Hadoop Interview Questions 2021[Updated]

Hadoop Interview Questions: Apache Hadoop is an open-source software library that controls information processing & storage at big data apps. Moreover, Hadoop assists in analyzing big amounts of data amount parallelly & extra swiftly. Additionally, Apache Hadoop was acquainted with the public in 2012 through Apache Software Foundation. This software is economical to utilize as information is kept at cheap commodity Servers which operate as clusters.

Hadoop is getting to be a more popular tech trending currently because it assists in solving a major Big-Data issue easily. There’s always some demand for Big-Data professionals that have experience using Hadoop. However, how does one find work as a Big-Data Analyst having Hadoop experience? Below are some of the interview questions that will help you get prepared for a Hadoop interview.

Hadoop Interview Questions for 5 years experience

What are the different vendor-specific supplies of Hadoop?

Various vendor-specific supplies of Hadoop include Cloudera, Amazon EMR, MAPR, Microsoft Azure, Hortonworks (Cloudera), and IBM InfoSphere.

What’s Hadoop, and what are its components.

The time “Big Data” is an issue, Apache Hadoop advances as an answer to it. Hadoop is a structure that offers various tools and services to store & process huge Data. This helps one in analyzing huge Data & making business decisions out of it. It can’t be efficiently & effectively done with the help of traditional systems.

The components include

• Processing framework– (NodeManager, ResourceManager) YARN
• Storage unit– (DataNode, NameNode) HDFS

What do the 4 V’s of Big-Data symbolize?

IBM contains a nice, easy explanation for the four critical sorts of big-data:

• Veracity –Doubt of data
• Velocity –Examination of streaming information
• Volume –Scale of information
• Variety – various kinds of data

What are the various configuration files for Hadoop?

The various configuration files for Hadoop include:

• mapred-site.xml
• Hadoop-env.sh
• core-site.xml
• hdfs-site.xml
• yarn-site.xml
• Master & Slaves

What’s Hadoop streaming?

At Hadoop distribution, it contains a generic app programming interface that’s used for writing Map & Reducing jobs in every wanted programming language, including Python, Ruby, Perl, etc. It’s known as Hadoop Streaming. Clients can create & operate jobs with every variety of shell scripts as Reducers or Mapper. The newest device for Hadoop streaming is known as Spark.

What are the more commonly stated input setups in Hadoop?

More common Input Setups stated in Hadoop includes:

Key Value-Input Format

The input format is utilized for plain-text files, where files are broken down into lines.

Text-Input Format

This is the automatic input format stated in Hadoop.

Sequence File-Input Format

The input format is utilized for reading the files in a sequence.

Which are some of the disadvantages of Hadoop?

Support for just batch-processing

Hadoop doesn’t process streamed information & promotes batch processing. This depresses the overall performance.

File size limits

HDFS can’t handle a large number of little files. If one utilizes this kind of file, then the NameNode will get overloaded.

Struggle in management

It can get difficult to manage complex apps in Hadoop.

What are the three modes from which Hadoop operates?

The three modes from which Hadoop operates include:

Pseudo-distributed mode

This utilizes single-node Hadoop placement, which executes every Hadoop service.

Standalone mode

It’s a default mode. This mode uses local FileSystem & a single Java process to operate Hadoop services.

Fully-distributed mode

This utilizes different nodes to operate Hadoop master & slave services.

State the reason HDFS is tolerant to a fault?

HDFS is tolerant to fault, the reason being it replicates information on various DataNodes. Automatically, a block of information is replicated at 3 DataNodes. The information blocks are kept in various DataNodes. If a node crashes, information can still get retrieved from extra DataNodes.

Explain many Hadoop daemons & their roles at the Hadoop cluster.

NameNode

It’s the master node that’s responsible for keeping metadata of every file & directories. It contains blocks, which makes a file, & where the blocks are situated in a cluster.

Datanode

It’s a slave node that contains actual data.

Secondary NameNode

It occasionally merges changes with FsImage, present in NameNode. It keeps the improved FsImage to persistent storage that can utilized in case a NameNode failure occurs.

ResourceManager

This is the vital authority that manages resources & schedules apps running at the uppermost of YARN.

JobHistoryServer

This maintains data on MapReduce jobs when the App Master terminates.

NodeManager

This runs on the slave machines, & it’s responsible for initiating the apps containers, monitoring the resource usage, & report to ResourceManager.

What’s shuffling about MapReduce?

Shuffling in Hadoop MapReduce is utilized in transferring information from mappers to significant reducers. It’s the procedure where the organization categories formless information & transmissions of the map output as the reducer input. It’s an important process for reducers. Otherwise, they wouldn’t accept data. Moreover, this process starts even before the map phase is finished. It assists in saving time & completing the process with a smaller time amount.

What’s Apache Pig?

A MapReduce requires programs translated to map & reduce stages as not every information analyst is habituated to MapReduce. Moreover, Yahoo researchers brought Apache pig to cover the gap. Apache-Pig was made on the uppermost of Hadoop, making a high level of abstraction & allowing programmers to use less time composing difficult MapReduce programs.

What’s Apache Hive?

Apache Hive is an open-source system that processes organized information in Hadoop, living at the top of the latter for summing Big-Data & easing analysis & queries. Also, the hive allows SQL developers to write Hive Query-Language statements same to normal SQL statements for information query & analysis. It’s made to create MapReduce programming simpler because one doesn’t understand & write long Java code.

What’s Yarn?

Yarn is a short form of Yet Another-Resource Negotiator. It’s a resource layer management of Hadoop. The yarn was started in Hadoop-2.x. Yarn offers numerous information processing engines, including graph processing, interactive processing, batch processing, & stream processing for executing & processing information saved in Hadoop Distributed-File System. Yarn provides job offer scheduling. This extends the ability of Hadoop to different evolving tech to allow them to take the best advantage of economic clusters & HDFS.

Apache Yarn refers to the information operating way for Hadoop-2.x. This consists of some master daemon referred to as “Resource-Manager,” a slave-daemon known as node manager, & Application Master.

What’s Apache ZooKeeper?

This is an open-source facility that supports regulating a huge set of hosts. Co-ordination and management in some distributed environments remain complex. Zookeeper computerizes this process & allows developers to focus on making software features instead of bothering about the distributed nature.

Furthermore, Zookeeper assists in maintaining configuration skills, group services on distributed applications & naming. This implements different protocols on the cluster, so the application shouldn’t execute it on its own. This offers a single coherent view of numerous machines.

Explain the way information is kept in a rack.

When the client is set to load a file in a cluster, its file substances are shared into data blocks or racks. Then the client allocates 3 DataNodes for every block. 2 copies of information are kept in a single rack, while 3rd copy is kept in a different rack.

What’s a checkpoint?

“Checkpointing” refers to a procedure that takes FsImage, correct log & compacts them to a fresh FsImage. Therefore, instead of repeating the edit log, NameNode could load the final in-memory-state direct from FsImage, which is an efficient operation & decreases NameNode startup time. Furthermore, Checkpointing is done through Secondary NameNode.

What do ‘jps’ commands do?

‘jps‘ command assists us in checking if Hadoop daemons will be operating or not. It displays all Hadoop daemons, including namenode, resourcemanager, datanode, nodemanager, etc., which are operating on the machine.

What’s “speculative execution” about Hadoop?

If nodes happen to be performing a job slower, then the master node redundantly performs another occurrence of a similar task on a different node. Then, the task that completes first will be acknowledged & the other one killed. This procedure is known as “speculative execution.”

How do “reducers” talk with every other?

“MapReduce” programming model doesn’t enable “reducers” to pass information with each other. Moreover, “Reducers” operates in isolation.

What happens if one store’s very many small files in a cluster at HDFS?

Keeping numerous small files at HDFS produces more metadata files. Storing these metadata at RAM remains a challenge as every file, directory, or block takes 150-bytes for metadata. Therefore, the cumulative size of every metadata will be very large.

What are various schedulers accessible in YARN?

Capacity scheduler

It’s a distinctly devoted queue that enables the small job to begin immediately as it’s submitted. Big jobs complete later about utilizing FIFO scheduler.

FIFO scheduler

This keeps apps in a queue & operates them in submission order (first-in, first-out). However, it’s not appropriate, as long-running apps may block small operating apps.

Fair scheduler

There’s no necessity to reserve a set capacity amount since this will energetically balance resources in between all running jobs.

What are the parts utilized in Hive-query processors?

The components utilized in Hive-query processors includes:

• Semantic Analyzer
• Parser
• Execution Engine
• Logical-Plan Generation
• User-Defined Functions
• Physical-Plan Generation
• Optimizer
• Type checking
• Operators

What are various ways of Pig script execution?

The various ways of a Pig script execution include:

• Script file
• Grunt shell
• Embedded script

What’s UDF?

If other functions are inaccessible at in-built operators, we programmatically make User-Defined Functions (UDF) which brings the functionalities utilizing different languages including Java, Ruby, Python, etc. & embed it inside the Script file.

What’s the default replication factor?

Automatically, replication factors are 3. There are no two copies that are on a similar data node. Normally, the 1st two copies are on the same rack, & the 3rd copy is off the shelf. It’s guided to set the replication factor to about three so that a copy will always be secure, even if anything occurs to rack.

We set the default replication factor of a file system of every file & directory exclusively. For files that aren’t essential, we lower the replication factor, & critical files need to have a high-replication factor.

What’s commodity hardware?

This refers to cheap systems which don’t have great availability or quality. Moreover, commodity hardware contains RAM, the reason being particular services require execution on RAM. Apache Hadoop can operate on every commodity hardware & doesn’t need a supercomputer or high-end hardware outline to execute the jobs.

When a client sends a Hadoop task, who accepts it?

NameNode accepts the Hadoop job, which appears for the information requested by the client & offers the block data. JobTracker takes care of the allocation of resources of the Hadoop job to guarantee timely completion.

Explain actions taken by Jobtracker at Hadoop.

• A client application is utilized to submit jobs to Jobtracker.
• JobTracker links with NameNode to decide data location.
• With the assistance of available slots & near information, JobTracker finds TaskTracker nodes.
• It gives the work on chosen TaskTracker-Nodes.
• When the task fails, the JobTracker alerts & decides further steps.
• JobTracker checks TaskTracker nodes
• State the features of Apache-sqoop.

Full Load

Sqoop is capable of loading the entire table with just a single command Sqoop. Moreover, it allows one to load every table of a database by utilizing a single-Sqoop command.

Robust

It’s more robust. Apache-sqoop even has public support & contribution & is more usable.

Incremental Load

Apache-sqoop supports incremental-load functionality. Utilizing Sqoop, we load sections of the table when it’s updated.

Import outcomes of SQL-query

This enables us to import output from SQL query to the Hadoop-Distributed File-System.

Parallel import/export

Apache-sqoop uses the YARN framework to import & export the information. That offers fault tolerance at the peak of parallelism.

Conclusion

With this blog, interview questions concerning Hadoop shouldn’t be an issue again. However, these are just a sample of questions; thus, you need to research more Hadoop interview questions.

Python vs JavaScript Differences

Python Interview Questions

Nexpose

Try Catch JavaScript?