Design scalable LRU cache and backtracking API

Q: Design scalable LRU cache and backtracking API

This question evaluates understanding of scalable system design, concurrent in-process caching and eviction semantics, distributed cache topologies, API design for compute-heavy services, observability and metrics, consistency models, rate limiting, autoscaling, and validation/testing strategies.

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 Adyen.

Q: What role is this question designed for?

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

Question

System Design: Two Production-Ready Services

A) Thread-Safe LRU Caching Service

Design and describe a production-ready, in-process LRU cache that can also be deployed as a networked cache service.

Specify:

API surface (in-process library and optional network endpoints): get/put/delete/computeIfAbsent, per-entry TTL, size limits, stats.
Concurrency control inside a process (e.g., fine-grained locks, lock striping, or lock-free techniques). Ensure eviction is correct under concurrent access.
TTL and capacity management (entry count limit, weighted by bytes, admission control).
Metrics and observability (hits/misses, evictions, latencies, saturation, traces, logs, debug endpoints).
Scaling across multiple instances:
- Using Redis (or another remote cache): topology, client behavior, backpressure, failure handling.
- Or client-side sharding with consistent hashing: hot key mitigation, replication/failover, rebalancing, consistency model.
Handling hot keys, backpressure, and data consistency semantics (cache-aside/write-through, stale reads, idempotency of updates, CAS).

Provide concrete choices and trade-offs.

B) Compute-Heavy "Find Transfer Combinations" API

Design an API that computes transfer route combinations (e.g., finding valid paths across payment rails/providers with constraints), with potential traffic spikes.

Specify:

Stateless vs. stateful design; avoid shared mutable state in workers and define per-request memory management.
Caching and precomputation strategies; request de-duplication (in-process and cross-instance).
Rate limiting, timeouts/deadlines, cancellation, idempotency.
Horizontal scaling and autoscaling policies; synchronous vs. async job modes.
Capacity estimates with clear assumptions and formulas.
A testing plan covering correctness, performance, reliability, and operability.

Make any minimal, explicit assumptions needed so the design is concrete.

Design scalable LRU cache and backtracking API

Quick Overview

System Design: Two Production-Ready Services

A) Thread-Safe LRU Caching Service

B) Compute-Heavy "Find Transfer Combinations" API

Solution

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