Hadoop interview questions and answers
Hadoop Administrator is one that manages Hadoop clusters and any other resources throughout the Hadoop Ecosystem. The job of a Hadoop admin is not visible to other IT or end-users. The role of a Hadoop Admin is primarily linked to tasks involving the installation and monitoring of Hadoop clusters.
Hadoop Admin jobs could include some ordinary tasks, although they are each important in order to prevent problems and improve the general performance for the efficient and continuous operation of the Hadoop cluster. A Hadoop admin is dully responsible for the safe and efficient operation of the Hadoop clusters.
So, make yourself a candidate for a high-quality Hadoop Admin work profile, speak about your knowledge and skills in Hadoop ventures, demonstrate multitasking and leading skills in your unique fields of interest and expertise.
If you have found your dream job at Hadoop Admin, but you are not sure how to crack the Hadoop Admin Interview. If you are not preparing enough time, it’s a tedious task to crack a Hadoop Admin Interview. Every interview is different, and the nature of a job is different too.
In this respect, we have created the most common questions and Answers for the Hadoop Admin interview to help your interview success. It is important, therefore, to review the following questions for the interview from Hadoop Admin, while you are planning for your interview.
Hadoop interview questions and answers
1- How can you read a file from HDFS?
This is the most common question asked during the interview with Hadoop Admin. So, by following the step below, you can read a file from HDFS.
First of all, you have to use a Hadoop client program to make the request.
Now, The client program reads a local machine cluster config file that will give you the location of the NameNode. This needs to be set up in advance.
You must then contact the NameNode and ask the file that you want to read.
The username or strong authentication mechanism, such as Kerberos, monitors your validation.
The owner and permissions of the file are reviewed for your validated request.
If a file exists and the user can access it, NameNode addresses the first ID block and provides a list of datanode where you can locate a copy of the block by its distance to the client (reader).
Now you can instantly contact the most suitable datanode and can read the block data directly. It continues until all frames in the file are read, or the user closes the flow of the file.
If the datanode dies during the file reading, a replica of data from another datanode will be automatically read by the library. If all replicas are inaccessible, the read process fails, and the consumer has an exception. If the information returned by the NameNode about block locations is outdated when the client tries to contact a datanode, an inspection will take place when other replicas exist, or the reading fails.
2- What is the default HDFS block size, and how do smaller block size benefit?
Most file systems with a block structure have a block size of about 4 or 8 KB. The default HDFS block size is 64 MB–and higher. This reduces the quantity of metadata stored per file provided by HDFS.
In addition, it enables fast data streaming readings by sequentially arranging large amounts of data on the disk. As a consequence, very large files should be read sequentially in HDFS. When compared to the NTFS or EXT filesystem, HDFS holds a limited number of very large files: hundreds of megabytes or gigabytes.
Hadoop interview questions 3- What are the applications of Hadoop in the real-time industry?
Hadoop, well known as Apache Hadoop, is a scalable and distributed open-source software platform for large-volume data computing. It provides quick, high-performance, and cost-effective data analysis produced on digital platforms and within the enterprise. It is currently in use in nearly all departments and sectors.
Many cases in which Hadoop is used are as follows:
1. Email archiving and content management
2. Hadoop is used to collect and analyze click source, payment, video, and social media information through advertising targeting platforms
3. Traffic management on highways
4. Managing social media platform content, comments, photos, and videos
5. The identification and prevention of fraud
6. Processing Streaming
7. Effective analysis of customer data to improve business performance
8. It is widely used in Public sector fields like defense, cybersecurity, intelligence, and scientific research.
4- Define the distributed cache and its advantages?
Hadoop distributed cache is a MapReduce framework service that caches files when required.
Once a file has been stored for a specific task, Hadoop places it in device and memory on each DataNode, where it maps and reduces tasks. You can then access, read and insert any cache files (such as list, hashmap) into your code. You can then read them easily.
The advantages of cache distribution are:
The distributed cache monitors cache time stamps, which warn that files should not be updated until a job is done.
This distributes basic files and/or complex text/data files, such as containers and archives. Then these files are unarchived at the slave node.
Hadoop interview questions 5. Explain the most common input formats in Hadoop?
There are three most common input formats available in Hadoop:
1. First, the most common input format that is available in Hadoop is key; it is the Value Input Format, which is used for simple text files in which the data are separated into rows.
2. The second most common input format in Hadoop is Text Input Format, which is the default input format in Hadoop.
3. The third widely used Input Format is the sequence that is generally used for reading files in sequence.
6- In which modes can code be executed with Hadoop?
You may deploy Hadoop in
1. Pseudo-distributed mode
2. Fully distributed mode
3. Standalone mode
Hadoop interview questions 7- What are the commands that can be used for coping with Hadoop shell?
The command for the copy operation are:
8- If NameNode is down, what will happen?
The file system will go offline while the NameNode is down.
9- How can you restart a NameNode? Explain it.
One of the simplest ways to restart a NameNode is to run the command to stop running sell script.
You need to click on stop.all.sh. Then reboot the NameNode by clocking on start-all-sh.
Hadoop interview questions 10- Define the use of ‘jps’ command?
The ‘jps’ command is one of the most useful commands that helps us to determine the Hadoop daemons are running or not. Apart from this, the ‘jps’ command also shows all the Hadoop daemons which are running on the machine such as resource manager, node manager, Datanode, Namenode, etc.
11- Define checkpointing in Hadoop and its importance .
Checkpointing is an important part of Hadoop as it helps to manage and persist filesystem metadata in HDFS. In addition to this, This is necessary for efficient Namenode recovery and restart and is an effective cluster health indicator.
Nameode persists with the metadata of the filesystem. The primary responsibility of a Namenode is to store the HDFS namespace at a high level. Things, such as file permissions, directory tree and file mapping for blocking IDs. It is important to maintain this metadata in a stable way for failure tolerance processing.
This metadata file system is stored in the fsimage and edit log parts. fsimage is the file that displays a point-in-time snapshot of the metadata of the filesystem. Although the format for the fsimage file is quite readable, it is not acceptable to update small incremental changes such as to rename a single file. Therefore, the NameNode will instead save the change operation in the Edit Log for consistency instead of writing a new fsimage every time the namespace is changed. Then it can restore its state when NameNode crashes by loading the fsimage first and replaying all the transactions in an edit log so as to catch up to the most recent state of the namesystem. This means that the NameNode collapses.
The edit log consists of a series of files called log-editing segments, which together represent all the changes to the name system made since the fsimage was created.
12- How many times are the Namenode required to reformat?
The Namenode needs to be formatted only once. It won’t format after that. Even Namenode recovery will result in data loss for the whole Namenode.
13- Explain rack awareness.
It is a process that determines how to base frames on the definitions of racks. Hadoop should seek to reduce network traffic in the same rack between data modes. So it’s just going to contact remotely.
Thus, we have provided you the various types of Hadoop Admin Interview questions, which can be asked usually. However, the questions of the Hadoop Admin Interview will differ and change based on your work experience and your business area.
Do not worry if you are inexperienced because companies would like to recruit you if you know your basics and know how to work on Hadoop projects. The main thing to start is preparations for a major career in Hadoop management, and a Hadoop admin interview can definitely be successful. Excellence and success are to be sought.