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This is a medium difficulty Coding & Algorithms question, commonly asked during Take-home Project rounds at Perplexity.

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

Implement time-versioned KV store with restore

Quick Overview

This question evaluates a candidate's ability to design and implement a time-versioned key–value store, assessing knowledge of temporal data structures, versioning semantics, time-travel and range queries, deletion semantics, and rollback/restoration.

Implement time-versioned KV store with restore

Company: Perplexity

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Take-home Project

Design and implement an in-memory key–value store where every operation is associated with a timestamp (monotonically increasing integer). The store must support time-travel queries and rollback. Implement the following operations (you may choose function signatures, but the semantics must match): 1. `set(key, value, ts)`: Set `key` to `value` effective at time `ts`. 2. `get(key, ts)`: Return the value of `key` at time `ts` (the most recent `set` at or before `ts` that hasn’t been deleted by time `ts`). Return `null`/`None` if the key does not exist at `ts`. 3. `delete(key, ts)`: Delete `key` effective at time `ts`. 4. Range query requirement: support querying a time interval for a key, e.g. `getRange(key, startTs, endTs)`, returning the value history within `[startTs, endTs]` (for example, a list of segments or (timestamp, value) change points within the interval). 5. `restore(ts)`: Restore the *entire store* to exactly the state it had at time `ts` (after all operations with timestamp `<= ts`), discarding the effects of any operations with timestamp `> ts`. Discuss and/or implement data structures that make these operations efficient. Clarify any assumptions (e.g., whether multiple operations may share the same timestamp, and how ties are resolved).

Quick Answer: This question evaluates a candidate's ability to design and implement a time-versioned key–value store, assessing knowledge of temporal data structures, versioning semantics, time-travel and range queries, deletion semantics, and rollback/restoration.

You are given a sequence of operations for an in-memory time-versioned key-value store. Implement a function `solution(operations)` that processes the operations in order and returns the answers for every `get` and `getRange` query. Operations are represented as tuples: - `("set", key, value, ts)`: set `key` to `value` effective at timestamp `ts`. - `("get", key, ts)`: return the value visible for `key` at time `ts`, or `None` if the key does not exist at that time. - `("delete", key, ts)`: delete `key` effective at timestamp `ts`. - `("getRange", key, startTs, endTs)`: return the visible value history for `key` inside `[startTs, endTs]` as a list of `[timestamp, value]` change points. - `("restore", ts)`: restore the entire store to exactly the state it had after all mutations with timestamp `<= ts`, permanently discarding every mutation with timestamp `> ts`. Important clarifications: - Process operations in input order. - Only `set` and `delete` create historical changes. - `get` and `getRange` are queries only; they do not change history. - `restore` truncates the current timeline; discarded future mutations do not come back later. - Mutation timestamps (`set`/`delete`) are non-decreasing in input order. Equal timestamps are allowed. - If multiple mutations share the same timestamp, input order breaks ties: the later mutation is the visible one at that timestamp. - For `getRange`, return visible change points, not every raw mutation. If the key already exists at `startTs`, include `[startTs, value_at_startTs]` as the first entry. Then include later timestamps in `(startTs, endTs]` where the visible value changes. Deletions are represented by `None`. Your goal is to support these operations efficiently.

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Examples

Input: [("set", "a", "x", 1), ("set", "a", "y", 4), ("get", "a", 3), ("getRange", "a", 1, 5), ("delete", "a", 6), ("get", "a", 6)]

Expected Output: ["x", [[1, "x"], [4, "y"]], None]

Explanation: At time 3, `a` is `x`. The range [1, 5] shows `a` starting as `x` and changing to `y` at time 4. After deleting at time 6, `get(a, 6)` returns None.

Input: [("set", "a", 10, 1), ("set", "b", 20, 2), ("set", "a", 30, 5), ("get", "a", 5), ("restore", 2), ("get", "a", 5), ("get", "b", 100), ("getRange", "a", 1, 10)]

Expected Output: [30, 10, 20, [[1, 10]]]

Explanation: Before restore, `a` is 30 at time 5. Restoring to time 2 discards the change at time 5, so later queries see `a = 10` and `b = 20`. The only visible history for `a` is its value from time 1.

Input: [("set", "k", "first", 2), ("set", "k", "second", 2), ("delete", "k", 2), ("set", "k", "final", 2), ("get", "k", 2), ("getRange", "k", 1, 3)]

Expected Output: ["final", [[2, "final"]]]

Explanation: All mutations happen at the same timestamp. Input order breaks ties, so the final visible value at time 2 is `final`. The range query collapses same-timestamp mutations into one visible change point.

Input: [("delete", "m", 1), ("get", "m", 1), ("getRange", "m", 1, 3), ("set", "m", "z", 4), ("getRange", "m", 2, 4)]

Expected Output: [None, [], [[4, "z"]]]

Explanation: Deleting a missing key leaves it absent, so `get(m, 1)` is None and there is no visible history in [1, 3]. After setting `m` at time 4, the range [2, 4] shows a single visible change at time 4.

Input: [("set", "a", "red", 1), ("set", "a", "blue", 5), ("set", "a", "blue", 7), ("delete", "a", 9), ("getRange", "a", 3, 10), ("restore", 5), ("get", "a", 10), ("getRange", "a", 6, 10)]

Expected Output: [[[3, "red"], [5, "blue"], [9, None]], "blue", [[6, "blue"]]]

Explanation: From time 3 to 10, `a` starts as `red`, changes to `blue` at 5, stays `blue` at 7, and is deleted at 9. Restoring to 5 discards the later update and delete, so afterwards `a` is still `blue` at time 10.

Input: []

Expected Output: []

Explanation: No operations means no query outputs.

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