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+ Hadoop Interview Questions and Answers, Question1: On What concept the you can download the Hadoop Installation osakeya.info file from our dropbox. We have put together a list of Hadoop Interview Questions that will come in handy. Top 50 Hadoop Interview Questions and Answers .. opens a large number of jobs every year for freshers as well as experienced ones. Hadoop Interview Questions & Answers: Advanced Technical Topics Covered | For Freshers & Professionals | Free Practice Test | Free Resumes. Read Now!.
This leads to various difficulties in making the hadoop cluster fast, reliable and scalable. To address all such problems, Apache ZooKeeper can be used as a coordination service to write correct distributed applications without having to reinvent the wheel from the beginning.
What does the overwrite keyword denote in Hive load statement? Answer: Overwrite keyword in Hive load statement deletes the contents of the target table and replaces them with the files referred by the file path i. For what kind of big data problems, did the organization choose to use Hadoop?
Answer: Asking this question to the interviewer shows the candidates keen interest in understanding the reason for hadoop implementation from a business perspective. This question gives the impression to the interviewer that the candidate is not merely interested in the hadoop developer job role but is also interested in the growth of the company. What is SerDe in Hive?
How can you write your own custom SerDe? Hive uses SerDe to read and write data from tables. Generally, users prefer to write a Deserializer instead of a SerDe as they want to read their own data format rather than writing to it. Answer: HDFS is a write once file system so a user cannot update the files once they exist either they can read or write to it. However, under certain scenarios in the enterprise environment like file uploading, file downloading, file browsing or data streaming —it is not possible to achieve all this using the standard HDFS.
NFS allows access to files on remote machines just similar to how local file system is accessed by applications. Namenode is the heart of the HDFS file system that maintains the metadata and tracks where the file data is kept across the Hadoop cluster. StandBy Nodes and Active Nodes communicate with a group of light weight nodes to keep their state synchronized. These are known as Journal Nodes. How can native libraries be included in YARN jobs? What are the various tools you used in the big data and hadoop projects you have worked on?
Answer: Your answer to these interview questions will help the interviewer understand your expertise in Hadoop based on the size of the hadoop cluster and number of nodes. Based on the highest volume of data you have handled in your previous projects, interviewer can assess your overall experience in debugging and troubleshooting issues involving huge hadoop clusters. The number of tools you have worked with help an interviewer judge that you are aware of the overall hadoop ecosystem and not just MapReduce.
To be selected, it all depends on how well you communicate the answers to all these questions.
How is the distance between two nodes defined in Hadoop? Answer: Measuring bandwidth is difficult in Hadoop so network is denoted as a tree in Hadoop. The distance between two nodes in the tree plays a vital role in forming a Hadoop cluster and is defined by the network topology and java interface D N Sto Switch Mapping.
The distance is equal to the sum of the distance to the closest common ancestor of both the nodes. The method getDistance Node node1, Node node2 is used to calculate the distance between two nodes with the assumption that the distance from a node to its parent node is always1. What is your favourite tool in the hadoop ecosystem?
Answer: The answer to this question will help the interviewer know more about the big data tools that you are well-versed with and are interested in working with. If you show affinity towards a particular tool then the probability that you will be deployed to work on that particular tool, is more.
If you say that you have a good knowledge of all the popular big data tools like pig, hive, HBase, Sqoop, flume then it shows that you have knowledge about the hadoop ecosystem as a whole. What is the size of the biggest hadoop cluster a company X operates? Answer: Asking this question helps a hadoop job seeker understand the hadoop maturity curve at a company. Based on the answer of the interviewer, a candidate can judge how much an organization invests in Hadoop and their enthusiasm to download big data products from various vendors.
The candidate can also get an idea on the hiring needs of the company based on their hadoop infrastructure. Based on the answer to question no 1, the candidate can ask the interviewer why the hadoop infrastructure is configured in that particular way, why the company chose to use the selected big data tools and how workloads are constructed in the hadoop environment.
Asking this question to the interviewer gives the impression that you are not just interested in maintaining the big data system and developing products around it but are also seriously thoughtful on how the infrastructure can be improved to help business growth and make cost savings. What are the features of Pseudo mode? Answer: 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. Data Quality — In the case of Big Data, data is very messy, inconsistent and incomplete. Discovery — Using a powerful algorithm to find patterns and insights are very difficult.
Hadoop is an open-source software framework that supports the storage and processing of large data sets. Apache Hadoop is the best solution for storing and processing Big data because: Apache Hadoop stores huge files as they are raw without specifying any schema. High scalability — We can add any number of nodes, hence enhancing performance dramatically. Reliable — It stores data reliably on the cluster despite machine failure. High availability — In Hadoop data is highly available despite hardware failure.
If a machine or hardware crashes, then we can access data from another path. Economic — Hadoop runs on a cluster of commodity hardware which is not very expensive case of hardware failure. It provides high throughput access to an application by accessing in parallel. MapReduce- MapReduce is the data processing layer of Hadoop.
It writes an application that processes large structured and unstructured data stored in HDFS. MapReduce processes a huge amount of data in parallel. It does this by dividing the job submitted job into a set of independent tasks sub-job. The Map is the first phase of processing, where we specify all the complex logic code.
Reduce is the second phase of processing. It provides Resource management and allows multiple data processing engines. For example real-time streaming, data science, and batch processing. Easy to use — No need of client to deal with distributed computing, the framework take care of all the things. So it is easy to use. How were you involved in data modelling, data ingestion, data transformation and data aggregation?
Answer: You are likely to be involved in one or more phases when working with big data in a hadoop environment. The answer to this question helps the interviewer understand what kind of tools you are familiar with. If you answer that your focus was mainly on data ingestion then they can expect you to be well-versed with Sqoop and Flume, if you answer that you were involved in data analysis and data transformation then it gives the interviewer an impression that you have expertise in using Pig and Hive.
What are the features of Fully-Distributed mode? Answer: In this mode, all daemons execute in separate nodes forming a multi-node cluster. Thus, we allow separate nodes for Master and Slave. Hadoop daemons run on a cluster of machines. There is one host onto which NameNode is running and the other hosts on which DataNodes are running.
Therefore, NodeManager installs on every DataNode. And it is also responsible for the execution of the task on every single DataNode. The ResourceManager manages all these NodeManager. ResourceManager receives the processing requests. After that, it passes the parts of the request to corresponding NodeManager accordingly.
Why wait notify and notifyAll called from synchronized block or method in Java What do you know about the big-O notation and can you give some examples with respect to different data structures What is the tradeoff between using an unordered array versus an ordered array? Why wait. Flag for inappropriate content. Related titles. Jump to Page. Search inside document. Amandeep Singh. Porselvi Balasubramaniam. Balakrishna Ganta. Mikky James. Ajay Singh.
Ashok Kumar K R. Thanos Peristeropoulos. Pranav Waila. Dheeraj Reddy. Rack is a storage area with all the datanodes put together. These datanodes can be physically located at different places. Rack is a physical collection of datanodes which are stored at a single location. There can be multiple racks in a single location.
When the client is ready to load a file into the cluster, the content of the file will be divided into blocks. Now the client consults the Namenode and gets 3 datanodes for every block of the file which indicates where the block should be stored. If both rack2 and datanode present in rack 1 fails then there is no chance of getting data from it. In order to avoid such situations, we need to replicate that data more number of times instead of replicating only thrice.
This can be done by changing the value in replication factor which is set to 3 by default. What Is A Secondary Namenode? The secondary Namenode constantly reads the data from the RAM of the Namenode and writes it into the hard disk or the file system. It is not a substitute to the Namenode, so if the Namenode fails, the entire Hadoop system goes down.
In Gen 1 Hadoop, Namenode is the single point of failure. If the active Namenode fails, passive Namenode takes over the charge. Namenode takes the input and divide it into parts and assign them to data nodes. These datanodes process the tasks assigned to them and make a key-value pair and returns the intermediate output to the Reducer.
The reducer collects this key value pairs of all the datanodes and combines them and generates the final output. Key value pair is the intermediate data generated by maps and sent to reduces for generating the final output. HDFS cluster is the name given to the whole configuration of master and slaves where data is stored.
Map Reduce Engine is the programming module which is used to retrieve and analyze data. Is Map Like A Pointer? Yes, we need two different servers for the Namenode and the datanodes. This is because Namenode requires highly configurable system as it stores information about the location details of all the files stored in different datanodes and on the other hand, datanodes require low configuration system.
The number of maps is equal to the number of input splits because we want the key and value pairs of all the input splits. No, a job is not split into maps.
Spilt is created for the file. The file is placed on datanodes in blocks. For each split, a map is needed. There are two types of writes in HDFS: Posted Write is when we write it and forget about it, without worrying about the acknowledgement. It is similar to our traditional Indian post. In a Non-posted Write, we wait for the acknowledgement. Naturally, non-posted write is more expensive than the posted write.
It is much more expensive, though both writes are asynchronous. Reading is done in parallel because by doing so we can access the data fast. But we do not perform the write operation in parallel. The reason is that if we perform the write operation in parallel, then it might result in data inconsistency.
For example, you have a file and two nodes are trying to write data into the file in parallel, then the first node does not know what the second node has written and vice-versa. So, this makes it confusing which data to be stored and accessed. Hadoop is not a database. When you enrol for the hadoop course at Edureka, you can download the Hadoop Installation steps.
How Jobtracker Schedules A Task? These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated.
There is only One Task Tracker process run on any hadoop slave node. Task Tracker runs on its own JVM process. Every TaskTracker is configured with a set of slots, these indicate the number of tasks that it can accept. The TaskTracker monitors these task instances, capturing the output and exit codes.
Where Does It Run? Task instances are the actual MapReduce jobs which are run on each slave node. Each of these daemon run in its own JVM. Following 3 Daemons run on Master nodes. Responsible for instantiating and monitoring individual Map and Reduce tasks.
Single instance of a Task Tracker is run on each Slave node. Task tracker is run as a separate JVM process. Single instance of a DataNode daemon is run on each Slave node. DataNode daemon is run as a separate JVM process.
One or Multiple instances of Task Instance is run on each slave node. NameNode periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. When NameNode notices that it has not recieved a hearbeat message from a data node after a certain amount of time, the data node is marked as dead.
Since blocks will be under replicated the system begins replicating the blocks that were stored on the dead datanode. The NameNode Orchestrates the replication of data blocks from one datanode to another. The replication data transfer happens directly between datanodes and the data never passes through the namenode. Nope, MapReduce programming model does not allow reducers to communicate with each other.
Reducers run in isolation. Yes, Setting the number of reducers to zero is a valid configuration in Hadoop. When you set the reducers to zero no reducers will be executed, and the output of each mapper will be stored to a separate file on HDFS. This is typically a temporary directory location which can be setup in config by the hadoop administrator.
What Are Combiners? The execution of combiner is not guaranteed, Hadoop may or may not execute a combiner. Also, if required it may execute it more then 1 times. Therefore your MapReduce jobs should not depend on the combiners execution. Writable is a Java interface.
Any key or value type in the Hadoop Map-Reduce framework implements this interface. Implementations typically implement a static read DataInput method which constructs a new instance, calls readFields DataInput and returns the instance. WritableComparable is a Java interface. Any type which is to be used as a key in the Hadoop Map-Reduce framework should implement this interface.
WritableComparable objects can be compared to each other using Comparators. IdentityMapper Implements the identity function, mapping inputs directly to outputs. IdentityReducer Performs no reduction, writing all input values directly to the output. Hadoop Tutorial. Hadoop Practice Tests. IT Skills. Management Skills. Communication Skills. Business Skills.
Digital Marketing Skills. Human Resources Skills. Health Care Skills. Finance Skills. All Courses. All Practice Tests. To enable you to plan better for your next Big Data interview, here are some of well-designed Big Data hadoop inquiries addresses that are by and large inquired: Question 2. What Is Mapreduce? The main MapReduce job usually splits the input data-set into independent chunks.
Big data sets in the multiple small datasets MapTask: The framework sorts the outputs of the maps. Reduce Task: And the above output will be the input for the reducetasks, produces the final result. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks Question 3. In most of the cases compute node and storage node would be the same machine.
Input location of data Output location of processed data. A map task. A reduced task. There would be each mapper for the a file For the given sample input the first map output: Reducer Question What Mapper Does? Context Question The number of reduces for the job is set by the user via Job. Shuffle, Sort and Reduce.
Only one Question JobTracker in Hadoop performs following actions Client applications submit jobs to the Job tracker. A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: When the work is completed, the JobTracker updates its status. The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they are deemed to have failed and the work is scheduled on a different TaskTracker.
Client applications can poll the JobTracker for information. Hadoop consists of two main parts. Hadoop distributed file system, a distributed file system with high throughput, Hadoop MapReduce, a software framework for processing large data sets. This daemon stores and maintains the metadata for HDFS. Secondary NameNode: Performs housekeeping functions for the NameNode. Following 2 Daemons run on each Slave nodes DataNode: Stores actual HDFS data blocks. What Is Nas?
Whereas in NAS data is stored on dedicated hardware. NAS is not suitable for MapReduce since data is stored separately from the computations. HDFS runs on a cluster of machines and provides redundancy using replication protocol. Whereas NAS is provided by a single machine therefore does not provide data redundancy.
An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients. No, Reducer runs in isolation. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. HDFS is designed to support very large files.
Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds.
HDFS supports write-once-read-many semantics on files. In , Yahoo ran 4, node Hadoop cluster and Hadoop won terabyte sort benchmark. Yes, we do. The mode of communication is SSH. Yes, this is to avoid datanode failure. No, Map is not like a pointer. Following 3 Daemons run on Master nodes NameNode: HDFS runs on a cluster of machines and provides redundancy using a replication protocol. A Deeper Look. Job Recommendation Latest.
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