【正文】 江 漢 大 學(xué) 畢 業(yè) 論 文（設(shè) 計(jì)） 外 文 翻 譯 原文來源 The Hadoop Distributed File System: Architecture and Design 中文譯文 Hadoop 分布式文件系統(tǒng)：架構(gòu)和設(shè)計(jì) 姓 名 XXXX 學(xué) 號(hào) 202108202137 2021 年 4 月 8 日 英 文原文 The Hadoop Distributed File System: Architecture and Design Source： Introduction The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on modity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. HDFS is highly faulttolerant and is designed to be deployed on lowcost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. HDFS relaxes a few POSIX requirements to enable streaming access to file system data. HDFS was originally built as infrastructure for the Apache Nutch web search engine project. HDFS is part of the Apache Hadoop Core project. The project URL is Assumptions and Goals Hardware Failure Hardware failure is the norm rather than the exception. An HDFS instance may consist of hundreds or thousands of server machines, each storing part of the file system’s data. The fact that there are a huge number of ponents and that each ponent has a nontrivial probability of failure means that some ponent of HDFS is always nonfunctional. Therefore, detection of faults and quick, automatic recovery from them is a core architectural goal of HDFS. Streaming Data Access Applications that run on HDFS need streaming access to their data sets. They are not general purpose applications that typically run on general purpose file systems. HDFS is designed more for batch processing rather than interactive use by users. The emphasis is on high throughput of data access rather than low latency of data access. POSIX imposes many hard requirements that are not needed for applications that are targeted for HDFS. POSIX semantics in a few key areas has been traded to increase data throughput rates. Large Data Sets Applications that run on HDFS have large data sets. A typical file in HDFS is gigabytes to terabytes in size. Thus, HDFS is tuned to support large files. It should provide high aggregate data bandwidth and scale to hundreds of nodes in a single cluster. It should support tens of millions of files in a single instance. Simple Coherency Model HDFS applications need a writeoncereadmany access model for files. A file once created, written, and closed need not be changed. This assumption simplifies data coherency issues and enables high throughput data access. A Map/Reduce application or a web crawler application fits perfectly with this model. There is a plan to support appendingwrites to files in the future. “Moving Computation is Cheaper than Moving Data” A putation requested by an application is much more efficient if it is executed near the data it operates on. This is especially true when the size of the data set is huge. This minimizes work congestion and increases the overall throughput of the system. The assumption is that it is often better to migrate the putation closer to where the data is located rather than moving the data to where the application is running. HDFS provides interfaces for applications to move themselves closer to where the data is located. Portability Across Heterogeneous Hardware and Software Platforms HDFS has been designed to be easily portable from one platform to another. This facilitates widespread adoption of HDFS as a platform of choice for a large set of applications. NameNode and DataNodes HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is split into one or more blocks and these blocks are stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories. It also determines the mapping of blocks to DataNodes. The DataNodes are responsible for serving read and write requests from the file system’s clients. The DataNodes also perform block creation, deletion, and replication upon instruction from the NameNode. The NameNode and DataNode are pieces of software designed to run on modity machines. These machines typically run a GNU/Linux operating system (OS). HDFS is built using the Java language。 any machine that supports Java can run the NameNode or the DataNode software. Usage of the highly portable Java language means that HDFS can be deployed on a wide range of machines. A typical deployment has a dedicated machine that runs only the NameNode software. Each of the other machines in the cluster runs one instance of the DataNode software. The architecture does not preclude running multiple DataNodes on the same machine but in a real deployment that is rarely the case. The existence of a single NameNode in a cluster greatly simplifies the architecture of the system. The NameNode is the arbitrator and repository for all HDFS metadata. The system is designed in such a way that user data never flows through the NameNode. The File System Namespace HDFS supports a traditional hierarchical file anization. A user or an application can create directories and store files inside these directories. The file system namespace hierarchy is similar to most other existing file systems。 one can create