Most Popular Hadoop Interview Questions and Answers
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This document provides a collection of Hadoop interview questions and answers, emphasizing the basic components of Hadoop, such as its processing framework and storage solutions. Key topics include Hadoop's architecture with HDFS and YARN, fault tolerance mechanisms, common input formats, and the roles of active and passive name nodes. The content serves as a guide for individuals preparing for Hadoop-related interviews, highlighting essential concepts and troubleshooting steps.
Most Popular Hadoop Interview Questions and Answers
- 2. Basic Hadoop Interview Questions and Answers Q1. what do you mean by Hadoop and its component? The ideal way to answer this question is by sticking to the main components that are the storage units and processing framework. When it comes to defining Hadoop, you have to start with big data. Below we have provided you a sample answer to which you can relate to and form your own answers. It is an open-source distributed processing framework pet stores, and the process is big data. The end users can use this software and have access to a network of many computers to resolve the problems related to mammoth amounts of data and its computation. It is commonly used for commodity hardware and is design for computer clusters. The best part is all the common occurrences of problems and failures in the hardware his fundamentally handled by the framework itself. . To Learn More Visit Link in the description
- 3. Q2. Define HDFS and YARN? Hadoop distributed file system is known as HDFS, file yet another resource negotiator is known as YARN. HDFS is designed to store data in blocks in a diverse environment and architecture. The environment consists of a master node, which is called a name node. This is where all the data are structured in blocks, location, and replication factors—making it to metadata information repository. The slave nodes which are responsible for the storage and blocks communication and replication factors are known as a data node. The name node is responsible for managing all the data nodes in our master and slave topology. While yet another resource negotiator can we define as a processing framework that provides execution and management of resources stored in the environment, it has a resource manager who is responsible for acting upon the received processing request. It corresponds with node managers and initiates actual processing. It works in a batch mode and allocates resources to applications based on their needs. An old manager, which is a part of YARN, can be found in every data node responsible for the execution of the task. To Learn More Visit Link in the description
- 4. FsImage, otherwise called metadata replica, is used to start a new name node in the file system. Then we start the configuration process. Further data notes, as well as the clients, are acknowledged as a new NameNode after the initiation of the first step. In the end, we get enough block reports from the data nodes that are loaded from the last checkpoint FsImage. H we have to follow a three-step approach in troubleshooting Hadoop cluster up problems, and they are: This usually takes up a lot of time to re-direct and extract the data, which may serve as a great challenge while doing routine maintenance. But with the use of high availability architecture, we can eliminate it in no time Example Q3. Illustrate the steps to fix the name node when it is a malfunction?
- 5. Q4. What do you mean by a checkpoint? User------fsimage-----checkpointing----- mkdir”/foo” ----- NameNode-----edit log. This is a process that takes the request of file system metadata replica, edits log, and further compacts them into a new FsImage. 1.Check preconditions----GET/ getimage?putimage=1-- ----- HTTP Get to getimage------ GET/ getimage----- new fsimage data----- saves to intermediate filename-- ---putimage completes----- save MD5 file & renames fsimage to final desitination. 1.
- 6. Q5. Illustrate how HDFS fault is tolerant? The problem with a single machine is that in a legacy system, the relational database performs both read and write operations by the users. If any contingency situation arises like a mechanical failure or power down from the user has to wait still, the issue is corrected manually. Another set of problems with legacy systems is that we have to store the data in a range of gigabytes. The data storage capacity was limited and enhanced data storage capacity. We have to buy a new server machine. It directly fixes the cost of maintaining file systems and issues related to it. With the all-new Hadoop distributed file system, we can overcome storage capacity problems and tackle favorable conditions like machine failure, RAM crash, and power down. To Learn More Visit Link in the description
- 7. Replication mechanism RAID or Redundant Array of Independent Disks makes practical usage of the Erasure coding by having effective space-saving methods. It can reduce up to 50% of storage overhead for each strip of the original dataset. Erasure Coding The idea here is to create a replica of the data block & store then in the DataNode. The replicas list entirely depends upon the replication factor that ensures no loss of data due to replicas stored on a variety of machines.
- 8. Q6. What are the common input formats in Hadoop? In Hadoop, we have provisions made accessible for input formats in three significant categories, and they are as follows: The input format for reading files in sequence, also known as Sequence File Input format. To Learn More Visit Link in the description
- 9. Pre-record to present anytime, anywhere In Hadoop, we have provisions made accessible for input formats in three significant categories, and they are as follows: The input format for reading files in sequence, also known as Sequence File Input format. The default input format of the Hadoop is known as the Text Input Format. The format that helps users to read plain text files is called Key-Value Input Format. Q8. Define Active and Passive NameNodes? The NameNode that helps to run the Hadoop cluster resource is called the Active NameNode. While the standby NameNode that helps in the storage of data for the Active NameNode is otherwise called as Passive NameNode. They both are the components of the High Availability Hadoop System, whose sole purpose is to provide fluidity and increase the effectiveness of the cluster and the system files. To Read Full Article Visit Link in the description
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