Optimize least-k revenue queries for read/write load

Q: Optimize least-k revenue queries for read/write load

This question evaluates a candidate's ability to design and maintain dynamic aggregate metrics from nested or incremental inputs, emphasizing data modeling, streaming updates, and the use of appropriate data structures and algorithms to compute least-k aggregates.

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Question

Follow-up Scenario

Now assume revenue is not provided as a flat list of events, but may be nested, for example:

Each customer has many orders, and each order has many line items.
Or a stream of updates arrives as (customer_id, delta_amount) events.

You need to support the query:

“Return the k customers with the smallest total revenue.”

Questions

How would you compute/maintain customer revenue totals when the input is nested (orders → items) or incremental ( delta_amount updates)?
What are the time and space complexities of your approach?
How would you change the design for:
- Read-heavy workload (many leastK() queries, fewer updates)
- Write-heavy workload (many updates, fewer queries)

Assume you do not need to write full code, but must clearly describe data structures, operations, and complexity.

Optimize least-k revenue queries for read/write load

Quick Overview

Follow-up Scenario

Questions

Solution

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