Design an in-memory key-value database

Q: Design an in-memory key-value database

This question evaluates system-design and storage engineering competencies, including in-memory data structures, concurrency control, persistence and durability mechanisms, replication and sharding strategies, and operational concerns like observability and backpressure.

Q: 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.

Q: What difficulty level is this interview question?

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

Q: What role is this question designed for?

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

Question

Design an In-Memory Key–Value Database (Technical Screen)

Context

Build an in-memory key–value (KV) database that offers high-throughput, low-latency operations. It should keep most data in RAM, support optional persistence, and scale across CPU cores and machines.

Assume a typical deployment runs on commodity servers with SSDs and multiple CPU cores. The system must handle high QPS with O(1) average-time reads and writes.

Functional Requirements

Core operations:
- set(key, value[, ttl])
- get(key)
- delete(key)
- compare-and-set (CAS): conditional update based on version or expected value
TTL expiration for keys
Atomic multi-key operations (within a shard)

Non-Functional Requirements

O(1) average-time reads/writes
Expiration tracking (e.g., timing wheel or min-heap)
Memory management and eviction policy (e.g., LRU)
Durability via append-only log (AOF) with periodic snapshots
Concurrency control strategy (single-threaded event loop vs locks)
Crash recovery procedure
Replication and sharding for scale
Consistency guarantees
Backpressure under overload
Observability: logs, metrics, tracing

Deliverables

API definitions (client-facing)
In-memory data structures and algorithms
On-disk record schemas (AOF and snapshot)
Concurrency and transaction model (incl. atomic multi-key)
Crash-recovery flow
Replication and sharding design
Consistency guarantees and trade-offs
Backpressure strategies
Failure modes and metrics

Design an in-memory key-value database

Quick Overview

Design an In-Memory Key–Value Database (Technical Screen)

Context

Functional Requirements

Non-Functional Requirements

Deliverables

Solution

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