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Hadoop is a popular solution to “Big Data” problems. And it’s available as an open software framework. Plus, since the software is free, the need for Hadoop among corporations is in demand. If you’re assisting businesses with their Hadoop framework, you’ll need to know about Hadoop interview questions.
This program helps users distribute storage. Hadoop also handles large sets of data. And it allows a system to continue working if its nodes fail.
Hadoop’s solutions to data problems help resolve issues with data mishaps. Those issues include storage, security, analytics, data quality, and discovery matters.
Since you’ll likely encounter Hadoop questions in your next interview, you should probably prepare for some Hadoop interview questions.
What Are Hadoop Interview Questions?
You want to prepare yourself with answers to the most popular Hadoop interview questions.
So, we’ll cover some of the answers to the most widely asked Hadoop interview questions below.
What is Apache Hadoop?
Hadoop exists as a part of Apache’s project. Apache’s project is called the Apache Software Foundation (ASF).
As mentioned, Hadoop is a free, open-source software. It helps better handle storage and the processing of substantial data sets.
Open-source software runs applications on a system that has a multitude of commodity hardware nodes. Hadoop works to transfer data among nodes rapidly.
Even if your system stalls due to node failure, Apache Hadoop will still work.
Hadoop’s Distributed File System (HDFS) storage layer can store and run large files efficiently. With an HDFS, you’ll get reliable data storage when your hardware system fails.
Hadoop also uses YARN. That means you can operate resource management tasks. You can also utilize several data processing engines simultaneously.
You’ll also get MapReduce, which is its batch processing engine that writes applications. This engine processes all types of data.
It also divides jobs into sub-tasks. MapReduce breaks up the processing information used by the system.
Why is Hadoop necessary?
Hadoop is necessary because of Big Data challenges. Those Big Data challenges are listed below.
Hadoop handles the discovery challenge. Discovery involves utilizing an algorithm to find information in data storing data. It is essential because data can be extensive. Storing large amounts of data is challenging.
Hadoop also helps analyze data. Analyzing data can be challenging to do. That’s because people don’t always know about the types of data they have.
Hadoop also keeps information secure. Security can be tough when dealing with large amounts of data.
Besides that, Hadoop ensures the data is high-quality. Big Data can be messy and incomplete.
Why is Hadoop the best solution for Big Data problems?
Hadoop is an open-source software framework that supports the storage and processing of large data sets. We listed below why Apache Hadoop is the best solution for storing and processing Big Data.
- Hadoop can store considerable files in their raw format
- It adds as many nodes as you need, boosting the way your system performs
- Hadoop stores data reliably even if the system fails
- Its data will stay available also if your hardware fails to operate
- Hadoop is affordable, and none of its equipment costs much to use
What are the most common input formats?
Hadoop offers three basic input formats. The default input format is known as Text Input Format. Next, the input format that reads files in order is known as Sequence File Input Format. Last, the Key-Value Input Format reads text files.
What is YARN?
Short for “Yet Another Resource Negotiator,” YARN is the data processing framework that assists with data resources. YARN also creates the processing environment.
It works to support a bevy of processing applications in the system.
YARN separates what it needs to do over several components. This framework also allocates resources to different applications. So, YARN helps to create the space and organization level to improve the system.
With YARN, your system continues to operate even when nodes or hardware failure.
What is rack awareness?
Rack awareness is the algorithm that NameNode needs to decipher the configuration of data blocks. Then, duplicate data information is stored in the Hadoop cluster.
Rack awareness helps to cut back on the overcrowding. Overcrowding can happen between data nodes sharing a rack.
What are NameNodes?
There are two standard NameNodes that you’ll find in any Hadoop system. Those two NameNodes are an Active NameNode and a Passive NameNode.
An Active NameNode runs a Hadoop cluster. The Passive NameNode stores the data used by the Active NameNode.
The use of two NameNodes demonstrates the true beauty of Hadoop. The second NameNode works as a back-up plan in case of failure.
So, if the Active NameNode stops working, then the Passive NameNode takes over. That means a NameNode is always running within its cluster. That way, the system cannot fail.
What are the Hadoop schedulers?
There are three schedulers in Hadoop. The first, COSHH, works with heterogeneity. It schedules decisions through a review of the cluster and workload. Next, the FIFO Scheduler creates a queue to organize jobs without heterogeneity.
Last, Fair Sharing makes space for separate users utilizing several maps. It also reduces slots on a resource. That way, jobs work more quickly
What is speculative execution in Hadoop?
When Hadoop is operating, some of the nodes won’t run as quickly as others. When that happens, the whole system constrains.
To combat this issue, Hadoop looks for, or “speculates” about how fast tasks are running. Then, Hadoop focuses on the slower ones.
Once Hadoop finds those slower tasks, it creates a back-up for the job. Then, the active node uses both tasks at the same time. It takes whatever one is done first and removes the other.
The use of a back-up process like this in Hadoop is called speculative execution.
What are the main components of Apache HBase?
Apache HBase has three significant components. First, it has a region server that forwards tables divided into several regions to the client.
Second, Apache HBase uses HMaster, which helps to control the Region server.
The final component, ZooKeeper, works within HBase. It helps keep a server state while the cluster is in the communication process.
What is the purpose of “checkpointing?”
In checkpointing, a FsImage and Edit log coordinate together and create a new FsImage. That way, the edit log is not replaced.
The NameNode loads directly from the final in-memory position of the FsImage. The passive NameNode handles the entire process.
Checkpointing helps cut back on the start-up time of the NameNode involved in the process.
How do you debug a Hadoop code?
When you begin to debug a coded in Hadoop, you’ll need to look at the MapReduce tasks. The MapReduce tasks tell you what is currently running.
After that, you’ll need to assess if any orphaned tasks are also running simultaneously. If they are, you’ll need to find the location of the Resource Manager logs.
To find the location of the Resource Manager logs, you can do the following. Start by running “ps –ef | grep –I ResourceManager.”
You’ll see the displayed result. Assess if there is an error in a job id.
Pinpoint the worker node that was previously executing the task. To do that, log into the node. Then run “ps –ef | grep –iNodeManager,”
Last, look over the Node Manager log. You’ll typically find errors in the user level logs.
What are the Hadoop modes?
Hadoop can run in three different modes.
The first mode, the standalone mode, is Hadoop’s default mode. It is mostly used to help you debug. Also, it doesn’t support the HDFS.
Next, the Pseudo-distributed mode is necessary for the configuration of mapped-site.xml, core-site xml, and hdfs-site-xml file types. You’ll also find the active and passive nodes here
Last, the fully-distributed mode is Hadoop’s production state. It distributes data across several nodes found in a single Hadoop cluster.
Know Your Hadoop
Data analytics is emerging as a growing profession. Many corporations need employees that can handle Big Data easily. Big Data is creating a lot of job opportunities for data analysts and scientists.
Companies expect their employees that are working with data to understand Hadoop. Reviewing the common Hadoop interview questions prepares you for your next potential job.
If you work with data, you’ll be working with Hadoop. So, you’ll need to answer Hadoop interview questions before you start.