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What is YARN, and how does it manage resources in a Hadoop ecosystem?

Spark/Big Dataeasy0.3 min read

**YARN**: Yet Another Resource Negotiator. Cluster resource manager. Allocates containers (CPU, memory) to applications (MapReduce, Spark, Hive). **Components**: ResourceManager (scheduler, allocates), NodeManager (per-node, runs containers), ApplicationMaster (per-app,...

🤖 Analyze Your Answer
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
spark

Why This Question Matters

This easy-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 (spark) will help you answer variations of this question confidently.

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Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.

Expert Answer
69 words

YARN: Yet Another Resource Negotiator. Cluster resource manager. Allocates containers (CPU, memory) to applications (MapReduce, Spark, Hive).

Components: ResourceManager (scheduler, allocates), NodeManager (per-node, runs containers), ApplicationMaster (per-app, negotiates resources).

Why It Matters: Multi-tenancy; multiple frameworks share cluster. Fair/Capacity schedulers for fairness.

Scalability Trade-offs: Single ResourceManager (or HA pair); scale NodeManagers. Tune container size for Spark (yarn.nodemanager.resource.memory-mb).

Cost Implications: Shared cluster = higher utilization than dedicated. Tune to avoid under/over-allocation.

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