# Design a Distributed Message Queue: System Design Mock Interview Aced (formerly Exponent) publishes a public search API over its indexed pages; the guide is at https://www.aced.io/agents/agent-access. Source: https://www.aced.io/blog/distributed-message-queue-system-design-mock-interview Source observed: 2026-11-04T22:21:22.965Z Last checked: 2026-11-04T22:21:22.965+00:00 Indexed: 2026-11-05T03:32:20.213+00:00 --- # Design a Distributed Message Queue: System Design Mock Interview [System Design](https://www.aced.io/blog/tag/system-design) **Aced Team • Last updated a month ago** We use this mock interview to work through the system design question: **“Design a distributed message queue.”** In the example, Neeraj Gupta, an eBay engineering manager, discusses his solution. Our [complete system design interview course](https://www.aced.io/courses/system-design-interviews?ref=exponent-blog.ghost.io) provides more mock interviews and is described as serving 19,000+ software engineers, engineering managers, technical program managers, and other tech professionals. ## What are message queues? Message queues let different parts of a distributed system communicate even when they do not operate at the same time. Messages remain in a queue until the consuming component uses them, helping prevent information loss. They act as temporary storage for exchanging information. Message queues use many servers, also called **brokers**. Brokers form a cluster to keep the system reliable and help components work more independently and efficiently. Example uses include: - Processing store orders - Dealing with money - Keeping information secure Examples of message-queue technologies include [RabbitMQ](https://www.rabbitmq.com/?ref=exponent-blog.ghost.io), Apache Kafka, Apache ActiveMQ, Google Pub/Sub, AWS SNS/SQS, and Azure Queue. ## Framework for this question Use our [system design interview framework](https://www.aced.io/blog/system-design-interview-guide?ref=exponent-blog.ghost.io) as a five-step structure: 1. **Understand the problem.** Design a distributed message queue that handles many messages while minimizing each operation's failure rate. 2. **Design the system.** Include topic-based queues, a pull model for consumers, and messages containing a topic, payload, and partitioning key. Make the queue highly scalable and able to handle abrupt traffic spikes. Consider SQL, NoSQL, or Write Ahead Log (WAL) storage. Use acknowledgments and replication to followers for fault tolerance and successful writes. 3. **Explore the design.** Discuss metadata and state storage in more detail. 4. **Improve the design.** Address a single file becoming too large by segmenting it and splitting based on buyer ID. Add back-end jobs to improve efficiency. 5. **Wrap up.** Confirm that the design meets requirements and suggest improvements such as leader-follower architecture, metadata and state storage, and storage designed for read-and-write-heavy workloads. ## Understanding queue functionality A queue inserts and removes messages, also called producing and consuming. Design discussions should also cover scalability and other non-functional requirements. Queue types include: - Topic-based queues - Fan-out-based broadcasting - Direct queues For scalability, use a **pull model** in which consumers pull messages from the queue instead of producers pushing them. Add servers or batch operations to improve scalability. A message should contain a **topic, payload, and key** for partitioning. The queue should scale substantially and handle abrupt traffic spikes. The example capacity target for topic-based queues is up to **10k topics**, an estimated **10 million messages daily**, **800 GB of storage per day**, and a **30-day retention period**. ## Storage solutions: SQL, NoSQL, or Write Ahead Log (WAL) For large message volumes and read-and-write-heavy workloads, consider SQL, NoSQL, or WAL. We advise WAL in this design because it is an append-only log: each message is added to the end of the file. A single append-only file can become too large. Divide it into multiple segments and split it based on buyer ID. Store segments on different servers to support scalability. You also need: - **Metadata storage** for configuration information - **State storage** for where each consumer last read ## Fault tolerance and successful message writes Fault tolerance means continuing to operate when a system fault occurs. The suggested approach uses leader-follower architecture, coordinated service, and a zookeeper to store and interact with leaders and followers. Possible approaches to successful writes include acknowledgments and replication to followers. These approaches help support fault tolerance and successful message writing. ## Continue preparing We offer free interview guides, insider tips, and courses. [Create your free account](https://www.aced.io/signup?src=blog). ## Related blog posts - [Design a Hotel Booking Service - System Design Interview (Question + Answer)](https://www.aced.io/blog/design-a-hotel-booking-service-system-design-interview-question-answer) — a month ago • 2 min read - [Design ChatGPT - System Design Mock Interview](https://www.aced.io/blog/design-chatgpt-system-design-mock-interview) — a month ago • 3 min read - [System Design Interview: Load Balancers](https://www.aced.io/blog/load-balancers-system-design-interview) — a month ago • 5 min read - [Design Food Delivery System like Uber Eats (Mock Interview)](https://www.aced.io/blog/design-food-delivery-uber-eats-system-design-interview) — a month ago • 5 min read --- [Read this page’s summary](https://www.aced.io/agents/api/public/v1/page-tldr?url=https%3A%2F%2Fwww.aced.io%2Fblog%2Fdistributed-message-queue-system-design-mock-interview&trace=t_679ff9ab98f3) ## Ask a follow-up about this page Need a more specific answer? 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