Commonly asked Hadoop famous interview questions and answers.
- What is the maximum size of a file that can be stored in HDFS?
- The maximum size of a file that can be stored in HDFS is determined by the block size, which is typically set to 128 MB or 256 MB. Therefore, the maximum size of a file is 128 MB or 256 MB multiplied by the number of data nodes in the cluster.
- What is the purpose of the JobTracker in Hadoop?
- The JobTracker in Hadoop is responsible for scheduling jobs, allocating resources, and monitoring job execution in the cluster.
- What is the purpose of the TaskTracker in Hadoop?
- The TaskTracker in Hadoop is responsible for running tasks assigned by the JobTracker and reporting their status back to the JobTracker.
- What is the difference between MapReduce and Spark?
- MapReduce is a batch processing framework used for processing large volumes of data, while Spark is a general-purpose data processing engine that supports batch processing, real-time processing, and machine learning.
- What is a speculative task in Hadoop?
- A speculative task is a copy of a task that is executed on a different node in the cluster if the original task is taking too long to complete. This is done to prevent slow tasks from delaying the entire job.
- What is the purpose of Hadoop MapReduce?
- Hadoop MapReduce is a programming model and software framework used for processing large volumes of data in a distributed environment. It allows users to write Map and Reduce functions to process data in parallel across a large cluster of machines.
- What is the purpose of HDFS?
- HDFS is a distributed file system used for storing and managing large datasets in a distributed environment. It is optimized for handling large files and is designed to be highly fault-tolerant and scalable.
- What is the purpose of YARN?
- YARN is a resource management and job scheduling framework used in Hadoop. It allows users to run multiple applications simultaneously and manage resources dynamically.
- What is a Rack in Hadoop?
- A Rack is a collection of nodes within the same network switch. In Hadoop, data replication is done across nodes in different racks to increase data reliability and fault tolerance.
- What is a Job in Hadoop?
- A Job in Hadoop is a unit of work that consists of one or more MapReduce tasks. It typically involves reading input data, processing it, and writing output data.
- What is a Speculative Execution in Hadoop?
- Speculative Execution is a feature in Hadoop that enables the JobTracker to run multiple copies of the same task in parallel on different nodes in the cluster. This is done to ensure that tasks complete in a timely manner and to improve overall job performance.
- What is the difference between Hadoop and traditional databases?
- Hadoop is designed to handle large volumes of unstructured data, while traditional databases are designed to handle structured data. Hadoop is optimized for batch processing and can handle data in various formats, while traditional databases are optimized for real-time transactional processing.
- What is a Distributed Cache in Hadoop?
- A Distributed Cache is a feature in Hadoop that allows data to be cached and shared across multiple nodes in the cluster. It is commonly used to distribute small, read-only files such as lookup tables and configuration files to all nodes in the cluster.
- What is the purpose of Hadoop Streaming?
- Hadoop Streaming is a utility in Hadoop that allows users to create and run MapReduce jobs using any programming language that can read and write to standard input and output streams.
- What is a SequenceFile in Hadoop?
- A SequenceFile is a file format in Hadoop used for storing binary key-value pairs. It is optimized for both sequential and random access and is commonly used as an intermediate format in MapReduce jobs.
- A SequenceFile is a binary file format used in Hadoop that is optimized for storing large amounts of structured or unstructured data.
- It provides fast and efficient serialization and deserialization of key-value pairs and is often used as an intermediate format in MapReduce jobs.
- What is the purpose of a Partitioner in Hadoop?
- A Partitioner in Hadoop is responsible for dividing the intermediate key-value pairs produced by the Mapper into separate partitions. It determines which reducer will receive which set of key-value pairs.
- What is the purpose of a Local JobRunner in Hadoop?
- A Local JobRunner in Hadoop is a mode that allows users to run MapReduce jobs on a single machine without the need for a full Hadoop cluster.
- It is commonly used for testing and debugging MapReduce jobs.
- What is a Task in Hadoop?
- A Task in Hadoop is a unit of work that is performed by a TaskTracker. Each Task corresponds to either a Map or a Reduce operation in a MapReduce job.
- What is a JobHistory Server in Hadoop?
- A JobHistory Server in Hadoop is responsible for storing information about completed MapReduce jobs.
- This information includes job configuration details, job status, and counters.
- What is a Side Data Distribution in Hadoop?
- A Side Data Distribution is a feature in Hadoop that allows users to distribute read-only data to all nodes in the cluster before a MapReduce job begins.
- This can improve job performance by reducing the amount of data that needs to be transferred over the network.
- What is the role of a Namenode in HDFS?
- The role of a Namenode in HDFS is to manage the file system namespace, regulate access to files by clients, and maintain the metadata about the files stored in HDFS.
- What is the role of a DataNode in HDFS?
- The role of a DataNode in HDFS is to store and retrieve data blocks as requested by clients. It is responsible for storing and maintaining replicas of the data blocks assigned to it.
- What is the purpose of a combiner function in Hadoop?
- A Combiner function in Hadoop is used to perform a local aggregation of the Map output key-value pairs before they are sent over the network to the Reducer.
- It is an optimization technique that reduces the amount of data transferred over the network, thus improving performance.
- What is the Hadoop Distributed File System (HDFS)?
- HDFS is a distributed file system that is designed to store and manage large datasets across multiple machines.
- It provides high availability and fault tolerance by storing data in multiple replicas across the cluster.
- What are the advantages of Hadoop?
- Some advantages of Hadoop include its ability to store and process large amounts of data, its fault tolerance and high availability, its scalability, and its ability to process data in parallel.
- What are the components of a Hadoop cluster?
- A Hadoop cluster typically consists of a NameNode, Secondary NameNode, DataNodes, JobTracker, TaskTrackers.
- What is the Hadoop Ecosystem?
- The Hadoop Ecosystem refers to the collection of open-source tools and frameworks that are built on top of Hadoop to extend its capabilities.
- These include tools for data processing, data analysis, data storage, and more.
- What is the purpose of a Secondary NameNode in Hadoop?
- The Secondary NameNode in Hadoop is responsible for periodically merging the edit logs and the file system image to create a new checkpoint for the NameNode.
- This helps to reduce the amount of time required to restart the NameNode in case of a failure.
- What is the purpose of a Combiner in Hadoop?
- A Combiner in Hadoop is used to perform a local aggregation of the output key-value pairs produced by the Mapper before they are sent over the network to the Reducer.
- It is an optimization technique that reduces the amount of data transferred over the network, thus improving performance.
- What is the difference between Hadoop and Spark?
- Hadoop is a distributed data processing framework that is designed to store and process large amounts of data, while Spark is a data processing engine that is designed to perform fast, in-memory data processing.
- Spark can be used with Hadoop, but it is not dependent on Hadoop.
- What is a Distributed Cache in Hadoop?
- A Distributed Cache in Hadoop is a feature that allows users to distribute small, read-only files such as lookup tables and configuration files to all nodes in the cluster. This can improve job performance by reducing the amount of data that needs to be transferred over the network.
- What is the purpose of a YARN Resource Manager in Hadoop?
- The YARN Resource Manager in Hadoop is responsible for managing the resources in the cluster and allocating them to applications.
- It is responsible for scheduling applications and managing the execution of application containers on the cluster.
- What is MapReduce in Hadoop?
- MapReduce is a programming model and software framework for processing large datasets in a distributed manner across a Hadoop cluster.
- It consists of two phases: Map and Reduce.
- The Map phase takes in a set of input data and transforms it into key-value pairs, and the Reduce phase takes in those key-value pairs and aggregates them into a final output.
- What is the difference between a block and a split in Hadoop?
- A block is a physical division of a large file into smaller pieces for storage on the Hadoop Distributed File System (HDFS).
- A split, on the other hand, is a logical division of a block for processing by a Mapper.
- What is the purpose of a NameNode in Hadoop?
- The NameNode in Hadoop is responsible for managing the file system metadata, such as the namespace, permissions, and block locations.
- It keeps track of where the data is stored in the cluster and provides clients with the information they need to access the data.
- What is the purpose of a JobTracker in Hadoop?
- The JobTracker in Hadoop is responsible for managing the MapReduce jobs submitted to the cluster.
- It is responsible for scheduling tasks, monitoring their progress, and rescheduling failed tasks.
- What is the purpose of a Reducer in Hadoop?
- A Reducer in Hadoop is responsible for taking the output of the Mapper as input and aggregating the key-value pairs based on the key.
- The Reducer produces the final output for the MapReduce job.
- What is the purpose of a Mapper in Hadoop?
- A Mapper in Hadoop is responsible for taking in a set of input data and transforming it into key-value pairs. It produces intermediate key-value pairs that are then sent over the network to the Reducer for aggregation.
- What is the purpose of HBase in Hadoop?
- HBase is a NoSQL database that is built on top of Hadoop and provides real-time access to large datasets.
- It is designed to handle large amounts of unstructured data and provides high scalability, fault tolerance, and performance.
- What is the purpose of Hive in Hadoop?
- Hive is a data warehouse system for Hadoop that provides a SQL-like interface for querying large datasets.
- It is designed to be used by analysts and data scientists who are familiar with SQL and provides a high-level abstraction over the Hadoop Distributed File System (HDFS).
