a distributed logging system
• Using a cloud-based logging service

13. Distributes task scheduler

A distributed task scheduler is a system that is responsible for scheduling and executing tasks in a distributed system. A task scheduler can be used to automate the execution of tasks at regular intervals, on a specific schedule, or in response to certain events.

There are several approaches to implementing a distributed task scheduler, including:

• Using a standalone task scheduler: A standalone task scheduler is a separate system that is responsible for scheduling and executing tasks in a distributed system. This approach can be simple to implement and allows for flexibility in terms of the types of tasks that can be scheduled.
• Using a built-in task scheduler: Some distributed systems, such as container orchestration platforms or cloud-based serverless platforms, have built-in task schedulers that can be used to schedule tasks within the system.
• Using a cloud-based task scheduler: Cloud-based task schedulers, such as Amazon Simple Notification Service (SNS) or Google Cloud Scheduler, can be used to schedule tasks in a distributed system.

It is important to choose a distributed task scheduler that meets the specific requirements of the system, taking into account factors such as scalability, performance, and cost.