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Data locality in Hadoop - explain

Spark/Big Dataeasy0.4 min readPremium

**Why locality matters**: Running tasks where data lives reduces network transfer—critical when network << disk. **Mechanism**: HDFS stores blocks on DataNodes; MapReduce/Spark prefer to schedule tasks on nodes holding those blocks. Rack-local as fallback; off-rack as last...

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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
JP Morgan
Key Concepts Tested
spark

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This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like JP Morgan. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark) will help you answer variations of this question confidently.

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Expert Answer
83 words

Why locality matters: Running tasks where data lives reduces network transfer—critical when network << disk. Mechanism: HDFS stores blocks on DataNodes; MapReduce/Spark prefer to schedule tasks on nodes holding those blocks. Rack-local as fallback; off-rack as last resort. Scalability trade-offs: Good locality = faster; cloud storage (S3) has no locality—all reads are network. Cost implications: On HDFS, locality saves network; on S3, optimize for request patterns (prefix, caching). Best practice: Co-locate compute and storage when possible; on cloud, optimize for throughput over locality.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data 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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