Amazon·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Apr 2026

Summary

Amazon system design follow-up round, focused entirely on caching layer design for an existing implementation. Pretty dense question with eight sub-parts, felt like a written exam more than a conversation.

Questions Asked (1)

Given an existing implementation that repeatedly resolves file paths or parses license strings, propose and fully analyze adding an in-memory dictionary-backed cache. Cover where to cache, key/value design, expected hit rate, eviction policy, invalidation strategy, concurrency safety, pseudocode for get/put with validation, and time/space complexity before and after.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

Eight parts.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the access patterns and performance requirements, then propose a cache design that balances hit rate, memory, and consistency. Walk through the design choices (key/value, eviction, invalidation, concurrency) and analyze trade-offs, finishing with complexity analysis and pseudocode.

Pro tip: Emphasize that caching is a trade-off: measure hit rate and memory overhead, and always consider invalidation and concurrency to avoid subtle bugs. Mention that for Amazon-scale systems, even small inefficiencies can have large impacts.

1. Clarify requirements and constraints

Ask about access patterns (read/write ratio, frequency), data size, consistency needs, and performance goals to tailor the cache design.

2. Design cache structure

Decide where to cache (e.g., in-process, near the computation), choose key/value types, and select an eviction policy (e.g., LRU) based on expected hit rate and memory.

3. Address invalidation and concurrency

Define invalidation strategy (TTL, explicit invalidation) and ensure thread safety using locks or concurrent data structures.

4. Provide pseudocode and complexity analysis

Write pseudocode for get/put with validation, and analyze time/space complexity before and after caching.

5. Discuss trade-offs and alternatives

Summarize benefits (reduced latency, CPU) and drawbacks (memory, staleness), and mention alternatives like memoization or external caches.

Key Points to Mention

Key design: use canonical file path or license string as key, and parsed result as value; consider normalization to avoid duplicate keys.
Eviction policy: LRU or LFU with a max size to bound memory; justify choice based on access patterns.
Invalidation: TTL for time-sensitive data, or explicit invalidation on file change/license update; discuss cache coherence.
Concurrency: use thread-safe structures (e.g., ConcurrentHashMap) or locks; consider read-write locks for high read concurrency.
Complexity: before caching O(n) for parsing/resolution; after caching O(1) average for hits, but O(n) on miss; space O(k) for k cached entries.
Validation: ensure cached values are still valid (e.g., file exists, license not expired) before returning; handle cache misses gracefully.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.