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Hadoop Interview Questions: After completing the Hadoop Certification training, students are supposed to appear for job interview in order to make their career financially stable and secure. This is the toughest time; students need to prepare thoroughly for Hadoop interview questions that are asked by the interviewer. This article provided below has been prepared by Aptron's Hadoop specialists, Hadoop experts, and Hadoop trainers to assist students are acquainted with the advanced Hadoop interview questions. The questionnaire provided below has been developed for Hadoop interview questions and answers for freshers, and experience professionals. The questionnaire is comprises of featured answers wrapping basic and advanced topics. Those who have focused on Hadoop interview questions, have been able to crack interviews, and found relevant answers to their doubts or confusions. The Hadoop Interview Questions is a sum-up of each vertical in the Hadoop technology.
Hadoop is an open source and file system HDFS. An allocated processing framework runs data processing and storage for big data submissions running in clustered systems. It is at the center of a mounting ecosystem of big data technologies that are mainly used to hold up advanced analytics initiatives, including predictive analytics, data mining and machine learning applications. Hadoop can knob a variety of forms of structured and unstructured data, giving users more flexibility for collecting, routing and analyzing data than relational databases and data warehouses structures.
Hadoop is an open-source framework used for storing large data sets and runs applications across clusters of commodity hardware.
It offers extensive storage for any type of data and can handle endless parallel tasks.
Core components of Hadoop:
Yet Another Resource Negotiator (YARN) is one of the core components of Hadoop and is responsible for managing resources for the various applications operating in a Hadoop cluster, and also schedules tasks on different cluster nodes.
YARN components:
Generally, the daemon is nothing but a process that runs in the background. Hadoop has five such daemons. They are:
Hadoop provides a feature called SkipBadRecords class for skipping bad records while processing mapping inputs.
Apache Hadoop is the future of the database because it stores and processes a large amount of data. Which will not be possible with the traditional database. There is some difference between Hadoop and RDBMS which are as follows:
Apache Hadoop runs in three modes:
By default, Hadoop run in a single-node, non-distributed mode, as a single Java process. Local mode uses the local file system for input and output operation. One can also use it for debugging purpose. It does not support the use of HDFS. Standalone mode is suitable only for running programs during development for testing. Further, in this mode, there is no custom configuration required for configuration files. Configuration files are:
Just like the Standalone mode, Hadoop can also run on a single-node in this mode. The difference is that each Hadoop daemon runs in a separate Java process in this Mode. In Pseudo-distributed mode, we need configuration for all the four files mentioned above. In this case, all daemons are running on one node and thus, both Master and Slave node are the same.
The pseudo mode is suitable for both for development and in the testing environment. In the Pseudo mode, all the daemons run on the same machine.
Apache Hadoop achieves security by using Kerberos.
At a high level, there are three steps that a client must take to access a service when using Kerberos. Thus, each of which involves a message exchange with a server.
The most important features of the Hadoop is its utilization of Commodity hardware. However, this leads to frequent Datanode crashes in a Hadoop cluster.
Another striking feature of Hadoop is the ease of scale by the rapid growth in data volume.
Hence, due to above reasons, administrator Add/Remove DataNodes in a Hadoop Cluster.
Rack Awareness is the algorithm in which the “NameNode” decides how blocks and their replicas are placed, based on rack definitions to minimize network traffic between “DataNodes” within the same rack. Let’s say we consider replication factor 3 (default), the policy is that “for every block of data, two copies will exist in one rack, third copy in a different rack”. This rule is known as the “Replica Placement Policy”.
If a node appears to be executing a task slower, the master node can redundantly execute another instance of the same task on another node. Then, the task which finishes first will be accepted and the other one is killed. This process is called “speculative execution”.
The “HDFS Block” is the physical division of the data while “Input Split” is the logical division of the data. HDFS divides data in blocks for storing the blocks together, whereas for processing, MapReduce divides the data into the input split and assign it to mapper function.
The three modes in which Hadoop can run are as follows:
HDFS supports exclusive writes only.
When the first client contacts the “NameNode” to open the file for writing, the “NameNode” grants a lease to the client to create this file. When the second client tries to open the same file for writing, the “NameNode” will notice that the lease for the file is already granted to another client, and will reject the open request for the second client.
NameNode periodically receives a Heartbeat (signal) from each of the DataNode in the cluster, which implies DataNode is functioning properly.
A block report contains a list of all the blocks on a DataNode. If a DataNode fails to send a heartbeat message, after a specific period of time it is marked dead.
The NameNode replicates the blocks of dead node to another DataNode using the replicas created earlier.
By default, the HDFS block size is 128MB for Hadoop 2.x.
Checkpoint Node is the new implementation of secondary NameNode in Hadoop. It periodically creates the checkpoints of filesystem metadata by merging the edits log file with FsImage file.
The smart answer to this question would be, DataNodes are commodity hardware like personal computers and laptops as it stores data and are required in a large number. But from your experience, you can tell that, NameNode is the master node and it stores metadata about all the blocks stored in HDFS. It requires high memory (RAM) space, so NameNode needs to be a high-end machine with good memory space.
The ‘jps’ command helps us to check if the Hadoop daemons are running or not. It shows all the Hadoop daemons i.e namenode, datanode, resourcemanager, nodemanager etc. that are running on the machine.
Custom partitioner for a Hadoop job can be written easily by following the below steps:
A “Combiner” is a mini “reducer” that performs the local “reduce” task. It receives the input from the “mapper” on a particular “node” and sends the output to the “reducer”. “Combiners” help in enhancing the efficiency of “MapReduce” by reducing the quantum of data that is required to be sent to the “reducers”.
“SequenceFileInputFormat” is an input format for reading within sequence files. It is a specific compressed binary file format which is optimized for passing the data between the outputs of one “MapReduce” job to the input of some other “MapReduce” job.
Sequence files can be generated as the output of other MapReduce tasks and are an efficient intermediate representation for data that is passing from one MapReduce job to another.
Heartbeat is the signals that NameNode receives from the DataNodes to show that it is functioning (alive). NameNode and DataNode do communicate using Heartbeat. If after the certain time of heartbeat NameNode do not receive any response from DataNode, then that Node is dead. The NameNode then schedules the creation of new replicas of those blocks on other DataNodes.
Heartbeats from a Datanode also carry information about total storage capacity. Also, carry the fraction of storage in use, and the number of data transfers currently in progress.
The default heartbeat interval is 3 seconds. One can change it by using dfs.heartbeat.interval in hdfs-site.xml.
Apache Hadoop HDFS stores and processes large (terabytes) data sets. However, storing a large number of small files in HDFS is inefficient, since each file is stored in a block, and block metadata is held in memory by the namenode.
Reading through small files normally causes lots of seeks and lots of hopping from datanode to datanode to retrieve each small file, all of which is inefficient data access pattern.
Hadoop Archive (HAR) basically deals with small files issue. HAR pack a number of small files into a large file, so, one can access the original files in parallel transparently (without expanding the files) and efficiently.
Hadoop Archives are special format archives. It maps to a file system directory. Hadoop Archive always has a *.har extension. In particular, Hadoop MapReduce uses Hadoop Archives as an Input.
In Hadoop 1.0, NameNode is a single point of Failure (SPOF). If namenode fails, all clients would unable to read/write files. In such event, whole Hadoop system would be out of service until new namenode is up.
Hadoop 2.0 overcomes this SPOF by providing support for multiple NameNode. High availability feature provides an extra NameNode to Hadoop architecture. This feature provides automatic failover. If active NameNode fails, then Standby-Namenode takes all the responsibility of active node. And cluster continues to work.
The initial implementation of Namenode high availability provided for single active/standby namenode. However, some deployment requires high degree fault-tolerance. So new version 3.0 enable this feature by allowing the user to run multiple standby namenode. For instance configuring three namenode and five journal nodes. So, the cluster is able to tolerate the failure of two nodes rather than one.
In Hadoop, by default HDFS replicates each block three times for several purposes. Replication in HDFS is very simple and robust form of redundancy to shield against the failure of datanode. But replication is very expensive. Thus, 3 x replication scheme has 200% overhead in storage space and other resources.
Thus, Hadoop 2.x introduced Erasure Coding a new feature to use in the place of Replication. It also provides the same level of fault tolerance with less space store and 50% storage overhead.
Erasure Coding uses Redundant Array of Inexpensive Disk (RAID). RAID implements EC through striping. In which it divide logical sequential data (such as a file) into the smaller unit (such as bit, byte or block). Then, stores data on different disk.
Encoding- In this process, RAID calculates and sort Parity cells for each strip of data cells. And recover error through the parity. Erasure coding extends a message with redundant data for fault tolerance. EC codec operates on uniformly sized data cells. In Erasure Coding, codec takes a number of data cells as input and produces parity cells as the output. Data cells and parity cells together are called an erasure coding group.
There are two algorithms available for Erasure Coding:
Pig Latin can handle both atomic data types like int, float, long, double etc. and complex data types like tuple, bag and map.
Atomic data types: Atomic or scalar data types are the basic data types which are used in all the languages like string, int, float, long, double, char[], byte[].
Complex Data Types: Complex data types are Tuple, Map and Bag.
We are in the digital age of instant expectation. Internet, Smartphones, social networking sites, and various online data sharing platforms have contributed in creating making data large and big. Big data is not a new term for us. Hadoop with HDFS (Hadoop Distributed File System) is used for managing such big data storage and system. Hadoop candidates with in-depth knowledge and skill-based training are able discover their smooth ways to attain their career goals in the job markets.
A Hadoop developer or programmer is expected a minimum salary of 38, 000 dollars per annum. However, the salary of an experienced Hadoop developer can reach to double the figure mentioned before. However, salaries are very dependent upon the location, business, and the company’s requirements.
The article ‘Hadoop interview questions’ has been productively answered every advanced Hadoop interview questions. Any student or professional have studied this Hadoop interview questions for experienced candidates can find success in the interview. Even then, if the learners still need more detailing on Hadoop designing, structure, storage and implementation, then they may drop in a message to our experts regarding Hadoop interview questions for experienced professionals. Our trainers would be happy to help and make your mind up your Hadoop-designing issues of the students. Join Hadoop Training in Noida, Hadoop Training in Delhi, Hadoop Training in Gurgaon
