Hadoop Is the trending technology with many subdivisions as its branches.\u00a0101\u00a0Hadoop Interview Questions with Answers<\/strong>\u00a0are divided with HDFS questions, Map reduce questions, HBase questions, SQoop questions, flume questions, Zookeeper interview questions, pig questions, hive questions and yarn questions. To help the students from the interview point of view, our Big Data Training professionals have listed down the 101 interview questions. Make use of our\u00a0Hadoop Training in Chennai<\/strong><\/a>\u00a0from our experts.<\/p>\r\n\r\n Mapper or reducer are used to create or run jobs using a generic application programming interface with a programming language like Python, Perl, and ruby etc. This is called Hadoop streaming.<\/p>\r\n\r\n The ECC memory is the greatest advantage of Hadoop and users have experienced errors by using the non-ECC memory. The hardware configuration depends upon the workflow requirement and memory. Hadoop jobs with dual-core machines or dual processors with 4GB or 8GB RAM uses the ECC memory and ECC memory is the best configuration for executing Hadoop jobs.<\/p>\r\n\r\n Text input format, Key value input format, and sequence file input format are some of the common input formats in the Hadoop.<\/p>\r\n\r\n Data ingestion, data storage, and data processing are the three steps involved in the big data solution. To extract the data there are different sources available like SAP, CRM, log files, flat files, documents, images, social media feeds and RDBMS like MySQL or Oracle<\/b><\/a>. Data can be ingested through batch jobs and real-time streaming. After extracting the data it is stored in HDFS or NoSQL database like HBase. After storage, the data is processed using MapReduce, spark, pig, and hive framework.<\/p>\r\n\r\n HDFS store the data in sequential order whereas HBase works with reading or write access.<\/p>\r\n\r\n Schema, usage pattern with respect to a number of columns, split of data to process parallel, Storage space, and the performance of data like reading, write or transfer are some of the factors which influence the decision of the file format in Apache Hadoop.<\/p>\r\n\r\n CSV, JSON, Columnar, Sequence files, AVRO and Parquet file are some of the files used in Apache Hadoop. Join the\u00a0Big Data\u00a0<\/strong>Training in Chennai<\/strong><\/a>\u00a0to prepare for the Hadoop interviews.<\/p>\r\n\r\n Block and block scanner works with the minimum amount of data which can be read or written in the HDFS. The size of a block is 64MB. Block scanner tracks the DataNode to checksum the errors.<\/p>\r\n\r\n NameNode manages the metadata which is saved in the directory tree of the HDFS file system on a Hadoop cluster.\u00a0fsimage\u00a0file and the edits file are the two in the NameNode. Backup Node keeps the up-to-date in-memory copy of the file which is in sync with the active NameNode. Checkpoint Node creates the checkpoints at regular intervals. Checkpoint Node downloads the edits and\u00a0fsimage\u00a0file from the NameNode and merges it locally. The image in the active NameNode is updated back after the Checkpoint Node.<\/p>\r\n\r\n Commodity hardware consists of RAM to execute the specific services and Hadoop does not require high-end hardware to configure.<\/p>\r\n\r\n The port number for name node, task tracker, and job tracker are 50070, 50030 and 50060 respectively.<\/p>\r\n\r\n The distributed data from the source to the destination works with DistCP in the Hadoop. The data copying within the Hadoop cluster is called as inter-cluster data copying. The version control is maintained by the DistCP.<\/p>\r\n\r\n Secondary NameNode performs the checkpoints in HDFS. It takes the Metadata file in the NameNode and merges the file with the FSImage to produce the new image. By this function, the edit logs stop from becoming too large.<\/p>\r\n\r\n The files associated with metadata are FSImage and Editlogs.<\/p>\r\n\r\n Yes, because the RAM in the HDFS saves the metadata of the files. If there are so many small files then it is difficult to store as the space in the RAM is 150 bytes. The number of files and the data in the Hadoop is restricted in some cases.<\/p>\r\n\r\n The common method to check whether the NameNode is working is the\u00a0jps\u00a0command. Jps Command checks the status of the daemons running in the HDFS.<\/p>\r\n\r\n Yes, it is possible by using \u2018-Ddfs.blocksize=block_size\u2019 where the block_size is specified in bytes. To change the block size from 120MB to 32MB with the command: Hadoop fs -Ddfs.blocksize=33554432 -copy from local\/home\/fita\/test.tst\/sample_hdfs and to check the block_size with the command Hadoop fs -stat%0\/sample_hdfs\/test.txt.<\/p>\r\n\r\n Rack awareness algorithm is used in the Hadoop to improve the network performance and to prevent the loss of data due to network failure. This algorithm helps to manage the traffic and improves the performance.<\/p>\r\n\r\n No, It is not possible to modify the HDFS because it follows the write once and read many systems.<\/p>\r\n\r\n Yes, it is possible to read a file which is already open. The\u00a0hflush\u00a0operation in the HDFS push all the data in the write pipeline and it wait for the acknowledgments from the data nodes. So after the\u00a0hflush\u00a0the data written in the file is visible to the readers through the file is open for writing.<\/p>\r\n\r\n The data integrity in the HDFS talks about the correctness of the data. The read operation and block scanner verify the correctness of the data stored in the HDFS periodically.<\/p>\r\n\r\n\r\n \t
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