Optimize Streaming Logistics Processing | Dataford Interview Questions

How would you optimize a Python script that processes large-scale streaming logistics data?

Implement top_k_hubs(events, k) to consume event records, aggregate delay values by hub, and return up to k hubs ordered by total delay descending, then hub name ascending. Each event is a map with string hub and integer delay; return an array of [hub, total_delay] pairs.

Example: events = [{"hub":"LAX","delay":2},{"hub":"LAX","delay":4},{"hub":"LHR","delay":6}], k = 2 returns [["LAX",6],["LHR",6]].

Constraints

0 <= events.length <= 9999
1 <= k
Each event contains a non-empty string hub
delay is an integer
Hub names are compared using ascending lexicographic order

Function Signature

def top_k_hubs(events, k):

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Problem

How would you optimize a Python script that processes large-scale streaming logistics data?

Example: events = [{"hub":"LAX","delay":2},{"hub":"LAX","delay":4},{"hub":"LHR","delay":6}], k = 2 returns [["LAX",6],["LHR",6]].

Constraints

0 <= events.length <= 9999
1 <= k
Each event contains a non-empty string hub
delay is an integer
Hub names are compared using ascending lexicographic order

Function Signature

def top_k_hubs(events, k):

Your question is Optimize Streaming Logistics Processing. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.