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 Technical Screen rounds at Google.

What role is this question designed for?

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

Design a prioritized log manager with eviction

Quick Overview

Design a prioritized log manager with eviction evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design a prioritized log manager with eviction

Design a Log Store with Priority- and Recency-Aware Eviction

Context

You are designing an in-memory (or on-disk) log storage component. Each inserted log belongs to a file (fileId) and has a priority and a timestamp. Storage is bounded by a global capacity and optional per-file capacities. When capacity is exceeded, the system must automatically evict the "least important" logs first. Importance is defined by priority (higher = more important) and recency (newer = more important).

Requirements

Global capacity: The total number of stored log records across all files must not exceed total_max .
Automatic eviction: addLog(fileId, record, priority, timestamp) must automatically evict when limits are exceeded.
Eviction policy: When eviction is required, delete the least important records first. Importance considers:
- Priority (higher is more important), and
- Recency (newer is more important).
Limits: Support both per-file limits (optional limit[fileId] = X ) and a global limit total_max .
APIs and complexity targets:
- Provide APIs to add logs, query by file, and observe capacities.
- Target complexities: O(1) (or amortized O(1)) to identify a deletion candidate and O(log n) or better per insertion/eviction.

Deliverable

Describe your data model, data structures, eviction policy, algorithms, and trade-offs that meet the above requirements and complexities.

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 Guidance

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 Guidance

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 Guidance

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

Requirements

Global capacity: The total number of stored log records across all files must not exceed total_max .

Automatic eviction: addLog(fileId, record, priority, timestamp) must automatically evict when limits are exceeded.

Eviction policy: When eviction is required, delete the least important records first. Importance considers:

Priority (higher is more important), and
Recency (newer is more important).

Limits: Support both per-file limits (optional limit[fileId] = X ) and a global limit total_max .

APIs and complexity targets:

Provide APIs to add logs, query by file, and observe capacities.
Target complexities: O(1) (or amortized O(1)) to identify a deletion candidate and O(log n) or better per insertion/eviction.

Deliverable

Describe your data model, data structures, eviction policy, algorithms, and trade-offs that meet the above requirements and complexities.

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 Guidance

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 Guidance

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 Guidance

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 a prioritized log manager with eviction

Quick Overview

Design a prioritized log manager with eviction

Design a prioritized log manager with eviction

Design a Log Store with Priority- and Recency-Aware Eviction

Context

Requirements

Deliverable

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Submit Your Answer to Earn 20XP

Design a prioritized log manager with eviction

Quick Overview

Design a prioritized log manager with eviction

Design a prioritized log manager with eviction

Design a Log Store with Priority- and Recency-Aware Eviction

Context

Requirements

Deliverable

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Submit Your Answer to Earn 20XP