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    hdfs block size is larger as compared to the size of the disk blocks so that

    Mohammed

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    get hdfs block size is larger as compared to the size of the disk blocks so that from screen.

    Why HDFS block size is large ?

    FileSystem block size is in KBs while disk block size is in bytes but why HDFS block size is large ?

    Why HDFS block size is large ?

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    September 20, 2018 at 2:21 pm

    #5133 DataFlair Team

    FileSystem block size is in KBs while disk block size is in bytes but why HDFS block size is large ?

    September 20, 2018 at 2:21 pm

    #5135 DataFlair Team

    HDFS blocks are large compared to disk blocks, because to minimize the cost of seeks. If we have many smaller size disk blocks, the seek time would be maximum (time spent to seek/look for an information). And also, having multiple small sized blocks is the burden on name node/master, as ultimately the name node stores metadata, so it has to save this disk block information.

    If the Data Block is large enough, the time it takes to transfer the data from the disk can be significantly longer than the time to seek to the start of the block. Thus, transferring a large file made of multiple blocks operates at the disk transfer rate.

    For each block we need a Mapper. So, in the case of small-sized blocks, there will be a lot of Mappers. Each will be processing the data, which isn’t efficient.

    Follow the link to learn more about HDFS Data Blocks

    September 20, 2018 at 2:21 pm

    #5136 DataFlair Team

    HDFS blocks are large, the reason is to lower the seek time(the time to locate the head of a file to read it completely).

    With smaller Data Block we have larger no of seek time and lesser number of transfer time, however, we wanted to reverse this process, i.e lesser no of seek time and more no.of transfer time( seek time/transfer time = .01), which is only possible with larger block sizes. Many times we won’t be interested to read the complete file and just find the seek, to process the files.

    With HDFS we deal with large files and quick processing hence helps in having lesser seek time.

    Also due to this large block size in HDFS (64mb), MapReduce to processes large single block file easily at one time.

    Follow the link to learn more about HDFS Blocks in Hadoop

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    Why HDFS Blocks are Large in Size?

    Why HDFS Blocks are Large in Size? - 226117

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    Why HDFS Blocks are Large in Size?

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    Labels: Apache Hadoop Cloudera Data Science Workbench (CDSW)

    patelharshali13 Explorer

    Created on ‎08-23-2018 09:49 AM - edited

    Why HDFS Blocks are Large in Size?

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    Tags:data-sciencehadoopHadoop CoreMapreduce

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    sharmadukool136 Explorer

    Created ‎08-23-2018 10:01 AM

    The main reason for having the HDFS blocks in large size is to reduce the cost of disk seek time. Disk seeks are generally expensive operations. Since Hadoop is designed to run over your entire dataset, it is best to minimize seeks by using large files. In general, the seek time is 10ms and disk transfer rate is 100MB/s. To make the seek time 1% of the disk transfer rate, the block size should be 100MB. Hence to reduce the cost of disk seek time HDFS block default size is 64MB/128MB.

    Reply 2,250 VIEWS 1 KUDO

    codelove New Contributor

    Created ‎07-18-2021 07:50 AM

    The ideas for the large size of blocks are:

    To reduce the expense of seek: Because of the large size blocks, the time consumed to shift the data from the disk can be longer than the usual time taken to commence the block. As a result, the multiple blocks are transferred at the disk transfer rate.

    If there are small blocks, the number of blocks will be too many in Hadoop HDFS and too much metadata to store. Managing such a vast number of blocks and metadata will create overhead and head to traffic in a network. Source: Link.

    Reply 1,067 VIEWS 0 KUDOS

    स्रोत : community.cloudera.com

    Hadoop Mock Test

    Hadoop Mock Test, This section presents you various set of Mock Tests related to Hadoop Framework. You can download these sample mock tests at your local machine and solve offli

    Hadoop Mock Test

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    This section presents you various set of Mock Tests related to Hadoop Framework. You can download these sample mock tests at your local machine and solve offline at your convenience. Every mock test is supplied with a mock test key to let you verify the final score and grade yourself.

    Hadoop Mock Test I

    Q 1 - The concept using multiple machines to process data stored in distributed system is not new.

    The High-performance computing (HPC) uses many computing machines to process large volume of data stored in a storage area network (SAN). As compared to HPC, Hadoop

    A - Can process a larger volume of data.

    B - Can run on a larger number of machines than HPC cluster.

    C - Can process data faster under the same network bandwidth as compared to HPC.

    D - Cannot run compute intensive jobs.

    Q 2 - Hadoop differs from volunteer computing in

    A - Volunteers donating CPU time and not network bandwidth.

    B - Volunteers donating network bandwidth and not CPU time.

    C - Hadoop cannot search for large prime numbers.

    D - Only Hadoop can use mapreduce.

    Q 3 - As compared to RDBMS, Hadoop

    A - Has higher data Integrity.

    B - Does ACID transactions

    C - IS suitable for read and write many times

    D - Works better on unstructured and semi-structured data.

    Q 4 - What is the main problem faced while reading and writing data in parallel from multiple disks?

    A - Processing high volume of data faster.

    B - Combining data from multiple disks.

    C - The software required to do this task is extremely costly.

    D - The hardware required to do this task is extremely costly.

    Q 5 - Which of the following is true for disk drives over a period of time?

    A - Data Seek time is improving faster than data transfer rate.

    B - Data Seek time is improving more slowly than data transfer rate.

    C - Data Seek time and data transfer rate are both increasing proportionately.

    D - Only the storage capacity is increasing without increase in data transfer rate.

    Q 6 - Data locality feature in Hadoop means

    A - store the same data across multiple nodes.

    B - relocate the data from one node to another.

    C - co-locate the data with the computing nodes.

    D - Distribute the data across multiple nodes.

    Q 7 - Which of these provides a Stream processing system used in Hadoop ecosystem?

    A - Solr B - Tez C - Spark D - Hive

    Q 8 - HDFS files are designed for

    A - Multiple writers and modifications at arbitrary offsets.

    B - Only append at the end of file

    C - Writing into a file only once.

    D - Low latency data access.

    Q 9 - A file in HDFS that is smaller than a single block size

    A - Cannot be stored in HDFS.

    B - Occupies the full block's size.

    C - Occupies only the size it needs and not the full block.

    D - Can span over multiple blocks.

    Q 10 - HDFS block size is larger as compared to the size of the disk blocks so that

    A - Only HDFS files can be stored in the disk used.

    B - The seek time is maximum

    C - Transfer of a large files made of multiple disk blocks is not possible.

    D - A single file larger than the disk size can be stored across many disks in the cluster.

    Q 11 - In a Hadoop cluster, what is true for a HDFS block that is no longer available due to disk corruption or machine failure?

    A - It is lost for ever

    B - It can be replicated form its alternative locations to other live machines.

    C - The namenode allows new client request to keep trying to read it.

    D - The Mapreduce job process runs ignoring the block and the data stored in it.

    Q 12 - Which utility is used for checking the health of a HDFS file system?

    A - fchk B - fsck C - fsch D - fcks

    Q 13 - Which command lists the blocks that make up each file in the filesystem.

    A - hdfs fsck / -files -blocks

    B - hdfs fsck / -blocks -files

    C - hdfs fchk / -blocks -files

    D - hdfs fchk / -files -blocks

    Q 14 - The datanode and namenode are respectiviley

    A - Master and worker nodes

    B - Worker and Master nodes

    C - Both are worker nodes

    D - None

    Q 15 - In the local disk of the namenode the files which are stored persistently are −

    A - namespace image and edit log

    B - block locations and namespace image

    C - edit log and block locations

    स्रोत : www.tutorialspoint.com

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