Big Data has become the "It" thing in the technological landscape, which has also resulted in the growing demand for Big data-related positions. Big Data Analytics is being used by one in five large businesses, so it's time to start looking for employment in this area. In order to assist you to succeed in the interview, we now present the Top 50 Hadoop Interview Questions & Answers.
We have formulated the most typical and probable big data Hadoop Interview questions and answers. You will be given questions related to these concepts.
These few Hadoop Interview Questions & answers are aimed at helping you prepare well to put you ahead of the competition. We will start off the list by concentrating on the typical and fundamental Hadoop interview questions and answers that individuals encounter when applying for a Hadoop-related job, regardless of position.
The Hadoop Framework is based on two fundamental ideas:
Hadoop has emerged as the solution to the problems of Big data. To store and process Big Data, Hadoop is a platform that provides a variety of tools and services. When utilizing the conventional approach is challenging, it is crucial for the analysis of massive data and for making effective business decisions.
Data processing and storing are extremely simple thanks to Hadoop's extensive toolkit. Here are all of the key elements of Hadoop:
The following three input formats are supported by Hadoop:
The Hadoop processing framework, known as YARN also referred to as Yet Another Resource Negotiator. The task of managing the resources and creating an environment in which the processes can run falls to YARN.
An administrator of a Hadoop cluster can add commission and remove decommission data nodes using the functionalities of the Hadoop framework listed below:
The mechanism used by NameNode to decide how blocks and their replicas are stored in the Hadoop cluster is known as Rack Awareness in Hadoop. Rack definitions, which reduce traffic between DataNodes inside a single rack, are used to accomplish this. Let's use the replication factor's default value of three as an example. Two duplicate copies of each block of data are to be kept in a single rack in accordance with the "Replica Placement Policy," while the third replica is kept in a different rack.
In Hadoop, Speculative Execution is a procedure that happens while a task is being executed at a node more slowly. The master node begins another instance of that task on the other node throughout this process. The work that is completed first is accepted, and by killing that, the execution of the other is halted
The key attributes of Hadoop include:
Google MapReduce, which is built on Google's Big Data File Systems, serves as the foundation of the Hadoop framework.
For Big Data analysis, the Hadoop platform can effectively address a wide range of issues.
Several well-known companies are now employing Hadoop. A few of the instances are:
Hadoop is used by Yahoo
The main characteristics that set RDBMS and Hadoop apart are:
Hadoop Interview Questions and Answers based on Hadoop Architecture
The following Hadoop Interview Questions and Answers are based on the Hadoop architecture. These questions and answers will help a great deal in your interview and preparation. It will enhance your knowledge and understanding of Hadoop architecture and help you answer any Hadoop Interview question with ease and confidence.
We can make the distinctions between Hadoop 1 and Hadoop 2 on the following grounds:
Both Active NameNode and Passive NameNode are high-availability Hadoop designs.
The Active NameNode runs in the Hadoop cluster
The backup NameNode known as the "Passive NameNode" stores the same information as the "Active NameNode" does.
When an active NameNode fails, a passive NameNode steps in and assumes control. The cluster never fails since there is always a running NameNode in it.
Apache HBase consists of the following primary components:
The correct function of each DataNode in the Hadoop cluster is specified in a signal that NameNode continuously gets from those DataNodes. A block report keeps track of every block that is present on a DataNode. After a predetermined amount of time, a DataNode is considered dead if it fails to relay the signal to the NameNode. The blocks of the deceased node are then replicated or copied by the NameNode to a different DataNode using the replicas that were previously generated.
The following steps can illustrate how NameNode recovery works and how it helps the Hadoop cluster remain operational.
Step 1: Use the file system metadata replica to launch a new NameNode (FsImage)
Step 2: Set up the DataNodes and clients to recognize the new NameNode.
Step 3: The new NameNode begins servicing the client after it has finished loading the last checkpoint FsImage and has received block reports from the DataNodes.
The various Hadoop schedulers that are available are:
Only because it can store data like laptops and personal computers, which are needed in large quantities, are DataNodes considered commodity hardware. Instead, NameNode serves as the master node and houses information about all of the HDFS blocks. It operates as a high-end system with a lot of memory because it requires a lot of memory.
The NameNode, Secondary NameNode, DataNode, NodeManager, ResourceManager, and JobHistoryServer are the Hadoop daemons. Their functions can be categorized as below:
A FsImage and Edit log are compressed into a new FsImage during checkpointing. Instead of replaying an edit log, the NameNode handles the loading of the final in-memory state directly from the FsImage. The checkpointing operation must be carried out by the secondary NameNode.
An advantage of checkpointing is that it reduces the NameNode's starting time and is a very effective process.
A Hadoop Administration performs certain administrative tasks to ensure the smooth running of the Hadoop cluster. The following Hadoop Interview questions & answers are Administrative, cluster, and environment based and will be a comprehensive guide for prospective candidates.
The following points are essential
An additional standalone system should always be used to deploy a secondary NameNode. This stops it from affecting how the principal node functions.
The various ways to run Hadoop are:
The most popular operating system for Hadoop is Linux. However, with the aid of some additional software, it may also be deployed on the windows operating system.
As opposed to small data chunks stored in multiple files, HDFS is more effective for maintaining a large number of data sets in a single file. The quantity of memory restricts the number of files in the HDFS file system since the NameNode stores the file system's information in RAM. Simply put, adding more files will result in the production of more metadata, which will increase the need for memory (RAM). A block, file, or directory's metadata should ideally take up 150 bytes.
The following three aspects of hdfs-site.xml are crucial:
The following are some of the crucial Hadoop tools that improve Big Data performance.
Clouds, Flume, SolrSee/Lucene, Hive, HDFS, HBase, Avro, SQL, NoSQL, Oozie, and ZooKeeper.
Hadoop Interview questions & answers for Experienced Level
It is a tool used for parallel data copying to and from Hadoop file systems with very huge amounts of data. Its distribution, error handling, recovery, and reporting are all impacted by MapReduce. A list of files and directories is expanded into a series of inputs to map jobs, each of which copies a specific subset of the files listed in the source list.
The HDFS data block has a default size of 128 MB. The following suggestions are for large-sized blocks:
To cut down on seek costs: Due to the enormous size of the blocks, it may take longer to move the data from the disc than it does to start the block. The numerous blocks are therefore sent at the disc transfer rate.
If there are small blocks, there will be an excessive number of blocks in Hadoop HDFS and an excessive amount of metadata to store. Managing such a large number of blocks and information will increase network traffic and add overhead.
The replication factor is 3 by default.
On the same data node, there won't be any duplicate copies. The first two copies are typically found on the same rack, but the third copy is typically taken off the shelf. In order to ensure that one copy is always secure, regardless of what happens to the rack, it is recommended to set the replication factor to at least three. The file system's default replication factor, as well as the replication factor for each individual file and directory, can be customized. We can set a lower replication factor for non-critical files, and a higher replication factor for key files.
When processing map inputs, Hadoop offers the ability to skip a specific set of poor input records. This feature can be controlled by applications using the SkipBadRecords class. When map tasks fail deterministically on a certain input, this capability might be employed. Typically, map function errors are the blame for this. They would need to be resolved by the user.
The NameNode server keeps two different kinds of metadata on disc and in RAM.
Metadata is connected to the following two files:
The NameNode will immediately record the file's deletion from HDFS in the EditLog.
The Secondary NameNode constantly reads all the file systems and metadata that are in the Namenode's RAM and records them into the file system or hard drive. FsImage and EditLogs are integrated in the NameNode. The EditLogs are periodically downloaded by Secondary NameNode from NameNode and then applied to FsImage. Once the NameNode has started the next time, the new FsImage is then copied back into it.
The following command is used to determine the block's status: hdfs fsck path> -files -blocks
The program used to determine the health of the file system is hdfs fsck/ -files -blocks -locations > dfs-fsck.log.
Hadoop fs -copyFromLocal [source][destination] is the command used to copy data from the Local system to HDFS.
The jobs are sent to Jobtracker via the client application.
To find the data location, the JobTracker connects to the NameNode.
JobTracker locates TaskTracker nodes using the slots that are open 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.
TaskTracker nodes are watched over by JobTracker.
When you need to exchange files around all nodes in a Hadoop cluster, the MapReduce Framework's Distributed Cache is a crucial tool that you can use. These files can be simple properties files or jar files.
Text files, zip files, jar files, and other small to medium-sized read-only files can all be cached and distributed across all Datanodes (worker-nodes) where MapReduce jobs are running thanks to Hadoop's MapReduce architecture. A local copy of the file is sent via Distributed Cache to each Datanode.
Text, LongWritable, text, and IntWritable are mappers' three main inputs. The first two are examples of input parameters, while the final two are examples of intermediate output parameters.
The fundamental data structure of Apache Spark is resilient distributed datasets. It has been put into the Spark Core. They are fault-tolerant and immutable. RDDs are created by transforming pre-existing RDDs or by storing an external dataset in a storage system with a solid foundation, such as HDFS or HBase.
They can be used in parallel operations since they have distributed collections of things. Parts of resilient distributed datasets are separated so they can be run on different cluster nodes.
For quicker processing and the building of machine learning models, Apache Spark stores the data in memory. To create an optimized model, machine learning algorithms may be repeated several times and move through different conceptual steps. Graph algorithms move along all the nodes and edges to construct a graph. Performance is improved by these low latency workloads, which demand a lot of repetitions.
The metadata data is kept in a meta store; alternatively, a relational database management system (RDBMS) and an open-source ORM layer can be used to transform object representation into a relational schema. It houses all of the metadata for Apache Hive. In a relational database, it maintains metadata for Hive partitions and tables (including information about their location and schema). Utilizing the meta store service API, it provides the client with access to this data. HDFS storage is separate from disc storage for the Hive metadata.
The variation between Local and Remote Met stores can be made in the following points:
No, only single-line comments are enabled at this time in Hive. Multiline comments are not yet supported.
Tables are divided into partitions using Apache Hive. Depending on the values of pertinent columns like date, city, and department, a table is partitioned into related components.
To receive your output file of a sqoop importation in a format other than.gz, use the -compress-codec argument.
For gathering, aggregating, and transporting massive amounts of streaming data, such as record files and events from various references, to a centralized data repository, Apache Flume is a tool, service, or data intake method.
Flume is a distributed utility that is very customizable. Typically, it is made to copy log data from various web servers that are streaming data to HDFS.
The following components make up the architecture of Apache Flume
The events are buffered until they are transmitted into the sink by an Intermediate Store, which receives the data from the flume source. The Flume Channel is the name of the Intermediate Store. An intermediary source is a channel. It connects a Sink Flume channel to a Source Flume channel. Both the Memory channel and the File channel are supported. Since the file channel is non-volatile, any data that is entered into it cannot be lost until you choose to delete it. The Memory Channel, in contrast, is very quick since events are kept in memory, making it volatile and subject to data loss.
The following file formats are the defaults that allow importing of Data:
Execution Engine processes the query by serving as a link between Hive and Hadoop. To carry out actions like creating or deleting tables, the Execution Engine engages in bidirectional communication with the Metastore.
That is the end to our list of the top Hadoop interview questions & answers. We hope this set of questions gives you a basic overview and helps you in your interview.
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