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Multiprocessing in Python - explain with example

Python/Codinghard0.5 min readPremium

**Why Multiprocessing vs Threading:** GIL limits threading to single-core CPU for CPU-bound work. Multiprocessing spawns separate processes—true parallelism, each with own GIL. Use for: CPU-bound (numeric, parsing), NOT for I/O-bound (use asyncio/threading). **Scalability...

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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
NAB
Key Concepts Tested
partitionpythonwindow

Why This Question Matters

This hard-level Python/Coding question appears frequently in data engineering interviews at companies like NAB. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, python, 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
108 words

Why Multiprocessing vs Threading: GIL limits threading to single-core CPU for CPU-bound work. Multiprocessing spawns separate processes—true parallelism, each with own GIL. Use for: CPU-bound (numeric, parsing), NOT for I/O-bound (use asyncio/threading).

Scalability Trade-offs: (1) Process spawn overhead: ~50–100ms each—amortize over chunk size. (2) IPC: Queue/Pipe have serialization cost; shared memory (multiprocessing.Value) for large arrays. (3) Pool.map: simple but materializes all results—use imap for streaming. (4) On Windows: if __name__ == '__main__' is mandatory (spawn semantics).

Cost: 4 workers = 4x memory. For data pipelines: partition data, process in parallel, avoid sending huge objects between processes.

from multiprocessing import Pool
with Pool(4) as pool:
results = pool.map(process_chunk, chunks)

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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 a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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