In this post, we aim to cover some of the most commonly asked Hadoop Interview Questions that will help you ace your future interviews with the best solutions.
It’s important that you prepare yourself adequately with these Hadoop interview questions. These questions should help you get an edge in the ever-escalating Big Data market where local and global enterprises, big or small, source for the best Big Data and Hadoop experts. And the first step relies on how well you grasp these Hadoop Interview Questions 2020.
Here’s everything you need to know:
1. What are the differences between HDFS and relational database?
Some of the key differences between relational database and HDFS include:
• Data types
• Schema on read and write
• Read-write speed
• Best for use case
2. What are the 5 V’s of big data?
Big Data has emerged as the next big opportunity for many companies. Businesses can now derive value from their data and use it to expand their organizational goals. The five V’s of Big Data include:
• Volume – represents the amount of data for your business
• Velocity – the rate at which this data is growing
• Variety – refers to the heterogeneity of the data types you have
• Veracity – the data in doubt or which you are uncertain about
• Value – refers to the data that you can turn into valuable data for your company
3. What is Hadoop and its components?
Apache Hadoop is a framework that provides you with different tools and services to process and store Big Data. When “Big Data” turned out to be a problem in many major companies, Apache Hadoop evolved and became the solution companies needed. Essentially, Hadoop helps to analyze Big Data and make critical business decisions out of it. Truthfully, you cannot perform any such operations effectively using conventional systems.
4.What is YARN and HDFS?
YARN (Yet Another Resource Negotiator) is Hadoop’s processing framework that manages resources and provides a proper execution environment for the processes.
HDFS (Hadoop Distributed File System) is Hadoop’s storage unit. It is the process responsible for storing various data as blocks in the processes’ distributed environment. This process follows the master and slave topology.
5. What are the various Hadoop daemons & their roles in a Hadoop cluster?
The best way to approach this question is by first explaining what the HDFS daemons is, i.e., DataNode, NameNode, and Secondary NameNode, then move on to the YARN daemons, i.e., NodeManager and ResourceManager. Lastly, it’s also crucial to explain the JobHistoryServer.
DataNode – this is the slave node that has the actual data.
• NameNode – this is the master node that’s responsible for storing the company’s metadata. This includes all its files and directories.
• Secondary NameNode – it merges any changes recorded in the logs periodically with the filesystem image.
• ResourceManager – this is the central authority that schedules applications and manages resources running on top of YARN.
• NodeManager – runs on slave machines. It is responsible for launching the app’s containers, monitoring their resource usage, and reporting these logs.
• JobHistoryServer – maintains information about any MapReduce jobs after the termination of the Application Master.
6. What’s the difference between HDFS and NAS?
The Network Attached Storage (NAS) is a file-level computer data storage server that’s connected to the computer network, providing absolute data access to its clients.
In HDFS Data Blocks, the filesystem is distributed across all users to ensure a well-distributed filesystem where you can store data using commodity hardware. This is also much more cost-effective than an HDFS.
7. What’s the difference between Hadoop 1 and Hadoop 2?
This is one of the most commonly asked questions and one of the most important ones as well. As you answer this question, ensure you focus on two main points, i.e., YARN architecture and NameNode architecture:
In Hadoop 1, the NameNode acts as a single point of failure, whereas the Hadoop 3 has both the Active and Passive “NameNodes.” Therefore, should one NameNode fail, the other NameNode takes charge.
In Hadoop2, YARN also provides a centralized resource manager. This allows it to perform other functions as well, like data processing, which is always a problem in Hadoop
8. What are active and passive “NameNodes”?
Active NameNode works and runs in a cluster, whereas the Passive NameMode is a standby NameNode that has similar data as the active NameNode.
9. Why does one remove or add nodes in a Hadoop cluster frequently?
You can commission or decommission Data Nodes if you don’t feel that the services we provide meet your preferences. You can use these nodes in accordance with rapid growth in data volume.
10. What happens when two clients try to access the same file in the HDFS?
The HDFS only supports exclusive writes. Our applications team will thoroughly review your application in accordance with the Hadoop administrator policy.
11. How does NameNode tackle and DataNode failures?
The NameMode occasionally received a heartbeat from each of its proper functioning. A block report should contain a list of all the blocks. NameNode replicates all blocks using duplicated to produce a helper. So, you never have to lose your data again.
12. What will you do when NameNode is down?
The recovery process of NameNode isn’t easy. It involves processes like using the filesystem metadata replica to start the new NameNode. Configure the DataNode so that they acknowledge the new NameNode that has started.
13. What is a checkpoint?
This process checks the FsImage, and compacts them into a newer FsImage. Therefore, instead of replaying the edit log, it can reload the final state in memory directly from the FsImage.
14. How is HDFS fault-tolerant?
Whenever data is stored over HDFS, its NameNode replicates that data to several DataNodes. This default replication is a factor of 3, but you can change it as per your needs. This setting helps to provide fault tolerance in HDFS.
15.Can NameNode and DataNode be a commodity hardware?
DataNodes are common commodity hardware like personal laptops and personal computers since they store data in a large number. NameNode is the master node needed to store metadata about all blocks stored in the HDFS.
16. Why do we use HDFS for applications having large data sets and not when there are a lot of small files?
Most enterprises and individuals prefer to use HDFS since it’s always suitable for storing large amounts of data sets in singe files. This is very efficient, as opposed to spreading data across multiple files.
17. How do you define “block” in HDFS? What is the default block size in Hadoop 1 and in Hadoop 2? Can it be changed?
Blocks are the smallest continuous sections on the hard drive that stores data. HDFS stores this data as blocks and distributes it across clusters. The default bock size for Hadoop 1 is 64MB, while that of Hadoop 2 is 128MB.
18. What does ‘jps’ command do?
This command helps to check whether the Hadoop Daemons are running or not.
19. How do you define “Rack Awareness” in Hadoop?
Rack Awareness is the algorithm used by the NameNode to decide how blocks are placed. This is usually meant to minimize network traffic.
20.What is “speculative execution” in Hadoop?
Speculative execution is the process used to speed up slower tasks. When two or more tasks run concurrently, immediately one task finishes, the other ones may be killed. This is the process called “speculative execution.”
21. How can I restart “NameNode” or all the daemons in Hadoop?
You can restart NameNode using the following procedure: you can stop each NameNode individually using the stop command. To stop or start all daemons, you can use the /sbin/stop-all.sh command.
22. What is the difference between an “HDFS Block” and an “Input Split”?
This is the physical division of data during the Input “Split:” as aforementioned, HDSF Block, the HDFS performs operations like processing and assign it to mapper function.
23. Name the 3 modes that Hadoop can run.
• The Standalone (local) mode
• The Pseudo=distributed mode
• The Fully distributed mode
24. What is “MapReduce”? What is the syntax to run a “MapReduce” program?
This is a framework model that constitutes the processing of large data. This syntax is always run on the MapReduce program.
25. What are the main configuration parameters in a “MapReduce” program?
Some of the main configuration parameters contain:
• Input format of data
• Jar file containing the mapper
• Output format data
• Class containing the reduced functions
• Jobs’ output location map function
• Jobs input location in map function
26. State the reason why we can’t perform “aggregation” in mapper? Why do we need the “reducer” for this?
Generally, no one can perform aggregation in mapper since mopping doesn’t occur in this region. During aggregation, it’s important to use the output of the mopper function to run on other aggregate functions.
27. What is the purpose of “RecordReader” in Hadoop?
Its high-time you used the InputSplit to slice your work. However, this doesn’t describe how you access this data. The RecordReader loads this data from its original source then converts it into pairs that are suitable for reading by the mapper.
28. Explain “Distributed Cache” in a “MapReduce Framework.”
This can be explained as a facility-provided by the MapReduce framework. It also caches files that you might not have remembered to backup.
29. How do “reducers” communicate with each other?
The MapReduce programming model doesn’t allow reducers to communicate. They run in isolation.
30. What does a “MapReduce Partitioner” do?
This is what ensures that everything that’s of any value for that particular day goes to the designated reducer. This allows for even distribution.
31. How will you write a custom partitioner?
Custom partitioners for the Hadoop gig can also be written using the following steps:
• Creating a new class that should scream happy hunting holidays
• Override method
• Add a custom petitioner
32. What is a “Combiner”?
A combiner is a comb really, or mini reducer that performs several local reduce tasks.
33. What do you know about sequence fileinputformat?
This is an input format that’s used for reading within sequence files. It’s a specific compressed binary that uses one of the easier to read file formats for your PC.
34. What are the benefits of Apache Pig over MapReduce?
• Apache requires a high level of desire and rest
• Apache Pig reduces the length for coding by about 20 minutes.
• Pig provides several built-in operators to support its data operations.
• Performing a joint operation is simpler.
• Pig provides nested data
35. This is an input format that may also require a little bit of reading
What are the benefits of Apache Pig over MapReduce?
36. What are the different data types in Pig Latin? the social science flinn
The Apache Hadoop is a platform that’s also used to analyze big data packages for its software.
The Pig Latin, on the other hand, is always the counter basics. It uses all language strings you can find online. It also uses the int, float, double chat, int, and byte.
37. What are the various relational operations in “Pig Latin”?
Some of the common relational operators include:
• For each
• Order by
38. What is a UDF?
If some of your progress’s functions don’t function smoothly or are unavailable. The system can practically create a new user and programmatically build a personalized (UDF)
The UDF helps to put certain information out. This is what is called a covenant — an agreement between two parties.
39. What is “SerDe” in “Hive”?
The Apache Hive is a data warehouse system that’s build on the Hadoop and used for analyzing both structured and semi-structured data developed by Facebook. This program abstracts the complexity of MapReduce.
40. Can the “Hive Metastore” be used by multiple users at the same time?
The simple answer here is “yes.”
41. What is the default location where “Hive” stores table data?
The default location usually has more than two Hive Stores that table their data from within the HDFS in hive/user.
42. What is Apache HBase?
The Apache Hbase will be a multilingual, scalable, multidimensional NoSQL database written in java. The Hbase also rushed on top of the HDSFand provides big Table google like propertied.
43. What are the components of Apache Hbase?
The Hbase come in three major segments, which include:
• Data Node
44. What are the components of region server?
The major components of this region server include:
• Black Cache
45. Explain “WAL” in HBase?
Write Ahead Log is the file that’s attached to every Region Server inside the distributed environment. It is imperative that you find a way to back up all your data early enough.
46. Mention the differences between “HBase” and “Relational Databases”?
Unlike relational Database that has thin tables, HBase has sparsely populated tables. Additionally, HBase is schema less and has a column oriented data store where as Relational Databases has a schema based data store that is row oriented.
These are the basic differences between HBase and Relational Databases.
47. Can you create “Spark” with any Hadoop version?
Yes, you can actually build “Spark” for your own customizable Hadoop version.
48. Define RDD.
RDD is an acronym for a Resilient Distributor Datasetsa that can be defined as a fault-tolerant collection of operational elements that run parallel to each other.
49.What is Apache ZooKeeper & Apache Oozie?
The Apache ZooKeeper is a software project from an open source that offers centralized services by indexing configuration information, synchronization, naming registry and group services over large clusters in distributed systems.
Apache Ooozie, on the hand, is a java web application system that schedules, runs and executes dependent Apache Hadoop Jobs.
50.How do you configure an “Oozie” job in Hadoop?
You can easily integrate Oozie with the rest of Hadoop, thanks mainly to its other supporting factors.
With all these questions to have in mind when stepping into a Hadoop interview, it’s quite easy to forget a thing or two. Feeling overwhelmed isn’t a bad thing, especially if this is your first time taking such interviews. But the questions mentioned above should help you get a grasp of what to expect in the interview.