Windows Server 2008 uses the NTFS V. 3.1 file system. Some of the basic features incorporated into NTFS include: Long filenames, Built-in security features, Better file compression tan FAT, Ability to use larger disks and files than FAT, File activity tracking for better recovery and stability than FAT, Portable Operating System Interface for UNIX support, Volume striping and Volume extensions, and Less disk fragmentation than FAT. Allowing for many security setups, and larger files to be stored/used on the computer, NTFS is a large improvement from previous FAT versions. Also, files can now be compressed by more than 40 percent, saving vital disk space for other needs!
Well-organized file names and folder structures make it easier to find and keep track of data files. A system needs to be practical and used consistently. Without organization, you cannot report data effectively.
When a file is written in HDFS, it is divided into fixed size blocks. The client first contacts the NameNode, which get the list of DataNode where actual data can be stored. The data blocks are distributed across the Hadoop cluster. Figure \ref{fig.clusternode} shows the architecture of the Hadoop cluster node used for both computation and storage. The MapReduce engine (running inside a Java virtual machine) executes the user application. When the application reads or writes data, requests are passed through the Hadoop \textit{org.apache.hadoop.fs.FileSystem} class, which provides a standard interface for distributed file systems, including the default HDFS. An HDFS client is then responsible for retrieving data from the distributed file system by contacting a DataNode with the desired block. In the common case, the DataNode is running on the same node, so no external network traffic is necessary. The DataNode, also running inside a Java virtual machine, accesses the data stored on local disk using normal file I/O
HDFS uses NameNode operation to realize data consistency. NameNodes utilizes a transactional log file to record all the changes of
GFS: Google File System is a distributed file system which is developed by Google in order to provide efficient, reliable access to data. . It is designed and implemented inorder to meet the requirements provided by Google’s data processing. The file system consists of hundreds of storage machines to provide inexpensive parts and it is accessed by different client machines. Here the search engine is providing huge amounts data that should be stored. GFS has 1,000 nodes with 300TB disk storage.
a. By reading the file system’s directory information, which is stored on the storage device
Blocks are the logical records which breaks the area used by a partition; clusters are physical bodies of a hard disk. Hard disk is usually broken in to cylinders and cylinders are broken down in to clusters. Most HDD arrive from the factory with a low level pattern where, block size = 512 bytes. The NTFS file system can produce cluster sizes of a multiple of 512 having a default of 8 blocks for every cluster. Size of a block is multiple of size of cluster, such that a logical block will fit a definite number of physical clusters “one file one cluster”. That is, in every cluster will be installed information belonging at most to a single file. As an aftermath, when scripting a file in a hard disk, some cluster remains incompletely filled or fully unused. As the operating system can only write an entire block, it pursues that the idle space should be fit with some strings of bytes that can be used by others. It should be remembered that these data are saved in a disk because of the operating system curbs to write only on an entire block, they could be detected by locating
HDFS is Hadoop’s distributed file system that provides high throughput access to data, high-availability and fault tolerance. Data are saved as large blocks making it suitable for applications
I would recommend a Windows OS and file system. This is because Windows is straight forward. “There may be too many distributions of Linux, it's possible that this is hurting Linux in the marketplace. It could be that the lack of a Linux distro from a major computer company is also hurting it in the marketplace.” (Horowitz, 2007) The type of architecture I would use is a web-based computing. This is
HFS+ is file system developed by apple to replace their Hierarchical file system as the primary file system used in Mac computers It is also used by IPod and it is referred to as Mac OS extended.
Hardware Pentium III or equivalent PC-compatible; Macintosh- or Unix-based machines are not supported 256-MB RAM; 512 MB preferred CD-ROM 2-GB free space on master hard drive; 5 GB preferred 56-KB modem; cable or DSL connection preferred
software. Please take some research to find if there is any other IED user software
It is a type of VHD disk which expand when more data is stored in it but do not shrink when data is deleted.
The purpose of a filing system is to reduce stress by being organized. Handling stress is not good for our being, so a good filing system is required. Life can become complicated when we let decisions and actions stack up without taking the time to thoughtfully process as to their short and long term effects on our life. Opportunities come at us like raindrops in a thunderstorm, pelting our brains until we go into brain overload. Our thinking becomes fuzzy, muddled, and confused; therefore, a good filing system is essential.
Abstract - Hadoop Distributed File System, a Java based file system provides reliable and scalable storage for data. It is the key component to understand how a Hadoop cluster can be scaled over hundreds or thousands of nodes. The large amounts of data in Hadoop cluster is broken down to smaller blocks and distributed across small inexpensive servers using HDFS. Now, MapReduce functions are executed on these smaller blocks of data thus providing the scalability needed for big data processing. In this paper I will discuss in detail on Hadoop, the architecture of HDFS, how it functions and the advantages.