Most Popular Hadoop Interview Questions and Answers
When we talk about the average salary of a Big Data Hadoop developer, it is close to 135 thousand dollars per annum. In European countries as well as in the United Kingdom, with the big data Hadoop certification, one can simply earn more than £67,000 per annum. These data reflect the reality of how great the career is. It was no less than a decade when companies are generating more than ten terabytes of data, we're paying heavily two database managers, and we are not satisfied with their services. For companies like Google, after a surge and lateral expansion, managing data became very cumbersome. Scientists and engineers of Google pioneer a project that was further known to be Hadoop. The idea here was to play with different types of data like XML, text, binary, SQL, log, and objects but further mapping them and reducing them do a single structured architecture.
The data management starts with architecture and then making a single node cluster as well as a multi-node cluster for transfer of data in a parallel way and not in a serial way. Then the communication protocols are maintained by building a cluster. And Lastly, the Hadoop cluster management comes into the picture. Now, if we compare Hadoop map-reduce with Sparks, then we'll find that spark is under terms faster than my produce, and it is written in Scala all its processing is done in batch, real-time com iterative and interactive graph processing matters. Where is Hadoop it's not as fast as a park, but it is faster than the traditional system, which is written in Java and follows a batch processing with complex and lengthy is of use? This article is all about answering the most ask questions in the interview of a Hadoop developer.
The only factor that separates Hadoop from spark is cost-effectiveness, and for a professional, with Hadoop's certification, the market offers a lot as it is still considered as a niche skill. To be a great Hadoop developer, you have to be literally a mastering writing Java. All the interviews that take place for a Hadoop developer focus more on the pragmatic side. That is the reason that the interviewer tends to indulge you more on competency mapping. In general, the question-answer round does not involve a high level of questions, but rather a trickier one, which is far more objective than thought.
You're hiring authority focuses more on direct answers, so it is advisable and wise for you as a candidate not to take abrupt turns while you're answering. Be crisp and clear!
Basic Hadoop Interview Questions and Answers
Q1. what do you mean by Hadoop and its component?
The ideal way to answer this question is by sticking to the main components that are the storage units and processing framework. When it comes to defining Hadoop, you have to start with big data. Below we have provided you a sample answer to which you can relate to and form your own answers.
It is an open-source distributed processing framework pet stores, and the process is big data. The end users can use this software and have access to a network of many computers to resolve the problems related to mammoth amounts of data and its computation. It is commonly used for commodity hardware and is design for computer clusters. The best part is all the common occurrences of problems and failures in the hardware his fundamentally handled by the framework itself.
In its core, we have a storage part, which is called a Hadoop distributed file system, followed by a processing part, which is known as the MapReduce programming model. In a way, we can work on a distributed file system and has the capabilities to work in a cross-platform operating system.
The base Apache framework consists of the following modules that contain libraries and utilities, a distributed file system for storing data on commodity machines. It also uses YARN that is a platform responsible for computing resources in clusters. All the large-scale data is processed through a programming model called MapReduce.
Q2. Define HDFS and YARN?
Hadoop distributed file system is known as HDFS, file yet another resource negotiator is known as YARN.
HDFS is designed to store data in blocks in a diverse environment and architecture. The environment consists of a master node, which is called a name node. This is where all the data are structured in blocks, location, and replication factors—making it to metadata information repository. The slave nodes which are responsible for the storage and blocks communication and replication factors are known as a data node. The name node is responsible for managing all the data nodes in our master and slave topology.
While yet another resource negotiator can we define as a processing framework that provides execution and management of resources stored in the environment, it has a resource manager who is responsible for acting upon the received processing request. It corresponds with node managers and initiates actual processing. It works in a batch mode and allocates resources to applications based on their needs. An old manager, which is a part of YARN, can be found in every data node responsible for the execution of the task.
Q3. Illustrate the steps to fix the name node when it is a malfunction?
H we have to follow a three-step approach in troubleshooting Hadoop cluster up problems, and they are:
- FsImage, otherwise called metadata replica, is used to start a new name node in the file system.
- Then we start the configuration process. Further data notes, as well as the clients, are acknowledged as a new NameNode after the initiation of the first step.
- In the end, we get enough block reports from the data nodes that are loaded from the last checkpoint FsImage.
This usually takes up a lot of time to re-direct and extract the data, which may serve as a great challenge while doing routine maintenance. But with the use of high availability architecture, we can eliminate it in no time.
Q4. What do you mean by a checkpoint?
This is a process that takes the request of file system metadata replica, edits log, and further compacts them into a new FsImage.
1.Check preconditions----GET/ getimage?putimage=1------- HTTP Get to getimage------ GET/ getimage----- new fsimage data----- saves to intermediate filename-----putimage completes----- save MD5 file & renames fsimage to final desitination.
- User------fsimage-----checkpointing-----mkdir”/foo” ----- NameNode-----edit log.
Q5. Illustrate how HDFS fault is tolerant?
The problem with a single machine is that in a legacy system, the relational database performs both read and write operations by the users. If any contingency situation arises like a mechanical failure or power down from the user has to wait still, the issue is corrected manually. Another set of problems with legacy systems is that we have to store the data in a range of gigabytes. The data storage capacity was limited and enhanced data storage capacity. We have to buy a new server machine. It directly fixes the cost of maintaining file systems and issues related to it. With the all-new Hadoop distributed file system, we can overcome storage capacity problems and tackle favorable conditions like machine failure, RAM crash, and power down.
HDFS, otherwise known as Highly fault-tolerant, handles the process of replica creation quite intuitively, making clusters of user data in different machines. The main component that helps to provide stability in fault-tolerant is called Erasure Coding. It improves the quality of the replication factors and enhances durability to contingencies.
It is achieved in two ways, and they are as follows:
Replication mechanism
The idea here is to create a replica of the data block & store then in the DataNode. The replicas list entirely depends upon the replication factor that ensures no loss of data due to replicas stored on a variety of machines.
Erasure Coding
RAID or Redundant Array of Independent Disks makes practical usage of the Erasure coding by having effective space-saving methods. It can reduce up to 50% of storage overhead for each strip of the original dataset.
Q6. What are the common input formats in Hadoop?
In Hadoop, we have provisions made accessible for input formats in three significant categories, and they are as follows:
The input format for reading files in sequence, also known as Sequence File Input format.
The default input format of the Hadoop is known as the Text Input Format.
The format that helps users to read plain text files is called Key-Value Input Format.
Q7. How would you define YARN?
YARN or Yet Another Resource Negotiator is a Hadoop Data processing framework that helps to manage data resources by creating an environment or architecture for data processing. It supports different varieties of the processing engines & applications by separating tits duties across multiple components and dynamically allocating the pools of resources to desired applications.
In many ways, it is uncommon for MapReduce to on cluster resource management.
Q8. Define Active and Passive NameNodes?
The NameNode that helps to run the Hadoop cluster resource is called the Active NameNode. While the standby NameNode that helps in the storage of data for the Active NameNode is otherwise called as Passive NameNode. They both are the components of the High Availability Hadoop System, whose sole purpose is to provide fluidity and increase the effectiveness of the cluster and the system files.
Q9. Define Speculative Execution?
When the entire program runs slower just because of some nodes, then to overcome this constrain, Hadoop Speculates the troubled nodes & launches a backup for the task. Here a master node executes both the task simultaneously of running and backing up & the whole scenario is called Speculation Execution.
Q10. List out some of the main components of Apache H base?
To be precise, there are three components of Apache H Base, and they are as follows:
H master: With the help of this tool, a user can manage as well as coordinate the functioning of the regional server.
Region server: reason server is a division of multiple reasons further into clusters of these reasons, which are then provided to the clients through the Region Server.
Zookeeper: each tool helps us to coordinate within the H base by maintaining a server state and communication in session inside the clusters.
Q11. How would you debug a Hadoop code?
The first step is to cheque and ascertains the list of map-reduce tasks that are running at present. Further, you have to cheque orphaned tasks whether or not they are running simultaneously with the map-reduce tasks. If you find any orphaned tasks, then you have to locate it, and the resource manager logs through the following steps given below:
Try to find out if there is an error related to a specific job ID by initiating the following command: Run "ps-ef | grep- | Resource Manager."
After identification of the worker node, then we have to execute the task by logging in to the node and run "ps –ef | grep- iNodeManager."
Under the final step, we have to scrutinize the node manager log for most of the errors that are generated from users' level logs that earlier created the problem in each MapReduce job are eliminated from the environment.
Q12. Define modes it helps Hadoop to run?
There are three different types of modes that help Hadoop to run, and they are as follows:
Pseudo distributed mode: the peculiarity about this mode is that both the slave as well as the master node are the same here. They mostly work for the configuration of mapred-site.xml, core-site.xml & hdfs-site.xml files.
Fully distributed mode: this is a production stage where data is distributed across various notes on a cluster separating the master and the slave node allotments differently.
Standalone mode: basically, this is the default mode used for debugging purposes, and in general, it does not support HD FS.
Q13. what are some of the practical applications of Hadoop?
In real-time, Hadoop and make a difference by fraud detection and its prevention. It also helps in managing St traffic. It adds to the customer service in real-time by analyzing the customer data. Practically with Hadoop, we get access to the unstructured data and improve services around it. The data can be related to medical science, banking, or any industry.
Q14. What do you mean by distributed cache?
We can define it as a service by no map-reduce framework for having access to cache files whenever needed. Once a file is listed as cached for a specific job, the framework will make it available both in the system as well as in memory. We can read the cache file and can add an array or hash map in the code.
Simple read-only text data files or complex files such as jars, archives, and others can be unarchived at the slave node and distributed further. The distributed cache Blacks notification if any alteration is made in that timestamp of the cache files.
Q15. What do you mean by WebDAV in Hadoop?
It is a set of extensions to HTTP but not only supports editing but also updating files related to WebDAV by sharing mounted as the file system and providing access to HDFS as a standard file system. It also helps us to expose the HDFS over WebDAV.
Q16. what is Sqoop in Hadoop?
It is a tool that is used to create transfer and enable this relationship of data transfer between an RDB MS & a Hadoop HDFS. It can work along with MySQL & Oracle and export data from HDFS to the RDBMS and vice versa.
Q17. How would you define a job tracker schedule as a task?
A job tracker usually stays up to date with the cluster work by informing through the message about the number of available slots. Here a task tracker is responsible for sending heartbeat messages for the job tracker in order to ensure its active condition a job functionality.
Q18. What do you mean by data ingestion & data storage?
It was storage can be defined as a subsequent step after the ingesting of data. When we deploy big data solutions to extract data from different sources or repositories, the data is extracted stored in HDFS. The NoSQL database, like HBase, helps to work along in randomly reading and granting writing access for sequential access.
The final step, the concludes data processing is done through frameworks such as MapReduce, Spark, Apache Pig, et cetera. The biggest question is to take the decision in choosing the particular file format that is needed to be processed. For this, we use schema evaluation and by using patterns like accessing 5 columns out of 50 columns with process split ability in parallel mode. Files format such as CSV, JSON, COLUMNAR, sequence files, and AVRO are used in Hadoop.
These files are an ideal fit for exchanging data between the existing and the external system. They also store both data and schema together in a record that best suits for long term storage with the schema. With these files, we can block the level compression. It helps us to specify an independent schema for reading the files.
Q19. What do you mean by rack awareness?
When all the data nodes are aligned and put together to form a storage area, especially in a physical location of the data node, then the whole concept is termed as a rack in HDFS. Each data node acquires a name node that helps us to select a closer data node depending upon the rack in formation. It helps us segregate the contents into data blocks in the Hadoop Cluster. The whole process is known as rack awareness.
Q20. What do you understand by a Reducer?
A reducer involves three important steps to set up and reduce associated tasks in the following manner:
Setup()- in this step of the reducer, we configure various parameters or metrics to get a context out of the input data.
Reduce() is the key component of the reducer that helps us to associate per key with the reduce task.
Clean-up()- at the end of the method, we clear the temp files and create the space.
Q21. Define a Row Key?
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