In my previous articles I have focused on multiple interview questions related to databases, data analysis with examples. In current situation there are so many databases and big data which is incredibly used in most of multinational companies. I would like to focus on Hadoop Interview Questions and answers where I can give you brief about multiple Hadoop interview questions which has been asked in Interviews. I would like to focus on Hadoop Interview Questions with answers for professionals.
Hadoop Interview Questions for Professionals :
Question 1 : What is big data Hadoop? (100% asked Hadoop Interview questions )
Big data refers to the storage, transport, and clustering of data using the open source Hadoop program. For every kind of data, this kind of software technology gives enormous storage management. Big data processing using Hadoop provides a way to handle an infinite number of activities or operations.
Question 2 : What are the components of Hadoop?
Apache Hadoop developed as a solution to “Big Data” as an issue. The framework Apache Hadoop offers us a number of tools or services to store and process Big Data. Big Data analysis and business decision-making are aided, which cannot be done effectively and efficiently with conventional methods.
Question 3 : What is Map Reduce?
The Hadoop MapReduce framework is used to handle massive data sets in parallel across a Hadoop cluster. Map and reduce is a two-step procedure used in data analysis.
Question 4 : How Map Reduce works?
During the map phase of the MapReduce algorithm, each document’s words are counted, and during the reduction phase, data is aggregated for each document over the whole collection. The input data is split up for analysis during the map phase by map processes executing concurrently throughout the Hadoop
Question 5 :What makes Hadoop unique compared to other parallel computing systems?
An example of a distributed file management system is Hadoop, which lessens data redundancy while assisting users in storing and managing massive data sets on cloud computing systems.
Data is stored in nodes in Hadoop, thus it’s best to process them in a distributed fashion. A relational database computing system called a parallel system makes it possible to query any kind of data in real time. In this case, storing data in records, columns, and tables isn’t necessarily a good idea.
Question 6 :What are the different modes in Hadoop? (80% asked Hadoop Interview Questions )
Standalone mode: This is the default setting, and it accesses both input and output operations through the local file system. This mode is frequently employed for debugging purposes and never requires HDFS support.
pseudo-distributed or single-node clusters of nodes: File systems are configured using pseudo-distributed or single-node clusters of nodes.
Fully Distributed nodes: The production phase of Hadoop occurs in the fully distributed mode (multi-core cluster), when data is used.
Question 7 : Why is it necessary to frequently add or remove nodes from a Hadoop cluster?
The Hadoop framework’s use of common hardware is one of its most appealing aspects. But as a result, a Hadoop cluster frequently experiences “DataNode” breakdowns. The Hadoop Framework’s ability to easily scale in response to the exponential development in data volume is another impressive trait. To commission (Add) and decommission (Remove) “Data Nodes” in a Hadoop Cluster is one of a Hadoop administrator’s most frequent tasks, as learned from the Hadoop Admin Training.
Question 8 :Explain about Rackawarness
Rack Awareness is the name of the algorithm used by the NameNode to make judgments in general and to determine the placement of blocks and replicas in particular. In order to reduce network traffic among DataNodes in the same rack, the NameNode makes decisions based on rack definitions.
Question 9 : Explain the sequence file.
The input/output formats are frequently reduced by the usage of sequence files. Binary key-value pairs are contained in this flat file. Map outputs are often kept in the Sequence file. There are three classes in this sequence file: a reader class, a writer class, and a sorter class.
Question 10 : How Do Hadoop Applications Look Or What Are Their Fundamental Elements?
The JobTracker takes on the responsibility of distributing the software / configuration to the slaves, scheduling tasks, and monitoring them, as well as providing status and diagnostic information to the job-client.
Question 11: What does “speculative execution” mean?
In Hadoop, a certain number of duplicate tasks are launched during Speculative Execution. Multiple instances of the same map or reduce task may be run using Speculative Execution on a different slave node. Hadoop will create a duplicate task on a different disc if a specific drive is taking a long time to execute a task. Disks that complete the task first are killed, and those that don’t are retained.
Question 12: How Many Maps Are Needed for a Specific Job?
Typically, there are 10–100 mappings per node. It is great if the maps take at least a minute to complete because task setup takes some time. With a block size of 128 MB and a 10 TB input data expectation, let’s say you’ll need 82,000 maps. Using the mapreduce.job.maps argument, you can limit the number of
blocks (which only provides a hint to the framework). The quantity of splits returned by the InputFormat.getSplits() method ultimately determines the number of tasks (which you can override).
Question 13 : What do you mean by the term “Checkpoint”?
A procedure called “checkpointing” involves taking a FsImage, an edit log, and condensing them into a new FsImage. As a result, the NameNode can load the final in-memory state straight from the FsImage rather than replaying an edit log. This procedure is much more effective and speeds up NameNode setup.
Secondary NameNode performs checkpointing.
Question 14 : What does the command ‘jps’ do?
We can use the ‘jps’ command to see if the Hadoop daemons are active or not. It displays every Hadoop daemon operating on the machine, including namenode, datanode, resourcemanager, nodemanager, etc.
Question 15 : What does a Hadoop task tracker do?
A slave node daemon in the cluster called a Task Tracker in Hadoop accepts tasks from a JobTracker. Every few minutes, it also transmits heartbeat messages to the JobTracker to make sure it is still alive.
Question 16 : Describe distributed cache context of Hadoop.
The MapReduce framework in Hadoop offers a feature known as distributed caching. It is utilized to cache files during job execution. Before any task is carried out at the slave node, the Framework copies the required files there.
Question 17 : What Are The Reducer’s Basic Techniques?
Reducers have an API very similar to Mappers; both have a run() function that takes a Context containing the job’s configuration and interface methods that return data from the reducer itself to the framework. For each key connected to the reduction job, the run() method calls setup() once, reduce() once for each
key, and cleanup() once at the conclusion. Utilizing Context.getConfiguration, each of these methods can retrieve the job’s configuration information ().
Question 18 : Explain about the term “Combiner”
A “Combiner” completes the local “reduction” duty as a miniature “reducer.” It sends the output to the “reducer” after receiving the input from the “mapper” on a certain “node.” By lowering the amount of data that must be delivered to the “reducers,” “Combiners” aid in improving the effectiveness of “MapReduce”.
Question 19 : Explain about the term “UDF”
If certain operations aren’t available in built-in operators, User Defined Functions (UDFs) can be dynamically created to add those functionalities using other languages like Java, Python, Ruby, etc. and then included in Script files.
Question 20 : What are actions followed by Hadoop?
Jobs are submitted via client applications to the job tracker. To ascertain the location of the data, JobTracker connects with Name mode. Close to the information or with open slots TaskTracker nodes are located by JobTracker. It submits the work to selected TaskTracker Nodes. Job tracker alerts you when a
task fails and lets you select what to do next.
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