分布式文件系统架构设计
使用分布式文件系统构建高可扩展性存储架构(二)
分布式文件系统是一种将数据分散存储在多个物理节点上的系统。
它通过将文件进行切块，并分散存储在多个节点上，以实现高可扩展性的存储架构。
本文将探讨分布式文件系统构建高可扩展性存储架构的方法和优势。
一、分布式文件系统的基本原理在传统的中心化文件系统中，文件存储在单个服务器上，对于大规模的数据存储和处理需求来说，这种架构很难满足高并发和大规模存储的要求。
而分布式文件系统通过将文件切块并分配到多个节点上进行存储，不仅可以提供更高的存储容量，还可以提供更高的性能和可靠性。
二、数据切块和冗余存储分布式文件系统将文件切分为较小的块，并将这些块分散存储在多个节点上。
这样做的好处在于，首先可以提高存储容量，可以根据需求动态地添加新节点，从而实现存储容量的无限扩展。
其次，将文件切块存储还可以提高系统的读写性能，因为多个节点可以同时进行读写操作，从而提高了存取速度。
同时，分布式文件系统还会对切块后的数据进行冗余存储，即将文件块复制到多个节点上。
这样做的好处在于，即使某个节点发生故障，系统仍然可以从其他节点上获取文件块，保证了数据的可靠性。
三、数据分发和负载均衡分布式文件系统会将文件块按照一定的规则分发到多个节点上进行存储，这样做的好处在于，可以实现数据的分布式存储和访问。
当用户请求某个文件时，系统可以根据文件块的位置信息，快速定位到存储该文件块的节点，从而提高了读取速度。
而对于写操作，分布式文件系统则会根据负载情况，动态地将文件块分配到相对空闲的节点上进行存储，从而实现了负载均衡，提高了系统的可扩展性。
四、数据一致性和故障容错在分布式存储系统中，数据一致性和故障容错是非常重要的。
分布式文件系统通过采用一致性协议，例如Paxos或Raft等，保证了多个节点之间的数据一致性。
当某个节点发生故障时，系统可以自动将该节点上的数据迁移到其他正常节点上，实现了故障容错，保证了数据的可靠性。
五、可扩展性和性能优势相比于传统的中心化存储架构，分布式文件系统具有更好的可扩展性和性能优势。
基于raft共识算法的分布式文件系统设计与实现
文章标题：基于Raft共识算法的分布式文件系统设计与实现一、引言在当今互联网时代，分布式系统已经成为了各种应用的重要组成部分。
其中，分布式文件系统作为分布式系统的重要应用之一，其设计与实现对于保障数据安全、提高系统可靠性和性能具有重要意义。
本文将基于Raft共识算法，探讨分布式文件系统的设计与实现。
二、分布式文件系统概述分布式文件系统是指将文件存储在多台计算机上，并通过网络进行访问和管理的系统。
它具有数据分布均衡、容错性强、可扩展性好等特点，被广泛应用于各种大型系统中。
然而，分布式文件系统的设计与实现面临着诸多挑战，如一致性、容错性、性能等问题。
三、Raft共识算法简介Raft是一种为分布式系统设计的共识算法，它可以保证系统中多个节点之间的一致性，并在故障发生时能快速选举出新的领导者，从而保证系统的稳定运行。
Raft算法包括领导者选举、日志复制、安全性等机制，使得其在分布式文件系统中具有重要的应用价值。
四、基于Raft的分布式文件系统设计1. 领导者选举：在分布式文件系统中，各个节点通过Raft算法进行领导者选举，确保系统中只有一个领导者进行控制和管理。
2. 日志复制：分布式文件系统中的数据通过Raft算法进行日志复制，保证数据在各个节点之间的一致性。
3. 安全性：Raft算法通过多数派决策的机制，保证系统在出现故障时能够快速选举出新的领导者，从而保障系统的安全性。
五、基于Raft的分布式文件系统实现基于Raft算法的分布式文件系统在实现时需要考虑到节点间通信、数据一致性、故障恢复等问题。
通过使用分布式一致性协议、高可用存储以及容错机制等技术，可以实现一个高性能、高可靠性的分布式文件系统。
六、个人观点与总结从上述分析可知，基于Raft共识算法的分布式文件系统设计与实现是一个复杂而重要的课题。
在实际应用中，我们需要充分考虑系统的容错性、一致性和性能，结合具体业务场景进行合理的设计与实现。
随着分布式系统领域的不断发展，我们也需要持续关注新的技术和算法，不断完善和优化分布式文件系统的设计与实现。
中小规模分布式文件系统集群构架的优化方案
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如何进行高效的分布式文件系统设计
如何进行高效的分布式文件系统设计随着互联网的飞速发展，数据量的爆炸式增长以及大型企业系统的普及，分布式文件系统已成为了数据存储和管理的重要方式。
从HDFS到GFS等知名分布式文件系统的诞生，各种分布式系统逐渐开始崭露头角，为企业和个人带来了更加高效稳定和可靠的数据存储解决方案。
如何进行高效的分布式文件系统设计，是当前企业和技术人员面临的一大难题。
本文将从以下几个方面进行探讨。
一、高效的设计目标设计分布式文件系统最根本的目标是数据能够尽可能地平均分布在各个节点上，同时保证数据的完整性和可靠性。
此外，高效的分布式文件系统设计还需要具备高可扩展性、高吞吐量、低延迟等特性。
因此，在设计阶段需要考虑诸多因素，包括存储映像的放置策略、块的大小、数据节点的备份数量、故障恢复机制等等，考虑周全，合理设计，才能够创造出高效可靠的分布式文件系统。
二、数据分布策略在分布式存储中，数据分布的策略是影响系统性能的重要因素。
为了实现数据的平均分布，我们需要引入分布式哈希表的概念，即通过哈希算法将文件的内容映射到一个特定的节点上。
在哈希冲突的情况下，我们需要采用一些特殊的冲突解决方法，例如Chord网络中采用的一致性哈希算法，通过虚拟节点的方式避免单点故障，保证系统能够在故障时保持高可用性。
此外，为了避免数据热点问题，在实现数据分布策略时需要遵循“主分区和副本分区”的原则，即主分区只存储一个副本，而副本分区则可以有多个副本存储到不同的节点上，以此来避免系统因用户频繁读取或写入数据而产生的热点问题。
三、故障恢复机制故障恢复机制是分布式文件系统中比较重要的部分之一。
由于每个节点都会存储一部分数据，当节点故障时，需要通过一些方式来保证数据不丢失。
因此，在分布式文件系统的设计过程中，我们需要考虑到节点宕机、网络故障等各种应急情况，确保系统能够在极端情况下保持数据的完整性和可靠性。
目前常见的故障恢复机制包括数据备份、数据镜像以及恢复点机制等，其中备份和镜像是最常用的方式，能够保障数据完整性和可靠性，但同时也牺牲了一些系统的性能。
如何进行软件分布式部署和系统架构设计
如何进行软件分布式部署和系统架构设计随着信息技术的发展，软件开发已经成为了现代企业必不可少的一部分。
而随着软件规模的扩大，单一机器的能力往往无法满足需求，因此分布式部署已经成为了软件开发中的重要问题。
本文将探讨如何进行软件分布式部署和系统架构设计。
一、软件分布式部署所谓分布式系统是指将任务分散到不同的计算机上，并通过计算机之间的通信进行协同工作的一种计算系统。
而软件分布式部署就是将软件部署到分布式系统中运行，以实现更高效和更灵活的服务。
1.1 选择适合的分布式系统架构分布式系统架构有很多种，比如中心节点、P2P、客户端-服务器等。
在进行软件分布式部署时，需要根据业务需求选择适合的分布式系统架构，以保证软件的高效和稳定。
1.2 保证数据一致性分布式系统中，由于数据存储在不同的计算机上，如何保证数据的一致性也是一个重要的问题。
为了保证数据一致性，可以采用主从复制、分布式事务等技术。
1.3 实现负载均衡由于分布式系统中计算机数量较多，任务的负载分布不均往往会导致某些计算机负载过重，从而影响整个系统的性能。
因此，在进行软件分布式部署时，需要实现负载均衡来避免出现负载不均的情况。
1.4 保证系统的安全性分布式系统中，由于系统架构复杂，安全问题往往会更为突出。
因此，在进行软件分布式部署时，需要采取一些措施来保证系统的安全性，比如防火墙、加密技术等。
二、系统架构设计系统架构设计是软件开发过程中不可忽视的一环。
好的系统架构设计能够保证软件的可维护性、可扩展性和可靠性，从而提高软件的使用价值。
2.1 定义系统架构的目标和要求在进行系统架构设计时，需要明确系统的目标和要求。
这些目标和要求包括性能要求、安全要求、可维护性要求、扩展性要求等。
只有明确目标和要求，才能有针对性地进行架构设计。
2.2 选择适合的架构风格系统架构设计中，架构风格的选择非常重要。
常见的架构风格有MVC、SOA、微服务等。
在选择架构风格时，需要考虑系统的规模和需求，并结合业务特点选择适合的架构风格。
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。
分布式文件系统设计与实现实验报告
分布式文件系统设计与实现实验报告引言:分布式文件系统是指将存储在不同物理位置的文件以一种透明、统一的方式组织起来，使用户能够像访问本地文件一样方便地对其进行存取。
本实验旨在设计和实现一个分布式文件系统，通过研究其原理和算法，探索其在分布式计算环境下的性能和可扩展性。
设计与实现:1. 架构设计1.1 主从架构1.2 对等架构1.3 混合架构2. 文件分配算法2.1 随机分配算法2.2 基于哈希的分配算法2.3 基于一致性哈希的分配算法3. 数据一致性管理3.1 副本机制3.2 一致性协议4. 容错与恢复4.1 容错机制4.2 数据恢复算法5. 性能优化5.1 负载均衡策略5.2 数据缓存技术实验过程与结果:在实验中，我们选取了对等架构作为设计的基础。
首先，我们搭建了一个由多台计算机组成的分布式系统，并在其上安装了相应的操作系统和软件环境。
然后，我们根据设计与实现的要求，编写了相应的代码，并进行了测试和优化。
实验结果表明，我们设计与实现的分布式文件系统具有较好的性能和可扩展性。
通过合理的文件分配算法和一致性管理策略，我们实现了文件的快速存取和数据的一致性维护。
同时，通过容错与恢复机制，我们提高了系统的可靠性和稳定性。
此外，我们还采用了负载均衡和数据缓存等技术，有效地优化了系统的性能。
结论:本实验的设计与实现进一步深化了对分布式文件系统的理解，并验证了相关算法和策略的可行性和有效性。
通过实验过程中遇到的问题和得到的经验，我们对分布式系统的设计与实现有了更深入的认识。
未来，我们将进一步改进和扩展分布式文件系统的功能，以适应更复杂的分布式计算环境。
参考文献:[1] Tanenbaum, A. S., & Van Steen, M. (2002). Distributed systems: principles and paradigms. Pearson Education.[2] Ghemawat, S., Gobioff, H., & Leung, S. T. (2003). The Google file system. ACM SIGOPS Operating Systems Review, 37(5), 29-43.[3] DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman,A., Pilchin, A., ... & Vosshall, P. (2007). Dynamo: Amazon’s highly available key-value store. ACM SIGOPS Operating Systems Review, 41(6), 205-220.。
如何设计可扩展的分布式系统架构
如何设计可扩展的分布式系统架构设计可扩展的分布式系统架构是保证系统能够应对日益增长的负载和需求，实现高可用性和高性能的关键。
在设计分布式系统架构时，需要考虑各种因素包括系统规模、性能需求、可用性需求、数据一致性、容错能力、可维护性等。
下面将从以下几个方面进行介绍如何设计可扩展的分布式系统架构。
1.业务拆分与模块化设计：在设计分布式系统架构时，首先需要将系统按照业务功能进行合理的拆分，将复杂的系统划分成多个相互独立的模块，每个模块负责一部分业务功能。
这种模块化的设计有助于实现横向扩展，即通过增加相同的模块来提高系统性能。
同时，模块化设计也可以通过不同的团队并行开发，提高开发效率。
2.数据分区与负载均衡：将系统中的数据进行分区是设计可扩展分布式系统的常见策略。
通过将数据按照某种规则分散到不同的存储节点中，可以实现数据的分布式存储和查询。
同时，在查询时可以借助负载均衡技术将请求分布到各个存储节点上，达到负载均衡的效果，提高系统的响应性能。
3.异步消息和消息队列：在分布式系统中，通常会涉及到多个模块之间的数据传递和协作。
为了实现解耦和高可扩展性，可以采用异步消息传递的方式。
即将模块间的数据改变通过消息进行通知，接收到消息的模块可进行相应的处理。
同时，引入消息队列可以实现消息的持久化和可靠传递，提高系统的可用性和容错能力。
4.缓存和分布式缓存：缓存是提高系统性能和扩展性的常用策略。
将高频访问的数据缓存在内存中，可以减少磁盘读写和网络传输的开销，从而提高系统的响应性能。
而分布式缓存是将缓存数据分布在多个节点上，减少单个节点的压力，并提高系统对于负载和故障的容错能力。
5.横向扩展与自动伸缩：为了应对不断增长的负载，可以通过横向扩展来提高系统的性能和可扩展性。
即通过增加相同类型的节点来分担负载，实现负载均衡。
同时，为了应对负载波动的情况，可以采用自动伸缩技术来动态地增加或减少系统节点数量，以满足实时的负载需求。
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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。
Namenode是HDFS的主节点，里面维护了文件系统的目录树，存储了文件系统的元数据信息，用户上传文件，下载文件等操作都必须跟NameNode进行交互，因为它存储了元数据信息，Namenode为了能快速响应用户的操作，启动的时候就把元数据信息加载到了内存里面。
DataNode是HDFS的从节点，干的活就很简单，就是存储block文件块。
01 / HDFS1架构缺陷缺陷一：单点故障问题（高可用）我们很清楚的看到，HDFS1里面只有一个NameNode，那么也就是说如果这个Namenode 出问题以后，整个分布式文件系统就不能用了。
缺陷二：内存受限问题NameNode为了能快速响应用户的操作，把文件系统的元数据信息加载到了内存里面，那么如果一个集群里面存储的文件较多，产生的元数据量也很大，大到namenode所在的服务器撑不下去了，那么文件系统的寿命也就到尽头了，所以从这个角度说，之前的HDFS1的架构里Namenode有内存受限的问题。
我们大体能看得出来，在HDFS1架构设计中，DataNode是没有明显的问题的，主要的问题是在NameNode这儿。
3.HDFS2HDFS1明显感觉架构不太成熟，所以HDFS2就是对HDFS1的问题进行解决。
01 / 单点故障问题解决（高可用）大家先跟着我的思路走，现在我们要解决的是一个单点的问题，其实解决的思路很简单，因为之前是只有一个NameNode，所以有单点故障问题，那我们把设计成两个Namenode就可以了，如果其中一个namenode有问题，就切换到另外一个NameNode上。
所以现在我们想要实现的是：其中一个Namenode有问题，然后可以把服务切换到另外一个Namenode上。
如果是这样的话，首先需要解决一个问题：如何保证两个NameNode之间的元数据保持一致？因为只有这样才能保证服务切换以后还能正常干活。
保证两个NameNode之间元数据一致为了解决两个NameNode之间元数据一致的问题，引入了第三方的一个JournalNode集群。
JournalNode集群的特点：JournalNode守护进程是相对轻量级的，那么这些守护进程可与其它Hadoop守护进程，如NameNode，运行在相同的主机上。
由于元数据的改变必须写入大多数（一半以上）JNs，所以至少存在3个JournalNodes守护进程，这样系统能够容忍单个Journal Node故障。
当然也可以运行多于3个JournalNodes，但为了增加系统能够容忍的故障主机的数量，应该运行奇数个JNs。
当运行N个JNs时，系统最多可以接受(N-1)/2个主机故障并能继续正常运行，所以Jounal Node集群本身是没有单点故障问题的。
引入了Journal Node集群以后，Active状态的NameNode实时的往Journal Node集群写元数据，StandBy状态的NameNode实时从Journal Node集群同步元数据，这样就保证了两个NameNode之间的元数据是一致的。
两个NameNode自动切换目前虽然解决了单点故障的问题，但是目前假如Active NameNode出了问题，还需要我们人工的参与把Standby N ameNode切换成为Active NameNode，这个过程并不是自动化的，但是很明显这个过程我们需要自动化，接下来我们看HDFS2是如何解决自动切换问题的。
为了解决自动切换问题，HDFS2引入了ZooKeeper集群和ZKFC进程。
ZKFC是DFSZKFailoverController的简称，这个服务跟NameNode的服务安装在同一台服务器上，主要的作用就是监控NameNode健康状态并向ZooKeeper注册NameNode，如果Active的NameNode挂掉后，ZKFC为StandBy的NameNode竞争锁（分布式锁），获得ZKFC锁的NameNode变为active，所以引入了ZooKeeper集群和ZKFC进程后就解决了NameNode自动切换的问题。
02 / 内存受限问题解决前面我们虽然解决了高可用的问题，但是如果NameNode的元数据量很大，大到当前NameNode所在的服务器存不下，这个时候集群就不可用了，换句话说就是NameNode的扩展性有问题。
为了解决这个问题，HDFS2引入了联邦的机制。
如上图所示这个是一个完整的集群，由多个namenode构成，namenode1和namenode2构成一套namenode，我们取名叫nn1，这两个namenode之间是高可用关系，管理的元数据是一模一样的；namenode3和namenode4构成一套namenode，假设我们取名叫nn2，这两个namenode之间也是高可用关系，管理的元数据也是一模一样的，但是nn1和nn2管理的元数据是不一样的，他们分别只是管理了整个集群元数据的一部分，引入了联邦机制以后，如果后面元数据又存不了，那继续扩nn3,nn4...就可以了。
所以这个时候NameNode就在存储元数据方面提升了可扩展性，解决了内存受限的问题。
联邦这个名字是国外翻译过来的，英文名是Federation，之所以叫联邦的管理方式是因为Hadoop的作者是Doug cutting，在美国上学，美国是联邦制的国家，作者从国家管理的方式上联想到元数据的管理方式，其实这个跟我们国家的管理方式也有点类似，就好比我们整个国家是一个完整的集群，但是如果所有的元数据都由北京来管理的话，内存肯定不够，所以中国分了34个省级行政区域，各个区域管理自己的元数据，这行就解决了单服务器内存受限的问题。
HDFS2引入了联邦机制以后，我们用户的使用方式不发生改变，联邦机制对于用户是透明的，因为我们会在上游做一层映射，HDFS2的不同目录的元数据就自动映射到不同的namenode 上。
03 / HDFS2的架构缺陷缺陷一：高可用只能有两个namenode为了解决单点故障问题，HDFS2设计了两个namenode,一个是active，另外一个是standby，但是这样的话，如果刚好两个NameNode连续出问题了，这个时候集群照样还是不可用，所以从这这个角度讲，NameNode的可扩展性还是有待提高的。
注意：这个时候不要跟联邦那儿混淆，联邦那儿是可以有无数个namenode的，咱们这儿说的只能支持两个namenode指的是是高可用方案。
缺陷二：副本为3，存储浪费了200%其实这个问题HDFS1的时候就存在，而且这个问题跟NameNode的设计没关系，主要是DataNode这儿的问题，DataNode为了保证数据安全，默认一个block都会有3个副本，这样存储就会浪费了200%。
4.HDFS3其实到了HDFS2，HDFS就已经比较成熟稳定了，但是HDFS3还是精益求精，再从架构设计上重新设计了一下。
01 / 高可用之解决只能有两个namenode当时在设计HDFS2的时候只是使用了两个NameNode去解决高可用问题，那这样的话，集群里就只有一个NameNode是Standby状态，这个时候假设同时两个NameNode都有问题的话，集群还是存在不可用的风险，所以在设计HDFS3的时候，使其可支持配置多个NameNode用来支持高可用，这样的话就保证了集群的稳定性。
02 / 解决存储浪费问题HDFS3之前的存储文件的方案是将一个文件切分成多个Block进行存储，通常一个Block 64MB或者128MB，每个Block有多个副本(replica)，每个副本作为一个整体存储在一个DataNode上，这种方法在增加可用性的同时也增加了存储成本。
ErasureCode通过将M个数据block进行编码(Reed-Solomon,LRC)，生成K个校验(parity)block, 这M+K个block 组成一个block group，可以同时容忍K个block失败,任何K个block都可以由其他M个block算出来. overhead是K/M。
以M=6,K=3为例，使用EC之前，假设block副本数为3,那么6个block一共18个副本，overhead是200%,使用EC后，9个block，每个block只需一个副本，一共9个副本，其中6个数据副本，3个校验副本，overhead是3/6=50%。
在存储系统中，纠删码技术主要是通过利用纠删码算法将原始的数据进行编码得到校验，并将数据和校验一并存储起来，以达到容错的目的。
其基本思想是将ｋ块原始的数据元素通过一定的编码计算，得到ｍ块校验元素。
对于这ｋ+ｍ块元素，当其中任意的ｍ块元素出错（包括数据和校验出错），均可以通过对应的重构算法恢复出原来的ｋ块数据。