Big Data Hadoop is one of the top competencies in today’s data-driven world. It is a potent technology that enables businesses and individuals to effectively and economically make sense of enormous amounts of data, especially unstructured data. Saying that data science and analytics are sweeping the globe would be an understatement given the present employment landscape that is dominated by technology. People with exceptional data analytics skills are in high demand as many firms seek to use the available data as effectively as possible.
Big Data-related jobs are currently in demand. Big Data Analytics is being used by one in five large businesses, so it’s time to start looking for employment in this area. These are some Hadoop MapReduce interview questions and responses for new and seasoned candidates seeking their ideal position. To assist you in succeeding in the interview, we present the Top 50 Hadoop Interview Questions and Answers.
List of 50 Top Interview Questions for Hadoop Big Data
1. Define big data and describe its features.
Big Data refers to a group or collection of significant datasets that are continuously expanding exponentially. Big Data is challenging to manage with conventional data management solutions. The volume of data produced daily by Facebook or the Stock Exchange Board of India are examples of big data.
The following are characteristics of big data:
- Volume: A lot of data that is kept in data warehouses is referred to as volume.
- Velocity: Velocity often refers to the rate at which real-time data is generated.
- Variety: Structured, unstructured, and semi-structured data that is gathered from various sources is referred to as the variety of big data.
- Veracity: The degree of accuracy of the data is often referred to as veracity.
- Value: Data must be reliable and valuable regardless of how quickly or much of it is produced. If not, the data is insufficient for processing or analysis.
2. What is Hadoop? What are the main elements of Hadoop?
Dealing with Big Data requires Apache Hadoop. Big Data can be stored, managed, processed, and analysed using various tools and services provided by the open-source framework known as Hadoop. Due to its effectiveness and efficiency, firms are now able to make important business decisions that were previously impossible to do so using conventional techniques and tools.
Hadoop is composed of three basic parts. They are as follows:
- HDFS: Big data storage can be distributed over a cluster of machines using HDFS technology. Moreover, it keeps up with the redundant data copies. However, HDFS can recover from that by producing a backup from a copy of the data that it had automatically saved, and you won’t even know if anything happened, if one of your machines happens to suddenly catch fire or if some technical troubles arise.
- YARN: YARN (Yet Another Resource Negotiator) comes next in the Hadoop ecosystem. It is the place where Hadoop’s data processing is utilized. The system that controls the resources on your computing network is called YARN.
- MapReduce: The next part of the Hadoop ecosystem is called MapReduce, and it’s essentially a programming model that lets you process data across an entire cluster.
3. Discuss the Hadoop Storage Unit (HDFS).
The storage layer for Hadoop is the Hadoop Distributed File System (HDFS). HDFS divides the files into units called data blocks that are one block in size. On the cluster’s slave nodes, these blocks are preserved. The size of the block is 128 MB by default, however, it can be changed to suit our needs. It adheres to the master-slave structure. Both DataNodes and NameNode are daemons that are present.
- NameNode: The master daemon running on the master node is called NameNode. It stores the filesystem metadata, such as file names, information about a file’s blocks, the locations of those blocks, permissions, etc. Datanodes are managed by it.
- DataNode: The slave daemon that manages the slave nodes is called the data node. The genuine business data is saved. Based on the NameNode instructions, it fulfils the client read/write requests. File blocks are kept there, and NameNode keeps track of metadata like permissions and block placements.
4. List the various features of HDFS.
Data is broken up into blocks by the Hadoop framework, which then duplicates each block many times throughout the cluster’s servers. Clients can therefore access their data from the other machine hosting a replica of the data blocks if any device in the cluster fails.
Data is divided into blocks by HDFS, and the Hadoop framework stores these blocks on cluster nodes. By creating a replica of each block that is currently in the cluster, it preserves data. Hence, a fault tolerance facility is presented. As a result, HDFS is quite dependable.
The data is stored using HDFS across several nodes. Hence, it can grow the cluster in the event of a rise in demand.
By creating a copy of the blocks, the data is duplicated in the HDFS environment. Because duplicate pictures of the blocks already exist in the other nodes of the HDFS cluster, users can easily retrieve their data from those nodes anytime they need it or in the unfortunate event of an emergency.
The issue of data loss in challenging circumstances, such as device failure, node crashing, etc., is solved via replication. It controls the replication process regularly. As a result, there is little chance that user data will be lost.
5. What is the 1.0 version of Hadoop’s limitations?
- It just executes Map/Reduce tasks.
- For real-time data processing, it is not ideal.
- The horizontal scalability of NameNode is not aided by it.
- The secondary NameNode was supposed to copy the NameNode’s hourly metadata backup.
- Only one Name No. and one Namespace is supported per Cluster.
- Only batch processing of large amounts of data that are already present in the Hadoop framework is appropriate for it.
- It just includes one component, called JobTracker, which can handle a variety of tasks, including resource management, job scheduling, job monitoring, and job rescheduling.
6. List a few of the largest Hadoop users in the world.
The following are some of the largest companies using Hadoop as a Big Data tool:
- Bank of Scotland
- The United States National Security Agency (NSA)
7. What are Hadoop’s real-time business applications?
The open-source software platform Hadoop also referred to as Apache Hadoop, is used for scalable and distributed computing of massive volumes of data. Structured and unstructured data generated on digital platforms and within businesses can be quickly, highly effective, and affordably analysed. Today, it is utilised by all divisions and industries.
Following are some scenarios in which Hadoop is utilised:
- Processing in real time
- directing traffic on roads
- Identifying and preventing fraud
- real-time customer data analysis to enhance business performance
- Taking care of posts, videos, photos, and other content on social media sites
- Hadoop is used in public sector industries like intelligence, defence, cyber security, and scientific research to collect and analyse clickstream, transaction, video, and social media data.
8. Explain HBase.
Apache A distributed, open-source, scalable, and multidimensional NoSQL database called HBase. Based on Java, HBase runs on HDFS and gives Hadoop capabilities and functionalities akin to those of Google Bigtable. Moreover, HBase’s failure tolerance makes it possible to store enormous amounts of sparse datasets. HBase provides faster access to huge datasets for reading or writing, resulting in low latency and great throughput.
9. Describe a combiner.
A combiner, which is used to carry out local reduction procedures, is a scaled-down counterpart of a reducer. The combiner receives the input from the mapper at a certain node, which then sends the corresponding output to the reducer. Also, it decreases the amount of data that must be transmitted to the reducers to increase MapReduce’s effectiveness.
10. What does shuffling in MapReduce mean?
Shuffling is used in Hadoop MapReduce to move data from the key mappers to the key reducers. It is the procedure by which the system organises the unstructured data and feeds the map’s output to the reducer as an input. For reducers, it is a significant process. They wouldn’t accept any information else. Also, because this procedure can start even before the map phase is finished, it helps to save time and finish the process more quickly.
11. What three operating modes does Hadoop support?
Standalone mode or Local mode
By default, Hadoop is configured to run in a non-distributed manner. It runs as a single Java process. Instead of using HDFS, this mode uses the local file system. As no setting of the core-site.xml, hdfs-site.xml, mapred-site.xml, masters, or slaves is required, this approach is more advantageous for debugging. Hadoop’s stand-alone mode is typically the fastest.
Model with Pseudo-distribution
Each daemon functions as a distinct Java process in this mode. This option needs a customised setup ( core-site.xml, hdfs-site.xml, mapred-site.xml). The input and output are handled by the HDFS. This deployment method is useful for testing and debugging.
Fully Distribution Mode
That is the Hadoop production mode. In essence, one machine in the cluster serves only as the NameNode, and another as the Resource Manager. These are specialists. Data nodes and node managers are the rest nodes. Slaves are those people. Hadoop Daemons require the definition of environment and configuration settings. This mode offers scalability, fault tolerance, security, and completely distributed computing capabilities.
12. Can algorithms or codes be improved to make them run more quickly? Yes, but why?
Certainly, optimising algorithms or scripts to make them operate more quickly is always advised. This is because optimised algorithms have been taught and are familiar with the business issue. The speed increases with the optimisation level.
13. Why is Apache Spark important?
An open-source framework engine called Apache Spark is renowned for its efficiency and quickness in handling and analysing large amounts of data. Moreover, it has built-in modules for SQL, streaming, graph processing, machine learning, etc. The Apache Spark execution engine allows cyclic data flow and in-memory computing. It can also access various data sources, including Cassandra, HDFS, and HBase.
14. Could you list Apache Spark’s components?
The following are the parts of the Apache Spark framework:
- Spark R
- Spark SQL
- Spark Streaming
- Spark Core Engine
It’s important to keep in mind that not all Spark components need to be used. The Spark Core Engine can, however, be utilised in conjunction with any of the other elements mentioned above.
15. Describe Apache Hive.
Hadoop uses Apache Hive, an open-source tool or system, to process structured data that is stored there. The system in charge of enabling analysis and queries in Hadoop is called Apache Hive. One advantage of adopting Apache Hive is that it makes it easier for SQL developers to create Hive queries that are essentially identical to the SQL statements provided for data processing and querying.
16. Describe Apache Pig in detail.
Programs must be converted into Map and Reduce stages for MapReduce to work. Because not all data analysts are familiar with MapReduce, Yahoo researchers developed Apache Pig to fill the gap. Due to the high level of abstraction produced by Apache Pig, which was built on top of Hadoop, programmers may now write complicated MapReduce processes with less effort.
17. Describe the architecture of Apache Pig.
A Pig Latin interpreter is part of the Apache Pig architecture, which uses Pig Latin scripts to process and analyse big datasets. To analyse massive datasets in the Hadoop environment, programmers use the Pig Latin language. A diverse collection of datasets in Apache Pig demonstrate various data operations including join, filter, sort, load, group, etc.
To address a Pig script to carry out a certain activity, programmers must become proficient in the Pig Latin language. To lessen the workload on programmers, Pig converts these Pig scripts into a series of Map-Reduce jobs. Pig Latin programmes can be executed using various tools, including Grunt shells, embedded code, and UDFs.
18. What is yarn?
Yet Another Resource Negotiator is referred known as Yarn. It is the Hadoop layer for resource management. With Hadoop 2.x, The Yarn was introduced. To execute and process data saved in the Hadoop Distributed File System, Yarn offers a variety of data processing engines, including graph processing, batch processing, interactive processing, and stream processing. Yarn also provides scheduling for jobs. It extends Hadoop’s capabilities to other developing technologies so they may benefit from HDFS and economic clusters.
The Hadoop 2.x data operating technique is Apache Yarn. It is made up of an Application Master, a slave daemon named node manager, and the “Resource Manager” master daemon.
19. List the elements of YARN.
- Resource Manager: It manages resource allocation in the cluster and runs as a master daemon.
- Node Manager: This programme runs on slave daemons and performs a task on each Data Node individually.
- Application Master: It manages the resource requirements and user job lifecycles for specific applications. It collaborates with the Node Manager and keeps track of task completion.
- Container: A single node’s worth of resources, such as RAM, CPU, Network, HDD, etc.
20. What does Hadoop Ecosystem mean?
The Hadoop Ecosystem is a collection of all the services involved in solving Big Data issues. In a precise sense, it is a platform made up of many parts and technologies that work together to carry out Big Data projects and address their problems. The Hadoop Ecosystem is made up of Apache projects as well as several additional elements.
21. What exactly is Apache ZooKeeper?
An open-source service called Apache Zookeeper supports managing a sizable number of hosts. It is difficult to manage and coordinate in a scattered setting. By automating this procedure, Zookeeper frees developers from worrying about the dispersed nature of the software and allows them to focus on creating new features.
For distributed applications, Zookeeper aids in the maintenance of configuration information, naming conventions, and group services. To prevent the programme from running different protocols on its own, it implements them on the cluster. It offers a unified, cohesive image of numerous machines.
22. What are the advantages of utilising Zookeeper?
- Simplified distributed coordination process: In Zookeeper, the coordination between all nodes is simple.
- Synchronisation: The cooperation and mutual exclusion of server processes.
- Ordered Messages: Zookeeper tracks messages with a number and indicates their order by stamping each update; as a result, communications are ordered here.
- Serialization: Follow predetermined procedures to encode the data. Make sure your programme functions consistently.
- Reliability: The zookeeper is quite trustworthy. If there is an update, all the data is kept until it is transmitted.
- Atomicity: No transaction is incomplete; data transmission either succeeds or fails.
23. List the different Znode kinds.
- Persistent Znodes: The persistent znode is the standard znode in ZooKeeper. It remains on the Zookeeper server indefinitely until another client removes it.
- Ephemeral Znodes: These are the znodes that are transient. Every time the creator client leaves the ZooKeeper server, it is destroyed.
- Sequential Znodes: Each sequential znode has a 10-digit number appended to its name in numerical order.
24. How does Hadoop Streaming work?
One of the methods that Hadoop provides for non-Java programming is Hadoop Streaming. Any language that can write to standard output and read from standard input can be used to create MapReduce programmes with the aid of Hadoop Streaming. The two main technologies are Hadoop Streaming, which enables any programme that uses standard input and output to be utilised for map and reduce activities, and Hadoop Pipes, which provides a native C++ interface to Hadoop. Any executable or script can be used as the mapper or reducer in a MapReduce job created and executed with Hadoop Streaming.
25. What distinguishes Hadoop from other parallel computing platforms?
With the help of a distributed file system called Hadoop, you may manage data redundancy while storing and managing massive volumes of data on a network of computers. The main advantage of this is that it is preferable to handle the data in a distributed fashion because it is stored across multiple nodes.
On the other hand, the relational database computer system allows for real-time data querying, but it is inefficient to store vast amounts of data in tables, records, and columns. A method for creating a column database using Hadoop is also provided.
26. What does distributed cache mean? What are its advantages?
The MapReduce framework’s distributed cache in Hadoop provides a service to cache files as needed. Hadoop will make a file accessible on each DataNode in the system and in memory, where map and reduce processes are carried out, once it has been cached for a particular job. Eventually, it will be simple for you to access and read the cache files and add data to any collection in your code, including an array or hashmap.
The following are some advantages of employing distributed cache:
- It disseminates straightforward, read-only text/data files as well as more complicated ones like jars, archives, and others. In the slave node, these archives are subsequently unarchived.
- The distributed cache keeps track of the cache files’ modification timestamps, which serve as a reminder that the files shouldn’t be changed until a job has been completed.
27. List the various Hadoop configuration files.
The names of the various Hadoop configuration files are shown below:
28. Can Hadoop skip the invalid records? How?
When processing map inputs in Hadoop, there is a setting that allows sets of input records to be skipped. The SkipBadRecords class is used by the applications to control this feature.
When map tasks encounter input record failures, the SkipBadRecords class is frequently utilised. Please be aware that the failure could be caused by problems with the map function. As a result, Hadoop may bypass faulty records by leveraging this.
29. How does one run a MapReduce programme?
A MapReduce programme is run using the syntax hadoop jar file.jar /input path /output path.
30. Describe DistCp.
It is a programme used to replicate data in parallel between Hadoop file systems that contain enormous volumes of data. MapReduce has an impact on its distribution, error handling, recovery, and reporting. Many inputs to map jobs are created from a list of files and directories, each of which duplicates a particular subset of the files listed in the source list.
31. What is the replication factor that is used by default?
The replication factor is 3 by default. There won’t be any duplicate copies on the same data node. The first two copies are frequently located on the same rack, whereas the third copy is usually withdrawn off the shelf. It is advised to set the replication factor to at least three so that one copy is always secure regardless of what happens to the rack.
Each file’s and directory’s replication factor, as well as the file system’s default replication factor, can be customised. For non-critical files, we can select a lower replication factor, and for crucial files, a larger replication factor.
32. In Hadoop, how do you skip the bad records?
Hadoop provides the option to skip a certain collection of subpar input records while processing map inputs. Applications employing the SkipBadRecords class can manage this capability.
When map tasks fail deterministically on input, this feature might be used. Errors in the map function are typically to blame. The user would have to deal with these.
33. Where are the two categories of metadata kept on the NameNode server?
Two different types of metadata are stored on the disc and in Memory by the NameNode server.
The following two files are associated with metadata:
- EditLogs: This file lists all changes made to the file system since the last FsImage.
- FsImage: It includes the complete namespace status of the file system as of the time it was created.
Hadoop provides the option to skip a certain collection of subpar input records while processing map inputs. There is a programme for that. When a file is deleted from HDFS, the NameNode instantly logs the event in the EditLog.
All the file systems and metadata stored in the Namenode’s RAM are continuously read by the Secondary NameNode and written to the file system or hard disc. The NameNode incorporates FsImage and EditLogs. Secondary NameNode periodically downloads the EditLogs from NameNode and applies them to FsImage. The new FsImage is copied back into the NameNode once it has booted up the following time.
34. Which Command is used to determine the health status of the File and Block systems?
hdfs fsck -files -blocks is the command used to determine the status of the block.
The programme used to determine the health of the file system is hdfs fsck/ -files -blocks -locations > dfs-fsck.log.
35. What are the most typical Hadoop input formats?
In Hadoop, the following three input formats are the most popular:
- Text Input Format: Hadoop’s default input format
- Key-value Input Format: Line-by-line plain text files are accepted as input in this format.
- Sequence File Input Format: Used for reading files in order, and file input format.
36. What are the most typical Hadoop output formats?
The following are the output formats that are frequently used in Hadoop:
- TextOutputFormat: Hadoop’s default output format is TextOutputFormat.
- Hadoop writes the output as map files using the mapfileoutputformat.
- DBoutputformat: DBoutputformat generates output for Hbase and relational databases.
- SequencefileOutputFormat: When writing sequence files, this format is utilised.
- SequencefileAsBinaryoutputformat: This function is used to write keys in binary format to a sequence file.
37. How do you run a Pig script?
Users can run a Pig script using one of the three techniques indicated below:
- Grunt shell
- Script file
- Embedded script
38. Why is Apache Pig chosen over MapReduce, and what is it?
A Hadoop-based framework called Apache Pig enables experts to examine big data volumes and portray them as data flows. Pig has an advantage over MapReduce since it lessens the complexity needed when writing a programme.
Pig is preferable to MapReduce for the reasons listed below, among others:
- MapReduce is a paradigm for low-level data processing, whereas Pig is a language for high-level data flow.
- Pig makes it simple to get a comparable result to MapReduce without having to create any complicated Java code.
- Pig roughly cuts development time in half compared to MapReduce, cutting the length of the code by about 20 times.
- Pig provides built-in capabilities to carry out a variety of operations, including sorting, filters, joins, ordering, etc.
39. Describe the steps taken by a Hadoop Jobtracker.
The assigned TaskTracker Nodes receive the work.
- When a task fails, JobTracker tells the user and decides what to do next.
- JobTracker locates TaskTracker nodes using the open slots and the nearby data.
- The work is submitted to the chosen TaskTracker Nodes.
- JobTracker notifies the user and determines the next steps when a task fails.
- The TaskTracker nodes are observed by JobTracker.
40. Explain the operation of the distributed Cache in the MapReduce framework.
The Distributed Cache of the MapReduce Framework is an essential tool that you can use when you need to transfer files among all nodes in a Hadoop cluster. Simple properties files or jar files can be used as these files.
With the help of Hadoop’s MapReduce framework, it is possible to distribute small to medium read-only files, such as text files, zip files, and jar files, to all the Datanodes (worker nodes) where MapReduce tasks are now running. Distributed Cache sends a copy of the file (local copy) to each Datanode.
41. Describe what occurs when a DataNode fails.
- Which blocks the DataNode failed on are detected by the job tracker and the name node, respectively.
- All jobs are rescheduled on the failing node by identifying other data nodes with copies of these blocks.
- To preserve the configured replication factor, user data will be copied from one node to another.
42. What are the fundamental characteristics of a mapper?
Text, LongWritable, text, and IntWritable are mappers’ three main inputs. The first two are considered input parameters, while the latter two are considered intermediate output parameters.
43. Briefly describe how Spark excels at low latency workloads like machine learning and graph processing.
Apache Spark stores the data in memory to speed up processing and the building of machine learning models, which may require numerous Machine Learning algorithms for repeated application and different conceptual processes to produce an optimum model. To build a graph, graph algorithms move along all of the nodes and edges. These low-latency workloads, which call for a large number of repeats, enhance performance.
44. What software programmes does Apache Hive support?
The programmes that Apache Hive supports include:
45. What does a metastore in Hive mean?
The metadata is stored in a metastore, but it is also possible to convert object representation into a relational schema using an open-source ORM layer and a relational database management system (RDBMS). It keeps track of the metadata for Hive partitions and tables in a relational database (including information about their location and schema). The client can retrieve this data by using the metastore service API. For the Hive metadata, HDFS storage is distinct from disc storage.
46. Does Hive enable multiline comments? Why?
No, only single-line comments are supported at this time in Hive. Multiline comments are not yet supported.
47. Why is partitioning in Hive necessary?
Tables are divided into partitions using Apache Hive. A table is divided into linked parts based on the values of relevant columns like date, city, and department.
To identify a certain partition, each table in the hive may have one or more partition keys. Slices of the data can be easily accessed through queries thanks to partitions.
48. What does Apache Flume in Hadoop stand for?
Apache Flume is a tool, service, and data ingestion methods for gathering, aggregating, and transporting enormous volumes of streaming data, such as record files and events from various references, to a centralised data store. A distributed utility with lots of customisation options is Flume. Most of the time, it is designed to copy log data from various web servers.
49. Describe Flume’s architecture.
The following elements make up the architecture of Apache Flume generally:
Flume Source: Flume Source is accessible on many social media sites, including Facebook and Instagram. Data is collected from the generator by a data generator, which then sends the data in the form of a flume to a flume channel.
Flume Channel: The events are buffered until they are sent into the sink once the data from the flume source is transmitted to an intermediate store. The Intermediate Store is known as The Flume Channel. It joins the Source Flume channel and the Sink Flume channel. As the file channel is non-volatile, any data entered into it cannot be lost until you choose to delete it.
Flume sink: Flume Sink is a feature of data repositories like HDFS. It receives Flume Channel events and stores them in the designated Destination, such as HDFS. The events should be delivered to the Store or another agent as a result of how it is done. The Flume supports several sinks such as the Hive Sink, Thrift Sink, etc.
Flume Agent: The Flume Agent is a Java process that operates on Source, Channel, and Sink combinations. Flume allows for the possibility of one or more agents. Distributed connected Flume agents can alternatively be referred to as Flume together.
Flume Event: An event is a unit of data that is transmitted through Flume. The term “Event” refers to the Data Object’s generic representation in Flume. The event consists of a byte array payload with optional headers.
50. Discuss how distributed applications have an impact.
Heterogeneity: The design of applications should allow users to access services and run applications over a heterogeneous collection of computers and networks, taking into consideration Hardware devices, OS, networks, and Programming languages.
Transparency: Designers of distributed systems must use every trick in the book to hide the system’s complexity. Location, accessibility, movement, relocation, and other transparency-related concepts are only a few.
Openness: This quality evaluates whether a system can be altered and reimplemented differently.
Security: The secrecy, integrity, and availability of designs must be considered.
Scalability: A system is deemed scalable if it can accommodate the addition of users and resources without experiencing a significant degradation in performance.