TOP 20 Hadoop Interview Questions and Answers [UPDATED 2024]

Big data has exploded in popularity over the last decade. It has resulted in the widespread use of Hadoop to address significant Big Data issues. Hadoop is one of the most widely used frameworks for storing, processing, and analyzing Big Data. As a result, there is always a need for specialists in this sector. However, how do you acquire a job in the Hadoop field? Well, we’ve got some answers for you!

In this breakthrough article, we’ll discuss the top 20 Hadoop interview questions that could be asked during a Hadoop interview. We’ll go through Hadoop interview questions from all parts of the Hadoop ecosystem, including HDFS, MapReduce, YARN, Hive, Pig, HBase, and Sqoop. But the question that repeats itself is what is Hadoop?

Hadoop is one of the most powerful, open-source big data frameworks out there. It aims to use data from disparate, and often unstructured sources such as files, databases, and sensor data to build complex workflows. Companies like Cloudera have been at the forefront of its growth, bringing a lot of innovation and leadership to the table.. Although Hadoop is not just a solution software like Microsoft SQL Server, it is definitely an option to find what makes the most sense for you in 2024.

Without further ado, Let’s jump into the TOP 20 Hadoop interview questions and answers in 2024. Before we start, it’s worth noting that these Hadoop interview questions and answers were hand-picked by experienced hiring managers with years of experience in the area.

TOP 20 Hadoop interview questions and answers:

Note: these Hadoop interview questions and answers are carefully selected, as we have mentioned before. However, it’s very important to conduct more research to strengthen your knowledge about Hadoop interview questions.

Mention what are the different Hadoop configuration files?

Answer: It is very important to mention that the six different vendor-specific distributions of Hadoop are the following: Cloudera, MAPR, Amazon EMR, Microsoft Azure, IBM InfoSphere, and Hortonworks (Cloudera).

Mention the 3 modes in which Hadoop can run?

Answer: Hadoop may run in three different modes:

• Standalone mode: is the default setting. The Hadoop services can run using the local FileSystem and a single Java process.
• Pseudo-distributed mode: A single-node Hadoop configuration is useful to run all Hadoop services.
• Fully distributed mode: This option runs Hadoop master and slave services on different nodes.

Tell what are the differences between regular FileSystem and HDFS?

Answer: Here is the difference between regular FileSystem and HDFS:

Regular FileSystem: A regular FileSystem keeps all of its data in one place. Due to the machine’s limited failure tolerance, data recovery is difficult. Because the seek time is longer, it takes longer to process the data.

HDFS: One file is split into multiple equal-sized parts and stored across the cluster, and during retrieval, one daemon process picks up all file pieces addresses and serves retrieval requests.

Why is HDFS fault-tolerant?

Answer: HDFS is a fault-tolerant file system. Prior to Hadoop 3, The replica creation mechanism dealt with any mistakes. It replicates users’ data across several computers in the HDFS cluster. As a result, if one of the machines in the cluster failed, data may still be accessible from other machines that have the same copy of the data.

Tell me the difference between a federation and high availability?

Answer:

How to restart NameNode and all the daemons in Hadoop?

Answer: NameNode and all daemons can restart using the following commands:

You may use the./sbin/Hadoop-daemon.sh stop NameNode command to stop the NameNode and then the./sbin/Hadoop-daemon.sh start NameNode command to restart it.

You may use the./sbin/stop-all.sh command to halt all daemons and then use the./sbin/start-all.sh command to restart them.

What would happen if you store too many small files in a cluster on HDFS?

Answer: When you store a lot of tiny files on HDFS, you end up with a lot of metadata files. It’s difficult to keep this metadata in RAM since each file, block, or directory requires 150 bytes for metadata. As a result, the total size of all metadata will be excessively big.

Who takes care of replication consistency in a Hadoop cluster and what do under/over replicated blocks mean?

Answer: In a cluster, the NameNode is always in charge of replication consistency. The fsck command displays information on over-replicated and under-replicated blocks.

Under-replicated blocks: The file replication objective fulfilled by these blocks. HDFS will build new replicas of under-replicated blocks until the goal replication is in place. Consider a three-node cluster with three replication levels. If one of the NameNodes fails at any time, the blocks will be under-replicated. It implies that a replication factor is ready to roll, but there aren’t enough copies to meet the replication factor’s requirements. If the NameNode does not get information on the replicas, it will wait a certain period of time before re-replicating the missing blocks from accessible nodes.

Over-replicated blocks: The file replication objective supasses by these blocks. Over-replication is usually not a concern, and HDFS will remove any duplicates automatically. Consider a scenario in which three nodes are operating with three nodes of replication, and one of the nodes fails to owe to a network failure. The NameNode re-replicates the data in a matter of minutes, and the failing node returns with its set of blocks. The NameNode will remove a group of blocks from one of the nodes since there is an over-replication problem.

Is it possible to modify the number of mappers that a MapReduce job generates?

Answer: Because the number of mappers is equal to the number of input divides, you can’t alter it by default. However, there are other ways to modify the number of mappers, including setting a property or customizing the code. For example, if you have a 1 GB file split into eight blocks (each of 128 MB), the cluster will only have eight mappers operating. However, there are other ways to modify the number of mappers, including setting a property or customizing the code.

Define what is speculative execution in Hadoop?

Answer: If a DataNode is taking too long to complete a job, the master node can redundantly do the identical operation on another node. The acceptance will go to the task that finishes first, while the other will have the cold shoulder. As a result, speculative execution is good if you work in a high-volume workload setting.

How is an identity mapper different from a chain mapper?

Answer:

What are the major configuration parameters need to in a MapReduce program?

Answer: The needed configuration settings:

• Input the job’s HDFS location.
• The job’s output location in HDFS.
• Formats for input and output.
• These classes include the map and reduce functions.
• A JAR file contains the mapper, reducer, and driver classes.

Hadoop Interview Question #13: What do you mean by map-side join and reduce-side join in MapReduce?

Answer:

Hadoop Interview Question #14: Explain, in detail, the process of spilling in MapReduce.

Answer: When the buffer use exceeds a certain amount, spilling is the process of transferring data from the memory buffer to disk. When there isn’t enough memory to fit all the mapper output, this happens. After 80 percent of the buffer capacity is full, a background thread starts dumping the material from memory to disk. If the buffer is 100 MB in size, spillage will begin when the buffer’s content exceeds 80 MB.

What happens when a node running a map task fails before sending the output to the reducer?

Answer: If this happens, map tasks pass to another node, and the complete job repeats to reproduce the output of the map. The YARN architecture in Hadoop v2 contains a temporary daemon named application master that handles application execution. If a job on a particular node fails to owe to a node’s unavailability, it is the application master’s responsibility to reschedule the task on another node.

Hadoop Interview Question #17: Can we have more than one ResourceManager in a YARN-based cluster?

Answer: True, we can have several ResourceManagers in Hadoop v2. You can have a high-availability YARN cluster with an active ResourceManager and a backup ResourceManager, with the coordination handled by the ZooKeeper. At any given moment, only one ResourceManager can be active. If one of the active ResourceManagers fails, the standby ResourceManager steps in to help.

What happens if a ResourceManager fails while executing an application in a high availability cluster?

Answer: There are two ResourceManagers in a high-availability cluster: one active and the other standby. In a high availability cluster, If a ResourceManager fails in a high-availability cluster, the standby becomes active, and the ApplicationMaster stops. By using the container statuses given by all node managers, the ResourceManager is able to restore its operating state.

Hadoop Interview Question #19: In a cluster of 10 DataNodes, each having 16 GB RAM and 10 cores, what would be the total processing capacity of the cluster?

Answer: A Hadoop cluster will have one or more processes operating on each node, each of which will require RAM. The machine, which runs on a Linux file system, would have its own processes that would require a certain amount of RAM. As a result, if you have ten DataNodes, you should budget at least 20% to 30% for overheads, Cloudera-based services, and so on. Every computer may have 11 or 12 GB of RAM and six or seven cores available for processing. That’s your processing capability multiplied by ten.

Hadoop Interview Question #20: What is the difference between an external table and a managed table in Hive?

Answer:

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