Top Most Asked System Design Interview Questions

System Design is defined as a process of creating an architecture for different components, interfaces, and modules of the system and providing corresponding data helpful in implementing such elements in systems.

1. Why is it better to use horizontal scaling than vertical scaling?

• Horizontal scaling is a cost-effective strategy, utilizing multiple affordable machines, as opposed to the expensive upgrades involved in vertical scaling.
• Improved fault tolerance is a key advantage of horizontal scaling, with multiple machines able to handle loads and avoid a single point of failure.
• Horizontal scaling allows for dynamic resource adjustments based on demand, offering flexibility, while vertical scaling may require downtime for changes.
• Efficient resource utilization is achieved through horizontal scaling by distributing workloads across machines, avoiding potential over-provisioning seen in vertical scaling.
• Achieving higher scalability is more straightforward with horizontal scaling by adding more machines, in contrast to vertical scaling’s inherent hardware limitations.

2. What is sharding, and how does it improve database scalability?

• Sharding refers to a process of splitting data and distributing them among various machines, or different databases.
• The independent operation of each shard facilitates parallel processing for optimal efficiency.
• It is good to note that sharding improves the database’s scalability since it distributes the data demand in such a manner that the system can comfortably accommodate many more transactions.

3. What is CAP theorem?

The three components of the CAP theorem are Consistency, Availability, and Partition Tolerance. 

• Consistency:
  • This means that all nodes in a distributed system have the same data at the same time. In simpler terms, if you read data from one part of the system, you should get the most recent write from another part of the system.
• Availability:
  • This implies that every request to the system receives a response without a guarantee that it contains the most recent version of the data. In other words, the system is always available for reads and writes, even if the data might be slightly out of date.
• Partition Tolerance:
  • This refers to the system’s ability to continue functioning even if communication between nodes is lost or delayed. Partitions can occur due to network issues, and a partition-tolerant system can still operate despite these communication problems.

The CAP theorem states that in a distributed system, you can only achieve two out of the three: Consistency, Availability, and Partition Tolerance. This means that if you prioritize Consistency, you may sacrifice Availability or Partition Tolerance, and vice versa.

4. What do you understand by load balancer? Why is it important in system design?

A load balancer works as a “traffic cop” sitting in front of your server and routing client requests across all servers. It simply distributes the set of requested operations (database write requests, cache queries) effectively across multiple servers and ensures that no single server bears too many requests that lead to degrading the overall performance of the application.

• Preventing Overload: By spreading the traffic, no single server gets too busy, avoiding slowdowns or crashes, especially during high-demand times.
• Ensuring Backup: If one server has a problem, the load balancer redirects traffic to other healthy servers, ensuring that the website or app stays up and running smoothly.

5. What are the various Consistency patterns available in system design?

• Weak Consistency:
  • After writing data, reading it may or may not immediately reflect the new information. This is often used in real-time applications multiplayer games where immediate consistency may not be critical. For instance, in a phone call, if there’s a brief loss of network, information about the conversation during that time may be lost.
• Eventual Consistency:
  • Following a data write, it takes some time, typically within milliseconds, for all reads to eventually show the latest data. This asynchronous replication is common in systems like DNS, providing high availability. It ensures that, given enough time, all replicas converge to the same data state.
• Strong Consistency:
  • After writing data, subsequent reads will immediately show the latest information. This is achieved through synchronous replication and is commonly found in systems like relational databases (RDBMS) and file systems. Strong consistency is essential in scenarios where precise, up-to-date data is crucial, especially in systems dealing with transactions.

6. When would you use cache layer of a system?

Answer: Caching layer helps in cases of important read while reading about stale data. This can help to offload from the underlying data storage, minimize response time for frequently read data, and generally enhance system responsiveness.

7. A reverse proxy in a web architecture means what?

• Reverse Proxy is an intermediate in web architecture that stands between different clients, and physical server resources.
• It is like an interface performing functions of load balancing, SSL terminating, caching among others. Load balancing enables incoming requests to be equally distributed on backend servers for better efficiency.
• Through SSL termination, secure HTTPS connections are decrypted from arriving at the application servers and this relieves the burden of these servers hence the high processing performance they offer.
• Cashing improves security, scalability, and performance by storing static content and decreasing the burden of application servers.

8. Outline the role played by a CDN in Web Architecture.

• CDN is a broad network of servers that are dispersed across areas and provide web content at different areas based on the users’ location.
• Most importantly, it helps reduce delays during content delivery.
• Through making use of servers that are closer to the end-users, a CDN reduces this transmission time and thus ultimately improves the user’s experience.
• Also, CDNs aid in scalability by effectively spreading out the load thus decreasing workload of a particular server and improving content delivery for several regions accordingly.

9. Describe the concepts of RESTful API principles.

• The principles followed by RESTful APIs include statelessness, uniform interface, and resource-oriented architecture.
• In statelessness, in every request of a client to a server, there are all needed information for understanding this request in order to deliver it properly for promotion of simplicity and scalability.
• Engineer is a tool dedicated to solving problems by developing strategies and implementing solutions. Uniform Interface dictates how a resource is called for and used through the conventional http methods like “GET”, “POST”, “PUT” and delete.
• Resource-based architecture centers around resource (entity or service), indicated with its unique URI, focusing on simplified and scalable design.
• The use of standard HTTP protocols in creating RESTful APIs means that they can easily be implemented since they are compatible with many applications.

10. How does a message broker operate within a distributed environment?

• Message brokering is a technique used in distributive system whereby message transmittion between distributed components is coordinated by a message broker.
• It made producers and consumers free, thus enabling asynchronous communication and increasing overall system scalability.

11. Why should we use NoSQL database?

Answer: Many considerations go in picking a NoSQL database that suits some special use case. Here are key reasons why opting for a NoSQL database might be advantageous:

• Schema Flexibility:
  • With noSQL database we will be using a flexible scheme since you do not get a preset structure to store and organise this.
• Scalability:
• • They can add more servers on the database cluster in order to handle the increasing volume of data and traffic.
  • The scalability is important because there are applications with constantly growing data sets and they must be efficient in order to remain effective.
• Variety of Data Models:
  • Not-only SQL databases support multiple data models such as documents, key value, column family and also graph databases.
• Performance and Speed:
  • The optimization of noSQL databases for certain use cases allows for improved speed of data operations.
  • NoSQL is more reliable because of properties such as in-memory processing, efficient indexing, and distributed architectures.
• Distributed and Fault-Tolerant:
  • They are able to work well between various node and also take care of failures smoothly.
  • Such distribution structure improves system reliability because it allows for continuous operations despite hardware and network problems.

12. What does the Singleton Design Pattern aim at?

• The singleton pattern is one of the simplest design patterns.
• Sometimes we need to have only one instance of our class for example a single DB connection shared by multiple objects as creating a separate DB connection for every object may be costly.
• Similarly, there can be a single configuration manager or error manager in an application that handles all problems instead of creating multiple managers.
• The singleton pattern is a design pattern that restricts the instantiation of a class to one object.

13. Discuss what is consistent hashing ?

• One of the data distribution techniques in a distributed system is known as consistent hashing.
• This is done to ensure that a minuscule adjustment within the system does not result in an entirely re-mapping of keys to nodes.
• In cases such as distributed caching and sharding, consistent hashing is vital in ensuring the uniform distribution of data.

14. Why is the two-phase commit protocol important?

• Two-phase commit provides an atomicity transaction protocol used between several nodes.
• The system comprises of a coordinator and a participant node. A coordinator asks all participants whether they could comply in the first stage.
• The second phase involves coordination between all the players and agreement that each one will commit on their part. The coordinator orders all participants to stop if any participant disagrees or refuses to respond.
• The two-phase commits guarantee that either everything commits, or nothing commits, making it consistent.

15. What are vector clocks in the context of the distributed system?

• Vector clock is a way of tracing causality in a distributed system.
• Every node has a vector which characterizes how it knows the sequence of events.
• The vector clock gets updated when an event takes place.
• These vector clocks allow for partial order on events to be defined and conflict resolutions for distributed systems that are done based upon causality and consistency.

16. Explain the function of consensus algorithms in distributed systems.

• Agreement about the status of individual nodes is achieved with the help of consensus algorithms in a distributed system. By ensuring that nodes come to agreement even in cases of failure, they serve this purpose.
• The distributed nodes can achieve a common understanding regarding a particular data unit/sequence through popular consensus algorithms like Paxos and Raft, thus increasing reliability and system capacity to deal with failures.

17. How does the “graceful degradation” principle impacts on system design?

• Graceful degradation refers to a design principle to ensure that system continues operating although it may have lost some its functions or failed to perform up to standards due to partial failure.
• When a system crashes, it leads to disruption and makes service provision difficult, but when it cracks, it is still serviceable and provides minimal disruption in case the system degenerates.

18. What are the bottlenecks in system design?

Bottlenecks in system design refer to points or components within a system that limit the overall performance or capacity. Identifying and addressing bottlenecks is crucial for optimizing system efficiency.

Common bottlenecks include:

• CPU Bottleneck: The central processing unit (CPU) becomes a limiting factor when it cannot process data fast enough to meet the demands of the system.
• Memory Bottleneck: Insufficient RAM or slow memory access can slow down the system when it cannot effectively handle data storage and retrieval.
• Storage Bottleneck: Slow read/write speeds or limited storage capacity can impede the performance of a system, especially in data-intensive applications.
• Database Bottleneck: Slow database queries, inefficient indexing, or database contention can impact the performance of applications heavily reliant on databases.

Conclusion

In this article, we talked about the questions you might get in a System Design interview. To do well in these interviews, it’s important to explain your ideas clearly when designing a system. For example, if you decide to use a certain type of database, like NoSQL, you should be able to say why you picked it instead of another kind, like SQL. Understanding the differences between these databases is key. Basically, every choice you make should have a good reason behind it, making your answers in interviews strong and logical. This will really help you stand out and do well in your interviews!