Big Data Interview Questions

1. What are your thoughts on the term

Ans: Big Data is a term that refers to complex and large datasets. A relational database cannot handle large amounts of data, which is why special tools and methods are used to perform operations on large amounts of data. Big data enables businesses to better understand their operations and derive meaningful information from unstructured and raw data collected on a regular basis. Big data also enables businesses to make better data-driven business decisions.

2. What are Big Data's five Vs?

Ans: The five Vs of Big Data are as follows:

1. Volume – Volume represents the amount of data that is growing rapidly, i.e. data volume in Petabytes.
2. Velocity – The rate at which data grows is referred to as velocity. Social media plays a significant role in the increasing velocity of data.
3. Variety refers to the various data types, i.e. various data formats such as text, audios, videos, and so on.
4. Veracity – The uncertainty of available data is referred to as veracity. The lack of veracity arises as a result of the large volume of data, which leads to incompleteness and inconsistency.
5. Value –Value refers to the process of converting data into monetary value. Businesses may benefit from converting accessed big data into values.

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3. Tell us about the relationship between big data and Hadoop.

Ans: Big data and Hadoop are nearly synonymous terms. With the rise of big data came the rise of Hadoop, a framework that specializes in big data operations. Professionals can use the framework to analyze big data and assist businesses in making decisions.

4. What role does big data analysis play in increasing business revenue?

Ans: Big data analysis is becoming increasingly important for businesses. It assists businesses in differentiating themselves from competitors and increasing revenue. Big data analytics, through predictive analytics, provides businesses with customized suggestions. Furthermore, big data allows enterprises to announce new products based on customer needs and preferences.As a result of these factors, businesses earn so much revenue, and as a result, businesses use big data analytics. Implementing big data analytics can result in a significant increase in revenue of 5-20%. Walmart, LinkedIn, Facebook, Twitter, and Bank of America are just a few examples of well-known companies that use big data analytics to boost revenue.

5. Define the various components of HDFS and YARN.

Ans: The two primary components of HDFS are-

NameNode – This is the master node responsible for processing metadata information for data blocks within the HDFS DataNode/Slave node – This is the node that acts as a slave node, storing data for processing and use by the NameNode

In addition to serving client requests, the NameNode performs one of the two roles listed below –

CheckpointNode – It runs on a different host than the NameNode BackupNode – It is a read-only NameNode that contains file system metadata but not block locations.

YARN's two main components are–

ResourceManager– This component receives processing requests and allocates them to the appropriate NodeManagers based on the processing needs.

NodeManager– It is in charge of carrying out tasks on each and every Data Node.

6. What is the purpose of using Hadoop for Big Data Analytics?

Ans: Because data analysis has become one of the most important business parameters, businesses are dealing with massive amounts of structured, unstructured, and semi-structured data. Analyzing unstructured data is difficult, which is where Hadoop comes in handy with its capabilities.

• Data collection and storage processing
• Hadoop is also open source and runs on commodity hardware. As a result, it's a cost-benefit solution for businesses.

7. What exactly is fsck?

Ans: The acronym fsck stands for File System Check. It is a command that is used by HDFS. This command is used to check for inconsistencies and problems in the file. For example, if a file has any missing blocks, HDFS is notified via this command.

8. What are the primary distinctions between NAS (Network-attached storage) and HDFS?

Ans: The primary distinctions between NAS (Network-attached storage) and HDFS –

HDFS is run on a cluster of machines, whereas NAS is run on a single machine. As a result, data redundancy is a common problem in HDFS. In the case of NAS, however, the replication protocol is distinct. As a result, the likelihood of data redundancy is much lower.In the case of HDFS, data is stored as data blocks on local drives. It is stored in dedicated hardware in the case of NAS.

9. What is the NameNode formatting command?

Ans: $ hdfs namenode -format

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Intermediate level big data question and answers:

10. Describe the steps involved in deploying a Big Data solution.

Ans: The three steps for deploying a Big Data Solution are as follows –

• Ingestion of Data

Data ingestion, or the extraction of data from various sources, is the first step in deploying a big data solution. The data source could be a CRM like Salesforce, an ERP like SAP, an RDBMS like MySQL, or any other log files, documents, social media feeds, and so on. Data can be ingested in two ways: batch jobs or real-time streaming. After that, the extracted data is saved in HDFS.

• Storage of Data

Following data ingestion, the extracted data must be stored. The data will be stored in either HDFS or a NoSQL database (i.e. HBase). HDFS storage is ideal for sequential access, whereas HBase is ideal for random read/write access.

• Information Processing

Data processing is the final step in deploying a big data solution. The data is processed using one of the processing frameworks such as Spark, MapReduce, Pig, and so on.

11. What are the different methods to deal with big data?

Ans: Because Big Data provides a business with a competitive advantage over its competitors, a business can decide to tap the potential of Big Data based on its needs and streamline the various business activities based on its goals.As a result, the approaches to dealing with Big Data must be determined based on your business requirements and available budgetary resources.

First, you must determine the nature of your current business concerns. What questions do you want your data to answer? What are your business goals, and how do you intend to achieve them?In terms of approaches to Big Data processing, there are two options: They are processing in batches and Stream.

You can process Big Data in batches daily or after a set period of time, depending on your business needs. If your business requires it, you can process it in a timely manner every hour or every 15 seconds or so.

It all depends on your business goals and the strategies you employ.

12. What are the platforms to deal with big data?

Ans: There are several Big Data platforms available. Some of these are open source, while others require a license.

Hadoop is the most popular open-source Big Data platform. The other option is to use HPCC. HPCC is an abbreviation for High-Performance Computing Cluster.

We have licensed Big Data platform offerings from Cloudera (CDH), Hortonworks (HDP), MapR (MDP), and others. (Hortonworks is now part of Cloudera.)
We have tools like Storm for stream processing.

• When we consider the landscape of Big Data platforms from the standpoint of usage, we can gain a better understanding of it.
• In the data storage and management category, for example, we have major players such as Cassandra, MongoDB, and others.
• Tools in the data cleaning category include OpenRefine, DataCleaner, and others.
• IBM SPSS, RapidMiner, Teradata, and other data mining tools are available.
• Tableau, SAS, Spark, Chartio, and other data visualization tools are available.

13. List the most commonly used big data tools.

There are numerous Big Data tools on the market today. Some offer storage and processing services.

Some only offer storage and various APIs for processing, while others offer analytical services, and so on. Hadoop, Spark, HPCC, CDH, and other Big Data tools are the most widely used.

• Ans: Hadoop is an Apache foundation open-source Big Data platform. The beauty of it is that it can run on common hardware. Spark is yet another Apache Foundation tool. It adds the ability to process streams. It also has in-memory data processing capabilities. As a result, it is much faster.
• HPCC is an abbreviation for High-Performance Computing Cluster. It is a highly scalable supercomputing platform.
• Cloudera is abbreviated as CDH.It is an enterprise level big data tool.

14. How is hadoop more suitable for big data?

Ans: Hadoop was most likely the first open-source Big Data platform. It is highly scalable and runs on standard hardware. It includes the Hadoop Distributed File System (HDFS). It has the ability to store a large amount of unstructured data in a distributed fashion.

MapReduce, a data processing framework, is also included in Hadoop. It performs data processing in a highly parallel manner.

The processing time is drastically reduced for large amounts of data. There are numerous APIs and other tools that can be integrated with Hadoop, extending its usefulness, improving its capability, and making it more suitable for Big Data.

The Hadoop Framework allows the user to create and test distributions.

It's indeed fault-tolerant and provided in the following section across the cluster of machines automatically. It makes extensive use of parallelism. Hadoop does not rely on the underlying hardware to provide high availability and fault tolerance.

It provides such assistance at the application layer. We can add or remove nodes based on our needs. There is no need for you to make any changes to the application.

15. How does big data assist in making decisions?

Ans: There are numerous factors to consider when making a business decision. A single bad decision can devastate an entire company. A Big Data set contains a wealth of information that, when used wisely, can greatly benefit a business.

It has the potential to transform any business that is willing to take advantage of it.

It is riddled with patterns, trends, and value. When this information is discovered, it can assist any business in making decisions based on actual data rather than human instinct. It aids in the development of various marketing, production, and inventory management strategies.

It has the potential to raise efficiency at all levels while significantly lowering overall costs.

A company that does not capitalize on the potential of Big Data may miss out on an opportunity and fall behind its competitors. It may make some mistakes if it fails to consider market and customer concerns. Because Big Data can provide valuable feedback and market thoughts, it can greatly assist a business in making wise, correct, and timely decisions and providing excellent business insights.

16. How does big data and data science are related to each other?

Ans: Data science encompasses a wide range of activities that involve the analysis of Big Data, the discovery of patterns and trends in data, the interpretation of statistical terms, and the prediction of future trends. Big Data is only one component of Data Science. Though Data Science is a broad term with many applications in business, it is nothing without Big Data.

All of our activities in Data Science are based on Big Data. As a result, Big Data and Data Science are inextricably linked and cannot be viewed in isolation.

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17. What are the tools used for extraction of big data?

Ans: There are numerous Big Data extraction tools available. Flume, Kafka, Nifi, Sqoop, Chukwa, Talend, Scriptella, Morphlines, and so on. Apart from data extraction, these tools also help with data modification and formatting.

There are several methods for extracting Big Data:

• Batched
• Continuous
• Real-time
• Asynchronous

There are many other issues that must be addressed as well. The source and destination systems may use different I/O formats, protocols, scalability, security concerns, and so on. As a result, data extraction and storage must be handled appropriately.

Open source tools: Open source tools may be more appropriate for users on a tight budget.

They are expected to have an adequate knowledge base as well as the necessary supporting infrastructure. Some vendors provide open source versions of their tools that are light or limited in scope.

• Batch data extraction tools: Existing Data data extraction tools combine/consolidate data in batches. It is usually done during off-hours to have the least amount of impact on the working systems.A batch extraction appears to be a good approach for on-premise, closed environments.
• Cloud-based data extraction tools: This is the next generation of data extraction tools. The emphasis here is on data extraction in real time.These tools provide an added benefit of data security while also addressing any data compliance issues. As a result, an enterprise does not need to be concerned about these issues.

18. What are the languages used in order to query the big data?

Ans: There are several languages available for querying Big Data. Some of these programming languages are functional, dataflow, declarative, or imperative. Big Data querying is frequently fraught with difficulties. As an example:

• Data that is unstructured
• Latency
• Fault tolerance

1. By 'unstructured data,' we mean that the data, as well as the various data sources, do not adhere to any specific format or protocol.
2. By 'latency,' we mean the amount of time it takes certain processes, such as Map-Reduce, to produce a result.
3. By 'fault tolerance,' we mean the steps in the analysis that allow for partial failures, reverting to previous results, and so on.

19. What is overfitting?

Ans: A model that really is overfit to the data is referred to as overfitting. It's a blunder in the modeling. It happens when a modeling function is too closely fitted to a small data set. The model is made too complex in this case to explain the peculiarity or individuality in the data under consideration.

Overfitting reduces the predictability of such models. The ability of such models to generalize is also harmed. Such models typically fail when applied to outside data, i.e. data that was not included in the sample data.

There are several methods for avoiding overfitting. They are as follows:

• Cross-validation
• Stopping
• Pruning,
• Regularization, and so on.

20. How are the missing values controlled in big data?

Ans: Missing values are the values which are not present in a single column. When we do not address the missing values, users may end up with incorrect data and, as a consequence, incorrect results. So, already when we handle Big Data, we must correctly treat the incomplete data in order to find the accurate sample. There are several approaches to dealing with missing values.

We can either discard the data or use data imputation to replace it.

If indeed the number of missing values is limited, it is normal practice to depart it alone. If the number of cases exceeds a certain threshold, data imputation is performed.

In facts and figures, there are several techniques for estimating so-called missing values:

• Regression
• MLE stands for Maximum Likelihood Estimation.
• Deletion in Lists/Pairs
• Imputation of multiple data.

Advanced level big data question and answers:

21. what are the issues in big data integration?

Ans: We must integrate various data sources and systems in Big Data integration. Policies governing data collection, extraction, storage, and processing are subject to change. Different data points have different formats, architectures, tools and technologies, data transfer protocols, and so on. So, deciding to capture and use Big Data for your business will entail integrating these various data points, making some changes to the formats, usage, and security, among other things. It will have an effect on the day-to-day operations of the company.

Before proceeding with the integration process, several issues in Big Data integration must be addressed. Some of them are:

• Consolidation of various verticals
• Business practices must evolve.
• Cultural shift
• Initial capital outlay
• Changes in operations.

Many businesses have most likely already deployed their IT infrastructures based on their needs. When deciding to implement Big Data integration, businesses must rethink their IT strategies and make the necessary capital investments.

So, initially, there is reluctance in the organization to plan for Big Data adoption because it requires drastic changes at various levels.

Historically, information is stored in storage facilities in several enterprises. Because these data silos have complex configurations and formats, integrating them is a difficult task.

As a result, when we plan for Big Data integration, we should focus on the long-term requirements of the overall Big Data infrastructure rather than just the current integration needs.

22. What is graph analytics counting to big data?

Ans: In Big Data Graph Analytics, we attempt to model the given problem into a graph database and then perform analysis on that graph to obtain the necessary answers to our questions. Graph analytics can be used in a variety of ways, including:

• Analyze the Path
• Analysis of Connectivity
• Analysis of the Community
• Analysis of Centrality

Graph analytics can be used to detect fraud, financial crimes, identify social media influencers, optimize routes and networks, and so on.

23. What are recent trends in big data?

Ans: In the early stages of Big Data, it's only available to large corporations. Small businesses were unable to use Big Data technologies because they were prohibitively expensive. However, with the rise of cloud and related technologies, even small businesses are recognizing the value of big data and capitalizing on it.

Predictive analytics is being used by an increasing number of businesses to drive sales and growth. The number of devices connected to the internet is also increasing. As a result, a massive quantity of data has been generated, that also includes insights that, when tapped, can be a huge benefit to businesses. The trend now is to use machine learning and AI to gain an advantage and stay competitive in the market.

The pattern is currently shifting away from on-premise handling and toward online/cloud processing. It relieves businesses of the need to make large investments in the future. They can now use the most up-to-date technologies and tools at a low/affordable cost. Because of these cost-per-usage trends, even small businesses can now gain access to Big Data Tools and Technologies, growing efficiency improvements throughout all levels.

24. Illustrate the data preparation in big data?

Ans: Data preparation entails gathering, incorporating, preparing, and organizing data in order to evaluate it for structures, patterns, and deep insight. Preprocessing, cleansing, validating, and transforming Big Data are all required. The necessary data is gathered from various sources for this purpose. One of the primary goals of data preparation is to ensure that data being analyzed is precise and reliable. This is due to the fact that only accurate data will yield valid results.

Once information is collected, it is insufficient. It may contain some missing values, outliers, and so on. Data preparation is the most important and time-consuming activity in any Big Data project. Only good data can yield good results. The majority of the time, data is stored in separate databases in silos. It is also available in a variety of formats. As a result, it must be reconciled. There are five D's associated with the data preparation process. They are as follows:

• Discover
• Detain
• Distill
• Document
• Deliver

The data preprocessing system is clearly automated. In data preprocessing, machine learning techniques could be used, such as stuffing null values, reassigning fields, maintaining accuracy, expelling redundant information, and so on. Data preprocessing, converting variables, removing the noise, information collection, data enrichment, data collection procedure and designing, and other terms are used to describe the process of information preparation. These phrases refer to the different processes or actions that take place during the data preparation phase.

It can be seen that the time cost of data preparation is usually longer than the time needed for data analysis.

However the techniques used only for data preparation seem to be streamlined, it takes a long time to process the data because the amount of information is indeed very large and continues to grow.

25. What is dimensionality reduction?

Ans: Dimensionality reduction refers to reducing the number of dimensions or variables taken into account. A large number of variables are present in Big Data. The majority of the time, some of these variables are linked. As a result, there is always the option of selecting only the major/distinctive variables that have a significant impact on the outcome. These variables are also known as Principal Components.

26. List the techniques for dimensionality reduction.

Ans: Dimensionality Reduction can be accomplished in two ways:

• Feature Selection 
• Feature Extraction 

The following are the various methods for performing 'Feature Selection':

• Filtering Method 
• Wrapper Method
• Embedded Method

In 'Feature Extraction,' we reduce data from a 'high dimensional space' to a 'lower-dimensional space' with fewer dimensions. The 'Dimensionality Reduction' process can be linear or nonlinear. Dimensionality Reduction employs a number of techniques.

27. What is data governance? How do we implement it?

28. What is the big data maturity model, categories of it and how to implement it?

Ans: A Big Data Maturity Model could also be used to track an organisation ’s objectives as it enters the world of Big Data. Also it assists in determining weak points and regions that require any additional attention in order to fit into the Data Analysis arena. A Big Data Maturity Model provides guidance on how an institution can end up making the most of its Big Data in order to reap the expected benefits. Use of the Big Data Maturity Model also implies that the more fully grown a model, the more the advantages a company can anticipate. It also aids in lowering overall operational costs.

Yes, there are various Big Data Maturity Model categories. It is generally divided into three levels:

• Descriptive Models 
• Comparison Models
• Prescriptive Models

The following are the criteria for evaluating Big Data Maturity Model:

• Completeness of model structure
• The model's development and evaluation quality
• Usability
• Creating value

We assess these Big Data Maturity Models by taking into account the various aspects of the business. These characteristics are as follows:

• Strategy for Business
• Information
• Analytics
• Governance
• Organizational Culture and Execution Architecture

29. Tell me whether the cloud based solution is a perfect choice for big data.

Ans: The Cloud is an excellent choice for Big Data Development. It will assist businesses in increasing operational efficiencies with a low initial investment. They just have to charge customers for the amenities they use. Moreover, they have the option to upgrade or downsize the amenities in response to changing business requirements.

Most Frequently Asked Big Data Hadoop Interview Questions

30. What is big data Hadoop?

Ans: As I said earlier Hadoop is open source software where Java framework is used to store, transfer, and clustering the data is called big data. This type of software technology offers huge storage management for any kind of data. Hadoop big data helps in processing enormous data power and offers a mechanism to handle limitless tasks or operations.

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31. What are the major differences between Hadoop and Spark?

Ans: The below table will explain the major differences between Hadoop and Spark;

32. What are the real-time industry applications of Hadoop?

Ans: The following are the important features of Hadoop;

• Helps in managing the traffics on streets
• Offers streaming processing
• Supports content management and archiving e-mails method
• Helps to process rat brain signal using Hadoop computing clusters
• Provides fraud detections and prevention
• Offers manage the contents, posts, images, and videos on many social media platforms
• Analyze customer data in real-time to improve business performance.

33. Can you please explain how Hadoop is different from other parallel computing systems?

Ans: The following points will explain how you can differentiate Hadoop from parallel computing system;

• Hadoop is a type of distributed file management system that helps users to store and handle the enormous data sets on cloud-computing machines and also reduces the data redundancy.
• In Hadoop data is stored in form of nodes, and it’s always good to process them in a distributed manner.
• Parallel system is a relational database computing system, which helps to query any type of data in real-time. Here it’s not always good to store data in records, columns, and tables.

34. Mention the different modes available in Hadoop?

Ans: There are 3 types of modes available in Hadoop; such as

• Standalone mode: This is the default mode and uses the local file system to access both input/ output operations. This mode is commonly used for debugging purposes and never takes the support of HDFS.
• Pseudo distributed or single-node cluster: this type of node cluster is used to configure the file systems.
• Fully-distributed mode (Multi-core cluster): this is the production phase of Hadoop, in this case, data is used and which is distributed across several nodes.

35. What is distributed Cache? What are its benefits?

Ans: Hadoop’s distributed cache is a type of service provided by MapReduce Framework used to manage cache file systems.

Uses:

This distributed cache reduces the execution time and it’s also possible to access the data.

Here you can store the data in arrays and hash map.

36. What are the most common input formats available in Hadoop?

Ans: There are three types of input formats available in Hadoop;

• Text input format
• Key-value input format
• Sequence file input format

37. What are the core methods of a Reducer?

Ans: The three core methods of Hadoop reducer such as;

1. Setup (): This type of core method is used for configuring parameters like input data size and distributed cache data. 

Public void setup (context_name)

2. Reduce (): this type of core method is used to reduce the redundancy file types. 

Public void reduce (key, value, context)

3. Cleanup (): this core method is used to clean the error and temporary files.

 Public void cleanup (context_name)

38. What is a sequence file in Hadoop?

Ans: Sequence files are extensively used to reduce the input/output formats. This is a flat-file that contains binary key-value pairs. Usually, map outputs are stored in the Sequence file. This sequence file contains three classes such as reader class, writer class, and sorter class.

39. What is the role of a job tracker in Hadoop?

Ans: Job tracker is a resource management system used to manage the task tracker. This is also used to track the task’s progress and fault tolerance. The job tracker is also used to communicate with the Name node mainly used to identify the location of data.

40. How can you debug the Hadoop code?

Ans: Steps:

1. Run:

This can be done by using the following command such as

Ps –ef I  grep –I ResourceManager

2. With the help of RM logs, you need to identify the worker node.
3. Now it’s time to log in to the node and run the below code;

 Ps –ef  I   grep –iNodemanager

4. It will examine the Node manager log files. You will get majority errors from the user-level logs.

41. How to compress a mapper output not touching reducer output?

Ans: To achieve this compression method,

Conf. set (“mapreduce.map.output.compress” , true)

Conf. set (“mapreduce. Output.fileoutputmat.compress”, false)

42. How can you transfer data from Hive to HDFS?

Ans: By writing the query:

Hive > insert overwrite directory ‘/’ select * from emp;

43. Which companies use Hadoop?

Ans: 

• Yahoo
• Facebook
• Amazon
• Netflix
• Adobe
• eBay
• Twitter

44. Mention the three different modes available in Hadoop?

Ans: Below are the three different modes of Hadoop such as;

• Name Mode
• Checkpoint name mode
• Backup node.

45. On What Concept The Hadoop Framework Works?

Ans: It works on MapReduce, and it is devised by Google.

46. What Is Mapreduce?

Ans: Map reduce is an algorithm or concept to process Huge amount of data in a faster way. As per its name you can divide it Map and Reduce.

The main MapReduce job usually splits the input data-set into independent chunks. (Big data sets in the multiple small datasets)

MapTask: will process these chunks in a completely parallel manner (One node can process one or more chunks).The framework sorts the outputs of the maps.

Reduce Task : And the above output will be the input for the reducetasks, produces the final result.

Your business logic would be written in the MappedTask and ReducedTask. Typically both the input and the output of the job are stored in a file-system (Not database). The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks

47. How Does Master Slave Architecture In The Hadoop?

Ans: The MapReduce framework consists of a single master JobTracker and multiple slaves, each cluster-node will have one TaskTracker. The master is responsible for scheduling the jobs' component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves execute the tasks as directed by the master.

48. How Does An Hadoop Application Look Like Or Their Basic Components?

Ans: Minimally an Hadoop application would have following components.

Input location of data

Output location of processed data.

A map task.

A reduced task.

Job configuration

The Hadoop job client then submits the job (jar/executable etc.) and configuration to the JobTracker which then assumes the responsibility of distributing the software / configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client.

49. Explain How Input And Output Data Format Of The Hadoop Framework?

Ans: The MapReduce framework operates exclusively on pairs, that is, the framework views the input to the job as a set of pairs and produces a set of pairs as the output of the job, conceivably of different types.

See the flow mentioned below

(input) -> map -> -> combine/sorting -> -> reduce -> (output)

50. What Are The Restriction To The Key And Value Class ?

Ans: the key and value classes have to be serialized by the framework. To make them serializable Hadoop provides a Writable interface. As you know from the java itself that the key of the Map should be comparable, hence the key has to implement one more interface Writable Comparable.

51. Explain The Wordcount Implementation Via Hadoop Framework ?

Ans: We will count the words in all the input file flow as below

input Assume there are two files each having a sentence Hello World Hello World (In file 1) Hello World Hello World (In file 2)

Mapper : There would be each mapper for the a file For the given sample input the first map output:

< Hello, 1>

< World, 1>

< Hello, 1>

< World, 1>

The second map output:

 < Hello, 1>

< World, 1>

< Hello, 1>

< World, 1>

Combiner/Sorting (This is done for each individual map) So output looks like this The output of the first map:

< Hello, 2>

< World, 2>

The output of the second map:

< Hello, 2>

< World, 2>

Reducer : It sums up the above output and generates the output as below

< Hello, 4>

< World, 4>

 Output

Final output would look like

Hello 4 times

World 4 times

52. What Mapper Does?

Ans: Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs.

53. What Is The Inputsplit In Map Reduce Software?

Ans: An InputSplit is a logical representation of a unit (A chunk) of input work for a map task; e.g., a file name and a byte range within that file to process or a row set in a text file.

54. What Is The Inputformat ?

Ans: The InputFormat is responsible for enumerate (itemise) the InputSplits, and producing a RecordReader which will turn those logical work units into actual physical input records.

55. Where Do You Specify The Mapper Implementation?

Ans: Generally mapper implementation is specified in the Job itself.

56. How Mapper Is Instantiated In A Running Job?

Ans: The Mapper itself is instantiated in the running job, and will be passed a MapContext object which it can use to configure itself.

57. Which Are The Methods In The Mapper Interface?

Ans: The Mapper contains the run() method, which call its own setup() method only once, it also call a map() method for each input and finally calls it cleanup() method. All above methods you can override in your code.

58. What Happens If You Don't Override The Mapper Methods And Keep Them As It Is?

Ans: If you do not override any methods (leaving even map as-is), it will act as the identity function, emitting each input record as a separate output.

59. What Is The Use Of Context Object?

Ans: The Context object allows the mapper to interact with the rest of the Hadoop system. It Includes configuration data for the job, as well as interfaces which allow it to emit output.

60. How Can You Add The Arbitrary Key-value Pairs In Your Mapper?

Ans: You can set arbitrary (key, value) pairs of configuration data in your Job, e.g. with

Job.getConfiguration().set("myKey", "myVal"), and then retrieve this data in your mapper with

Context.getConfiguration().get("myKey"). This kind of functionality is typically done in the Mapper's setup() method

61. How Does Mapper's Run() Method Works?

Ans: The Mapper.run() method then calls map(KeyInType, ValInType, Context) for each key/value pair in the InputSplit for that task

62. Which Object Can Be Used To Get The Progress Of A Particular Job ?

Ans: Context

63. What Is Next Step After Mapper Or Maptask?

Ans: The output of the Mapper are sorted and Partitions will be created for the output. Number of partition depends on the number of reducer.

64. How Can We Control Particular Key Should Go In A Specific Reducer?

Ans: Users can control which keys (and hence records) go to which Reducer by implementing a custom Partitioned.

65. What Is The Use Of Combiner?

Ans: It is an optional component or class, and can be specify via Job.setCombinerClass(ClassName), to perform local aggregation of the intermediate outputs, which helps to cut down the amount of data transferred from the Mapper to the Reducer.

66. How Many Maps Are There In A Particular Job?

Ans: The number of maps is usually driven by the total size of the inputs, that is, the total number of blocks of the input files.

Generally it is around 10-100 maps per-node. Task setup takes awhile, so it is best if the maps take at least a minute to execute.

Suppose, if you expect 10TB of input data and have a block size of 128MB, you'll end up with 82,000 maps, to control the number of block you can use the mapreduce.job.maps parameter (which only provides a hint to the framework). Ultimately, the number of tasks is controlled by the number of splits returned by the InputFormat.getSplits() method (which you can override).

67. What Is The Reducer Used For?

Ans: Reducer reduces a set of intermediate values which share a key to a (usually smaller) set of values.

The number of reduces for the job is set by the user via Job.setNumReduceTasks(int).

68. Explain The Core Methods Of The Reducer?

Ans: The API of Reducer is very similar to that of Mapper, there's a run() method that receives a Context containing the job's configuration as well as interfacing methods that return data from the reducer itself back to the framework. The run() method calls setup() once, reduce() once for each key associated with the reduce task, and cleanup() once at the end. Each of these methods can access the job's configuration data by using Context.getConfiguration().

As in Mapper, any or all of these methods can be overridden with custom implementations. If none of these methods are overridden, the default reducer operation is the identity function; values are passed through without further processing.

The heart of Reducer is its reduce() method. This is called once per key; the second argument is an Iterable which returns all the values associated with that key.

69. What Are The Primary Phases Of The Reducer?

Ans: Shuffle, Sort and Reduce.

70. Explain The Shuffle?

Ans: Input to the Reducer is the sorted output of the mappers. In this phase the framework fetches the relevant partition of the output of all the mappers, via HTTP.

71. Explain The Reducer's Sort Phase?

Ans: The framework groups Reducer inputs by keys (since different mappers may have output the same key) in this stage. The shuffle and sort phases occur simultaneously; while map-outputs are being fetched they are merged (It is similar to merge-sort).

72. Explain The Reducer's Reduce Phase?

Ans: In this phase the reduce(MapOutKeyType, Iterable, Context) method is called for each pair in the grouped inputs. The output of the reduce task is typically written to the FileSystem via Context.write (ReduceOutKeyType, ReduceOutValType). Applications can use the Context to report progress, set application-level status messages and update Counters, or just indicate that they are alive. The output of the Reducer is not sorted.

73. How Many Reducers Should Be Configured?

Ans: The right number of reduces seems to be 0.95 or 1.75 multiplied by

( * mapreduce.tasktracker.reduce.tasks.maximum).

 With 0.95 all of the reduces can launch immediately and start transfering map outputs as the maps finish. With 1.75 the faster nodes will finish their first round of reduces and launch a second wave of reduces doing a much better job of load balancing. Increasing the number of reduces increases the framework overhead, but increases load balancing and lowers the cost of failures.

Final Note:

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