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What Hadoop command would you use to merge multiple files into one?

Spark/Big Datamedium0.5 min read

**getmerge** (HDFS to local): `hadoop fs -getmerge /input/dir /local/merged.txt` **In-HDFS merge** (cat + put): `hadoop fs -cat /input/dir/* | hadoop fs -put - /output/merged` **Spark** (preferred for large data): `spark.read.text("/input/*").coalesce(1).write.text("/output")`...

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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
Infosys
Key Concepts Tested
partitionspark

Why This Question Matters

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

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Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.

Expert Answer
109 words

getmerge (HDFS to local): hadoop fs -getmerge /input/dir /local/merged.txt

In-HDFS merge (cat + put): hadoop fs -cat /input/dir/* | hadoop fs -put - /output/merged

Spark (preferred for large data): spark.read.text("/input/*").coalesce(1).write.text("/output")

Why Avoid Single Huge File: (1) No parallelism for downstream reads. (2) Single block limits map tasks. (3) Memory pressure if read entirely. Merge only when consumer requires single file (e.g., export to external system).

Scalability Trade-offs: getmerge pulls to driver/local—fails for TB. Spark coalesce(1) shuffles all data to one partition; expensive. Prefer multiple files with reasonable size (64–128MB).

Cost Implications: Coalesce(1) on 1TB = full shuffle; use only when necessary. Small-file consolidation (OPTIMIZE in Delta) is different—keeps parallelism.

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