Hadoop Interview Questions

Hadoop Interview Questions

Hadoop is a framework for distributed processing of large data sets across the clusters of commodity computers.

Hadoop developers have a great market to capture as every other MNC is looking to recruit one. You can now build a career as a Hadoop developer and get placed in one of your dream companies. You can prepare for your interview and clear the biggest barrier. If you are looking for Hadoop HDFS questions and answers and seek to become a Hadoop Developer or Hadoop Admin, you have come to the right place. We have provided a list of the Hadoop Interview Questions that will prove to be useful. These are the most widely recognized and prominently asked Big Data Hadoop Interview Questions, which you will undoubtedly get in huge information interviews.

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Hadoop Interview Questions

Hadoop Distributed File System (HDFS) stores documents as information blocks and circulates these blocks over the whole cluster. As HDFS was intended to be fault tolerant and to keep running on ware equipment, blocks are replicated in various circumstances to guarantee high data accessibility.

Furthermore Yes, I can change the block size of HDFS records by changing the default size parameter show in hdfs-site.xml. But after changing, I have to restart the cluster for this property change to take effect.

Initially, it looks not possible as I am a regular RDBMS user like other programmers, but then I tried to connect in Hive context. I found it is possible as Hive creates  schema and append on top of an existing data file. One can have multiple schema for one data file, schema would be saved in hive’s metastore and data will not be parsed read or serialized to disk in given schema. When s/he will try to retrieve data schema will be used. Lets say if my file have 5 column (Id, Name, Class, Section, Course) we can have multiple schema by choosing any number of column

Secondary Name-Node in Hadoop is a particularly devoted node in HDFS group whose primary function is to take checkpoints of the document structure metadata present on name-node. It’s anything but a checkpoints name-node. It just checkpoints name node’s file framework namespace. The Secondary NameNode is a helping hand to the primary Name-Node but not substitute for primary name-node.

No, multiple customers can’t write into an HDFS document simultaneously. HDFS takes after single writer multiple readers model.

Data-Node stores information in HDFS; it is a node where the real information resides in the document. Each data node sends a pulse message to notify that it is alive. If the name-node does not get a message from data-node for 10 minutes, it is considered to be dead or out of place and begins replication of obstructs that were facilitated on that information node with the end goal that they are facilitated on some other information node. A Block-Report consists of the list rundown of all blocks on a Data-Node.

Data Integrity discusses the accuracy of the information. It is essential for us to have a guarantee or assurance that the information kept in HDFS is right. However, there is dependably a slight chance that the information will be corrupted during I/O tasks on the disks. HDFS makes the checksum for all of the information kept in touch with it and confirmed the information with the checksum during the read activity of course. Additionally, each Data-Node runs a block scanner occasionally, which checks the accuracy of the information blocks kept in the HDFS.

Here are some of the differences between NAS and HDFS:

  • NAS keeps running on a single machine, and in this way, there is no probability of information repetition though HDFS keeps running on a cluster of machines subsequently there is information excess due to the replication convention.
  • Hadoop HDFS is intended to work with Map Reduce structure. In Map Reduce structure calculation move to the data rather than Data to the calculation. NAS isn’t appropriate for Map Reduce, as it stores information independently from the calculations.
  • In HDFS, data blocks are appropriated over all of the machines in a group. Though in NAS, data is put away on devoted hardware.
  • HDFS utilizes ware hardware, which is financially effective, though a NAS is a high-end storage gadget which incorporates high cost.

Rack Awareness algorithm in Hadoop guarantees that all the block copies are not stored on a similar rack or a solitary rack. Considering the reproduction factor is 3, the Rack Awareness Algorithm says that the primary replica of a block will be socked on a local rack and the following two replicas will be put away on an alternate (remote) rack at the same time, on an alternate Data-Node inside that (remote) rack. There are two purposes for utilizing Rack Awareness:

  • To enhance the network performance-: You will discover more prominent system data transmission between machines in a similar rack than the machines living in various racks. In this way, the Rack Awareness helps to compose movement in the middle of various racks and therefore gives a superior write performance.
  • To prevent loss of data: You need not worry about the information even if the whole rack fails because of switch failure or electrical power failure.

Name-Node Metadata stores the record for Block mapping, locations of blocks on DataNodes, dynamic data nodes, and much more metadata are altogether stored in memory on the Name-Node. When we check the Name-Node status site, basically the greater part of that data is kept in memory somewhere.

The main thing stored on disk is the fsimage, edit log, and status logs. Name-Node never truly utilizes these records on disk, aside from when it begins. The fsimage and edits record practically exist to have the capacity to bring the Name-Node back up if it should be halted or it crashes.

  • 1) fsimage An fsimage document contains the entire state of the file system at a point in time. Each document system modification is doled out in a unique process, monotonically expanding transaction ID. A fsimage document speaks to the file system state after all alterations up to a particular transaction ID.
  • 2) Edits – An edited file is a log that lists each document system change (record creation, deletion or alteration) that was made after the latest fsimage. When a document is put into HDFS, it is converted into blocks (of configurable size).

The HDFS is one of the storage systems of the Hadoop structure. It is a circulated file structure that can helpfully keep running on item equipment for processing unstructured information. Because of this functionality of HDFS that is worked to keep running on commodity equipment, it can be very fault tolerant. Similar information is kept in numerous areas and in the case of one storage area neglecting to give the required information; similar information can be effortlessly fetched from another area.

Throughput is the measure of work done in a unit time. HDFS gives great throughput because of the followings:

  • The HDFS depends on Write Once and Read Many Model, it improves the information coherency issues as the information written once can’t be altered and consequently, gives high throughput data access.
  • In Hadoop, the calculation part is moved towards the information which decreases the system blockage and in this way, improves the overall system throughput.

In Hadoop, Speculative Execution is a procedure that happens during the slower execution of an errand at a node. In this procedure, the master node begins executing another occurrence of that same task on the other hub. Furthermore, the errand, which is done first is acknowledged, and killing that halts the execution of other.

Check pointing is a fundamental part of keeping up and holding on file system metadata in HDFS. It’s urgent for proficient Name-Node recovery and restart, and is a vital indicator of general cluster health. However, check pointing can also because of confusion for operators of Apache Hadoop clusters. Check pointing is a procedure that takes a fsimage and alters log and compacts them into another fsimage. Along these lines, rather than replaying a possibly unbounded alter log, the NameNode can load the final in-memory state straightforwardly from the fsimage.

Block Scanner is fundamentally used to recognize corrupt data-node Block. During a writing task, when a data node writes into the HDFS, it confirms a checksum for that information. This checksum helps in confirming the information corruptions during the information transmission.

At the point when similar information is perused from the HDFS, the customer confirms the checksum returned by the data-node against the checksum it figures against the information to check the information corruption that may have caused by the information node that may have happened during the shortage of information in the data node.