Apache Hadoop is an open-source platform for efficiently storing and processing gigabyte- to petabyte-sized datasets. Instead of using a single huge computer to store and analyze data, Hadoop enables the clustering of several computers to analyze massive datasets in parallel and with greater speed. As a result, big data has experienced exponential growth during the past decade. With Big Data comes broad use of Hadoop to handle Big Data’s most difficult problems. Therefore, there will always be a need for professionals in this industry. But how does one obtain employment in the Hadoop field? This post will examine the top 25 Hadoop interview questions you may be asked in a Hadoop interview, as well as some sample answers that will help you understand how to respond to these.
1. What Is Hadoop, In Your Own Words?
Hadoop is a platform that stores and manages large amounts of data using distributed storage and parallel computing. It is the most popular program data analysts use to manage large amounts of data, and its market share continues to expand. There are three Hadoop components:
- Hadoop HDFS: HDFS serves as Hadoop’s storage layer. On HDFS, data is always stored as data blocks, with a customizable default size of 128 MB per data block.
- Hadoop MapReduce – MapReduce functions as a Hadoop processing layer. It is developed for the parallel processing of data distributed across multiple devices.
- YARN is the Hadoop resource management and task scheduling layer.
2. What Are The Most Important Aspects Of The Hadoop System?
The Hadoop framework is capable of answering numerous problems in Big Data analysis. It is built on Google MapReduce, based on the Big Data file systems developed by Google. Scalability is the greatest strength of the MapReduce framework. Once a MapReduce program has been created, you can readily scale it up to operate on a cluster of hundreds or thousands of nodes. In this architecture, computation is performed at the location of the data.
3. According To Your Understanding, What Is Big Data?
Big Data is a collection of data that is enormous in volume and exponentially rising over time. The data is so massive and complicated that no conventional data management technologies can effectively store or process it. Big data is data but of enormous size. Social Media is an illustration of big data. This social media data is created mostly through photo and video uploads, message exchanges, commenting, etc. There are structured, unstructured, and semi-structured types of big data.
4. Which Operating Systems Do Hadoop Deployments Support?
Hadoop may operate on various Linux distributions, including CentOS, RedHat, Ubuntu, Debian, SUSE, etc. In real-time production, most Hadoop Clusters are built on RHEL/CentOS; for demonstration purposes, we will utilize CentOS 7 in this series of tutorials.
Using kickstart, an organization can install an operating system. Manual installation is feasible for clusters with 3 to 4 nodes; however, installing OS manually on clusters with more than ten nodes is difficult and time-consuming. In this circumstance, the Kickstart approach comes into play; with kickstart, we can proceed with the mass installation. Provisioning suitable Hardware and Software is crucial to get optimal performance from a Hadoop environment.
5. What Is The Most Significant Distinction Between RDBMS And Hadoop?
An RDMS is an information management system based on a data model. In RDBMS, tables are used to store information. Each table row represents a record, while each column represents a data property. RDBMS differs from other databases in its approach to data organization and manipulation.
On the other hand, Hadoop is an open-source software framework utilized for data storage and application execution on a collection of commodity hardware. It can simultaneously manage several concurrent processes. It has applications in predictive analysis, data mining, and machine learning. It is capable of handling both organized and unstructured data. Hadoop, unlike conventional systems, permits concurrent execution of numerous analytical processes on the same data.
6. Explain The Different Schedulers That Are Available In Hadoop.
- FIFO Scheduler: This scheduler disregards system heterogeneity and sorts jobs according to their arrival times in a queue.
- This scheduler considers the workload, cluster, and user heterogeneity while making scheduling decisions.
- This Hadoop scheduler defines a pool for each user to ensure equitable sharing. Several maps and reduced slots on a resource are contained in the pool. Each user can run jobs using their pool.
7. How Does Speculative Execution Function In Hadoop?
Hadoop MapReduce is used to initiate all of the tasks for the job. The speculative tasks are initiated for those that have been operating for some time (at least one minute) and have not made much progress, on average, relative to other tasks in the job. If the original task completes before the speculative task, the speculative task is terminated. Likewise, the original task is terminated if the original task is completed before the speculative task.
8. When Adopting Hadoop In A Production Setting, What Are The Most Critical Hardware Concerns To Keep In Mind?
- Depending on the type of application being run, the Memory-worker System’s and management services’ memory requirements will differ.
- Operating System: A 64-bit operating system prevents constraints from being imposed on the amount of memory available for worker nodes. These restrictions could limit the total amount of storage space available for use.
- To achieve scalability and high performance, it is desirable to create a Hadoop platform by moving the computational activity to the data.
- The number of MapReduce slots available across all of the nodes that make up a Hadoop cluster can be used to calculate a Hadoop cluster’s computational capacity.
9. In Your Own Words, Kindly Explain What Check Pointing Is In HDFS.
Checkpointing is the process of merging the edit log and FSImage. Secondary Namenode receives a copy of the image and edit log from Name Node and combines the files to generate the final FSImage file.
Therefore, instead of replaying an edit log, the Name Node can be loaded directly from the image in its final in-memory state. This operation is unquestionably more efficient and reduces the Name Node starting time.
10. What Are The Three Operating Modes For Hadoop?
The three modes of operation for Hadoop include:
- The default mode is the standalone mode. Hadoop services are executed with a single Java process and the Local File System.
- Hadoop may also operate in pseudo-distributed mode on a single node. Each daemon operates on a distinct Java process in this mode, and configuration customization is required. In this case, HDFS is used for input and output. This deployment technique is beneficial for testing and troubleshooting.
- Fully Distributed mode: It is Hadoop’s production mode. One machine in the cluster is often designated as the Name Node and another as the Resource Manager. Separate nodes are used to execute the Hadoop master and slave services.
11. Can The Number Of Mappers Created For A Mapreduce Task Be Altered?
The number of mappers cannot be changed by default because it equals the number of input splits. There are multiple ways to set a property or modify the code to adjust the number of mappers.
For instance, if a 1 GB file is divided into eight blocks, there will only be eight mappers running on the cluster. There are multiple ways to set a property or modify the code to adjust the number of mappers.
12. What Are The Various Configuration Files For Hadoop?
The following are the Hadoop Configuration Files:
- HADOOP-ENV.sh: This file specifies the environment settings influencing the Hadoop Daemon’s JDK.
- CORE-LOCATION: XML is one of the essential configuration files for the Hadoop cluster runtime environment setting. It tells Hadoop daemons of the cluster location of the name node.
- HDFS-SITE: It is used to define block replication by default. When a file is created, the number of replications can also be given.
- MAPRED-SITE.XML: It holds the MapReduce setup settings.
- Experts: It is used to determine the Hadoop cluster’s controller Nodes. In addition, it will inform Hadoop Daemon of the location of the secondary name node.
- Slave: It is utilized to identify the slave Nodes in the Hadoop cluster.
13. What Are The Distinctions Between A Standard File System And HDFS?
In a regular File System, information is preserved in a single system. However, due to low fault tolerance, data recovery is difficult in the event of a system failure. In addition, the longer seeks time increases the time required to process the data.
On the other hand, HDFS distributes and manages data across multiple platforms. Therefore, one can recover data from other nodes in the cluster if a Data Node fails. However, local data is read from the disc, and data synchronization from many systems increases the time required to read data.
14. Can The Output Of Mapreduce Be Written In Several Formats?
Yes. Hadoop supports different input and output file formats, including:
TextOutputFormat: This is the default format for outputting records as lines of text.
- SequenceFileOutputFormat: This is used to write sequence files when it is a must to utilize the output files for another MapReduce process.
- MapFileOutputFormat: This is used to create map files as output.
- SequenceFileAsBinaryOutputFormat: It is an alternative implementation of SequenceFileInputFormat. It writes keys and values in binary format to a sequence file.
- DBOutputFormat: It is utilized for writing to relational databases and HBase. This format also stores the output of reduction in a SQL table.
15. What Occurs If A Node Executing A Mapping Job Fails Before Passing The Output To The Reducer?
If this occurs, map tasks will be assigned to a new node, and the entire task will be re-executed to recreate the map output. In Hadoop v2, the YARN framework includes a temporary daemon named application master responsible for application execution. If a job on a specific node fails to owe to the node’s unavailability, it is the responsibility of the application master to schedule this task on another node.
16. Why Do You Believe Hdfs Is Fault-Tolerant?
Fault tolerance is the capacity of a system to function or operate, notwithstanding adverse situations. HDFS is robust because it duplicates data across several Data Nodes. Different Data Nodes are used to hold the data blocks. Data can still be obtained from other Data Nodes if a node crashes. In addition, Error Coding is used to give Fault Tolerance. Erasure Coding in HDFS enhances storage efficiency while maintaining the same fault tolerance and data durability as replication-based HDFS deployments.
17. What Is The Function Of Rack Awareness In Hdfs?
By default, each data block is replicated three times over multiple Data Nodes distributed across many racks. It is not possible to place two identical blocks on the same Data Node. When a cluster is “rack-aware,” it is impossible to place all clones of a block on the same rack. You can obtain the data block from different Data Nodes if a Data Node collapses.
18. Identify Some Hadoop-Specific Data Types Utilized In Mapreduce Programs.
In Hadoop, each Java data type has its equivalent. Consequently, the following Hadoop-specific data types in your MapReduce program:
- Integer: It is utilized to pass integer values and keys.
- Float: Hadoop variant of Float for passing floating-point numbers as key or value.
- Long: A Hadoop-specific variation of the Long data type for storing long numbers.
- Short: A variation of the Short data type for storing short values in Hadoop.
- Double: A variation of Double for storing double values in Hadoop.
String: Hadoop variation of String allows string characters to be sent as keys or values.
- Byte: A Hadoop-specific variation of byte used to store sequences of bytes.
19. What Is The Purpose Of The Hadoop ‘JPS’ Command?
We can determine whether or not the Hadoop daemons are operating by using the ‘JPS’ command. It also displays all of the Hadoop daemons currently operating on the machine, such as the name node, data node, node manager, and resource manager, among others.
20. What Exactly Is Mapreduce’s Distributed Cache?
A distributed cache is a method that allows disk-sourced data to be cached and made accessible to all worker nodes. For example, when a MapReduce program is running, rather than reading data from the disk each time, the data would be retrieved from the distributed cache to improve MapReduce processing.
Once a file has been cached for a job, Apache Hadoop will make it accessible on all data nodes where map/reduce tasks are executing. Consequently, we can access files from each data node in a MapReduce task.
21. What Occurs When Two Clients Attempt To Access The Same Hdfs File?
When the first client contacts “Name Node” to open a file for writing, “Name Node” provides the client a lease to create the file. When the second client attempts to open the same file for writing, the “Name Node” detects that the file lease has already been granted to another client and rejects the second client’s open request.
22. What Function Do Record Reader, Combiner, And Practitioner Provide Throughout A Mapreduce Operation?
- Record Reader connects with the Input Split and turns the data into key-value pairs that the mapper can read.
- Combiner is an optional phase that resembles a mini-reducer. It gets data from the map tasks, manipulates it, and then sends the results to the reducer phase.
- The practitioner determines the number of reduced jobs required to summarize the data. Additionally, it verifies how outputs from combiners are given to the reducer and controls intermediate map outputs’ key partitioning.
23. What Must The Hadoop Administrator Do After Adding New Data Nodes?
They should initiate the balancer for evenly dispersing data among all nodes for the Hadoop cluster to discover new data nodes automatically. To enhance the cluster’s performance, they should use rebalance to redistribute data between data nodes.
24. Explain How The Spilling Process Works In Mapreduce.
Spilling is copying data from the memory buffer to the disk when the buffer utilization exceeds a predetermined threshold. It occurs when there is insufficient memory to accommodate the entire mapper output. For example, after 80% of the buffer’s size has been consumed, the default behavior of a background thread is to begin spilling the content to the disk. Once the buffer’s contents reach 80 MB for a 100 MB buffer, spillage will begin.
25. What Characteristics Make Hadoop The Most Popular And Powerful Large Data Tool?
The following characteristics contribute to Hadoop’s popularity:
- Hadoop is open-source, which means that it is free to use.
- Very Scalable Cluster: Hadoop is a highly scalable model. In a cluster, a significant volume of data is distributed across numerous inexpensive processors and processed simultaneously.
- User-Friendly: Hadoop is user-friendly since the processing work is handled by Hadoop itself, relieving developers of any responsibility for it.
- Hadoop employs Data Locality: The Data Locality idea is used to speed up Hadoop processing. In the data locality notion, computation logic is relocated close to data instead of relocating data to computation logic. Moving data on HDFS is the most expensive operation, and the data locality principle reduces the system’s bandwidth consumption.
After reviewing this collection of often asked Hadoop interview questions, I hope you feel prepared. Review the questions and rehearse with the sample responses so that you can secure the Hadoop position. As you have noted, the majority of these questions are challenging and require that you read and comprehend this technology.