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This is a medium difficulty Coding & Algorithms question, commonly asked during Onsite rounds at Scale AI.

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

Compute party time blocks per neighborhood | Scale AI Interview Question

Quick Overview

This question evaluates a candidate's ability to perform interval merging and temporal aggregation across joined datasets, testing algorithmic efficiency, data-processing skills, and handling of sorting and grouping in the Coding & Algorithms domain.

You are given two datasets:

Parties :
- party_id (string or integer)
- start_timestamp (e.g., UNIX timestamp or datetime)
- end_timestamp (same type as start_timestamp , and end_timestamp > start_timestamp )
PartyLocations :
- party_id
- neighborhood (string)
- city (string)
- state (string)

Assume each party_id appears exactly once in Parties and once in PartyLocations.

Define a party time block for a given (neighborhood, city, state) as a maximal continuous time interval during which there is at least one ongoing party in that neighborhood. If two party intervals in the same neighborhood overlap or touch (i.e., the end of one equals the start of another), they belong to the same block.

Task

Given the Parties and PartyLocations data, compute all party time blocks for each neighborhood.

Output

Return a list of records with the following fields:

neighborhood
city
state
block_start_timestamp
block_end_timestamp

Each record represents one maximal merged time block for that neighborhood. The list should be sorted by state, then city, then neighborhood, then block_start_timestamp.

You may assume there are up to $10^5$ parties in total and should design an algorithm that is efficient for this input size.

Quick Overview

You are given two datasets:

Parties :
- party_id (string or integer)
- start_timestamp (e.g., UNIX timestamp or datetime)
- end_timestamp (same type as start_timestamp , and end_timestamp > start_timestamp )
PartyLocations :
- party_id
- neighborhood (string)
- city (string)
- state (string)

Assume each party_id appears exactly once in Parties and once in PartyLocations.

Task

Given the Parties and PartyLocations data, compute all party time blocks for each neighborhood.

Output

Return a list of records with the following fields:

neighborhood
city
state
block_start_timestamp
block_end_timestamp

Each record represents one maximal merged time block for that neighborhood. The list should be sorted by state, then city, then neighborhood, then block_start_timestamp.

You may assume there are up to $10^5$ parties in total and should design an algorithm that is efficient for this input size.

Compute party time blocks per neighborhood

Quick Overview

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Compute party time blocks per neighborhood

Quick Overview

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