☸️ Hadoop Interview Questions and Answers (2025)
Basic Level Questions
What is Hadoop?▶
Hadoop is an open-source framework that enables distributed storage and processing of large data sets using commodity hardware.
What is HDFS?▶
Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware and store large volumes of data reliably.
What is MapReduce?▶
MapReduce is a programming model for processing and generating large data sets with a parallel, distributed algorithm on a Hadoop cluster.
What are the main components of Hadoop?▶
The core components are HDFS for storage and YARN for resource management and job scheduling.
What is YARN?▶
Yet Another Resource Negotiator (YARN) is Hadoop’s cluster resource management system that schedules and manages tasks.
What is a JobTracker?▶
JobTracker was the service in Hadoop 1 responsible for resource management and scheduling MapReduce jobs (replaced by YARN in Hadoop 2).
What is a NameNode?▶
NameNode is the master server that manages the file system namespace and regulates access to files by clients.
What is a DataNode?▶
DataNodes are slave nodes that manage storage attached to nodes and serve read/write requests from clients.
What is a block in HDFS?▶
HDFS stores files in blocks (default size 128MB/256MB), distributed across DataNodes for fault tolerance.
How does Hadoop ensure fault tolerance?▶
HDFS replicates data blocks across different DataNodes; if a node fails, data is retrieved from replicated nodes.
Intermediate Level Questions
How does YARN work in Hadoop?▶
YARN manages resources in the cluster and schedules applications by allocating containers to execute tasks.
What is a Reducer in MapReduce?▶
Reducer processes intermediate data from Mappers and aggregates or summarizes the results to produce the final output.
What is a Combiner?▶
Combiner is an optional mini-reducer that performs local aggregation to reduce data transfer between Mapper and Reducer.
What is InputSplit?▶
InputSplit divides a distributed file into logical splits for parallel processing by individual Mapper tasks.
What is a Job in Hadoop?▶
A Job represents a MapReduce program submitted by a user, which is divided into tasks executed in the cluster.
What is speculative execution?▶
Speculative execution runs duplicate copies of slow tasks to improve job completion time by avoiding stragglers.
What is job scheduling in Hadoop?▶
Schedulers (FIFO, Capacity, Fair) allocate cluster resources to jobs, managing priorities and resource sharing.
How is data locality achieved?▶
Hadoop schedules tasks where data resides to minimize network traffic and increase processing efficiency.
What is a shuffle and sort phase?▶
During shuffle, Map outputs are transferred to Reducers; sorting organizes data by keys before reduction.
Explain Hadoop streaming.▶
Hadoop streaming allows MapReduce jobs to be written in any language using standard input/output.
What is the role of JobTracker and TaskTracker in Hadoop 1?▶
JobTracker manages jobs’ scheduling and resource allocation; TaskTracker runs tasks on slave nodes.
Explain YARN architecture components.▶
Includes ResourceManager, NodeManager, ApplicationMaster, and Containers to manage cluster resource allocation and task execution.
What is an ApplicationMaster?▶
ApplicationMaster negotiates resources from ResourceManager and works with NodeManagers to execute and monitor tasks.
What is recordReader?▶
It converts data into key-value pairs suitable for MapReduce processing from input splits.
What is InputFormat and OutputFormat?▶
InputFormat defines how input files are read and split; OutputFormat defines how job output is written.
How do you debug MapReduce jobs?▶
Check logs, counters, use debugging tools, and monitor job progress and task failures.
What are Hadoop counters?▶
Counters are metrics for tracking job progress and performance indicators.
Explain how you can achieve fault tolerance in Hadoop jobs.▶
By replicating data in HDFS and retrying failed tasks in MapReduce.
What is speculative execution?▶
Running duplicate copies of slow tasks on other nodes to reduce job latency.
Explain the concept of a heartbeat in Hadoop.▶
DataNodes send regular heartbeat signals to NameNode to indicate they are alive and functioning.
Advanced Level Questions
What is the Namenode High Availability (HA) in Hadoop?▶
NameNode HA uses active and standby nodes with a quorum-based mechanism (using ZooKeeper) to provide failover and eliminate single point of failure.
Explain Hadoop Federation.▶
Federation allows multiple independent NameNodes to manage separate namespaces to improve scalability.
How does Hadoop handle security?▶
Hadoop uses Kerberos authentication, HDFS permissions, SSL/TLS encryption, and Apache Ranger/Knox for centralized security management.
What is Hadoop MapReduce internals?▶
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.
What is speculative execution and how does it improve job performance?▶
Running backup copies of slower tasks allows jobs to complete faster by minimizing delays caused by slow or failing nodes.
How do you optimize Hadoop cluster performance?▶
Tune memory settings, parallelism, data locality, compression, and monitor to identify bottlenecks; balance resource allocation.
How is data read and written in HDFS?▶
Data is split into blocks and replicated; writes go to closest DataNode with replication; reads fetch blocks from nearby nodes in parallel.
How do Hadoop ecosystem tools integrate with security?▶
Tools like Hive, HBase, and Spark support Kerberos, use encryption for data in transit/rest, and integrate with centralized authorization systems.
Explain the process of data ingestion in Hadoop.▶
Data ingestion uses tools like Sqoop for RDBMS import, Flume for streaming data, and Kafka for real-time pipelines into Hadoop storage.
What are the best practices for Hadoop cluster administration?▶
Regular monitoring, resource tuning, data backup, security hardening, upgrade planning, and capacity management.