Top 50 Bigdata Hadoop Interview Questions And Answers Pdf, For you to crack your Hadoop Interview Questions and Answers for Freshers – xumodaperma.tk- 4,5,6,9. We have further categorized Big Data Interview Questions for Freshers and Hadoop Interview Questions and Answers for Freshers – xumodaperma.tk- 1,2,4,5,6,7,8,9. hadoop interview questions hadoop, pig,hive,hbase, hdfs, mapreduce. Copyright: © All Rights Reserved. Download as PDF, TXT or read online from Scribd . / interview-questions/pig-interview-questions-for-experienced-and-freshers/.
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+ Hadoop Interview Questions and Answers, Question1: On What concept the you can download the Hadoop Installation xumodaperma.tk 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 and Answers for Freshers - xumodaperma.tk- 1,2,4,5,6,7,8,9 For a detailed PDF report on Hadoop Salaries - CLICK HERE.
Hadoop 1. However, in case of Hadoop 2. In Hadoop 2. This helps Hadoop to share resources dynamically between multiple parallel processing frameworks like Impala and the core MapReduce component. In the development of distributed systems, creating own protocols for coordinating the hadoop cluster results in failure and frustration for the developers. The architecture of a distributed system can be prone to deadlocks, inconsistency and race conditions. 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. 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. 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. SerDe is a Serializer DeSerializer. 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.
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.
Top Hadoop Interview Questions And Answers
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. 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. 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. 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.
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.
JAVAInterview Questions and answers:
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.
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.
In Hadoop, MapReduce works by breaking the processing into phases: Map and Reduce. 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? 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. 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.
In you previous project, did you maintain the hadoop cluster in-house or used hadoop in the cloud. Most of the organizations still do not have the budget to maintain hadoop cluster in-house and they make use of hadoop in the cloud from various vendors like site, Microsoft, Google, etc. Interviewer gets to know about your familiarity with using hadoop in the cloud because if the company does not have an in-house implementation then hiring a candidate who has knowledge about using hadoop in the cloud is worth it.
Apache Hadoop runs in three modes: Local Standalone Mode — Hadoop by default 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. It is also used for debugging purpose, and it does not support the use of HDFS. Further, in this mode, there is no custom configuration required for configuration files.
Pseudo-Distributed Mode — Just like the Standalone mode, Hadoop also runs on a single-node in a Pseudo-distributed mode. The difference is that each daemon runs in a separate Java process in this Mode. Fully-Distributed Mode — In this mode, all daemons execute in separate nodes forming a multi-node cluster.
Thus, it allows. 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. Whereas Hadoop is distributed computing framework having two main components: While Hadoop can accept both structured as well as unstructured data.
It is a great feature of hadoop, as we can store everything in our database and there will be no data loss. So if the data increases for storing then we have to increase particular system configuration.
While Hadoop provides horizontal scalability. So we just have to add one or more node to the cluster if there is any requirement for an increase in data. Cost — Licensed software, therefore we have to pay for the software. If you have any doubts or queries regarding Hadoop Interview Questions at any point you can ask that Hadoop Interview question to us in comment section and our support team will get back to you.
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.
Authentication — The client authenticates itself to the authentication server. Service Request — The client uses the service ticket to authenticate itself to the server. This reduces network congestion and therefore, enhances the overall system throughput. Various limitations of Hadoop are: Issue with small files — Hadoop is not suited for small files.
Small files are the major problems in HDFS. As HDFS works with a small number of large files for storing data sets rather than larger number of small files. If one use the huge number of small files, then this will overload the namenode. Since namenode stores the namespace of HDFS. HAR files, Sequence files, and Hbase overcome small files issues.
Processing Speed — With parallel and distributed algorithm, MapReduce process large data sets. MapReduce performs the task: MapReduce requires a lot of time to perform these tasks thereby increasing latency. As data is distributed and processed over the cluster in MapReduce. So, it will increase the time and reduces processing speed. Support only Batch Processing — Hadoop supports only batch processing.
It does not process streamed data and hence, overall performance is slower. MapReduce framework does not leverage the memory of the cluster to the maximum. Iterative Processing — Hadoop is not efficient for iterative processing. As hadoop does not support cyclic data flow. That is the chain of stages in which the input to the next stage is the output from the previous stage.
Vulnerable by nature — Hadoop is entirely written in Java, a language most widely used. Hence java been most heavily exploited by cyber-criminal. Therefore it implicates in numerous security breaches.
Security- Hadoop can be challenging in managing the complex application.
Hadoop is missing encryption at storage and network levels, which is a major point of concern. Hadoop supports Kerberos authentication, which is hard to manage. The core Hadoop Interview Questions are for experienced, but freshers and Students can also read and refer them for advanced understanding. Hadoop major drawback was cross-switch network traffic due to the huge volume of data. To overcome this drawback, Data locality came into the picture.
It refers to the ability to move the computation close to where the actual data resides on the node, instead of moving large data to computation. Data locality increases the overall throughput of the system. Datasets are divided into blocks and stored across the datanodes in Hadoop cluster. Data locality has three categories: Data local — In this category data is on the same node as the mapper working on the data. In such case, the proximity of the data is closer to the computation.
Hadoop Interview Questions
This is the most preferred scenario. Intra — Rack- In this scenarios mapper run on the different node but on the same rack. As it is not always possible to execute the mapper on the same datanode due to constraints. Inter-Rack — In this scenarios mapper run on the different rack. As it is not possible to execute mapper on a different.
Then, to stop all the Hadoop daemons use:. Much faster when compared to other modes. Pseudo-Distributed Mode Single Node Cluster : In this case, you need configuration for all the three files mentioned above. In this case, all daemons are running on one node and thus, both Master and Slave node are the same. Fully Distributed Mode Multiple Cluster Node : This is the production phase of Hadoop what Hadoop is known for where data is used and distributed across several nodes on a Hadoop cluster.
Separate nodes are allotted as Master and Slave. Learn more about Hadoop in this Hadoop Certification course to get ahead in your career!
In simple terms, block is the physical representation of data while split is the logical representation of data present in the block. Split acts a s an intermediary between block and mapper. Suppose we have two blocks: Block 1: ii nntteell Block 2: Ii ppaatt Now, considering the map, it will read first block from ii till ll, but does not know how to process the second block at the same time. Here comes Split into play, which will form a logical group of Block1 and Block 2 as a single block.
It then forms key-value pair using inputformat and records reader and sends map for further processing With inputsplit, if you have limited resources, you can increase the split size to limit the number of maps. This will form a logical group of MB, with only 5 maps executing at a time.
What is distributed cache and what are its benefits? Distributed Cache, in Hadoop, is a service by MapReduce framework to cache files when needed. Learn more in this MapReduce Tutorial now. Once a file is cached for a specific job, hadoop will make it available on each data node both in system and in memory, where map and reduce tasks are executing. Later, you can easily access and read the cache file and populate any collection like array, hashmap in your code.
These archives are then un-archived at the slave node. Distributed cache tracks the modification timestamps of cache files, which notifies that the files should not be modified until a job is executing currently. Give your career a big boost by going through our Hadoop Online Training Videos now! NameNode is the core of HDFS that manages the metadata — the information of what file maps to what block locations and what blocks are stored on what datanode.
It uses following files for namespace: fsimage file- It keeps track of the latest checkpoint of the namespace. Checkpoint NameNode has the same directory structure as NameNode, and creates checkpoints for namespace at regular intervals by downloading the fsimage and edits file and margining them within the local directory.
The new image after merging is then uploaded to NameNode. Backup Node provides similar functionality as Checkpoint, enforcing synchronization with NameNode. The backup node needs to save the current state in-memory to an image file to create a new checkpoint.
What are the most common Input Formats in Hadoop? Key Value Input Format: used for plain text files where the files are broken into lines Sequence File Input Format: used for reading files in sequence 9. Each datanode sends a heartbeat message to notify that it is alive. If the namenode does noit receive a message from datanode for 10 minutes, it considers it to be dead or out of place, and starts replication of blocks that were hosted on that data node such that they are hosted on some other data node.
A BlockReport contains list of all blocks on a DataNode. Now, the system starts to replicate what were stored in dead DataNode.
The NameNode manages the replication of data blocksfrom one DataNode to other. In this process, the replication data transfers directly between DataNode such that the data never passes the NameNode.
CTA What are the core methods of a Reducer? The three core methods of a Reducer are: setup : this method is used for configuring various parameters like input data size, distributed cache.
What is SequenceFile in Hadoop? The map outputs are stored as SequenceFile internally. It provides Reader, Writer and Sorter classes. What is Job Tracker role in Hadoop?Minimally an Hadoop application would have following components. What happens to a NameNode that has no data? How to compress mapper output but not the reducer output? Then, check whether orphaned jobs is running or not; if yes, you need to determine the location of RM logs. It is different from the traditional fsck utility for the native file system.
Question 3. Sequential Znode is the one in which sequential number is chosen by the ZooKeeper ensemble and is pre-fixed when the client assigns name to the znode.
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