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Optimize a function to calculate moving averages of user engagement.

Python/Codinghard0.5 min readPremium

**Why Moving Averages in Engagement:** DAU/MAU curves, session duration trends, retention metrics—all use rolling windows. Naive O(n*window) per row is unacceptable at scale. **Optimization:** (1) Cumulative sum: precompute cumsum, then avg[i] = (cumsum[i] - cumsum[i-w]) / w....

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
Category
179
questions in Python/Coding
Difficulty Split
127E|24M|28H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Disney+ Hotstar
Key Concepts Tested
optimizationsparkwindow

Why This Question Matters

This hard-level Python/Coding question appears frequently in data engineering interviews at companies like Disney+ Hotstar. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, spark, window) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
105 words

Why Moving Averages in Engagement: DAU/MAU curves, session duration trends, retention metrics—all use rolling windows. Naive O(n*window) per row is unacceptable at scale.

Optimization: (1) Cumulative sum: precompute cumsum, then avg[i] = (cumsum[i] - cumsum[i-w]) / w. O(n) total. (2) Pandas: rolling(window=7).mean()—vectorized, uses underlying C. (3) Streaming: maintain deque of size w, running sum—O(1) per update. (4) Exponential: EMA = αnew + (1-α)prev—O(1), no window storage.

Cost: At Hotstar, 10M users * 30 days with window=7: naive = 2.1B ops; cumsum = 300M. Use Spark window functions for distributed: rowsBetween(-6, 0).

def opt_moving_avg(arr, w):
cumsum = np.cumsum(np.insert(arr, 0, 0))
return (cumsum[w:] - cumsum[:-w]) / w

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