Hoop分布式文件系统架构和设计
Hadoop分布式文件系统的原理与优化
Hadoop分布式文件系统的原理与优化一、Hadoop分布式文件系统的原理Hadoop分布式文件系统（HDFS）是Apache Hadoop项目的核心组件之一，具有高可靠性、高吞吐量的能力，可应用于大数据的存储和分析。
其原理主要分为以下几个方面：1.1 HDFS的基本结构HDFS采用主从结构，主要包括一个NameNode和若干个DataNode。
其中，NameNode负责管理整个文件系统的命名空间、文件的目录和文件块信息等，而DataNode则负责存储实际的数据块。
1.2 文件块的存储方式HDFS将一个大文件划分成若干个固定大小的数据块（默认为64MB），并将其存储在不同的DataNode中。
这些数据块可以在不同的机器上进行并行处理，从而提高了数据处理的效率。
1.3 副本的备份方式在HDFS中，每一个数据块默认会存储三个副本，分别存储在不同的DataNode上。
这样可以保证数据的可靠性，在某个DataNode失效时，可以通过其它副本进行数据的恢复。
1.4 HDFS的读写流程当客户端请求读取一个文件时，首先会向NameNode发送请求，NameNode会返回该文件所在的DataNode列表，客户端再通过TCP/IP协议与这些DataNode建立连接，获取文件块并进行合并。
当客户端进行写入操作时，会先向NameNode发送请求，并创建一个新的文件，将数据写入其中，NameNode再将写入请求发送给相应的DataNode，进行数据块的存储。
1.5 HDFS的权限控制为了保证数据的安全性，HDFS引入了基于ACL的权限控制机制。
该机制通过对文件的ACL列表进行管理，实现对不同用户的权限限制。
二、Hadoop分布式文件系统的优化由于HDFS的工作原理和设计特点决定了其在大数据存储和分析方面拥有良好的性能表现，但也存在一些需要优化的问题，主要表现在以下几个方面：2.1 数据块大小的设置HDFS默认的数据块大小为64MB，如果我们需要存储大量小文件时，则需要频繁地读写磁盘，会导致IO瓶颈，影响性能。
Hadoop分布式文件系统：架构和设计外文翻译
外文翻译原文来源The Hadoop Distributed File System: Architecture and Design 中文译文Hadoop分布式文件系统：架构和设计姓名 XXXX学号 ************2013年4月8 日英文原文The Hadoop Distributed File System: Architecture and DesignSource：/docs/r0.18.3/hdfs_design.html IntroductionThe Hadoop Distributed File System (HDFS) 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 onlow-cost 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/core/.Assumptions and GoalsHardware FailureHardware 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 components and that each component has a non-trivial probability of failure means that some component of HDFS is always non-functional. Therefore, detection of faults and quick, automatic recovery from them is a core architectural goal of HDFS.Streaming Data AccessApplications 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 notneeded for applications that are targeted for HDFS. POSIX semantics in a few key areas has been traded to increase data throughput rates.Large Data SetsApplications 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 ModelHDFS applications need a write-once-read-many 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. AMap/Reduce application or a web crawler application fits perfectly with this model. There is a plan to support appending-writes to files in the future.“Moving Computation is Cheaper than Moving Data”A computation 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 network congestion and increases the overall throughput of the system. The assumption is that it is often better to migrate the computation 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 PlatformsHDFS 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 DataNodesHDFS 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 blocksare 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 commodity 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 ofmachines. 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 NamespaceHDFS supports a traditional hierarchical file organization. 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 and remove files, move a file from one directory to another, or rename a file. HDFS does not yet implement user quotas or access permissions. HDFS does not support hard links or soft links. However, the HDFS architecture does not preclude implementing these features.The NameNode maintains the file system namespace. Any change to the file system namespace or its properties is recorded by the NameNode. An application can specify the number of replicas of a file that should be maintained by HDFS. The number of copies of a file is called the replication factor of that file. This information is stored by the NameNode.Data ReplicationHDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time.The NameNode makes all decisions regarding replication of blocks. It periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster.Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode.Replica Placement: The First Baby StepsThe placement of replicas is critical to HDFS reliability and performance. Optimizing replica placement distinguishes HDFS from most other distributed file systems. This is a feature that needs lots of tuning and experience. The purpose of a rack-aware replica placement policy is to improve data reliability, availability, and network bandwidth utilization. The current implementation for the replica placement policy is a first effort in this direction. The short-term goals of implementing this policy are to validate it on production systems, learn more about its behavior, and build a foundation to test and research more sophisticated policies.Large HDFS instances run on a cluster of computers that commonly spread across many racks. Communication between two nodes in different racks has to go through switches. In most cases, network bandwidth between machines in the same rack is greater than network bandwidth between machines in different racks.The NameNode determines the rack id each DataNode belongs to via the process outlined in Rack Awareness. A simple but non-optimal policy is to place replicas on unique racks. This prevents losing data when an entire rack fails and allows use of bandwidth from multiple racks when reading data. This policy evenly distributes replicas in the cluster which makes it easy to balance load on component failure. However, this policy increases the cost of writes because a write needs to transfer blocks to multiple racks.For the common case, when the replication factor is three, HDFS’s placement policy is to put one replica on one node in the local rack, another on a different node in the local rack, and the last on a different node in a different rack. This policy cuts the inter-rack write traffic which generally improves write performance. The chance of rack failure is far less than that of node failure; this policy does not impact data reliability and availability guarantees. However, it does reduce the aggregate network bandwidth used when reading data since a block is placed in only two unique racks rather than three. With this policy, the replicas of a file do not evenly distribute across the racks. One third of replicas are on one node, two thirds of replicas are on one rack, and the other third are evenly distributed across the remaining racks. This policy improves write performance without compromising data reliability or read performance.The current, default replica placement policy described here is a work in progress. Replica SelectionTo minimize global bandwidth consumption and read latency, HDFS tries to satisfy a read request from a replica that is closest to the reader. If there exists a replica on the same rack as the reader node, then that replica is preferred to satisfy the read request. If angg/ HDFS cluster spans multiple data centers, then a replica that is resident in the local data center is preferred over any remote replica.SafemodeOn startup, the NameNode enters a special state called Safemode. Replication of data blocks does not occur when the NameNode is in the Safemode state. The NameNode receives Heartbeat and Blockreport messages from the DataNodes. A Blockreport contains the list of data blocks that a DataNode is hosting. Each block has a specified minimum number of replicas. A block is considered safely replicated when the minimum number of replicas of that data block has checked in with the NameNode. After a configurable percentage of safely replicated data blocks checks in with the NameNode (plus an additional 30 seconds), the NameNode exits the Safemode state. It then determines the list of data blocks (if any) that still have fewer than the specified number of replicas. The NameNode then replicates these blocks to other DataNodes.The Persistence of File System MetadataThe HDFS namespace is stored by the NameNode. The NameNode uses a transaction log called the EditLog to persistently record every change that occurs to file system metadata. For example, creating a new file in HDFS causes the NameNode to insert a record into the EditLog indicating this. Similarly, changing the replication factor of a file causes a new record to be inserted into the EditLog. The NameNode uses a file in its local host OS file system to store the EditLog. The entire file system namespace, including the mapping of blocks to files and file system properties, is stored in a file called the FsImage. The FsImage is stored as a file in the NameNode’s local file system too.The NameNode keeps an image of the entire file system namespace and file Blockmap in memory. This key metadata item is designed to be compact, such that a NameNode with 4 GB of RAM is plenty to support a huge number of files and directories. When the NameNode starts up, it reads the FsImage and EditLog from disk, applies all the transactions from the EditLog to the in-memory representation of the FsImage, and flushes out this new version into a new FsImage on disk. It can then truncate the old EditLog because its transactions have been applied to the persistent FsImage. This process is called a checkpoint. In the current implementation, a checkpoint only occurs when the NameNode starts up. Work is in progress to support periodic checkpointing in the near future.The DataNode stores HDFS data in files in its local file system. The DataNode has no knowledge about HDFS files. It stores each block of HDFS data in a separatefile in its local file system. The DataNode does not create all files in the same directory. Instead, it uses a heuristic to determine the optimal number of files per directory and creates subdirectories appropriately. It is not optimal to create all local files in the same directory because the local file system might not be able to efficiently support a huge number of files in a single directory. When a DataNode starts up, it scans through its local file system, generates a list of all HDFS data blocks that correspond to each of these local files and sends this report to the NameNode: this is the Blockreport.The Communication ProtocolsAll HDFS communication protocols are layered on top of the TCP/IP protocol. A client establishes a connection to a configurable TCP port on the NameNode machine. It talks the ClientProtocol with the NameNode. The DataNodes talk to the NameNode using the DataNode Protocol. A Remote Procedure Call (RPC) abstraction wraps both the Client Protocol and the DataNode Protocol. By design, the NameNode never initiates any RPCs. Instead, it only responds to RPC requests issued by DataNodes or clients.RobustnessThe primary objective of HDFS is to store data reliably even in the presence of failures. The three common types of failures are NameNode failures, DataNode failures and network partitions.Data Disk Failure, Heartbeats and Re-ReplicationEach DataNode sends a Heartbeat message to the NameNode periodically. A network partition can cause a subset of DataNodes to lose connectivity with the NameNode. The NameNode detects this condition by the absence of a Heartbeat message. The NameNode marks DataNodes without recent Heartbeats as dead and does not forward any new IO requests to them. Any data that was registered to a dead DataNode is not available to HDFS any more. DataNode death may cause the replication factor of some blocks to fall below their specified value. The NameNode constantly tracks which blocks need to be replicated and initiates replication whenever necessary. The necessity for re-replication may arise due to many reasons: a DataNode may become unavailable, a replica may become corrupted, a hard disk on a DataNode may fail, or the replication factor of a file may be increased.Cluster RebalancingThe HDFS architecture is compatible with data rebalancing schemes. A scheme might automatically move data from one DataNode to another if the free space on a DataNode falls below a certain threshold. In the event of a sudden high demand for a particular file, a scheme might dynamically create additional replicas and rebalance other data in the cluster. These types of data rebalancing schemes are not yet implemented.Data IntegrityIt is possible that a block of data fetched from a DataNode arrives corrupted. This corruption can occur because of faults in a storage device, network faults, or buggy software. The HDFS client software implements checksum checking on the contents of HDFS files. When a client creates an HDFS file, it computes a checksum of each block of the file and stores these checksums in a separate hidden file in the same HDFS namespace. When a client retrieves file contents it verifies that the data it received from each DataNode matches the checksum stored in the associated checksum file. If not, then the client can opt to retrieve that block from another DataNode that has a replica of that block.Metadata Disk FailureThe FsImage and the EditLog are central data structures of HDFS. A corruption of these files can cause the HDFS instance to be non-functional. For this reason, the NameNode can be configured to support maintaining multiple copies of the FsImage and EditLog. Any update to either the FsImage or EditLog causes each of the FsImages and EditLogs to get updated synchronously. This synchronous updating of multiple copies of the FsImage and EditLog may degrade the rate of namespace transactions per second that a NameNode can support. However, this degradation is acceptable because even though HDFS applications are very data intensive in nature, they are not metadata intensive. When a NameNode restarts, it selects the latest consistent FsImage and EditLog to use.The NameNode machine is a single point of failure for an HDFS cluster. If the NameNode machine fails, manual intervention is necessary. Currently, automatic restart and failover of the NameNode software to another machine is not supported.SnapshotsSnapshots support storing a copy of data at a particular instant of time. One usage of the snapshot feature may be to roll back a corrupted HDFS instance to a previously known good point in time. HDFS does not currently support snapshots but will in a future release.Data OrganizationData BlocksHDFS is designed to support very large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds. HDFS supports write-once-read-many semantics on files. A typical block size used by HDFS is 64 MB. Thus, an HDFS file is chopped up into 64 MB chunks, and if possible, each chunk will reside on a different DataNode.StagingA client request to create a file does not reach the NameNode immediately. In fact, initially the HDFS client caches the file data into a temporary local file. Application writes are transparently redirected to this temporary local file. When the local file accumulates data worth over one HDFS block size, the client contacts the NameNode. The NameNode inserts the file name into the file system hierarchy and allocates a data block for it. The NameNode responds to the client request with the identity of the DataNode and the destination data block. Then the client flushes the block of data from the local temporary file to the specified DataNode. When a file is closed, the remaining un-flushed data in the temporary local file is transferred to the DataNode. The client then tells the NameNode that the file is closed. At this point, the NameNode commits the file creation operation into a persistent store. If the NameNode dies before the file is closed, the file is lost.The above approach has been adopted after careful consideration of target applications that run on HDFS. These applications need streaming writes to files. If a client writes to a remote file directly without any client side buffering, the network speed and the congestion in the network impacts throughput considerably. This approach is not without precedent. Earlier distributed file systems, e.g. AFS, have used client side caching to improve performance. APOSIX requirement has been relaxed to achieve higher performance of data uploads.Replication PipeliningWhen a client is writing data to an HDFS file, its data is first written to a local file as explained in the previous section. Suppose the HDFS file has a replication factor of three. When the local file accumulates a full block of user data, the client retrieves a list of DataNodes from the NameNode. This list contains the DataNodes that will host a replica of that block. The client then flushes the data block to the first DataNode. The first DataNode starts receiving the data in small portions (4 KB), writes each portion to its local repository and transfers that portion to the second DataNode in the list. The second DataNode, in turn starts receiving each portion of the data block, writes that portion to its repository and then flushes that portion to the third DataNode. Finally, the third DataNode writes the data to its local repository. Thus, a DataNode can be receiving data from the previous one in the pipeline and at the same time forwarding data to the next one in the pipeline. Thus, the data is pipelined from one DataNode to the next.AccessibilityHDFS can be accessed from applications in many different ways. Natively, HDFS provides a Java API for applications to use. A C language wrapper for this Java API is also available. In addition, an HTTP browser can also be used to browse the files of an HDFS instance. Work is in progress to expose HDFS through the WebDAV protocol.FS ShellHDFS allows user data to be organized in the form of files and directories. It provides a commandline interface called FS shell that lets a user interact with the data in HDFS. The syntax of this command set is similar to other shells (e.g. bash, csh) that users are already familiar with. Here are some sample action/command pairs:FS shell is targeted for applications that need a scripting language to interact with the stored data.DFSAdminThe DFSAdmin command set is used for administering an HDFS cluster. These are commands that are used only by an HDFS administrator. Here are some sample action/command pairs:Browser InterfaceA typical HDFS install configures a web server to expose the HDFS namespace through a configurable TCP port. This allows a user to navigate the HDFS namespace and view the contents of its files using a web browser.Space ReclamationFile Deletes and UndeletesWhen a file is deleted by a user or an application, it is not immediately removed from HDFS. Instead, HDFS first renames it to a file in the /trash directory. The file can be restored quickly as long as it remains in /trash. A file remains in/trash for a configurable amount of time. After the expiry of its life in /trash, the NameNode deletes the file from the HDFS namespace. The deletion of a file causes the blocks associated with the file to be freed. Note that there could be an appreciable time delay between the time a file is deleted by a user and the time of the corresponding increase in free space in HDFS.A user can Undelete a file after deleting it as long as it remains in the /trash directory. If a user wants to undelete a file that he/she has deleted, he/she can navigate the /trash directory and retrieve the file. The /trash directory contains only the latest copy of the file that was deleted. The /trash directory is just like any other directory with one special feature: HDFS applies specified policies to automatically delete files from this directory. The current default policy is to delete files from /trash that are more than 6 hours old. In the future, this policy will be configurable through a well defined interface.Decrease Replication FactorWhen the replication factor of a file is reduced, the NameNode selects excess replicas that can be deleted. The next Heartbeat transfers this information to the DataNode. The DataNode then removes the corresponding blocks and the corresponding free space appears in the cluster. Once again, there might be a time delay between the completion of the setReplication API call and the appearance of free space in the cluster.中文译本原文地址:/docs/r0.18.3/hdfs_design.html一、引言Hadoop分布式文件系统(HDFS)被设计成适合运行在通用硬件(commodity hardware)上的分布式文件系统。
分布式文件系统架构设计
分布式文件系统架构设计目录1.前言 (3)2.HDFS1 (3)3.HDFS2 (5)4.HDFS3 (11)5.结语 (15)1.前言Hadoop是一个由Apache基金会所开发的分布式系统基础架构。
用户可以在不了解分布式底层细节的情况下，开发分布式程序。
充分利用集群的威力进行高速运算和存储。
Hadoop实现了一个分布式文件系统（Hadoop Distributed File System），简称HDFS，解决了海量数据存储的问题；实现了一个分布式计算引擎MapReduce，解决了海量数据如何计算的问题；实现了一个分布式资源调度框架YARN，解决了资源调度，任务管理的问题。
而我们今天重点给大家介绍的是Hadoop里享誉世界的优秀的分布式文件系统-HDFS。
Hadoop重要的比较大的版本有:Hadoop1，Hadoop2，hadoop3。
同时也相对应的有HDFS1，HDFS2，HDFS3三个大版本。
后面的HDFS的版本，都是对前一个版本的架构进行了调整优化，而在这个调整优化的过程当中都是解决上一个版本的架构缺陷，然而这些低版本的架构缺陷也是我们在平时工作当中会经常遇到的问题，所以这篇文章一个重要的目的就是通过给大家介绍HDFS不同版本的架构演进，通过学习高版本是如何解决低版本的架构问题从而来提升我们的系统架构能力。
2.HDFS1最早出来投入商业使用的的Hadoop的版本，我们称为Hadoop1，里面的HDFS就是HDFS1，当时刚出来HDFS1，大家都很兴奋，因为它解决了一个海量数据如何存储的问题。
HDFS1用的是主从式架构，主节点只有一个叫：Namenode，从节点有多个叫：DataNode。
我们往HDFS上上传一个大文件，HDFS会自动把文件划分成为大小固定的数据块（HDFS1的时候，默认块的大小是64M，可以配置），然后这些数据块会分散到存储的不同的服务器上面，为了保证数据安全，HDFS1里默认每个数据块都有3个副本。
Hadoop分布式文件系统方案
Hadoop分布式文件系统：架构和设计要点Hadoop分布式文件系统：架构和设计要点原文：/core/docs/current/hdfs_design.html一、前提和设计目标1、硬件错误是常态，而非异常情况，HDFS可能是有成百上千的server组成，任何一个组件都有可能一直失效，因此错误检测和快速、自动的恢复是HDFS的核心架构目标。
2、跑在HDFS上的应用与一般的应用不同，它们主要是以流式读为主，做批量处理；比之关注数据访问的低延迟问题，更关键的在于数据访问的高吞吐量。
3、HDFS以支持大数据集合为目标，一个存储在上面的典型文件大小一般都在千兆至T字节，一个单一HDFS实例应该能支撑数以千万计的文件。
4、 HDFS应用对文件要求的是write-one-read-many访问模型。
一个文件经过创建、写，关闭之后就不需要改变。
这一假设简化了数据一致性问题，使高吞吐量的数据访问成为可能。
典型的如MapReduce框架，或者一个web crawler应用都很适合这个模型。
5、移动计算的代价比之移动数据的代价低。
一个应用请求的计算，离它操作的数据越近就越高效，这在数据达到海量级别的时候更是如此。
将计算移动到数据附近，比之将数据移动到应用所在显然更好，HDFS提供给应用这样的接口。
6、在异构的软硬件平台间的可移植性。
二、Namenode和DatanodeHDFS采用master/slave架构。
一个HDFS集群是有一个Namenode和一定数目的Datanode 组成。
Namenode是一个中心服务器，负责管理文件系统的namespace和客户端对文件的访问。
Datanode在集群中一般是一个节点一个，负责管理节点上它们附带的存储。
在部，一个文件其实分成一个或多个block，这些block存储在Datanode集合里。
Namenode执行文件系统的namespace操作，例如打开、关闭、重命名文件和目录，同时决定block到具体Datanode节点的映射。
Hadoop分布式文件系统原理与实现解析
Hadoop分布式文件系统原理与实现解析Hadoop分布式文件系统（Hadoop Distributed File System，简称HDFS）是Hadoop生态系统中的一个核心组件，它是为了解决大规模数据存储和处理问题而设计的。
本文将对HDFS的原理和实现进行解析，帮助读者更好地理解和应用HDFS。
一、HDFS的基本原理HDFS是一个基于分布式文件系统的存储解决方案，它的设计目标是能够在大规模集群上存储海量数据，并且具备高可靠性和高性能。
HDFS的基本原理可以概括为以下几点：1. 数据切块：HDFS将大文件切分成多个固定大小的数据块，通常为64MB或128MB。
这些数据块会被分散存储在集群中的不同节点上，以实现数据的分布式存储和并行处理。
2. 数据复制：HDFS采用数据冗余的方式来提供高可靠性。
每个数据块都会被复制多次，通常是三份。
这些副本会被存储在不同的节点上，以防止数据丢失。
3. 主从架构：HDFS采用主从架构，其中包括一个主节点（NameNode）和多个从节点（DataNode）。
主节点负责管理整个文件系统的元数据和命名空间，从节点负责存储实际的数据块。
4. 数据流传输：HDFS采用数据流的方式来传输数据。
当客户端需要读取文件时，主节点会告知客户端数据块的位置，并直接与数据节点进行通信，实现数据的快速传输。
二、HDFS的实现细节HDFS的实现细节主要包括数据块的存储和复制、元数据的管理以及数据的读写操作等方面。
1. 数据块的存储和复制：当客户端上传文件到HDFS时，主节点会将文件切分成数据块，并将这些数据块分散存储在不同的从节点上。
主节点会定期与从节点通信，确保数据块的完整性和可用性。
如果某个从节点出现故障，主节点会将其上的数据块复制到其他节点上，以保证数据的冗余备份。
2. 元数据的管理：HDFS的元数据包括文件的名称、大小、位置等信息。
这些信息由主节点进行管理，并存储在内存中。
为了提高元数据的可靠性，主节点会将元数据的变化记录在日志中，并定期将日志同步到磁盘上。
Hadoop分布式文件系统-架构和设计要点(翻译)
Hadoop分布式文件系统：架构和设计要点(翻译)一、前提和设计目标1、硬件错误是常态，而非异常情况，HDFS可能是有成百上千的server组成，任何一个组件都有可能一直失效，因此错误检测和快速、自动的恢复是HDFS的核心架构目标。
2、跑在HDFS上的应用与一般的应用不同，它们主要是以流式读为主，做批量处理；比之关注数据访问的低延迟问题，更关键的在于数据访问的高吞吐量。
3、HDFS以支持大数据集合为目标，一个存储在上面的典型文件大小一般都在千兆至T字节，一个单一HDFS实例应该能支撑数以千万计的文件。
4、HDFS应用对文件要求的是write-one-read-many访问模型。
一个文件经过创建、写，关闭之后就不需要改变。
这一假设简化了数据一致性问题，使高吞吐量的数据访问成为可能。
典型的如MapReduce框架，或者一个web crawler应用都很适合这个模型。
5、移动计算的代价比之移动数据的代价低。
一个应用请求的计算，离它操作的数据越近就越高效，这在数据达到海量级别的时候更是如此。
将计算移动到数据附近，比之将数据移动到应用所在显然更好，HDFS提供给应用这样的接口。
6、在异构的软硬件平台间的可移植性。
二、Namenode和DatanodeHDFS采用master/slave架构。
一个HDFS集群是有一个Namenode和一定数目的Datanode组成。
Namenode是一个中心服务器，负责管理文件系统的namespace和客户端对文件的访问。
Datanode在集群中一般是一个节点一个，负责管理节点上它们附带的存储。
在内部，一个文件其实分成一个或多个block，这些block存储在Datanode集合里。
Namenode执行文件系统的namespace操作，例如打开、关闭、重命名文件和目录，同时决定block到具体Datanode节点的映射。
Datanode在Namenode的指挥下进行block 的创建、删除和复制。
基于Hadoop的分布式文件存储与计算平台设计与部署
基于Hadoop的分布式文件存储与计算平台设计与部署一、引言随着大数据时代的到来，数据量的爆炸式增长给传统的数据处理方式带来了挑战。
传统的单机存储和计算已经无法满足海量数据的处理需求，因此分布式存储和计算技术应运而生。
Hadoop作为一个开源的分布式存储和计算框架，被广泛应用于大数据领域。
本文将介绍基于Hadoop的分布式文件存储与计算平台的设计与部署。
二、Hadoop简介Hadoop是一个由Apache基金会开发的开源软件框架，用于可靠、可扩展、分布式计算。
它最核心的两个模块是HDFS（Hadoop Distributed File System）和MapReduce。
HDFS是一个高度容错性的分布式文件系统，适合存储大规模数据；MapReduce是一种编程模型，用于将大规模数据集分解成小块进行并行处理。
三、设计与部署步骤1. 硬件环境准备在设计与部署基于Hadoop的分布式文件存储与计算平台之前，首先需要准备好硬件环境。
通常情况下，一个Hadoop集群包括多台服务器，其中包括主节点（NameNode）、从节点（DataNode）以及资源管理节点（ResourceManager）。
主节点负责管理文件系统的命名空间和数据块映射信息，从节点负责存储实际的数据块，资源管理节点负责集群资源的调度和管理。
2. 软件环境准备在硬件环境准备完成后，接下来需要安装配置Hadoop软件。
可以从Apache官网下载最新版本的Hadoop压缩包，并解压到每台服务器上。
然后根据官方文档进行配置，主要包括core-site.xml、hdfs-site.xml、mapred-site.xml和yarn-site.xml等配置文件的修改。
3. HDFS部署（1）NameNode部署NameNode是HDFS的核心组件之一，负责管理文件系统的命名空间和数据块映射信息。
在部署NameNode时，需要配置core-site.xml 和hdfs-site.xml，并启动NameNode服务。
基于Hadoop的大数据平台架构设计
基于Hadoop的大数据平台架构设计随着互联网的普及和各种数字化设备的普及，现代社会已经进入了信息时代。
数据普及了每个角落，数据正在成为信息化时代的核心资源。
数据的速度、容量和多样性已经远远超出了人类处理的极限，人们需要采用更加高效和智能的方式来处理庞大的数据，这时候大数据技术就应运而生了。
而Hadoop的出现，正是为了解决大数据存储和处理的问题，它是目前使用最广泛的大数据平台之一。
本文将介绍如何基于Hadoop构建一个高效的大数据平台，以满足组织和企业的不同需求。
一、Hadoop架构Hadoop由HDFS（分布式文件系统）和MapReduce（分布式计算）构成，其架构如下图所示。
图一：Hadoop架构HDFS是Hadoop的存储组件，它将文件拆分成块（block），并将它们存储在集群的不同节点上。
MapReduce是Hadoop的计算组件，其中Map任务和Reduce任务是将大数据拆分成小块并进行分布式计算的核心算法。
二、大数据平台构建流程1.架构设计在构建大数据平台时，首先应该根据数据的特征、业务需求以及架构要求来设计架构。
根据Hadoop的架构特点，大数据平台的架构可以概括为以下几个层次：（1）数据层：数据是大数据平台的核心，数据层是大数据平台的基础，它包括数据采集、存储、清洗、预处理等环节；在Hadoop中，该层的实现可以通过HDFS、Sqoop、Flume等工具来完成。
（2）计算层：计算层是处理大数据的核心，它可以根据业务需求来编写MapReduce、Hive、Pig等计算框架，以实现对数据的处理。
（3）服务层：服务层是将计算结果整合为可视化、操作性强的服务。
比如通过HBase实现实时查询、通过Impala进行SQL分析等。
（4）接口层：接口层是大数据平台和外部系统进行交互的入口。
通过接口层，外部系统可以调用大数据平台提供的服务，通过数据的交换来实现信息的共享。
（5）安全层：安全层是保障大数据平台安全和合法性的重要保障，它可以通过Kerberos、Apache Ranger、Apache Sentry等工具来实现。
Hadoop大数据平台架构的设计与实现
Hadoop大数据平台架构的设计与实现随着互联网和移动互联网的广泛普及，数据量呈现爆炸式增长。
传统的关系型数据库已经无法胜任海量数据的处理和分析工作。
因此，需要一种新的技术来处理和分析大数据。
Hadoop作为大数据时代的代表性技术，其架构设计和实现具有非常重要的意义。
一、Hadoop平台的架构设计Hadoop平台的核心组件包括分布式文件系统HDFS和分布式计算框架MapReduce。
HDFS用来存储大规模数据，MapReduce用来处理大规模数据。
其中，HDFS是一个具有高度容错性的文件系统，它能够自动将数据分为多个块，并在集群中的多台机器上存储副本。
而MapReduce是一个分布式计算框架，它能够将大规模数据分成多个小块并行处理。
除了HDFS和MapReduce之外，Hadoop平台还包括Hbase、Hive、Sqoop、Pig、Mahout、Flume等开源组件。
这些组件能够帮助用户更方便地利用Hadoop进行数据管理和分析。
Hbase是一个NoSQL数据库，能够存储非常庞大的数据量。
Hive是基于Hadoop的数据仓库，可以帮助用户进行数据的ETL（抽取、转换、加载）操作。
Sqoop是一种工具，能够将数据库的数据导入到Hadoop集群中，或将Hadoop集群中的数据导出到传统数据库中。
Pig是一种分析工具，能够让用户使用简单的脚本来完成数据的查询和分析。
Mahout是一个机器学习框架，它能够帮助用户进行大规模数据的挖掘和分析。
Flume是一个实时数据收集工具，能够将日志等实时数据收集到Hadoop集群中。
总体来说，Hadoop平台的架构设计具有如下特点：（1）分布式存储和计算：Hadoop平台采用分布式存储和计算的方式，可以充分利用集群中的多台机器的计算能力和存储能力。
（2）高可用性：Hadoop平台采用多副本技术，可以在某些节点出现故障的情况下，仍然能够保证数据的安全性和可用性。
（3）基于开放标准：Hadoop平台基于开放的标准和协议开发，能够在不同的系统和平台上运行，具有非常高的灵活性和可扩展性。
解密Java中的分布式文件系统
解密Java中的分布式文件系统分布式文件系统是一种能够在多台计算机上存储和访问文件的系统。
它的主要目标是提供高可用性、可扩展性和容错性。
Java作为一种广泛应用于分布式系统开发的编程语言，也提供了一些强大的工具和框架来支持分布式文件系统的实现。
在Java中，有几个主要的分布式文件系统框架值得关注。
其中最著名的是Apache Hadoop和Apache HDFS。
Hadoop是一个开源的分布式计算框架，它提供了一个分布式文件系统HDFS（Hadoop Distributed File System）。
HDFS是Hadoop 的核心组件之一，它被设计用来存储和处理大规模数据集。
HDFS的设计思想是将大文件切分成多个块，并将这些块分布在多台计算机上。
每个块都有多个副本，分布在不同的计算机上，以提供容错性和高可用性。
HDFS使用了一种称为“NameNode”的中心服务器来管理文件系统的元数据，以及一组称为“DataNode”的数据服务器来存储实际的文件块。
在Java中使用HDFS可以通过Hadoop的Java API来实现。
通过这个API，我们可以编写Java程序来读取和写入HDFS中的文件。
下面是一个简单的例子，演示了如何使用Java API来读取HDFS中的文件：```javaimport org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;public class HDFSExample {public static void main(String[] args) throws Exception {// 创建Hadoop配置对象Configuration conf = new Configuration();// 创建文件系统对象FileSystem fs = FileSystem.get(conf);// 创建文件路径对象Path path = new Path("/user/hadoop/input/file.txt");// 打开文件输入流FSDataInputStream in = fs.open(path);// 读取文件内容byte[] buffer = new byte[1024];int bytesRead = in.read(buffer);// 输出文件内容System.out.println(new String(buffer, 0, bytesRead));// 关闭文件输入流in.close();}}```除了Hadoop和HDFS之外，还有其他一些Java分布式文件系统框架值得一提。
hadoop框架设计原理
hadoop框架设计原理
Hadoop框架设计原理主要包括以下几个方面：
1. 分布式存储：Hadoop采用Hadoop分布式文件系统（HDFS）作为底层的分布式存储系统，将文件切分成多个块并存储在不同的计算节点上，实现数据的分布式存储和高可用性。
2. 分布式计算：Hadoop采用MapReduce计算模型，将计算任
务拆分成Map和Reduce两个阶段。
Map阶段将输入数据拆分
成若干个键值对，并由不同的计算节点并行处理；Reduce阶
段将Map阶段的输出结果归并处理，并最终输出计算结果。
3. 容错机制：Hadoop通过数据冗余和任务重试等机制来保证
系统的高可靠性。
在HDFS中，每个数据块都会默认有多个
副本存储在不同的节点上，当某个节点宕机时可以通过备份的副本进行数据恢复。
而在MapReduce中，任务失败时可以通
过重新执行任务的方式来完成容错。
4. 扩展性：Hadoop框架可以轻松扩展到大规模的集群上，可
以通过增加计算节点和存储节点来提高处理能力和存储容量。
同时，Hadoop的设计原理也考虑了负载均衡的问题，可以动
态地将计算任务分配到空闲的节点上，实现任务的并行处理。
5. 数据局部性：Hadoop框架在调度任务时会尽量将计算任务
分配到存储数据所在的节点上，以减少数据传输的开销。
这是通过节点感知和数据本地性调度策略实现的，可以有效提高计算效率。
综上所述，Hadoop框架设计原理主要包括分布式存储、分布式计算、容错机制、扩展性和数据局部性等方面，通过这些原理可以实现大规模数据的高效处理和存储。
分布式文件系统HDFS的分析
分布式文件系统HDFS的分析Hadoop分布式文件系统（Hadoop Distributed File System，简称HDFS）是一种设计用于在大规模集群上存储和处理大数据的分布式文件系统。
HDFS由Apache Hadoop项目开发，旨在提供高容错性、高可靠性和高可扩展性。
以下是对HDFS的分析。
1.架构和组件：HDFS的架构由两个主要组件组成，即NameNode和DataNode。
NameNode负责管理文件系统的元数据，如文件名、文件权限和目录结构。
DataNode则负责存储实际的文件块数据。
这种架构使HDFS具有高度可扩展性和容错性。
2.数据存储和复制：HDFS将大文件分割成固定大小的块并存储在不同的DataNode上，使数据能够并行处理。
每个块都会进行默认三次复制，以提供容错性。
这意味着即使一些DataNode发生故障，数据仍然可用。
3.数据访问和处理：HDFS采用了一种基于位置的数据访问模型，使数据尽可能地放在离计算节点更近的位置上。
这种模型适用于批处理和大规模数据分析。
HDFS还提供了一套Java API和Shell命令，以方便用户进行文件的读取、写入和管理。
4.容错性和可靠性：HDFS具有高度的容错性和可靠性。
当DataNode发生故障时，NameNode会自动将其数据复制到其他正常运行的DataNode上。
此外，HDFS还具有自动化的错误检测和恢复机制，以确保数据的完整性和可用性。
5.可扩展性：HDFS的设计目标之一是在集群中规模化地存储和处理大数据。
它通过水平扩展来实现这一目标，可以根据需求增加DataNode的数量，以提高存储容量和处理能力。
同时，HDFS还支持数据的动态添加和删除，使得系统能够灵活地适应不断变化的需求。
6.适用场景：HDFS适用于大规模数据存储和处理的场景，特别是适合批处理和离线分析。
由于HDFS具有高度的容错性和可靠性，它也可以用于存储和处理关键数据和重要应用程序。
Hadoop分布式文件系统：架构和设计外文翻译
外文翻译原文来源The Hadoop Distributed File System: Architecture and Design 中文译文Hadoop分布式文件系统：架构和设计姓名 XXXX学号 200708202137英文原文The Hadoop Distributed File System: Architecture and DesignSource：/docs/r0.18.3/hdfs_design.html IntroductionThe Hadoop Distributed File System (HDFS) 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 onlow-cost 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/core/.Assumptions and GoalsHardware FailureHardware 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 components and that each component has a non-trivial probability of failure means that some component of HDFS is always non-functional. Therefore, detection of faults and quick, automatic recovery from them is a core architectural goal of HDFS.Streaming Data AccessApplications 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 lowlatency 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 SetsApplications 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 ModelHDFS applications need a write-once-read-many 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. AMap/Reduce application or a web crawler application fits perfectly with this model. There is a plan to support appending-writes to files in the future.“Moving Computation is Cheaper than Moving Data”A computation 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 network congestion and increases the overall throughput of the system. The assumption is that it is often better to migrate the computation 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 PlatformsHDFS 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 DataNodesHDFS 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 tobe 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 commodity 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 highlyportable 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 NamespaceHDFS supports a traditional hierarchical file organization. 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 and remove files, move a file from one directory to another, or rename a file. HDFS does not yet implement user quotas or access permissions. HDFS does not support hard links or soft links. However, the HDFS architecture does not preclude implementing these features.The NameNode maintains the file system namespace. Any change to the file system namespace or its properties is recorded by the NameNode. An application can specify the number of replicas of a file that should be maintained by HDFS. The number of copies of a file is called the replication factor of that file. This information is stored by the NameNode.Data ReplicationHDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time.The NameNode makes all decisions regarding replication of blocks. It periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster.Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode.Replica Placement: The First Baby StepsThe placement of replicas is critical to HDFS reliability and performance. Optimizing replica placement distinguishes HDFS from most other distributed file systems. This is a feature that needs lots of tuning and experience. The purpose of a rack-aware replica placement policy is to improve data reliability, availability, and network bandwidth utilization. The current implementation for the replica placement policy is a first effort in this direction. The short-term goals of implementing this policy are to validate it on production systems, learn more about its behavior, and build a foundation to test and research more sophisticated policies.Large HDFS instances run on a cluster of computers that commonly spread across many racks. Communication between two nodes in different racks has to go through switches. In most cases, network bandwidth between machines in the same rack is greater than network bandwidth between machines in different racks.The NameNode determines the rack id each DataNode belongs to via the process outlined in Rack Awareness. A simple but non-optimal policy is to place replicas on unique racks. This prevents losing data when an entire rack fails and allows use of bandwidth from multiple racks when reading data. This policy evenly distributes replicas in the cluster which makes it easy to balance load on component failure. However, this policy increases the cost of writes because a write needs to transfer blocks to multiple racks.For the common case, when the replication factor is three, HDFS’s placement policy is to put one replica on one node in the local rack, another on a different node in the local rack, and the last on a different node in a different rack. This policy cuts the inter-rack write traffic which generally improves write performance. The chance of rack failure is far less than that of node failure; this policy does not impact data reliability and availability guarantees. However, it does reduce the aggregate network bandwidth used when reading data since a block is placed in only two unique racks rather than three. With this policy, the replicas of a file do not evenly distribute across the racks. One third of replicas are on one node, two thirds of replicas are on one rack, and the other third are evenly distributed across the remaining racks. This policy improves write performance without compromising data reliability or read performance.The current, default replica placement policy described here is a work in progress. Replica SelectionTo minimize global bandwidth consumption and read latency, HDFS tries to satisfy a read request from a replica that is closest to the reader. If there exists a replica on the same rack as the reader node, then that replica is preferred to satisfy the read request. If angg/ HDFS cluster spans multiple data centers, then a replica that is resident in the local data center is preferred over any remote replica.SafemodeOn startup, the NameNode enters a special state called Safemode. Replication of data blocks does not occur when the NameNode is in the Safemode state. The NameNode receives Heartbeat and Blockreport messages from the DataNodes. A Blockreport contains the list of data blocks that a DataNode is hosting. Each block has a specified minimum number of replicas. A block is considered safely replicated when the minimum number of replicas of that data block has checked in with the NameNode. After a configurable percentage of safely replicated data blocks checks in with the NameNode (plus an additional 30 seconds), the NameNode exits the Safemode state. It then determines the list of data blocks (if any) that still have fewer than the specified number of replicas. The NameNode then replicates these blocks to other DataNodes.The Persistence of File System MetadataThe HDFS namespace is stored by the NameNode. The NameNode uses a transaction log called the EditLog to persistently record every change that occurs to file system metadata. For example, creating a new file in HDFS causes the NameNode to insert a record into the EditLog indicating this. Similarly, changing the replication factor of a file causes a new record to be inserted into the EditLog. The NameNode uses a file in its local host OS file system to store the EditLog. The entire file system namespace, including the mapping of blocks to files and file system properties, is stored in a file called the FsImage. The FsImage is stored as a file in the NameNode’s local file system too.The NameNode keeps an image of the entire file system namespace and file Blockmap in memory. This key metadata item is designed to be compact, such that a NameNode with 4 GB of RAM is plenty to support a huge number of files and directories. When the NameNode starts up, it reads the FsImage and EditLog from disk, applies all the transactions from the EditLog to the in-memory representation of the FsImage, and flushes out this new version into a new FsImage on disk. It can then truncate the old EditLog because its transactions have been applied to the persistent FsImage. This process is called a checkpoint. In the current implementation, a checkpoint only occurs when the NameNode starts up. Work is in progress to support periodic checkpointing in the near future.The DataNode stores HDFS data in files in its local file system. The DataNode has no knowledge about HDFS files. It stores each block of HDFS data in a separatefile in its local file system. The DataNode does not create all files in the same directory. Instead, it uses a heuristic to determine the optimal number of files per directory and creates subdirectories appropriately. It is not optimal to create all local files in the same directory because the local file system might not be able to efficiently support a huge number of files in a single directory. When a DataNode starts up, it scans through its local file system, generates a list of all HDFS data blocks that correspond to each of these local files and sends this report to the NameNode: this is the Blockreport.The Communication ProtocolsAll HDFS communication protocols are layered on top of the TCP/IP protocol. A client establishes a connection to a configurable TCP port on the NameNode machine. It talks the ClientProtocol with the NameNode. The DataNodes talk to the NameNode using the DataNode Protocol. A Remote Procedure Call (RPC) abstraction wraps both the Client Protocol and the DataNode Protocol. By design, the NameNode never initiates any RPCs. Instead, it only responds to RPC requests issued by DataNodes or clients.RobustnessThe primary objective of HDFS is to store data reliably even in the presence of failures. The three common types of failures are NameNode failures, DataNode failures and network partitions.Data Disk Failure, Heartbeats and Re-ReplicationEach DataNode sends a Heartbeat message to the NameNode periodically. A network partition can cause a subset of DataNodes to lose connectivity with the NameNode. The NameNode detects this condition by the absence of a Heartbeat message. The NameNode marks DataNodes without recent Heartbeats as dead and does not forward any new IO requests to them. Any data that was registered to a dead DataNode is not available to HDFS any more. DataNode death may cause the replication factor of some blocks to fall below their specified value. The NameNode constantly tracks which blocks need to be replicated and initiates replication whenever necessary. The necessity for re-replication may arise due to many reasons: a DataNode may become unavailable, a replica may become corrupted, a hard disk on a DataNode may fail, or the replication factor of a file may be increased.Cluster RebalancingThe HDFS architecture is compatible with data rebalancing schemes. A scheme might automatically move data from one DataNode to another if the free space on a DataNode falls below a certain threshold. In the event of a sudden high demand for a particular file, a scheme might dynamically create additional replicas and rebalance other data in the cluster. These types of data rebalancing schemes are not yet implemented.Data IntegrityIt is possible that a block of data fetched from a DataNode arrives corrupted. This corruption can occur because of faults in a storage device, network faults, or buggy software. The HDFS client software implements checksum checking on the contents of HDFS files. When a client creates an HDFS file, it computes a checksum of each block of the file and stores these checksums in a separate hidden file in the same HDFS namespace. When a client retrieves file contents it verifies that the data it received from each DataNode matches the checksum stored in the associated checksum file. If not, then the client can opt to retrieve that block from another DataNode that has a replica of that block.Metadata Disk FailureThe FsImage and the EditLog are central data structures of HDFS. A corruption of these files can cause the HDFS instance to be non-functional. For this reason, the NameNode can be configured to support maintaining multiple copies of the FsImage and EditLog. Any update to either the FsImage or EditLog causes each of the FsImages and EditLogs to get updated synchronously. This synchronous updating of multiple copies of the FsImage and EditLog may degrade the rate of namespace transactions per second that a NameNode can support. However, this degradation is acceptable because even though HDFS applications are very data intensive in nature, they are not metadata intensive. When a NameNode restarts, it selects the latest consistent FsImage and EditLog to use.The NameNode machine is a single point of failure for an HDFS cluster. If the NameNode machine fails, manual intervention is necessary. Currently, automatic restart and failover of the NameNode software to another machine is not supported.SnapshotsSnapshots support storing a copy of data at a particular instant of time. One usage of the snapshot feature may be to roll back a corrupted HDFS instance to a previously known good point in time. HDFS does not currently support snapshots but will in a future release.Data OrganizationData BlocksHDFS is designed to support very large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds. HDFS supports write-once-read-many semantics on files. A typical block size used by HDFS is 64 MB. Thus, an HDFS file is chopped up into 64 MB chunks, and if possible, each chunk will reside on a different DataNode.StagingA client request to create a file does not reach the NameNode immediately. In fact, initially the HDFS client caches the file data into a temporary local file. Application writes are transparently redirected to this temporary local file. When the local file accumulates data worth over one HDFS block size, the client contacts the NameNode. The NameNode inserts the file name into the file system hierarchy and allocates a data block for it. The NameNode responds to the client request with the identity of the DataNode and the destination data block. Then the client flushes the block of data from the local temporary file to the specified DataNode. When a file is closed, the remaining un-flushed data in the temporary local file is transferred to the DataNode. The client then tells the NameNode that the file is closed. At this point, the NameNode commits the file creation operation into a persistent store. If the NameNode dies before the file is closed, the file is lost.The above approach has been adopted after careful consideration of target applications that run on HDFS. These applications need streaming writes to files. If a client writes to a remote file directly without any client side buffering, the network speed and the congestion in the network impacts throughput considerably. This approach is not without precedent. Earlier distributed file systems, e.g. AFS, have used client side caching to improve performance. APOSIX requirement has been relaxed to achieve higher performance of data uploads.Replication PipeliningWhen a client is writing data to an HDFS file, its data is first written to a local file as explained in the previous section. Suppose the HDFS file has a replication factor of three. When the local file accumulates a full block of user data, the client retrieves a list of DataNodes from the NameNode. This list contains the DataNodes that will host a replica of that block. The client then flushes the data block to the first DataNode. The first DataNode starts receiving the data in small portions (4 KB), writes each portion to its local repository and transfers that portion to the second DataNode in the list. The second DataNode, in turn starts receiving each portion of the data block, writes that portion to its repository and then flushes that portion to the third DataNode. Finally, the third DataNode writes the data to its local repository. Thus, a DataNode can be receiving data from the previous one in the pipeline and at the same time forwarding data to the next one in the pipeline. Thus, the data is pipelined from one DataNode to the next.AccessibilityHDFS can be accessed from applications in many different ways. Natively, HDFS provides a Java API for applications to use. A C language wrapper for this Java API is also available. In addition, an HTTP browser can also be used to browse the files of an HDFS instance. Work is in progress to expose HDFS through the WebDAV protocol.FS ShellHDFS allows user data to be organized in the form of files and directories. It provides a commandline interface called FS shell that lets a user interact with the data in HDFS. The syntax of this command set is similar to other shells (e.g. bash, csh) that users are already familiar with. Here are some sample action/command pairs:FS shell is targeted for applications that need a scripting language to interact with the stored data.DFSAdminThe DFSAdmin command set is used for administering an HDFS cluster. These are commands that are used only by an HDFS administrator. Here are some sample action/command pairs:Browser InterfaceA typical HDFS install configures a web server to expose the HDFS namespace through a configurable TCP port. This allows a user to navigate the HDFS namespace and view the contents of its files using a web browser.Space ReclamationFile Deletes and UndeletesWhen a file is deleted by a user or an application, it is not immediately removed from HDFS. Instead, HDFS first renames it to a file in the /trash directory. The file can be restored quickly as long as it remains in /trash. A file remains in/trash for a configurable amount of time. After the expiry of its life in /trash, the NameNode deletes the file from the HDFS namespace. The deletion of a file causes the blocks associated with the file to be freed. Note that there could be an appreciable time delay between the time a file is deleted by a user and the time of the corresponding increase in free space in HDFS.A user can Undelete a file after deleting it as long as it remains in the /trash directory. If a user wants to undelete a file that he/she has deleted, he/she can navigate the /trash directory and retrieve the file. The /trash directory contains only the latest copy of the file that was deleted. The /trash directory is just like any other directory with one special feature: HDFS applies specified policies to automatically delete files from this directory. The current default policy is to delete files from /trash that are more than 6 hours old. In the future, this policy will be configurable through a well defined interface.Decrease Replication FactorWhen the replication factor of a file is reduced, the NameNode selects excess replicas that can be deleted. The next Heartbeat transfers this information to the DataNode. The DataNode then removes the corresponding blocks and the corresponding free space appears in the cluster. Once again, there might be a time delay between the completion of the setReplication API call and the appearance of free space in the cluster.中文译本原文地址:/docs/r0.18.3/hdfs_design.html一、引言Hadoop分布式文件系统(HDFS)被设计成适合运行在通用硬件(commodity hardware)上的分布式文件系统。
基于Hadoop的分布式文件系统设计与优化
基于Hadoop的分布式文件系统设计与优化第一章：引言随着数据量的不断增长，单机存储和计算都已经无法满足大规模数据的需求。
分布式系统已经成为了处理大规模数据的一种重要方式。
而分布式文件系统则是分布式系统的重要组成部分之一，其设计和优化对于整个系统的性能和可靠性都有着重要的影响。
本文将介绍基于Hadoop的分布式文件系统的设计和优化，包括系统架构、存储模型、数据传输和调度等方面的设计和优化，旨在帮助读者更好地理解和应用分布式文件系统。
第二章：Hadoop分布式文件系统概述Hadoop分布式文件系统是一款开源的分布式文件系统，其核心是基于Google的GFS文件系统。
Hadoop分布式文件系统被广泛应用于大规模数据的处理和存储，如Facebook、Yahoo等公司的大数据处理平台。
Hadoop分布式文件系统的主要特点包括：1.高可靠性：通过数据冗余和心跳检测等手段确保数据的可靠性。
2.高性能：基于数据本地化和分布式存储等方式提高了数据读写的性能。
3.可扩展性：支持集群规模的扩展，可以应对不同规模的数据处理需求。
4.兼容性：支持多种操作系统和文件格式，方便和其他系统的集成。
第三章：Hadoop分布式文件系统架构设计Hadoop分布式文件系统的架构主要由NameNode、DataNode和客户端三个部分组成。
Node：负责存储文件元数据的信息，如文件名、目录结构、块列表等。
NameNode还负责文件系统的命名空间管理和访问控制等职责。
2.DataNode：负责存储数据块的实际数据内容。
每个DataNode 都会向NameNode定期发送心跳信号和块报告，以更新和维护文件系统元数据的信息。
3.客户端：通过和NameNode和DataNode通讯实现文件的读取和写入等操作。
Hadoop分布式文件系统的架构可以实现存储空间的动态扩展和文件权限的管理等功能。
第四章：Hadoop分布式文件系统存储模型设计Hadoop分布式文件系统的存储模型包括管理空间的文件系统和存储数据的块模型。
基于Hadoop的分布式文件系统设计
基于Hadoop的分布式文件系统设计Hadoop是一个被广泛应用于大数据处理的分布式框架，其核心组成部分之一就是分布式文件系统（Hadoop Distributed File System，简称HDFS）。
HDFS作为Hadoop的文件系统，其功能强大，可靠性高，并且可以在物理服务器上分布式存储大文件。
在本文中，我们将讨论HDFS的基本原理及其核心组件，然后深入探讨在Hadoop上如何设计一个高效的分布式文件系统。
1. Hadoop分布式文件系统概述Hadoop分布式文件系统（HDFS）是一个高度可靠的文件系统，设计用于存储大型文件和工作负载。
HDFS是一个master/slave架构，它主要由两个组成部分：NameNode和DataNode。
这两个组件都是用Java编写的，可以在相同的物理服务器上或在不同的物理服务器上运行。
NameNode负责存储文件系统的元数据，包括文件名称、文件路径、文件的块大小、块所在的DataNode等信息。
DataNode实际存储文件的块和文件的数据。
当一个客户端请求某一个文件时，NameNode会提供给客户端文件所在的DataNode 的位置，然后客户端通过DataNode读取该文件。
在HDFS中，文件分成多个块，每个块的默认大小为128MB。
当一个文件被保存在HDFS中时，它会被分割成多个块，并将这些块分布式地保存在不同的DataNode上。
这种方法确保了文件的冗余性和可靠性，同时还支持Hadoop进行高并发的工作负载。
2. 分布式文件系统设计的核心考虑因素在设计基于Hadoop的分布式文件系统时，需要考虑以下几个核心因素：2.1 可伸缩性：分布式文件系统应该能够管理大量的文件和数据，并能够在工作负载的压力下增加存储和处理能力。
2.2 可靠性：分布式文件系统需要能够支持数据备份和恢复，保证数据的完整性和可靠性。
2.3 性能：分布式文件系统应该能够快速地存储和检索数据，并且能够在高并发负载下高效地工作。
Hadoop分布式文件系统的配置与使用教程
Hadoop分布式文件系统的配置与使用教程Hadoop分布式文件系统（Hadoop Distributed File System，简称HDFS）是一种适用于大数据处理的可靠、安全且高扩展性的分布式文件系统。
它能够将大容量的数据分散存储在集群的多台计算机上，并提供高效的数据访问方式。
本文将为您提供关于Hadoop 分布式文件系统的配置和使用教程。
**1. 配置Hadoop集群**首先，我们需要准备一个Hadoop集群，该集群包括主节点和若干个从节点。
主节点负责协调和管理整个集群，而从节点则负责存储和处理数据。
2. 安装Hadoop在配置Hadoop集群之前，我们需要将Hadoop安装在每个节点上。
您可以从Hadoop官方网站下载最新版本的Hadoop。
下载完成后，解压缩文件并将其移动到您选择的安装目录。
3. 配置Hadoop集群文件在配置Hadoop集群之前，您需要对一些配置文件进行修改。
这些配置文件位于Hadoop的安装目录中的“etc/hadoop”文件夹中。
以下是一些需要注意的主要配置文件：- core-site.xml: 设置Hadoop核心属性，如HDFS的命名节点和文件系统的URI。
- hdfs-site.xml: 配置HDFS的属性，如数据块大小、副本数量等。
- mapred-site.xml：配置Hadoop MapReduce属性，如MapReduce框架的任务分配方式等。
- yarn-site.xml：配置Hadoop资源管理器(YARN)属性，如内存和CPU分配等。
配置完成后，将这些文件复制到Hadoop集群的每个节点。
4. 格式化文件系统在配置完成后，我们需要格式化HDFS文件系统以准备存储数据。
在主节点上, 打开终端并使用以下命令格式化文件系统：```hadoop namenode -format```5. 启动Hadoop集群在所有节点上启动Hadoop集群。
首先进入Hadoop的安装目录并输入以下命令：```start-dfs.sh```这个命令将启动HDFS服务。
基于Hadoop的分布式文件系统设计
基于Hadoop的分布式文件系统设计分布式文件系统（Distributed File System，简称DFS）是一种存储大规模数据的解决方案，它旨在将数据分布式存储在多个计算机节点上，并提供高可靠性、可扩展性和高性能的数据访问。
基于Hadoop的分布式文件系统设计采用了Hadoop Distributed File System（HDFS），它是Hadoop生态系统的核心组件之一，为大规模数据处理提供了可靠的分布式存储支持。
本文将介绍HDFS的基本概念和架构，以及其在分布式文件系统设计中的应用。
首先，让我们来了解HDFS的基本概念。
HDFS是一种以块（block）为单位进行数据存储的文件系统，在文件上传时将文件分割成多个固定大小的数据块，并分布式存储在不同的计算机节点上。
每个数据块都有多个副本存储在不同的节点上，以增强数据的可靠性和容错性。
HDFS还提供了高吞吐量的数据读写操作，适合处理大规模数据集。
HDFS的架构包括两个核心组件：NameNode和DataNode。
NameNode负责管理文件系统的命名空间和元数据信息，它记录了文件和目录的结构、权限和位置信息，并负责处理客户端的元数据操作请求。
DataNode负责存储和管理实际的数据块，它接收来自客户端和NameNode的读写请求，并根据指令进行相应的数据块操作。
在分布式文件系统设计中，基于Hadoop的分布式文件系统可以提供高可靠性和可用性的数据存储方案。
通过将数据分布式存储在多个节点上，并多副本备份，即使某个节点发生故障，系统仍然可以继续运行，不会丢失数据。
此外，HDFS还支持自动故障监测和修复，能够在发生节点故障时自动恢复数据的完整性。
另外，基于Hadoop的分布式文件系统设计还具有良好的可扩展性。
Hadoop集群可以根据需求进行水平扩展，即通过增加计算机节点来增加存储容量和处理能力。
在节点增加或减少时，HDFS能够自动进行数据块的重新分布和数据副本的重新复制，以保证整个系统的负载均衡和数据一致性。
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H o o p分布式文件系统架构和设计Hessen was revised in January 2021Hadoop分布式文件系统：架构和设计引言云计算（cloud computing)，由位于网络上的一组服务器把其计算、存储、数据等资源以服务的形式提供给请求者以完成信息处理任务的方法和过程。
在此过程中被服务者只是提供需求并获取服务结果，对于需求被服务的过程并不知情。
同时服务者以最优利用的方式动态地把资源分配给众多的服务请求者，以求达到最大效益。
Hadoop分布式文件系统(HDFS)被设计成适合运行在通用硬件(commodity hardware)上的分布式文件系统。
它和现有的分布式文件系统有很多共同点。
但同时，它和其他的分布式文件系统的区别也是很明显的。
HDFS是一个高度容错性的系统，适合部署在廉价的机器上。
HDFS能提供高吞吐量的数据访问，非常适合大规模数据集上的应用。
一前提和设计目标1 hadoop和云计算的关系云计算由位于网络上的一组服务器把其计算、存储、数据等资源以服务的形式提供给请求者以完成信息处理任务的方法和过程。
针对海量文本数据处理,为实现快速文本处理响应,缩短海量数据为辅助决策提供服务的时间,基于Hadoop云计算平台,建立HDFS分布式文件系统存储海量文本数据集,通过文本词频利用MapReduce原理建立分布式索引,以分布式数据库HBase存储关键词索引,并提供实时检索,实现对海量文本数据的分布式并行处理.实验结果表明,Hadoop 框架为大规模数据的分布式并行处理提供了很好的解决方案。
2 流式数据访问运行在HDFS上的应用和普通的应用不同，需要流式访问它们的数据集。
HDFS的设计中更多的考虑到了数据批处理，而不是用户交互处理。
比之数据访问的低延迟问题，更关键的在于数据访问的高吞吐量。
3 大规模数据集运行在HDFS上的应用具有很大的数据集。
HDFS上的一个典型文件大小一般都在G字节至T字节。
因此，HDFS被调节以支持大文件存储。
它应该能提供整体上高的数据传输带宽，能在一个集群里扩展到数百个节点。
一个单一的HDFS实例应该能支撑数以千万计的文件。
4 简单的一致性模型HDFS应用需要一个“一次写入多次读取”的文件访问模型。
一个文件经过创建、写入和关闭之后就不需要改变。
这一假设简化了数据一致性问题，并且使高吞吐量的数据访问成为可能。
Map/Reduce应用或者网络爬虫应用都非常适合这个模型。
目前还有计划在将来扩充这个模型，使之支持文件的附加写操作。
5 异构软硬件平台间的可移植性HDFS在设计的时候就考虑到平台的可移植性。
这种特性方便了HDFS作为大规模数据应用平台的推广。
6 硬件错误硬件错误是常态而不是异常。
HDFS可能由成百上千的服务器所构成，每个服务器上存储着文件系统的部分数据。
我们面对的现实是构成系统的组件数目是巨大的，而且任一组件都有可能失效，这意味着总是有一部分HDFS的组件是不工作的。
因此错误检测和快速、自动的恢复是HDFS最核心的架构目标。
二HDFS重要名词解释HDFS采用master/slave架构。
一个HDFS集群是由一个Namenode和一定数目的Datanodes组成。
Namenode是一个中心服务器，负责管理文件系统的名字空间(namespace)以及客户端对文件的访问。
集群中的Datanode一般是一个节点一个，负责管理它所在节点上的存储。
HDFS暴露了文件系统的名字空间，用户能够以文件的形式在上面存储数据。
从内部看，一个文件其实被分成一个或多个数据块，这些块存储在一组Datanode上。
Namenode执行文件系统的名字空间操作，比如打开、关闭、重命名文件或目录。
它也负责确定数据块到具体Datanode节点的映射。
Datanode负责处理文件系统客户端的读写请求。
在Namenode的统一调度下进行数据块的创建、删除和复制。
集群中单一Namenode的结构大大简化了系统的架构。
Namenode是所有HDFS元数据的仲裁者和管理者，这样，用户数据永远不会流过Namenode。
1 Namenode（1）HDFS的守护程序。
（2）记录文件时如何分割成数据块的，以及这些数据块被存数到那些借点上。
（3）对内存和I/O进行集中管理（4）namenode是单个节点，发生故障将使集群崩溃。
2 secondary Namenode（1）监控HDFS状态的辅助后台程序（2）secondary Namenode与namenode通讯，定期保存HDFS元数据快照。
（3）当namenode故障时候，secondary Namenode可以作为备用namenode使用。
3 DatanodeDatanode将HDFS数据以文件的形式存储在本地的文件系统中，它并不知道有关HDFS文件的信息。
它把每个HDFS数据块存储在本地文件系统的一个单独的文件中。
Datanode并不在同一个目录创建所有的文件，实际上，它用试探的方法来确定每个目录的最佳文件数目，并且在适当的时候创建子目录。
4 jobTracker（1）用于处理作业的后台程序（2）决定有哪些文件参与处理，然后切割task并分配节点（3）监控task，重启失败的task（4）每个集群只有唯一一个jobTracker，位于Master。
5 TaskTracker（1）位于slave节点上，与datanode结合（2）管理各自节点上的task（由jobTracker分配）（3）每个节点只有一个tasktracker，但一个tasktracker可以启动多个JVM,（4）与jobtracker交互三 HDFS数据存储1 HDFS数据存储特点（1）HDFS被设计成能够在一个大集群中跨机器可靠地存储超大文件。
（2）它将每个文件存储成一系列的数据块，除了最后一个，所有的数据块都是同样大小的，数据块的大小是可以配置的。
（3）文件的所有数据块都会有副本。
每个副本系数都是可配置的。
（4）应用程序可以指定某个文件的副本数目。
（5）HDFS中的文件都是一次性写入的，并且严格要求在任何时候只能有一个写入者。
2 心跳机制Namenode全权管理数据块的复制，它周期性地从集群中的每个Datanode接收心跳信号和块状态报告(Blockreport)。
接收到心跳信号意味着该Datanode节点工作正常。
块状态报告包含了一个该Datanode上所有数据块的列表。
3 副本存放副本的存放是HDFS可靠性和性能的关键。
HDFS采用一种称为机架感知(rack-aware)的策略来改进数据的可靠性、可用性和网络带宽的利用率。
4 副本选择为了降低整体的带宽消耗和读取延时，HDFS会尽量让读取程序读取离它最近的副本。
如果在读取程序的同一个机架上有一个副本，那么就读取该副本。
如果一个HDFS集群跨越多个数据中心，那么客户端也将首先读本地数据中心的副本。
5 安全模式Namenode启动后会进入一个称为安全模式的特殊状态。
处于安全模式的Namenode是不会进行数据块的复制的。
Namenode从所有的 Datanode接收心跳信号和块状态报告。
四 HDFS数据健壮性HDFS的主要目标就是即使在出错的情况下也要保证数据存储的可靠性。
常见的三种出错情况是：Namenode出错, Datanode出错和网络割裂(network partitions)。
1 磁盘数据错误，心跳检测和重新复制每个Datanode节点周期性地向Namenode发送心跳信号。
网络割裂可能导致一部分Datanode跟Namenode失去联系。
Namenode通过心跳信号的缺失来检测这一情况，并将这些近期不再发送心跳信号Datanode标记为宕机，不会再将新的IO请求发给它们。
任何存储在宕机Datanode上的数据将不再有效。
Datanode的宕机可能会引起一些数据块的副本系数低于指定值，Namenode不断地检测这些需要复制的数据块，一旦发现就启动复制操作。
在下列情况下，可能需要重新复制：某个Datanode节点失效，某个副本遭到损坏，Datanode上的硬盘错误，或者文件的副本系数增大。
2 集群均衡HDFS的架构支持数据均衡策略。
如果某个Datanode节点上的空闲空间低于特定的临界点，按照均衡策略系统就会自动地将数据从这个Datanode移动到其他空闲的Datanode。
当对某个文件的请求突然增加，那么也可能启动一个计划创建该文件新的副本，并且同时重新平衡集群中的其他数据。
这些均衡策略目前还没有实现。
3 数据完整性从某个Datanode获取的数据块有可能是损坏的，损坏可能是由Datanode的存储设备错误、网络错误或者软件bug造成的。
HDFS客户端软件实现了对HDFS文件内容的校验和(checksum)检查。
当客户端创建一个新的HDFS文件，会计算这个文件每个数据块的校验和，并将校验和作为一个单独的隐藏文件保存在同一个HDFS名字空间下。
当客户端获取文件内容后，它会检验从Datanode获取的数据跟相应的校验和文件中的校验和是否匹配，如果不匹配，客户端可以选择从其他Datanode获取该数据块的副本。
4 元数据磁盘错误FsImage和Editlog是HDFS的核心数据结构。
如果这些文件损坏了，整个HDFS实例都将失效。
因而，Namenode可以配置成支持维护多个FsImage和Editlog的副本。
任何对FsImage或者Editlog的修改，都将同步到它们的副本上。
这种多副本的同步操作可能会降低Namenode 每秒处理的名字空间事务数量。
然而这个代价是可以接受的，因为即使HDFS的应用是数据密集的，它们也非元数据密集的。
当Namenode重启的时候，它会选取最近的完整的FsImage和Editlog来使用。
Namenode是HDFS集群中的单点故障(single point of failure)所在。
如果Namenode机器故障，是需要手工干预的。
目前，自动重启或在另一台机器上做Namenode故障转移的功能还没实现。
5 快照快照支持某一特定时刻的数据的复制备份。
利用快照，可以让HDFS在数据损坏时恢复到过去一个已知正确的时间点。
HDFS目前还不支持快照功能，但计划在将来的版本进行支持。