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Distributed Hadoop (HDFS) system for storing large amounts of reliable data and data transfer that allocates high bandwidth for user applications. In large groups, thousands of hosts are directly hosted and do their job. By distributing savings and having many investors, resources can grow even in different economies. We describe the HDFS architecture and experience reports with HDFS to manage the user's business information on Yahoo. Under the tree is three divisions skills and hash policy and hash metadata management consistent. One of them, and the three divisions of art are applied in the bundle of a plurality of reasons is the most appropriate hash NameNodes "the guts of" reasoning power pushes the words of NameNodes, he left in the efforts. Keeping the NameNodes ray beam of faith and replication are replicated on different techniques is adopted to maintain consistency. KeywordsHadoop, HDFS, distributed file system, Metadata Management, File Management System. I.INTRODUCTION With the rapid development of Internet, the amount of data is growing exponentially, and the largescale data storage and processing has become a problem. Cloud computing is one of the most popular solutions to meet the demand. Cloud computing provides decreased cost of hardware resource and increased equipment utilization. Users can access all kinds of Internet services and virtual computing power through lightweight portable devices rather than traditional Pc. Cloud storage is a key issue for cloud computing, and metadata management plays a critical role in Cloud storage. Metadata is the data that describes the structure of a file system, such as hierarchical namespace, file and directory attributes and mapping of file to data chunks. Although the size of metadata is relatively small in the file system, metadata operations can take up over 50 percent of the whole file system operations. So the metadata management is critically important for file system performance. There are various distributed file systems like PVFS (Parallel Virtual File System), Lustre, GFS (Google File System), HDFS (Hadoop Distributed File System).The Google File System (GFS) [1] and Hadoop Distributed File System (HDFS) [2] are the most common file system deployed in large scale distributed systems such as Face book, Google and Yahoo today. PVFS [3] is an open source RAID-0 style parallel file system for clusters. It partitions a file into stripe units and distributes these stripes to disks in a round robin fashion. PVFS consists of one metadata server and several data servers. All data traffic of file content flows between clients and data server nodes in parallel without going through the metadata server. The fatal disadvantage of PVFS is that it does not provide any fault-tolerance in its current form. The failure of any single server node will affect the whole file system. International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 04, Issue 03; March2018 [ISSN: 2455-1457] @IJRTER-2018, All Rights Reserved 629 Lustre is a shared disk file system. Commonly used for large scale cluster computing. It is an openstandard based system with great modularity and compatibility with interconnects, networking components and storage hardware. Currently, it is only available for Linux. A. Introduction to Hadoop Hadoop provides a distributed file system and a framework forthe analysis and transformation of large datasets using the Map Reduce the paradigm. An important feature of Hadoop is data partition and calculation through many (thousands) of hosts and perform application calculations in parallel closed to your data. Figure 1: Hadoop System The table 1 shows the components of hadoop. Hadoop is an Apache project; all components are available via the Apache open source license. Yahoo! has developed and contributed to 80% of the core of Hadoop (HDFS and MapReduce). HBase was originally developed at Powerset, now a department at Microsoft. Table 1.Hadoop project components Hive was developed and developed on Facebook. Pig, guardian of the zoo, and Chukwa were on Yahoo! It was Avro comes from Yahoo! and is in co-development with Cloudera. HDFS is the component of the Hadoop file system. During the The interface for HDFS is modeled on the UNIX International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 04, Issue 03; March2018 [ISSN: 2455-1457] @IJRTER-2018, All Rights Reserved 630 file system, Loyalty to the standards was sacrificed in favor of improvement Performance for applications at your fingertips. The distributed Hadoop file system (HDFS) [7] is distributed file system designed to work on raw hardware. There are many similarities with existing distributed file systems. Anyway the differences compared to other distributed file systems are significant. HDFS is extremely error prone and is designed to be distributed low-cost hardware. HDFS offers access to high performance and is suitable for applications that have large data set. HDFS was originally designed as an Apache infrastructure Web Search Engine project. HDFS stores file system metadata and application data separately. The HDFS architecture consists of NameNode, DataNode,and HDFS clients. The HDFS cluster can be made up of thousands DataNode and tens of thousands of HDFS cluster clients, such as each DataNode can perform multiple application tasks at the same time. The main features of HDFS are that it is very wrong tolerance, suitable for applications with large data sets. HDFS [2] is a master / slave and one-man architecture NameNode, a master server that manages the file system namespace and file access control for clients. HDFS The namespace is a hierarchy of files and directories. These files are directories that register the attribute, such as permissions, changes, access time space and disk space quotas. The file is divided into one or more blocks and has a series of blocks stored in DataNode. [2] DataNodes stores data in the files it contains local system and has knowledge of HDFS files system. Each block of HDFS data is stored in a separate file. The HDFS cluster has a single node that manages the file system namespace. B. Hadoop Distributed File System The Hadoop Distributed File System (HDFS) [7] 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 was originally built as infrastructure for the Apache Nutch web search engine project. HDFS stores file system metadata and application data separately. HDFS architecture consists of NameNode, DataNode, and HDFS Client. A HDFS Cluster may consist of thousands of DataNode and tens of thousands of HDFS clients per cluster, as each DataNode may execute multiple application tasks concurrently. The main features of HDFS are that, it is highly fault tolerance, suitable for applications with large data sets. II. METADATA MANAGEMENT SCHEMES To distribute metadata across multiple servers, some techniques are used such as secondary partitioning, hashing techniques, and consistent hashing. A. Sub-Tree Partitioning Subtree Partitioning [4] [6] is used in the Ceph file system and in the Coda file system. The key design idea is that of hashing the directory near the root of the hierarchy to the partition initially, and as the server load grows, the less loaded server automatically migrates some subdirectories to another server with a lower load. We also recommend prefix caching to efficiently use the RAM available on all servers to improve performance. There are three main disadvantages of this approach. First, let's assume that there is an accurate load measurement method available on each server and that all servers periodically exchange load information. Secondly, all directories must be reconfigured to reflect changes to the server infrastructure when Microsoft joins or departs due to an error or recovery. In fact, there is a huge overhead in petabyte-sized storage systems. Third, when the International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 04, Issue 03; March2018 [ISSN: 2455-1457] @IJRTER-2018, All Rights Reserved 631 hotspots of metadata operations change as the system evolves; Performing frequent metadata migrations to eliminate these hot spots can result in significant overhead and offset the benefits of load balancing. In the partitioning of the subtree, the namespace is divided into different sub-levels of directories, managed by individual metadata servers. This strategy provides a good position for metadata in the same subtree to be assigned to the same metadata server, but metadata is not equally distributed, and metadata calculation and transmission can result in significant time and network overhead. B. Hashing Technique Hashing technology [10] is used by Luster, the zFS file system. The hashing technique uses a hash function on the path name to get the location of the metadata. In this schema, metadata can be equally distributed across clusters, but the attribute of the directory locale is lost and some metadata must be migrated when the path is renamed. However, when the root directory is renamed, or when the total number of MS changes, there is a serious problem when the hashing mapping needs to be reimplemented, so all affected metadata must be migrated between MS. The file metadata size is small, but a large number of files may be involved. In particular, if MS joins or leaves, it is necessary to reposition the metadata of all the files. This can result in peaks of disk and network traffic and a severe performance degradation. The hash-based mapping method can balance metadata workloads and can essentially perform fast met

Distributed Metadata Scheme in HDFS