Preparing for Data Engineering Interviews? Practice with these distributed systems interview questions and see if you got the right answer.
If you’re hoping to land a job in software engineering with almost any major tech company, you’ll be expected to know how distributed systems work. More to the point, you’ll be expected to be able to show off that knowledge in an interview setting – which means you’ll have to be ready to answer some common interview questions about distributed systems. Here are just a few of the questions you could face:
And that’s just the beginning! Distributed systems are a broad, wide-ranging topic, so you’ll need to prepare thoroughly if you want to nail your interview.
On this page, we’ve prepared a list of common interview questions about distributed systems. We’ve also supplied some example answers, so you can get a head start on your preparation. Read on, and you’ll be an expert in the field in no time!
If you want the best chance of knocking your interview out of the park, you’ll need to be ready to answer the most common distributed systems interview questions. Some of the questions you might be asked include:
A distributed system has components spread out or ‘distributed’ across several devices. All those devices are networked together, often via the internet and more specifically via the Cloud. This means that the system’s work can be coordinated between the devices, increasing efficiency and reducing the risk of system failure.
From a user perspective, a distributed system looks just like a centralized system – so a distributed system works without making anything inaccessible to a regular user.
While distributed systems have historically taken a lot of effort to build and maintain, today’s distributed systems can be created fairly easily using the internet – specifically the Cloud. All of the devices networked via the Cloud take on part of the whole task that the user is trying to accomplish. While the task might have taken a single computer a full day to complete, distributing the work across multiple devices means it can be carried out in a matter of minutes.
Distributed systems are extremely common in day-to-day usage, and many people barely notice that they’re there! Some examples of distributed systems include:
The key advantages of distributed systems are their scalability, fault tolerance, and reliability. They can easily be expanded to meet increasing demands – you only have to add more servers or nodes to the network to increase its capacity. What’s more, if one device on the network fails, the other devices can continue to operate; in a centralized system, this could be enough to cause a system failure.
Distributed systems are therefore ideal for use in situations where demand for system use could grow substantially over time. They are also suitable in situations where system failure simply isn’t an option, and a degree of fault tolerance is necessary.
Because distributed systems are so much more complex than centralized systems, they do come with challenges. For example, if a system is designed poorly, a single node failure can cause the whole system to go under – their fault tolerance has to be built in from the beginning, and it isn’t foolproof. Scaling a system effectively can also be challenging; you need to make sure that the system is built to accommodate the levels of scalability that you will need, to maximize efficiency as your organization grows.
It’s also important to remember that distributed systems, due to their complexity, can be hard for laypeople to manage and understand. When designing a distributed system, it’s critical to ensure that its eventual users have the support and documentation they need to operate it effectively. Name some different types of distributed systems. Client-server systems are the most simple, and perhaps the most common; they involve a large number of networked computers interacting with a central server to carry out a common goal. Peer-to-peer networks distribute workloads between hundreds or thousands of computers, all running the same software. And web-based distributed systems can create dozens of Cloud-based virtual server instances to handle tasks as needed, then delete them when their work is completed.
Distributed systems deal with the management of distributed resources across networked computers. On the other hand, distributed computing involves writing and creating software applications that can run on a distributed system. Though there is some overlap between the two fields, as distributed computing has to work with distributed systems, each one has a different purpose.
Distributed deployments can vary wildly in scale, and are usually categorized accordingly. They are categorized based on factors including the amount of data they will consume, the size of their computer network, and the number of users accessing the system. The most common categories for distributed deployments are departmental, small enterprise, medium enterprise, and large enterprise.
The CAP theorem for distributed computing was initially proposed by Eric Brewer, and is now considered to be a truism of distributed system design. It says that no system can guarantee more than two out of three security features – consistency, availability, and partition tolerance.
Because distributed systems work across a number of devices, it’s important to ensure that all devices on the network are adequately protected from vulnerabilities or attacks. A single weak point in the system could enable an attacker to access the whole system. As a result, any organization maintaining a distributed system needs to make sure that all users are familiar with security protocols, and that the system’s security measures are always kept up to date.
A single point of failure is any element within a system that can cause the entire system to fail. A properly-designed distributed system will have a more limited risk of a single point of failure, due to the distributed nature of its nodes. What are patterns in a distributed system? Patterns are solutions to common problems that represent the best practices available in the moment. Although they don’t provide completed code, they can be reused over and over again, and they can offer guidance on implementation or problem-solving.
Patterns are often used to describe and design distributed systems. Most distributed system designers understand what is meant by command and query responsibility segregation (CQRS), for instance – that’s a pattern. Entire systems can be built from unique combinations of patterns, depending on the needs of the user and the intended aims of the system.
Distributed tracing is a method for monitoring applications commonly used on distributed systems. It’s a form of distributed computing in its own right, as it tracks multiple applications and processes across multiple nodes and computing environments. It’s designed to help uncover any problems within the system, as well as to monitor applications running on large and complex distributed systems in ways that would otherwise be impossible.
While a range of access control approaches are viable in distributed systems, one of the most promising mechanisms for access control is attribute-based access control (ABAC). This access control mechanism controls access to objects and processes using rule involving user information, the requested action, and the request environment. This enables access control to take into account all elements of the distributed system, making full use of the information available.
SN architecture is a distributed system in which each update request is answered by a single node in a computer cluster. The nodes involved do not share memory or storage – hence ‘shared-nothing’ – and the architecture is designed to eradicate any contention between different nodes. It eliminates the risk of a single point of failure, because a failure in an individual node cannot cascade into failures in other nodes.
Load balancing is the process of distributing network traffic across multiple servers, ensuring that no one server has to support too much user demand. This improves application responsiveness and availability. These are both crucial for user experience, as well as for the functionality of a distributed system.
Round robin load balancing distributes client requests across a group of servers by forwarding a client request to each server in turn. The round robin algorithm then instructs the load balancer to go back to the top of the list and repeat it again. This means that requests are distributed evenly between servers, avoiding undue strain on any one server and improving user experience.
Horizontal scaling involves adding additional nodes or machines to your system infrastructure to meet increasing demands. If your distributed system no longer has the capacity to handle traffic or demand, adding a server may solve the problem. However, too much horizontal scaling can make your system too complex to manage, and can increase the costs of maintaining your system.
Vertical scaling is the process of adding more power to your existing system infrastructure to meet an increase in demand. This might mean upgrading existing machines, or replacing them with new ones. While vertical scaling is more cost-effective, it can require extensive downtime – and there is usually a limit to how much a machine can be upgraded, so it may not always be the most effective route.
Asynchronous programming is a subtype of parallel programming, and it allows a unit of work to run separately from the primary application thread. It notifies the main thread of completion (or failure) when the work is finished. It can lead to improved responsiveness and better application performance.
Parallel programming, on the other hand, allows you to run multiple processes simultaneously. Each process runs on a separate core, so there’s no need to generate a new thread for a second process. The crucial difference is that parallel programming requires multiple cores, where asynchronous programming can take place on a single core.
Distributed debugging is a way to address the challenges of debugging a distributed system. It can be challenging to carry out debugging processes when processes are distributed across multiple nodes. This is particularly true given that the communication between processes can become bugged in its own right, and can be hard to monitor using conventional means of debugging.
A distributed debugging process can record what happens on each individual node throughout the execution of a process. This makes it easier to track down any bugs and rectify them quickly. What is the bully algorithm? The bully algorithm is used in distributed systems to elect a coordinator from among a group of distributed computer processes. It selects the process with the highest process ID number from the non-failed processes. If an elected leader node then fails, the bully algorithm kicks in again to select a new coordinator process without a long delay.
Inter-process communication is the process by which data is exchanged between multiple independent processes within a distributed environment. There are many ways to establish inter-process communication, ranging from the unidirectional pipe method to the more complex shared memory system. It’s important for inter-process communication to be synchronized, either within the communication process itself or using an inter-process control mechanism.