Problem and Scope
Design a distributed storage system similar to AWS S3 or Azure Blob Storage that allows users to store and retrieve files of any size in the cloud with high durability and availability.
Start by confirming the core user journey, exclusions, success criteria, and the constraints that materially affect the architecture.
Requirements to Clarify
A strong answer should establish scope before choosing components.
Functional requirements:
- Users should be able to upload files of various sizes (from small KB files to multi-GB files)
- Users should be able to download their files at any time
- Users should be able to delete their files
- The system should support listing files in a user's storage
- The system should support multi-tenancy, allowing many users to use the same infrastructure
- The system should provide virtual hosting style access (e.g., bucket.storageservice.com)
Non-functional requirements:
- High durability (99.999999% or higher) - files should not be lost once stored
- High availability (99.99% or higher) - the service should be accessible most of the time
- Scalability - the system should scale automatically as more storage is needed
- Security - data should be securely stored and transferred
- Region-specific storage with possible replication between regions
- Cost-effective storage solution
Scale and Capacity
Use the workload to justify storage, partitioning, caching, and reliability decisions. Clarify or challenge these assumptions rather than treating them as unquestionable facts:
- Potentially billions of files stored in the system
- File sizes from a few KB to multiple GB
- Thousands of concurrent users uploading and downloading files
- Total storage requirements in petabytes and growing
- Users distributed across the globe requiring low-latency access
Architecture Discussion
Walk through the important read and write paths, identify ownership boundaries, and explain how the design behaves when dependencies fail. Cover these areas explicitly:
- Load balancing and API management layer
- Metadata service to track file information
- Partitioning strategy for distributing data
- Storage servers or nodes for actual data persistence
- Replication mechanism for durability and availability
- Caching layer for frequently accessed content
- Authentication and authorization system
- Monitoring and health check systems
- Ensuring extremely high durability despite hardware failures
- Managing efficient replication across multiple regions
- Handling large file uploads/downloads reliably
- Scaling metadata management as the number of files grows
- Balancing between cost-efficiency and performance
- Managing consistency across replicated data
- Ensuring secure access to stored data
Follow-up Questions
Expect the interviewer to test the consequences of your choices. Practise answering these questions with a concrete decision, its benefit, and its cost:
- How would you ensure the 99.999999% durability requirement is met?
- Focus: Assess the candidate's answer to "How would you ensure the 99.999999% durability requirement is met" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
- What approach would you take for handling very large file uploads?
- Focus: Assess the candidate's answer to "What approach would you take for handling very large file uploads" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
- How would your system manage metadata at scale?
- Focus: Assess the candidate's answer to "How would your system manage metadata at scale" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
- What strategies would you employ for data replication across regions?
- Focus: Assess the candidate's answer to "What strategies would you employ for data replication across regions" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
- How would your garbage collection process work?
- Focus: Assess the candidate's answer to "How would your garbage collection process work" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
- How would you handle hot spots in your storage system?
- Focus: Assess the candidate's answer to "How would you handle hot spots in your storage system" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
- What trade-offs did you consider in your partitioning strategy?
- Focus: Assess the candidate's answer to "What trade-offs did you consider in your partitioning strategy" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
Evaluation Rubric
MockMe evaluates the answer across the following dimensions. A complete answer should connect claims to requirements and explain consequential trade-offs.
- Requirements and scope (15%): Clarifies and prioritizes the required behavior for Users should be able to upload files of various sizes (from small KB files to multi-GB files); Users should be able to download their files at any time; Users should be able to delete their files. Establishes the constraints that materially affect Design AWS S3, including High durability (99.999999% or higher) - files should not be lost once stored; High availability (99.99% or higher) - the service should be accessible most of the time. Strong evidence includes Separates the critical path from secondary features and resolves ambiguous requirements before choosing components.
- Architecture and interfaces (20%): Presents coherent ownership boundaries and end-to-end flows covering Load balancing and API management layer; Metadata service to track file information; Partitioning strategy for distributing data; Storage servers or nodes for actual data persistence. Strong evidence includes Defines interfaces and traces important success, retry, and failure paths across the proposed components.
- Data and scaling (25%): Uses workload assumptions such as Potentially billions of files stored in the system; File sizes from a few KB to multiple GB to justify capacity and partitioning decisions. Explains the data, state, or model strategy for Metadata service to track file information; Partitioning strategy for distributing data; Storage servers or nodes for actual data persistence. Strong evidence includes Quantifies a dominant workload, identifies the first bottleneck, and explains how the design evolves as that workload grows.
- Reliability, correctness, and safety (20%): Explains concrete failure behavior, recovery, and operational safeguards for Replication mechanism for durability and availability; Monitoring and health check systems; Ensuring extremely high durability despite hardware failures. Strong evidence includes States the required correctness or consistency boundary and covers retries, partial failure, observability, and safe degradation.
- Communication and trade-offs (20%): Drives a structured discussion and compares consequential alternatives for Balancing between cost-efficiency and performance; Managing consistency across replicated data; Ensuring secure access to stored data. Strong evidence includes Makes assumptions explicit, answers the question asked, and explains both the benefit and cost of major decisions.