HDFS Interview Questions and Answers
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Best HDFS Interview Questions and Answers
If you are truly aspiring to start your career in the Hadoop Ecosystem (HDFS), then you must have a basic idea about fundamental concepts of the Hadoop Distributed File System to face the interviewer. Need not worry about this because you can easily gain basic knowledge in HDFS by learning the Hadoop course. This blog covers the most frequently asked HDFS interview questions and answers to help you out in cracking the interview. Mastering these top HDFS interview questions and answers will help you build your level of knowledge step higher in HDFS. The demand for HDFS professionals is rising in the market, so there are an increasing number of job opportunities on HDFS to help aspirants provide the best workplace. These top HDFS interview questions and answers will be helpful for the aspirants to have a glance and provide useful information. Without late, let’s go through the most frequently asked HDFS interview questions and answers.
Apache Hadoop is the most popular open-source software framework. Actually, it is defined as a collection of software utilities that mainly allows you to solve problems involving a huge amount of data and computation using a network of many computers. Apache Hadoop provides storage for any kind of data. This framework will allow you to perform distributed processing of large data sets across clusters of computers using simple programming models.
The essential Hadoop components are:
- HDFS
- MapReduce
- YARN
HDFS: HDFS stands for Hadoop Distributed File System. It is designed and structured to run on commodity hardware. It provides storage for large volumes of data sets and is known as a primary data storage system for Hadoop applications.
MapReduce: MapReduce is a programming model designed for performing distributed computing. It mainly contains two important tasks such as Map and Reduce.
YARN: YARN is one of the essential components of Hadoop. The main aim of YARN is to split up the functionalities of job scheduling and resource management into separate daemons.
HDFS is abbreviated as Hadoop Distributed File System. It is considered as the key essential component of Apache Hadoop. The main goal of the Hadoop Distributed File System is to provide storage for any kind of data and large data sets. It makes use of commodity hardware and is cost-effective. In this file system, data is distributed over many machines and replicated to ensure high availability. It contains key fields like blocks, Name node, Data Node, etc.
HDFS has a master/slave architecture. The below image provides a clear view of HDFS Architecture.
HDFS contains a NameNode and a DataNode. Both the nodes are designed to run on commodity machines. The NameNode is considered as a master server, it is used to manage file system namespace. There are a number of data nodes present in HDFS, commonly one per node in the cluster. The DataNode is mainly used to manage the storage attached to the nodes that they run on and they are also responsible for serving read and write requests from clients of the file system.
HDFS Block Replication is the key process performed on blocks of a file. It allows you to reliably store very massive files across many machines in a large cluster. HDFS stores each file as a sequence of blocks and all blocks in a file except the last block are of the same size. In HDFS, blocks of a file are replicated for fault tolerance. The replication factor and block size are configurable per file. The replication factor can be specified at the time of file creation and changed later. Files in HDFS are write-once and read many times. The decisions regarding HDFS block replication are taken by NameNode.
The comparison between NAS and HDFS is as follows:
NAS | HDFS |
NAS stands for Network Attached Storage | HDFS stands for Hadoop Distributed File System |
Network Attached Storage is a file storage system that mainly enables multiple users to retrieve data from centralized disk. | HDFS stands for Hadoop Distributed File System. It provides storage for large volumes of data sets and is known as a primary data storage system for Hadoop applications. |
HDFS Data Blocks are distributed across different machines | Data is stored on dedicated hardware |
Data Redundancy is available due to the presence of replication protocol | No probability of data redundancy |
The process of validating the developed and integrated code several times to ensure it works fine before it reaches the end-user is known as continuous
HDFS is the key component of Hadoop. The working of HDFS mainly revolves around NameNode and the DataNode. The NameNode is a master and is usually used to manage file system namespace. The DataNode follows the instructions provided by NameNode. The data separated into blocks is distributed among different DataNodes for storage. Blocks in a file are replicated for fault tolerance and to avoid failure.
OLAP is abbreviated as Online Analytical Processing. OLAP on Hadoop mainly solves Big Data analytics problems without moving data out of the Hadoop platform. Multi-dimensional OLAP cubes are designed directly on the Apache Hadoop platform, to provide an immediate response to multiple queries for enabling quick analytics reports on huge amounts of data on a wide variety of metrics.
In HDFS, the NameNode is the master node. It is mainly used to store the metadata of HDFS. The NameNode is the head of all the DataNodes because it has all the capabilities to manage and maintain DataNodes.
Whereas, DataNode is mainly used to store actual data present in HDFS. The DataNode is known as a slave. DataNodes remains constant in communication. They are also responsible for serving read and write requests from clients of the file system.
The put command in HDFS is similar to that of copyFromLocal command. The fs -put command is used to copy single src or multiple srcs from the local file system to the destination file system. This command is used to read input from stdin and writes to the destination files. The usage of put command is Hadoop fs -put [-f] [-p] [-l] [-d] [ – | <localsrc1> .. ]. <dst>. The output for the put command returns 0 on success and -1 on error.
Syntax:
bin/hdfs dfs -copyFromLocal <local file path> <dest(present on hdfs)>
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In HDFS, the command mkdir is used to create a directory. By default there is no home directory present in Hadoop dfs.
Syntax:
bin/hdfs dfs -mkdir <folder name>
creating home directory:
hdfs/bin -mkdir /user
hdfs/bin -mkdir /user/username -> write the username of your computer
A Is command is used to list all the files in HDFS.
Rack in HDFS is usually a collection of DataNodes connected using the same network switch in a cluster. Rack Awareness is the concept chosen in HDFS to improve network traffic while writing/reading HDFS files in large clusters of Hadoop. The racks used in the Hadoop cluster must be smaller than the number of replicas.
The HDFS should be formatted first before starting it.
- Initially, the command used for formatting HDFS is $ hadoop namenode -format.
- The command used to start HDFS is $ start-dfs.sh
Fault Tolerance in HDFS refers to the working strength of the system in unfavorable conditions. HDFS is highly fault-tolerant. Before versions of Hadoop handled faults by the process of replica creation. Fault Tolerance provides the ability for the system to continue operating without even if one or two components fail. Backup components are used by the fault-tolerance to automatically take the place of failed components.
Block in HDFS is the physical representation of data. In HDFS, the large files are split into small chunks known as blocks. The block size in HDFS is large to just minimize the cost of seeking. The DataNodes perform block replication, creation, and deletion according to the NameNode instructions.
Hadoop Scheduler is used when you run a massive Hadoop cluster with a number of clients and priorities and types of jobs. The scheduler is the correct choice to provide guaranteed access with the potential to reuse the unused prioritized jobs within queues.
The different types of Hadoop Schedulers are as follows:
- Capacity Scheduler
- First In First Out (FIFO) Scheduler
- Hadoop Fair Scheduler
HDFS Federation plays a key role in improving the existing HDFS Architecture through a separation of storage and namespace for enabling the general block storage layer. HDFS Federation enables support for multiple namespaces in the cluster to provide enhanced isolation and scalability.
Block Scanner in HDFS is mainly used to track the list of blocks present on DataNode. It is also used to identify corrupted DataNode Blocks. Block Scanner runs periodically on every Data Node to cross-check whether the Data Blocks stored are correct or not. Furthermore, it verifies the checksum of every block stored on the DataNode.
HDFS Metadata plays a prominent role in representing the structure of HDFS files and directories in a tree. The metadata in HDFS also includes different attributes of files and directories such as quotas, permissions, replication factor, ownership, etc.
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The process of indexing in HDFS is different from the local file system. The Indexing process is done using the HDFS Node memory where the data resides. The index files are stored in one folder present in the main directory. The process of indexing in HDFS mainly depends on block size. Indexing in HDFS can be achieved by two types such as InputSplit Indexing and File-Based Indexing.
The NameNode is known as a master node in HDFS.
No but you can do this way or try this way to modify file – first to modify the file present in HDFS, the file should be locally brought. You need to modify the file and then put it back again into HDFS. In order to place the modified file into HDFS, you need to remove the old file from it. In another way, you need to rewrite the entire file and replace the old one.
Yes, you can overwrite or modify the replication factor in HDFS. The replication factor can be overwritten in two different ways. They are
- As per-file basis
- The second way is directly in hdfs-site.xml
You can overwrite the replication factor in HDFS using Hadoop FS Shell. The first way is to modify the replication factor based on file basis by making use of the command $hadoop fs –setrep –w 2 /my/sample.xml. The second way is to modify the replication factor of all files under one directory using the command $hadoop fs –setrep –w 6 /my/sample_dir.
The Hadoop utility called Distributed Copy or the command distscp is used to perform a large intra/inter-cluster data copying process. The Distributed Copy utility is used to copy massive amounts of HDFS files in between or within the HDFS Clusters.
In HDFS, heartbeats are the signals sent by DataNodes to the NameNode to provide indication that the DataNode is functioning/working properly (alive). In HDFS, the interval of the heartbeat is 3 seconds by default and that can be configured using dfs.heartbeat.interval.
The main function of Data Integrity in HDFS is to ensure that no data is corrupted or lost during data processing or storage. Moreover, HDFS software also implements checksums for checking the contents of HDFS files.
The key features of HDFS are:
- It is Highly Scalable
- Fault Tolerance – signifies the system robustness in the event of failure.
- It is Portable
- Replication
- Distributed Data Storage
Streaming Data Access in HDFS is needed for the applications run on HDFS to their data sets. HDFS is mainly designed and developed for batch processing. Streaming data access in HDFS indicates a continuous reading of data with constant bitrate.
The advantages of Hadoop are:
- Cost-Effective
- Fast and Flexible
- Highly Scalable
- Resilient to Failure
- Fault-Tolerant
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