Distributed System Interview Questions
Last Updated :
23 Jul, 2025
This article breaks down key interview questions for distributed systems in clear, straightforward terms. this resource will help you ace your interview. Let's get started!
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Top Interview Questions for Distributed System
Q1: What is a distributed system?
A distributed system is a collection of multiple interconnected computers or nodes that work together to achieve a common goal. In a distributed system, these nodes communicate and coordinate with each other through a network, typically sharing resources and collaborating on tasks.
Q2: What are the key challenges in building distributed systems?
Some key challenges in building distributed systems include:
- Concurrency Management: Coordinating concurrent operations across multiple nodes while ensuring consistency and avoiding race conditions.
- Consistency and Replication: Maintaining consistency of data across distributed nodes, especially in the presence of failures, replication, and eventual consistency requirements.
- Fault Tolerance: Designing systems resilient to node failures, network partitions, and other types of faults, often requiring redundancy, replication, and fault detection mechanisms.
- Scalability: Ensuring that the system can scale horizontally to handle increasing workload and user demand without sacrificing performance or reliability.
Q3: What is the CAP theorem? Explain its implications.
CAP theorem states that in networked shared-data system or distributed system can share/have only two of the three desired characteristics for a database: Consistency, Availability, and Partition tolerance.
Q4: What is consistency in distributed systems?
The consistency of a distributed system denotes the requirement that all the nodes maintain a consistent view of the data. This is what we hope for: consistency against read operations on the system; every such operation should perform on the latest write, regardless of the node from which the read is made.
Q5: Explain the difference between strong consistency, eventual consistency, and eventual strong consistency.
- Strong consistency: In distributed systems, strong consistency ensures that, regardless of where a node accesses the data, it is always visible to all nodes at the same time.
- Eventual consistency: Eventual consistency is a consistency model used in distributed systems where, after some time with no updates, all data replicas will eventually converge to a consistent state.
- Eventual strong consistency: Just like in the case of eventual consistency as well, but with the additional guarantee that every replica will get any update, the group of replicators is applied to a consistent state.
- Horizontal Scaling: Also known as scaling out, refers to the process of increasing the capacity or performance of a system by adding more machines or servers to distribute the workload across a larger number of individual units.
- Vertical Scaling: Also known as scaling up, refers to the process of increasing the capacity or capabilities of an individual hardware or software component within a system.
Q7: What is fault tolerance in distributed systems? How is it achieved?
Fault tolerance is a term used to describe a system's capability to work correctly when failure of some parts occurs. It does so by means of multiple copies, restatements, and techniques such as error recovery and detection.
Q8: What is a distributed hash table (DHT)?
A distributed hash table is a decentralized system that uses a linked look-up service like a hash table. It offers a mechanism of data indexing by which keys are mapped with values and the distribution of storage and retrieval operations is spread over multiple nodes of a network.
Q9: What is the role of a load balancer in a distributed system?
A load balancer distributes the incoming network traffic over multiple servers, avoiding a server failure that could cause website unavailability and some reliability issues.
Q10: Explain ACID properties and how they apply to distributed systems.
ACID is a group of properties called atomicity, consistency, isolation, and durability (ACID), which provides the assurance of database consistency. The ACID properties in distributed systems may be harder to support if network latency and/or partition tolerance arise, which make those systems non-fault-tolerant.
Q11: What is the difference between a distributed transaction and a local transaction?
- Local Transaction: Operations confined to a single database or resource, managed by a single transaction manager within one node.
- Distributed Transaction: Involves multiple databases or resources across different nodes, requiring coordination between multiple transaction managers for consistency across the distributed system
Q12: What are some common concurrency control mechanisms in distributed systems?
Some common concurrency control mechanisms in distributed systems include:
- Locking: Control access to shared resources by acquiring locks.
- Timestamp Ordering: Order transactions based on timestamps to maintain consistency.
- Two-Phase Locking (2PL): Acquire locks in two phases to ensure serializability.
- Multi-Version Concurrency Control (MVCC): Allow concurrent access to multiple data versions.
- Distributed Snapshot Isolation (DSI): Provide consistent snapshots of the database for transactions.
Q13: Explain the concept of distributed consensus. What are some algorithms used for achieving consensus?
Consensus in a distributed system is defined as the event that a group of nodes agree on a digital value or the way for this system to work. Creation of algorithms like Paxos, Raft, and Zab are some the ways of implementing the consensus in this distributed system.
Q14: What is the role of leader election in distributed systems?
Leader election in a distributed system refers to the algorithm through which how the group nodes will vote for a leader to conduct their orders. The role of the leader is designated to become the decision-maker, and the date between the nodes should be coordinated.
Q15: What is a distributed lock and why is it necessary?
The distributed lock is a construct which consists of a set of rules and protocols to assign of shared resources among different nodes of the distributed system. It merely allows one node to access the resources, and therefore prevents conflicts for data privacy and ensuring data consistency.
Q16: What is Sharding and how does it help in distributed databases?
Splitting scheme applies while splitting the data into several servers or nodes of the distributed database. It embraces the parallel processing of multiple machines which reduces the workload through the nodes.
- Synchronous communication: In the act of sending a message, the transmitter waits for an acknowledgement before continuing.
- Asynchronous communication: The sender does not sit waiting for the response and goes on with the overall task.
Q18: What are message queues and how are they used in distributed systems?
A Message Queue is a form of communication and data transfer mechanism used in system design and distributed systems. It functions as a temporary storage and routing system for messages exchanged between different components, applications, or systems within a larger software architecture.
Q19: Explain the concept of eventual message delivery.
Eventual message delivery ensures that messages sent between nodes in a distributed system will eventually be delivered, even if there are temporary failures or network partitions. Unlike guaranteed message delivery, which ensures immediate delivery or notification of failure, eventual message delivery prioritizes system availability and scalability over immediate consistency.
Q20: What is the difference between RPC (Remote Procedure Call) and RESTful services?
- RPC: By way of communication between distributed systems, the program operates remotely and is executed on the destination machine.
- RESTful services: a representational style of designing application networks that run on HTTP (through HTTP’s REST principles).
The distributed caching practice consists of keeping in memory the most accessed data close to the different nodes in the system. It results in better system responsiveness by making reoccurred data available from the memory without the need to access a slower storage system like the database or thereby.
Q22: How does data replication work in distributed databases?
Data redundancy represents keeping multiple copies of data on different nodes of a fence in a distributed database. It is very important for data resilience, availability, and improved performance since data could still be accessible despite some nodes having failed.
Q23: Explain the concept of vector clocks and how they are used for ordering events in distributed systems.
Vector clocks are the clocking mechanisms used to create a partial ordering of events across distributed systems. Each "node" of the system sees the "vector time-clock" as an element as it tracks the order of events that have been observed, allowing for the detection of causal relationships, among other things.
Q24: What are gossip protocols in the category of distributed systems?
Gossip protocols are decentralized communication algorithms used in distributed systems for peer-to-peer communication and information dissemination. Nodes in the system randomly select a small set of peers to share information with, spreading messages throughout the network like gossip in a social network.
Q25: How do you handle network partitions in distributed systems?
Network partitions might be handled by different methods; among them are quorum-based protocols, leader election, and replication of data to maintain consistency and system availability in the event of the network being unavailable or partitioned.
Q26: What is the difference between a distributed system and a decentralized system?
- Distributed System:
- In a distributed system, multiple nodes work together to achieve a common goal, typically connected through a network.
- These nodes may share resources, coordinate actions, and communicate to provide a unified service or functionality.
- Decentralized System:
- A decentralized system is a subset of distributed systems where there is no single point of control or authority.
- Instead, control is distributed among multiple nodes, often operating autonomously or in a peer-to-peer fashion.
Q27: Explain the concept of microservices and how they relate to distributed systems.
‘Microservices' is a software architecture that uses the collection of numerous small components (i.e., services), which are able to be deployed and released independently of one another. Microservices individually work into their own separate processes and have the functionality of distributing service communication over the network, which is a type of distributed system.
Q28: What is the role of service discovery in microservices architecture?
Service discovery in microservices architecture automates the process of finding and connecting to services within the system. It enables dynamic registration, lookup, load balancing, and failover of services, simplifying communication and management in distributed environments.
Q29: What are some common challenges in deploying and managing distributed systems in cloud environments?
The list of challenges can be very extensive and encompasses many of them: safeguarding a cloud infrastructure and complying with the regulatory standards; managing elasticity and scalability efficiently; resolving issues with network delay and stability; and last but not least, integrating with other cloud-native services and infrastructure.
Conclusion
Today, distributed systems are one of the major components of modern computing infrastructures, which at the same time ensure the synchronization and smooth connection of such apps to be deployed in varied environments. The need for more distributed systems experts is increasing, and expertise is becoming more important for this field of professionals in order to have a deep understanding of the key principles and challenges.
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