Hadoop Interview Questions And Answers

Compare Hadoop & Spark

Criteria	Hadoop	Spark
Dedicated storage	HDFS	None
Speed of processing	average	excellent
Libraries	Separate tools available	Spark Core, SQL, Streaming, MLlib, GraphX

Modes Hadoop can be run in?

Hadoop can run in three modes:

• Standalone Mode: Default mode of Hadoop, it uses local file stystem for input and output operations. This mode is mainly used for debugging purpose, and it does not support the use of HDFS. Further, in this mode, there is no custom configuration required for mapred-site.xml, core-site.xml, hdfs-site.xml files. Much faster when compared to other modes.

• Pseudo-Distributed Mode (Single Node Cluster): In this case, you need configuration for all the three files mentioned above. In this case, all daemons are running on one node and thus, both Master and Slave node are the same.

• Fully Distributed Mode (Multiple Cluster Node): This is the production phase of Hadoop (what Hadoop is known for) where data is used and distributed across several nodes on a Hadoop cluster. Separate nodes are allotted as Master and Slave.

Difference between HDFS block and InputSplit.

In simple terms, block is the physical representation of data while split is the logical representation of data present in the block. Split acts a s an intermediary between block and mapper.
Suppose we have two blocks:
Block 1: ii nntteell
Block 2: Ii ppaatt
Now, considering the map, it will read first block from ii till ll, but does not know how to process the second block at the same time. Here comes Split into play, which will form a logical group of Block1 and Block 2 as a single block.

It then forms key-value pair using inputformat and records reader and sends map for further processing With inputsplit, if you have limited resources, you can increase the split size to limit the number of maps. For instance, if there are 10 blocks of 640MB (64MB each) and there are limited resources, you can assign ‘split size’ as 128MB. This will form a logical group of 128MB, with only 5 maps executing at a time.

However, if the ‘split size’ property is set to false, whole file will form one inputsplit and is processed by single map, consuming more time when the file is bigger.

Difference between NameNode, Checkpoint NameNode and BackupNode.

• NameNode is the core of HDFS that manages the metadata – the information of what file maps to what block locations and what blocks are stored on what datanode. In simple terms, it’s the data about the data being stored. NameNode supports a directory tree-like structure consisting of all the files present in HDFS on a Hadoop cluster. It uses following files for namespace:
  fsimage file- It keeps track of the latest checkpoint of the namespace.
  edits file-It is a log of changes that have been made to the namespace since checkpoint.

• Checkpoint NameNode has the same directory structure as NameNode, and creates checkpoints for namespace at regular intervals by downloading the fsimage and edits file and margining them within the local directory. The new image after merging is then uploaded to NameNode.
  There is a similar node like Checkpoint, commonly known as Secondary Node, but it does not support the ‘upload to NameNode’ functionality.

• Backup Node provides similar functionality as Checkpoint, enforcing synchronization with NameNode. It maintains an up-to-date in-memory copy of file system namespace and doesn’t require getting hold of changes after regular intervals. The backup node needs to save the current state in-memory to an image file to create a new checkpoint.

Common Input Formats in Hadoop?

There are three most common input formats in Hadoop:

• Text Input Format: Default input format in Hadoop.
• Key Value Input Format: used for plain text files where the files are broken into lines
• Sequence File Input Format: used for reading files in sequence

Core methods of a Reducer?

The three core methods of a Reducer are:

1. setup(): this method is used for configuring various parameters like input data size, distributed cache.
   public void setup (context)
2. reduce(): heart of the reducer always called once per key with the associated reduced task
   public void reduce(Key, Value, context)
3. cleanup(): this method is called to clean temporary files, only once at the end of the task
   public void cleanup (context)

What is SequenceFile in Hadoop?

Extensively used in MapReduce I/O formats, SequenceFile is a flat file containing binary key/value pairs. The map outputs are stored as SequenceFile internally. It provides Reader, Writer and Sorter classes. The three SequenceFile formats are:

1. Uncompressed key/value records.
2. Record compressed key/value records – only ‘values’ are compressed here.
3. Block compressed key/value records – both keys and values are collected in ‘blocks’ separately and compressed. The size of the ‘block’ is configurable.

Job Tracker role in Hadoop?

Job Tracker’s primary function is resource management (managing the task trackers), tracking resource availability and task life cycle management (tracking the taks progress and fault tolerance).

• It is a process that runs on a separate node, not on a DataNode often.
• Job Tracker communicates with the NameNode to identify data location.
• Finds the best Task Tracker Nodes to execute tasks on given nodes.
• Monitors individual Task Trackers and submits the overall job back to the client.
• It tracks the execution of MapReduce workloads local to the slave node.

Use of RecordReader in Hadoop?

Since Hadoop splits data into various blocks, RecordReader is used to read the slit data into single record. For instance, if our input data is split like:
Row1: Welcome to

Row2: Intellipaat
It will be read as “Welcome to Intellipaat” using RecordReader.