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Process a large log file (in GBs) to identify the top 10 users by event frequency. Optimize for memory efficiency and handle streaming input.

Spark/Big Datahard0.4 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
Google
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
73 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Process large log file for top 10 users: Use Spark: `spark.read.text(path).select(split(col('value'),' ').getItem(0).alias('user')).groupBy('user').count().orderBy(desc('count')).limit(10)`. For streaming: `readStream` with watermark. Optimize: partition by date; use reduceByKey equivalent; avoid collect....
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 Google. The answer also includes follow-up discussion points that interviewers commonly explore.

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