2. Hadoop Interview Questions on Page 7. Hadoop Certification Exam Simulator + Study Material o Contains 4 practice Question Paper o realistic Hadoop. hadoop interview questions hadoop, pig,hive,hbase, hdfs, mapreduce Download as PDF, TXT or read online from Scribd amadeus complete manual. Audience: Hadoop job candidates. Rating: 4. Reviewer: Ian Stirk. This site- only e-book aims to help you pass an interview for a job as a.
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Hadoop Interview Guide - site edition by Monika Singla, Sneha Poddar, Shivansh Kumar. Download it once and read it on your site device, PC, phones or. This book is designed to provide in-depth knowledge of Hadoop components. It will equip you to apply for a job as a Hadoop Developer right from beginner to. O'Reilly Media, Inc. Hadoop: The Definitive Guide, the image of an African elephant, and .. Martin Gardner, the mathematics and science writer, once said in an interview: . collateral/analyst-reports/diverse-exploding-digital-universe. pdf).
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Answer : Speculative execution is a way of coping with individual Machine performance. In large clusters where hundreds or thousands of machines are involved there may be machines which are not performing as fast as others.
This may result in delays in a full job due to only one machine not performaing well. To avoid this, speculative execution in hadoop can run multiple copies of same map or reduce task on different slave nodes. The results from first node to finish are used.
Answer : In a MapReduce job reducers do not start executing the reduce method until the all Map jobs have completed. Reducers start copying intermediate key-value pairs from the mappers as soon as they are available.
The programmer defined reduce method is called only after all the mappers have finished.
Why reducers progress percentage is displayed when mapper is not finished yet? The progress calculation also takes in account the processing of data transfer which is done by reduce process, therefore the reduce progress starts showing up as soon as any intermediate key-value pair for a mapper is available to be transferred to reducer. Though the reducer progress is updated still the programmer defined reduce method is called only after all the mappers have finished.
What Is Hdfs? This is a distributed file system designed to run on commodity hardware.
It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant.
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.
Hadoop Tutorial & Learning PDF guides
HDFS supports write-once-read-many semantics on files. What Is Hdfs Block Size? Each block is typically 64Mb or Mb in size. Each block is replicated multiple times. For e.
InputSplit is a Java class that points to start and end location in the block. Data is not read as chunks or packets but rather comes in at a constant bit rate.
The application starts reading data from the beginning of the file and continues in a sequential manner. All slaves send a signal to their respective master node i.
However, the other machine can copy only 7. No, the blocks cannot be broken, it is the responsibility of the master node to calculate the space required and accordingly allocate the blocks. Master node monitors the number of blocks that are in use and keeps track of the available space.
Hadoop ecosystem works on dividing tasks into smaller sub tasks, which are then spread over the nodes for processing. While processing a task, there is a possibility that some of the systems could be slow, which may slow down the overall process thus requiring lot of time to complete a particular task. Multiple copies of MapReduce tasks are run on other DataNodes.
As most of the tasks finish, Hadoop creates redundant copies of the remaining tasks, and assigns it to the nodes that are not executing any other task.
This process is referred to as Speculative Execution. This way if the same task is finished by some other node then Hadoop will stop all the other nodes which are processing that task.
Everything You Need to Know for a Hadoop Developer Interview
Whenever a system fails, the whole MapReduce process has to be executed again. Even if the fault occurs after the mapping process, the process has to be restarted. The backup intermediary key value pairs help improve the performance at failure time. The intermediary key value pairs help retrieve or resume the job when there is any fault in the system. Apart from this, since HDFS assumes that the data stored in nodes is unreliable, it creates copies of the data which are available across all the nodes that can be used on failure.
What are the similarities between Impala and hive? I often wonder why anyone would want to use Hive queries when Impala is available - since it can query the Hive tables much faster.
Chapter 9 Pig Pig provides workflow and scripting functionality, at a higher level than Java and MapReduce programming. Example questions include: In which scenario MapReduce is a better fit than Pig? What the different ways to develop PigLatin scripts? What is a relation in Pig? What is a skewed join? Java can be a difficult language to learn, Pig provides an easier way of programming MapReduce.
Perhaps in the future, higher level tools e. Pig and the various querying languages will be used for most processing, and Java for only the low-level complex work. Chapter 10 Java Refresher for Hadoop Java is a general purpose language, often used as the default language with various Hadoop components.
Example questions include: What is final, finally, and Finalize? What is the difference between an ArrayList and a LinkedList?
Does Java support multiple inheritance? This section really is just a brief refresher, it contains a list of Java questions that you might be asked when going for a junior Java developer role.
Conclusion This book contains a wide-range of questions about Hadoop and its components, and the answers generally provide accurate explanations with sufficient detail. The tasks to program at the end of each chapter should prove useful in demonstrating your practical understanding of the topics.
Top Hadoop Interview Questions To Prepare In 2019 – Apache Hive
Many other common Hadoop components could have been included e. Perhaps it can be expanded in the future to include these topics - that said, the book does cover many of the core Hadoop technologies. Generally the book is well written, however, some of the questions have substandard English grammar. Some of the longer example code is difficult to read because it is not formatted adequately.
There is a large list of websites at the end of the book, however, none of the sites is annotated.Reducers run in isolation. Question 5. JobTracker is a daemon service which submits and tracks the MapReduce tasks to the Hadoop cluster. I feel, the authors work is really not worth of Rs. This book contains a wide-range of useful questions about Hadoop and many of its components.