Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Why Recursion Fails at Scale:** The naive recursive fib(n) = fib(n-1) + fib(n-2) exhibits O(2^n) time and O(n) stack depth. Each subproblem is recomputed exponentially—fib(40) triggers ~1B calls. In distributed data pipelines, this pattern causes task explosion....
This hard-level Python/Coding question appears frequently in data engineering interviews at companies like Goldman Sachs. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition) will help you answer variations of this question confidently.
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.
Why Recursion Fails at Scale: The naive recursive fib(n) = fib(n-1) + fib(n-2) exhibits O(2^n) time and O(n) stack depth. Each subproblem is recomputed exponentially—fib(40) triggers ~1B calls. In distributed data pipelines, this pattern causes task explosion.
Architectural Trade-offs: (1) Memoization (@lru_cache): O(n) time, O(n) space—suitable for bounded n in single-process. (2) Iterative DP: O(n) time, O(1) space—production default. (3) Matrix exponentiation: O(log n) time—for algorithmic interviews; rarely used in data eng.
Cost Implication: At Goldman, a batch job using recursive Fibonacci on large partitions would exhaust executor memory. Prefer iterative or vectorized (NumPy) for production.
def fib_iter(n):
a, b = 0, 1
for _ in range(n):
a, b = b, a + b
return a
Red Flag: Reciting memoization without explaining when it fails (e.g., multi-worker Spark—each task has its own cache). Pro-Move: 'We use iterative in prod; lru_cache only for service-layer APIs with n < 10^4.'
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According to DataEngPrep.tech, this is one of the most frequently asked Python/Coding interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.