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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 Adyen.

What role is this question designed for?

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

Design scalable LRU cache and backtracking API

Quick Overview

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.

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.

Quick Overview

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

Submit Your Answer

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

Submit Your Answer