Interview Questions and Answers

Hadoop is a distributed computing platform. It is written in Java. It consists of the features like Google File System and MapReduce.

Java 1.6.x or higher versions are good for Hadoop, preferably from Sun. Linux and Windows are the supported operating system for Hadoop, but BSD, Mac OS/X, and Solaris are more famous for working.

Hadoop can run on a dual processor/ dual core machines with 4-8 GB RAM using ECC memory. It depends on the workflow needs.

These are the most common input formats defined in Hadoop:

1. TextInputFormat
2. KeyValueInputFormat
3. SequenceFileInputFormat

TextInputFormat is a by default input format.

The big data can be categorized using the following features:

• Volume
• Velocity
• Variety

We use panels in bootstrap from the boxing of DOM components.

Button groups are used for the placement of more than one buttons in the same line.

• Ordered list
• Unordered list
• Definition list

The 'jps' command is used for the retrieval of the status of daemons running the Hadoop cluster.

When a Hadoop job runs, it splits input files into chunks and assigns each split to a mapper for processing. It is called the InputSplit.

In TextInputFormat, each line in the text file is a record. Value is the content of the line while Key is the byte offset of the line. For instance, Key: longWritable, Value: text

In Hadoop, SequenceFileInputFormat is used to read files in sequence. It is a specific compressed binary file format which passes data between the output of one MapReduce job to the input of some other MapReduce job.

Hadoop makes 5 splits as follows:

• One split for 64K files
• Two splits for 65MB files, and
• Two splits for 127MB files

InputSplit is assigned with a work but doesn't know how to access it. The record holder class is totally responsible for loading the data from its source and convert it into keys pair suitable for reading by the Mapper. The RecordReader's instance can be defined by the Input Format.

JobTracker is a service within Hadoop which runs MapReduce jobs on the cluster.

Following are the three configuration files in Hadoop:

• core-site.xml
• mapred-site.xml
• hdfs-site.xml

WebDAV is a set of extension to HTTP which is used to support editing and uploading files. On most operating system WebDAV shares can be mounted as filesystems, so it is possible to access HDFS as a standard filesystem by exposing HDFS over WebDAV.

Sqoop is a tool used to transfer data between the Relational Database Management System (RDBMS) and Hadoop HDFS. By using Sqoop, you can transfer data from RDBMS like MySQL or Oracle into HDFS as well as exporting data from HDFS file to RDBMS.

These are the main tasks of JobTracker:

• To accept jobs from the client.
• To communicate with the NameNode to determine the location of the data.
• To locate TaskTracker Nodes with available slots.
• To submit the work to the chosen TaskTracker node and monitors the progress of each task.

TaskTracker is a node in the cluster that accepts tasks like MapReduce and Shuffle operations from a JobTracker.

Map/Reduce job is a programming paradigm which is used to allow massive scalability across the thousands of server.

MapReduce refers to two different and distinct tasks that Hadoop performs. In the first step maps jobs which takes the set of data and converts it into another set of data and in the second step, Reduce job. It takes the output from the map as input and compresses those data tuples into the smaller set of tuples.

Map: In Hadoop, a map is a phase in HDFS query solving. A map reads data from an input location and outputs a key-value pair according to the input type.

Reducer: In Hadoop, a reducer collects the output generated by the mapper, processes it, and creates a final output of its own.

Shuffling is a process which is used to perform the sorting and transfer the map outputs to the reducer as input.

NameNode is a node, where Hadoop stores all the file location information in HDFS (Hadoop Distributed File System). We can say that NameNode is the centerpiece of an HDFS file system which is responsible for keeping the record of all the files in the file system, and tracks the file data across the cluster or multiple machines.

Heartbeat is a signal which is used between a data node and name node, and between task tracker and job tracker. If the name node or job tracker doesn't respond to the signal then it is considered that there is some issue with data node or task tracker.

There is a very unique way of indexing in Hadoop. Once the data is stored as per the block size, the HDFS will keep on storing the last part of the data which specifies the location of the next part of the data.

If a data node fails the job tracker and name node will detect the failure. After that, all tasks are re-scheduled on the failed node and then name node will replicate the user data to another node.

Hadoop streaming is a utility which allows you to create and run map/reduce job. It is a generic API that allows programs written in any languages to be used as Hadoop mapper.

A Combiner is a mini-reduce process which operates only on data generated by a Mapper. When Mapper emits the data, combiner receives it as input and sends the output to a reducer.

Following are the network requirement for using Hadoop:

• Password-less SSH connection.
• Secure Shell (SSH) for launching server processes.

Storage node: Storage Node is the machine or computer where your file system resides to store the processing data.

Compute Node: Compute Node is a machine or computer where your actual business logic will be executed.

If you have a background in any programming language like C, C++, PHP, Python, Java, etc. It may be really helpful, but if you are nil in java, it is necessary to learn Java and also get the basic knowledge of SQL.

There are many ways to debug Hadoop codes but the most popular methods are:

• By using Counters.
• By web interface provided by the Hadoop framework.

Yes, It is possible. The input format class provides methods to insert multiple directories as input to a Hadoop job.

In Hadoop, A job is divided into multiple small parts known as the task.

The Logical division of data is called Input Split and physical division of data is called HDFS Block.

HDFS data blocks are distributed across local drives of all machines in a cluster whereas, NAS data is stored on dedicated hardware.

Hadoop facilitates you to increase or decrease the number of mappers without worrying about the volume of data to be processed.

Distributed cache is a facility provided by MapReduce Framework. It is provided to cache files (text, archives etc.) at the time of execution of the job. The Framework copies the necessary files to the slave node before the execution of any task at that node.

JobTracker is a giant service which is used to submit and track MapReduce jobs in Hadoop. Only one JobTracker process runs on any Hadoop cluster. JobTracker runs it within its own JVM process.

Functionalities of JobTracker in Hadoop:

• When client application submits jobs to the JobTracker, the JobTracker talks to the NameNode to find the location of the data.
• It locates TaskTracker nodes with available slots for data.
• It assigns the work to the chosen TaskTracker nodes.
• The TaskTracker nodes are responsible to notify the JobTracker when a task fails and then JobTracker decides what to do then. It may resubmit the task on another node or it may mark that task to avoid.