Hadoop Interview Questions and Topics – HDFS, MapReduce, YARN, and Optimization
This article compiles a comprehensive set of Hadoop interview questions covering HDFS write and read processes, architecture, fault‑tolerance, NameNode metadata management, MapReduce scheduling, combiner and partition roles, YARN scheduling strategies, and various optimization techniques for both MapReduce and HDFS.
HDFS Section
Questions explore the HDFS write and read workflows, detailed architecture, block replication factor, default block size, data storage components, SecondaryNameNode purpose, file size impact, and mechanisms ensuring data safety and high availability.
MapReduce Section
Deep‑dive queries address serialization, custom bean handling, InputSplit concept, determination of map and reduce task numbers, task counting logic, MapTask and ReduceTask mechanisms, sorting phases, shuffle workflow and optimization, combiner usage and differences from reducers, partitioning behavior, load balancing, Top‑N implementation, caching (DistributedCache), join strategies, and scenarios unsuitable for MapReduce acceleration.
HDFS Deep Dive
Additional items probe block replication count, block size evolution, storage responsibilities, SecondaryNameNode functions, file size considerations, and overall HDFS architecture.
YARN Section
Questions compare Hadoop 1 and Hadoop 2 architectures, motivations behind YARN, its advantages, HDFS compression algorithms, scheduler summaries, MapReduce 2.0 fault tolerance, and speculative execution algorithms.
Optimization and Other Issues
Topics include reasons for slow MapReduce performance, optimization methods, and HDFS small‑file handling techniques.
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Big Data Technology & Architecture
Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
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