Coding (Algo)

LRU cache simulation: combine constant-time lookup with recency ordering and deterministic eviction

LRU cache simulation: Simulate an LRU cache. put stores or replaces a key and get returns its value or -1; accessing or writing a key makes it most recently used. Return the results of get operations.

Input: A JSON object { "capacity": number, "operations": { "type": "get"|"put", "key": string, "value"?: number }[] }.

Output: A JSON number[] containing get results in operation order.

Constraints:

0 <= operations.length <= 100000
capacity is a non-negative integer
get and put should be O(1) expected time

Read one JSON value from stdin and print exactly one JSON-serialized answer to stdout with no extra text.

Difficulty: HARD. Variant: 21. Balance correctness, maintainability, and clear review-ready code.

Practice context

This coding (algo) exercise trains prompt interpretation, explicit assumptions, a concrete response, and a concise explanation of the decisions made. It i

What this task practices

This coding (algo) exercise trains the same sequence expected in an interview: understand the prompt, make assumptions explicit, produce a concrete answer, and explain the decisions behind it. The task is marked hard difficulty. The private workspace adds the tools required by this round, such as recording, code execution, diagrams, evidence panels, or structured notes. It also preserves the attempt so later feedback can be compared with previous work. Evaluation is based on task-specific criteria and the candidate seniority selected in the preparation path. Public pages never expose the reference solution, hidden tests, evaluator instructions, or another candidate’s work.