Implement a Python function to compute streaming, per-campaign CTR over a sliding 24-hour window with click de-duplication and late-arriving events. Requirements: - Input: two iterators of dicts sorted non-decreasing by ts (ISO 8601 strings): impressions: {"imp_id", "campaign_id", "ts"} clicks: {"click_id", "imp_id", "campaign_id", "ts"} - Dedup: count at most one click per imp_id (keep earliest click). Ignore clicks whose imp_id was never seen. - Sliding window: maintain CTR for each campaign over the last 24 hours at every new event. Late events may arrive up to 10 minutes late; include them if they fall within the 24-hour window based on their ts. - Performance: O(log n) amortized updates per event, memory proportional to events within the last 24 hours. Provide big-O justification. - Output: an iterator of tuples (ts, campaign_id, impressions_in_window, dedup_clicks_in_window, ctr_in_window) emitted whenever the metric changes for that campaign. - Edge cases: multiple clicks referencing same imp_id, clock skew between streams, and daylight saving transitions. Provide a brief explanation of your data structures (e.g., heaps/queues + hash maps) and how you handle late events and expirations.

This question evaluates streaming data-processing and algorithmic design skills, specifically time-windowed aggregation, click deduplication, late-event handling, state management, and selection of data structures for per-campaign CTR computation.

How do I approach Coding & Algorithms interview questions?

Coding & Algorithms questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master coding & algorithms interviews.

What difficulty level is this interview question?

This is a Medium difficulty Coding & Algorithms question, commonly asked during Technical Screen rounds at Roblox.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Roblox during technical interviews.

Implement streaming CTR with deduplication

Implement a Python function to compute streaming, per-campaign CTR over a sliding 24-hour window with click de-duplication and late-arriving events. Requirements:

Input: two iterators of dicts sorted non-decreasing by ts (ISO 8601 strings): impressions: {"imp_id", "campaign_id", "ts"} clicks: {"click_id", "imp_id", "campaign_id", "ts"}
Dedup: count at most one click per imp_id (keep earliest click). Ignore clicks whose imp_id was never seen.
Sliding window: maintain CTR for each campaign over the last 24 hours at every new event. Late events may arrive up to 10 minutes late; include them if they fall within the 24-hour window based on their ts.
Performance: O(log n) amortized updates per event, memory proportional to events within the last 24 hours. Provide big-O justification.
Output: an iterator of tuples (ts, campaign_id, impressions_in_window, dedup_clicks_in_window, ctr_in_window) emitted whenever the metric changes for that campaign.
Edge cases: multiple clicks referencing same imp_id, clock skew between streams, and daylight saving transitions. Provide a brief explanation of your data structures (e.g., heaps/queues + hash maps) and how you handle late events and expirations.

Implement a Python function to compute streaming, per-campaign CTR over a sliding 24-hour window with click de-duplication and late-arriving events. Requirements:

Input: two iterators of dicts sorted non-decreasing by ts (ISO 8601 strings): impressions: {"imp_id", "campaign_id", "ts"} clicks: {"click_id", "imp_id", "campaign_id", "ts"}
Dedup: count at most one click per imp_id (keep earliest click). Ignore clicks whose imp_id was never seen.
Sliding window: maintain CTR for each campaign over the last 24 hours at every new event. Late events may arrive up to 10 minutes late; include them if they fall within the 24-hour window based on their ts.
Performance: O(log n) amortized updates per event, memory proportional to events within the last 24 hours. Provide big-O justification.
Output: an iterator of tuples (ts, campaign_id, impressions_in_window, dedup_clicks_in_window, ctr_in_window) emitted whenever the metric changes for that campaign.
Edge cases: multiple clicks referencing same imp_id, clock skew between streams, and daylight saving transitions. Provide a brief explanation of your data structures (e.g., heaps/queues + hash maps) and how you handle late events and expirations.

Implement streaming CTR with deduplication

Quick Overview

Comments (0)

Implement streaming CTR with deduplication

Quick Overview

Comments (0)