Hadoop Interview Questions and Answers (2025) | JaganInfo

Hadoop is an open-source framework that enables distributed storage and processing of large data sets using commodity hardware.

Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware and store large volumes of data reliably.

MapReduce is a programming model for processing and generating large data sets with a parallel, distributed algorithm on a Hadoop cluster.

The core components are HDFS for storage and YARN for resource management and job scheduling.

Yet Another Resource Negotiator (YARN) is Hadoop’s cluster resource management system that schedules and manages tasks.

JobTracker was the service in Hadoop 1 responsible for resource management and scheduling MapReduce jobs (replaced by YARN in Hadoop 2).

NameNode is the master server that manages the file system namespace and regulates access to files by clients.

DataNodes are slave nodes that manage storage attached to nodes and serve read/write requests from clients.

HDFS stores files in blocks (default size 128MB/256MB), distributed across DataNodes for fault tolerance.

HDFS replicates data blocks across different DataNodes; if a node fails, data is retrieved from replicated nodes.

YARN manages resources in the cluster and schedules applications by allocating containers to execute tasks.

Reducer processes intermediate data from Mappers and aggregates or summarizes the results to produce the final output.

Combiner is an optional mini-reducer that performs local aggregation to reduce data transfer between Mapper and Reducer.

InputSplit divides a distributed file into logical splits for parallel processing by individual Mapper tasks.

A Job represents a MapReduce program submitted by a user, which is divided into tasks executed in the cluster.

Speculative execution runs duplicate copies of slow tasks to improve job completion time by avoiding stragglers.

Schedulers (FIFO, Capacity, Fair) allocate cluster resources to jobs, managing priorities and resource sharing.

Hadoop schedules tasks where data resides to minimize network traffic and increase processing efficiency.

During shuffle, Map outputs are transferred to Reducers; sorting organizes data by keys before reduction.

Hadoop streaming allows MapReduce jobs to be written in any language using standard input/output.

JobTracker manages jobs’ scheduling and resource allocation; TaskTracker runs tasks on slave nodes.

Includes ResourceManager, NodeManager, ApplicationMaster, and Containers to manage cluster resource allocation and task execution.

ApplicationMaster negotiates resources from ResourceManager and works with NodeManagers to execute and monitor tasks.

It converts data into key-value pairs suitable for MapReduce processing from input splits.

InputFormat defines how input files are read and split; OutputFormat defines how job output is written.

Check logs, counters, use debugging tools, and monitor job progress and task failures.

Counters are metrics for tracking job progress and performance indicators.

By replicating data in HDFS and retrying failed tasks in MapReduce.

Running duplicate copies of slow tasks on other nodes to reduce job latency.

DataNodes send regular heartbeat signals to NameNode to indicate they are alive and functioning.

NameNode HA uses active and standby nodes with a quorum-based mechanism (using ZooKeeper) to provide failover and eliminate single point of failure.

Federation allows multiple independent NameNodes to manage separate namespaces to improve scalability.

Hadoop uses Kerberos authentication, HDFS permissions, SSL/TLS encryption, and Apache Ranger/Knox for centralized security management.

MapReduce splits job into tasks, with Map tasks processing input splits producing intermediate data that Reducers aggregate; data is shuffled and sorted efficiently during execution.

Running backup copies of slower tasks allows jobs to complete faster by minimizing delays caused by slow or failing nodes.

Tune memory settings, parallelism, data locality, compression, and monitor to identify bottlenecks; balance resource allocation.

Data is split into blocks and replicated; writes go to closest DataNode with replication; reads fetch blocks from nearby nodes in parallel.

Tools like Hive, HBase, and Spark support Kerberos, use encryption for data in transit/rest, and integrate with centralized authorization systems.

Data ingestion uses tools like Sqoop for RDBMS import, Flume for streaming data, and Kafka for real-time pipelines into Hadoop storage.

Regular monitoring, resource tuning, data backup, security hardening, upgrade planning, and capacity management.