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How would you handle a large-scale data shuffle in a Dataflow pipeline?

Spark/Big Datahard0.5 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Aarete
Key Concepts Tested
optimizationpartitionwindow
Expert AnswerPremium
102 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Large-scale shuffle in Dataflow: (1) Increase worker count and disk. (2) Use `GroupByKey` sparingly; prefer `CombinePerKey` or `CombineGlobally` to reduce data. (3) Use side inputs for small data. (4) Co-locate data—use consistent keys. (5) Increase `maxNumWorkers`; use `dataflowFlexTemplates` for custom config. (6) Consider shuffle service....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Aarete. The answer also includes follow-up discussion points that interviewers commonly explore.

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