How do I approach System Design interview questions?

System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

What difficulty level is this interview question?

This is a hard difficulty System Design question, commonly asked during HR Screen rounds at Databricks.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Databricks during technical interviews.

Design concurrent range-aware file caching client

Quick Overview

This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Design concurrent range-aware file caching client states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design concurrent range-aware file caching client

System Design: High-throughput Client-side Ranged-read File Cache

You are asked to design and specify a client-side cache that accelerates ranged reads against a remote storage service.

Context and Assumptions

The remote service supports HTTP Range-like reads: fetch(filename, offset, length).
There is also an API to download an entire file by filename and known size.
Workloads consist of many concurrent ranged reads, often overlapping or adjacent.
The cache persists on local disk across process restarts.

Requirements

Concurrency and coalescing
- Efficiently handle concurrent requests for overlapping/adjacent ranges.
- Deduplicate overlapping in-flight downloads so only one fetch per chunk occurs.
- Provide thread-safe access and correctness under concurrency.
Chunking, request strategy, and prefetch
- Choose chunk size and justify trade-offs.
- Coalesce requests to reduce remote calls and over-fetch sensibly.
- Implement prefetch/read-ahead strategies for sequential access.
Backpressure and rate limiting
- Limit remote QPS/throughput and disk I/O concurrency.
- Disable prefetch under load; bound queues.
Cache management
- Cache indexing and lookup.
- Eviction policy (e.g., LRU) with byte budget.
- Persistence across restarts.
- Validation and invalidation if the remote file changes.
Reliability
- Handle partial failures, retries, and timeouts.
- Resume or refetch partial chunks.
API semantics
- Define read(filename, offset, length) (sync or async), and streaming semantics if applicable.
- Define write policy (e.g., read-through only, or write-through with invalidation).
Deliverables
- Describe key data structures and on-disk layout.
- Provide pseudocode for coordinating fetch, cache lookup, coalescing, and concurrency control.
- Analyze scalability, correctness under concurrency, and time/space complexity.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.
State explicit assumptions before making sizing or architecture decisions.
Prioritize the functional path first, then address reliability, security, observability, and rollout.

What a Strong Answer Covers

A scoped requirements summary with concrete non-goals and success metrics.
API, data model, architecture, consistency, capacity, and operations.
Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.
A validation, monitoring, migration, and launch plan appropriate for the risk level.

Follow-up Questions

What breaks first at 10x traffic or data volume?
How would you degrade gracefully during dependency failures?
What metrics and alerts would prove the design is healthy after launch?

Quick Overview

Context and Assumptions

The remote service supports HTTP Range-like reads: fetch(filename, offset, length).

There is also an API to download an entire file by filename and known size.

Workloads consist of many concurrent ranged reads, often overlapping or adjacent.

The cache persists on local disk across process restarts.

Requirements

Concurrency and coalescing

Efficiently handle concurrent requests for overlapping/adjacent ranges.
Deduplicate overlapping in-flight downloads so only one fetch per chunk occurs.
Provide thread-safe access and correctness under concurrency.

Chunking, request strategy, and prefetch

Choose chunk size and justify trade-offs.
Coalesce requests to reduce remote calls and over-fetch sensibly.
Implement prefetch/read-ahead strategies for sequential access.

Backpressure and rate limiting

Limit remote QPS/throughput and disk I/O concurrency.
Disable prefetch under load; bound queues.

Cache management

Cache indexing and lookup.
Eviction policy (e.g., LRU) with byte budget.
Persistence across restarts.
Validation and invalidation if the remote file changes.

Reliability

Handle partial failures, retries, and timeouts.
Resume or refetch partial chunks.

API semantics

Define read(filename, offset, length) (sync or async), and streaming semantics if applicable.
Define write policy (e.g., read-through only, or write-through with invalidation).

Deliverables

Describe key data structures and on-disk layout.
Provide pseudocode for coordinating fetch, cache lookup, coalescing, and concurrency control.
Analyze scalability, correctness under concurrency, and time/space complexity.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.

If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.

Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.

State explicit assumptions before making sizing or architecture decisions.

Prioritize the functional path first, then address reliability, security, observability, and rollout.

What a Strong Answer Covers

A scoped requirements summary with concrete non-goals and success metrics.

API, data model, architecture, consistency, capacity, and operations.

Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.

A validation, monitoring, migration, and launch plan appropriate for the risk level.

Follow-up Questions

What breaks first at 10x traffic or data volume?

How would you degrade gracefully during dependency failures?

What metrics and alerts would prove the design is healthy after launch?

Design concurrent range-aware file caching client

Quick Overview

Design concurrent range-aware file caching client

Design concurrent range-aware file caching client

System Design: High-throughput Client-side Ranged-read File Cache

Context and Assumptions

Requirements

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Submit Your Answer to Earn 20XP

Design concurrent range-aware file caching client

Quick Overview

Design concurrent range-aware file caching client

Design concurrent range-aware file caching client

System Design: High-throughput Client-side Ranged-read File Cache

Context and Assumptions

Requirements

Constraints & Assumptions

Clarifying Questions to Ask

What a Strong Answer Covers

Follow-up Questions

Submit Your Answer to Earn 20XP