全文搜索引擎的设计与实现-文献综述

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学术搜题引擎的设计和实现

学术搜题引擎的设计和实现一、前言随着互联网的飞速发展，学术研究已经成为许多人生活的一部分。

因此，在如此庞大的网络信息中，如何快速找到相关的学术文献成为了一个急需解决的问题。

学术搜题引擎应运而生，旨在为用户提供高效便捷的文献检索服务。

在这篇文章中，我们将深入探讨学术搜题引擎的设计和实现。

二、需求分析学术搜题引擎的主要用户群体是高等院校教师、研究生等优秀学者，他们需要在浩瀚的信息中快速地找到自己所需要的学术文献。

因此，学术搜题引擎需要满足如下需求：1.快速检索：用户需要在最短时间内找到自己需要的文献2.准确性：对用户输入的关键词进行精准匹配，避免检索结果过多或过少3.多维度检索：引入多个维度检索，如作者、期刊、出版时间等4.结果推荐：根据用户需求，对搜索结果进行智能化推荐5.用户体验：提供高质量的用户体验，如操作简便、响应迅速等三、技术选型1.搜索引擎：学术搜题引擎需要使用搜索引擎来进行搜索，常见的搜索引擎有Elasticsearch、Solr、Lucene等，经过比对，我们选用Elasticsearch作为搜索引擎。

2.数据源：根据需求分析，我们需要收集大量的学术文献，常见数据源有CNKI、WanFang、Web of Science、Google Scholar等，为了获取更为全面的学术数据，我们选择综合使用这些数据源。

3.技术架构：我们采用前后端分离架构，前端使用Vue.js，后端使用Spring Boot框架。

四、技术实现1. 数据采集为了获取更为全面的学术数据，我们需要从多个数据源中采集数据。

由于各个数据源的数据结构不同，我们需要针对不同数据源进行数据抓取，将抓取到的数据进行清洗、去重、存储等操作。

2. 数据存储在数据存储方面，我们采用Elasticsearch作为搜索引擎，并且将数据以文档的形式存储。

每一个文档由多个字段组成，如标题、作者、出版时间等。

3. 搜索算法在搜索算法方面，我们采用了基于BM25（Okapi与BM25的比较）的排序算法，该算法能够根据文本的相关性对搜索结果进行排序。

基于大数据的智能文献检索系统设计与实现

基于大数据的智能文献检索系统设计与实现随着信息化时代的不断发展，人们获取信息的方式也在不断变革和升级。

由于互联网时代大数据的快速增长以及信息的多样性和丰富性，文献检索系统成为学术研究和实践的重要渠道。

大数据技术以其高效、快速的特点赋能文献检索系统，使其在众多领域中功效显著。

本文将介绍如何基于大数据技术设计和实现智能文献检索系统。

一、大数据技术在文献检索系统中的应用在过去，文献检索的常用方式是使用全文搜索，即输入关键词查询匹配的文献。

随着对数据的处理和存储能力的提高以及大数据技术的迅速发展，借助大数据技术来实现对文献进行全面分析已成为可能。

具体实现方式如下：1. 数据的采集、存储和处理一方面，可以通过网络爬虫技术，自动地从各大学术数据库、文献数据库中爬取文献原始数据，包括作者、标题、摘要等信息。

将这些原始数据存储在分布式文件系统中，如Hadoop，方便大数据技术进行高效处理。

另一方面，采用自然语言处理技术对文献进行语义分析和处理，构建字词、词组、句子和段落等语义单元，建立语义关系模型。

2. 文献的处理和分类借助大数据技术，在对所有文献数据进行语义分析和处理的基础上，将其按照不同文献类型划分，形成不同的文献数据集。

根据用户对文献的需求不同，将这些文献数据集进行匹配和筛选，只返回符合用户需求的文献。

3. 文献的查询和推荐通过对用户历史查询记录、已读过的文献以及关注的主题等信息进行分析和挖掘，对用户需求进行预测和推断，然后从大数据库中检索和推荐符合用户需求的文献和研究报告。

二、设计和实现智能文献检索系统在了解了大数据技术在文献检索中的应用后，下面介绍如何设计和实现一个智能文献检索系统，满足人们日益增加的高质量、高效率的文献信息检索需求。

1. 功能需求分析从用户角度出发，对其需求进行分析如下：- 应支持基本的关键词搜索功能；- 针对文献类型（如论文、专利、技术报告等）进行分类检索；- 提供高级搜索选项，支持组合式检索、高亮显示、文献筛选等功能；- 推荐相关的研究题目、主题、作者以及未来研究方向等文献信息；- 根据个人喜好或者历史浏览行为，提供个性化的推荐服务。

智能文献检索系统的设计与实现

智能文献检索系统的设计与实现随着信息技术的迅猛发展，文献检索系统也越来越受到人们的关注。

智能文献检索系统是一种应用人工智能技术来实现文献检索的新型系统，主要通过数据挖掘、机器学习等技术对文献信息进行处理和分析，从而实现快速、准确的检索。

本文将介绍智能文献检索系统的设计和实现过程。

一、需求分析在设计智能文献检索系统前，需要对用户需求进行分析。

一般用户检索文献的需求包括以下几个方面：1.快速检索：用户需要快速找到自己需要的文献信息，因此系统需要实现快速和准确的检索。

2.精准匹配：用户需要检索结果与自己的需求尽可能地匹配，因此系统需要实现语义分析和匹配。

3.分类检索：用户需要对文献按照不同的分类进行检索，因此系统需要实现文献分类功能。

4.个性化推荐：用户需要根据自己的兴趣和需求推荐相关文献，因此系统需要实现个性化推荐功能。

基于以上需求，设计智能文献检索系统应该包括文献数据采集、数据预处理、检索算法设计、用户界面设计、个性化推荐等基本模块。

二、系统实现1.文献数据采集文献数据采集是智能文献检索系统的基础，文献数据来源可以包括各种数据库、论文库、学术搜索引擎等。

在数据采集过程中，需要注意文献数据的质量和完整性，尽可能获取大量优质的文献数据。

2.数据预处理文献数据采集后，需要进行数据预处理，包括数据清洗、分词、词干提取、停词处理等。

数据清洗是指对文献数据中存在的无用信息、重复信息和错误信息进行过滤和清理。

分词是指将文献数据分解成一个个词语，逐个处理。

词干提取是指将不同的词形还原成同一词干，以减少处理时间和提高检索效率。

停词处理是指将一些常见的词语（如“的”、“是”、“在”等）从文献数据中去除，以减少处理时间和降低搜索干扰。

3.检索算法设计检索算法是智能文献检索系统的核心，主要包括词频统计、TF-IDF算法、向量空间模型、余弦相似度等。

词频统计是指通过统计文献中各个词语的频率来判断该文献和用户需求的相似程度，这种方法简单易用，但不够准确。

电子文献检索系统设计与实现

电子文献检索系统设计与实现电子文献检索系统是指一个能够帮助人们检索到相关电子文献的系统。

设计和实现一个高效可靠的电子文献检索系统是很重要的，能够提高人们获取文献的效率，使其能够更方便的应用于各种领域。

一、系统需求分析首先，需要确定系统的使用场景和要解决的问题，进而分析系统的需求。

在对使用场景和问题的分析方面，我们可以从以下几个方面来考虑：1.谁会使用此系统？2.用户需要什么样的关键词检索功能？3.用户是否需要查看电子文献的详细信息？4.如何确保检索的准确性和文献质量？5.如何规范管理已有的文献资源？基于以上分析，我们可以定义出电子文献检索系统的基本需求：1.提供良好的用户界面：要求系统的操作界面简单易用，能够帮助用户快速完成各种操作。

2.支持多种检索功能：系统需要支持全文、关键词、作者、标题等多种检索方式，能够满足不同用户的需求。

3.提供详细的文献信息：用户需要能够查看文献的作者、摘要、目录、引用等详细信息，从而对电子文献进行更好的管理和应用。

4.提高检索的准确性：为了减少用户产生的误导，要求系统采用先进的算法和模型，优化文献检索和匹配的结果，并尽量排除一些错误的信息。

5.规范化管理已有的文献资源：要求系统能够按照标准的规范对已有的电子文献进行分类和管理，方便用户检索处理。

二、系统设计基于需求分析的结果，开始进行系统设计。

设计过程主要关注以下几个方面：1.系统架构的选择：根据系统的需求，选择合适的系统架构方案。

2.数据库的设计：根据不同类型和格式的文献，确定数据库的结构和字段，以便存储、管理和检索文献信息。

3.索引设计：根据文献的特点，设计合适的索引结构，提高检索效率。

4.算法和模型的设计：选择合适的算法和模型，以减少检索误差和提高检索效率。

在具体实现中，我们可以考虑采用以下方案：1.采用B/S架构：基于浏览器的架构，方便用户随时进行检索，提高用户体验。

2.数据库选择：可以选择MySQL或者Oracle等关系型数据库管理系统，以保证数据的稳定性和完整性。

全文搜索引擎的设计与实现-外文翻译

江汉大学毕业论文（设计）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 2007082021372013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。

文献综述检索方法

文献综述检索方法
文献综述的检索方法主要有以下几种：
1. 学术搜索引擎：利用Google学术、百度学术、CNKI等学术搜索引擎，输入关键词加上“综述”或“综述文献”进行检索，可以找到该领域相关的文献综述。

2. 文献数据库：利用Web of Science、Scopus、PubMed等文献数据库，在高级检索中选择“综述”或“综述文献”进行检索，可以找到该领域相关的文献综述。

3. 学科主题网站：如、等学科主题网站，可以浏览该网站所属的学科领域，找到该领域的文献综述。

4. 学术期刊：浏览相关领域的学术期刊，找到其中发表的文献综述。

5. 学术论坛：浏览相关领域的学术论坛，可以获得该领域的最新进展和热点问题，并找到其中提到的文献综述。

在搜索文献综述时，需要注意关键词的选择，以及对搜索结果的筛选和评估，找到高质量、权威的文献综述。

基于Lucene的全文搜索引擎的设计与实现

效性。
图１Ｌｃｎｕｅｅ系统的结构组织图
２Ｌｕｅｅ的系统结构分析ｃｎ
２２ｏｇａａｈ．ｃｎ．ｉｅ索引包是整个系统核心，．ｒ．ｐｃｅ［ｅｅｎｘｕｄ主要提供库的读写接口，过该包可以创建库．加删除记录及通添读取记录等。全文检索的根本就为每个切出来的词建立索引，查询时只需要遍历索引，不需要遍历整个正文，而极大地而从提高了检索效率，引创建的质量直接关系整个系统的质量。索Ｌｃｎ的索引树是非常优质高效的，这个包中，要有Ｉ．ｕｅｅ在主ｎ
查询结果。图１是Ｌｃｎｕｅｅ系统的结构组织图。２．分析器Ａｎｌｚｒ分析器主要用于切词，段文档输入１ａｙｅ一
以后，过Ａａｚｒ输出时只剩下有用的部分，他部分被剔经ｎｌｅ，ｙ其除。分析器提供了抽象的接口，因此语言分析（ｎｌ）Ａａ￣ｒ是可以ｙ定制的。因为Ｌｃｎ缺省提供了２个比较通用的分析器Ｓｕｅｅｉｍ．ｐＡａｓ和ＳａｄｒＡａｓｒ这２个分析器缺省都不支持中ｌｅｌｅｎｙｒｔｎａｄｎｌｅ，ｙ文，以要加入对中文语言的切分规则，要修改这２个分析所需

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江汉大学毕业论文（设计）
文献综述
综述名称全文搜索引擎的设计与实现
姓名cccc
学号200708202137
2013年4月8日
一、绪论
目前定制和维护搜索引擎的需求越来越大，对于处理庞大的网络数据，如何有效的去存储它并访问到我们需要的信息，变得尤为重要。

Web搜索引擎能有很好的帮助我们解决这一问题。

本文阐述了一个全文搜索引擎的原理及其设计和实现过程。

该系统采用B/S 模式的Java Web平台架构实现，采用Nutch相关框架，包括Nutch，Solr，Hadoop,以及Nutch的基础框架Lucene对全网信息的采集和检索。

文中阐述了Nutch相关框架的背景，基础原理和应用。

Nutch相关框架的出现，使得在java平台上构建个性化搜索引擎成为一件简单又可靠的事情。

Nutch 致力于让每个人能很容易, 同时花费很少就可以配置世界一流的Web搜索引擎。

目前国内有很多大公司，比如百度、雅虎，都在使用Nutch相关框架。

由于Nutch是开源的，阅读其源代码，可以让我们对搜索引擎实现有更加深刻的感受，并且能够更加深度的定制需要的搜索引擎实现细节。

本文首先介绍了课题研究背景，然后对系统涉及到的理论知识，框架的相关理论做了详细说明，最后按照软件工程的开发方法逐步实现系统功能。

二、文献研究
2.1 Nutch技术
Nutch 是一个开源Java 实现的搜索引擎。

它提供了我们运行的搜索引擎所需的全部工具。

包括全文搜索和Web爬虫。

尽管Web搜索是漫游Internet的基本要求, 但是现有web搜索引擎的数目却在下降。

并且这很有可能进一步演变成为一个公司垄断了几乎所有的web搜索为其谋取商业利益.这显然不利于广大Internet用户。

Nutch为我们提供了这样一个不同的选择. 相对于那些商用的搜索引擎, Nutch作为开放源代码搜索引擎将会更加透明, 从而更值得大家信赖. 现在所有主要的搜索引擎都采用私有的排序算法, 而不会解释为什么一个网页会排在一个特定的位置。

除此之外, 有的搜索引擎依照网站所付的费用, 而不是根据它们本身的价值进行排序. 与它们不同, Nucth没有什么需要隐瞒, 也没有动
机去扭曲搜索的结果。

Nutch将尽自己最大的努力为用户提供最好的搜索结果。

Nutch 致力于让每个人能很容易, 同时花费很少就可以配置世界一流的Web搜索引擎。

2.1.1 特色和缺点
特色：
1、透明度:Nutch是开放源代码的，因此任何人都可以查看他的排序算法是如何工作的。

商业的搜索引擎排序算法都是保密的，我们无法知道为什么搜索出来的排序结果是如何算出来的。

更进一步，一些搜索引擎允许竞价排名，比如百度，这样的索引结果并不是和站点内容相关的。

因此 Nutch 对学术搜索和政府类站点的搜索来说，是个好选择。

因为一个公平的排序结果是非常重要的。

2、对搜索引擎的理解:我们并没有google的源代码，因此学习搜索引擎Nutch是个不错的选择。

了解一个大型分布式的搜索引擎如何工作是一件让人很受益的事情。

在写Nutch的过程中，从学院派和工业派借鉴了很多知识：比如：Nutch的核心部分目前已经被重新用 Map Reduce 实现了。

看过开复演讲的人都知道 Map Reduce 的一点知识吧。

Map Reduce 是一个分布式的处理模型，最先是从 Google 实验室提出来的。

你也可以从下面获得更多的消息。

/bbs/list.asp?boardid=29
/bbs/list.asp?boardid=29
并且 Nutch 也吸引了很多研究者，他们非常乐于尝试新的搜索算法，因为对Nutch 来说，这是非常容易实现扩展的。

3、扩展性你是不是不喜欢其他的搜索引擎展现结果的方式呢？那就用Nutch 写你自己的搜索引擎吧。

Nutch 是非常灵活的：他可以被很好的客户订制并集成到你的应用程序中：使用Nutch 的插件机制，Nutch 可以作为一个搜索不同信息载体的搜索平台。

当然，最简单的就是集成Nutch到你的站点，为你的用户提供搜索服务。

缺点：
1.Nutch是通用的网路爬虫，这是优点也是缺点。

缺点是不适应垂直搜索
平台。

2.Nutch是机遇Java平台的，虽然架构很清爽，但是使用起来，速度还是
比其他语言平台的应用要慢一些。

3.Nutch目前配套的资料较少，学习起来困难度较大。

4.
最新版本：
Nutch可以在官方网站上获得/目前Nutch的最新版为：Apache Nutch v2.1 Release。

由于Nutch目前官方只是在Linux系统上对其进行了测试，所以在选择开发环境的时候，最好选用Linux系统。

2.2 Solr技术
Solr是一个独立的企业级搜索应用服务器，它对外提供类似于Web-service 的API接口。

用户可以通过http请求，向搜索引擎服务器提交一定格式的XML 文件，生成索引；也可以通过Http Get操作提出查找请求，并得到XML格式的返回结果。

2.2.1 特色和缺点
特色：
1. Solr集成了搜索引擎中的所要建立和查询，能够很好地集成其他Nutch 相关平台。

2. Solr使用方便，灵活性强，效率和稳定性能也较其他框架好。

3. Solr支持多种配置方式的运行，比如分词器，可以集成我们自定义的分词，对分词做到个性化配置。

缺点：
虽然Solr效率较高，但是毕竟是基于Java平台，运行速度上还是有待提高。

最新版本：
Solr可以在官方网站上获得/dyn/closer.cgi/lucene/solr/，目前Nutch的最新版为：solr-4.3.0。

由于Solr目前官方只是在Linux系统上对其进行了测试，所
以在选择开发环境的时候，最好选用Linux系统。

三、总结
本全文搜索引擎的设计与实现正是利用以上技术，使得系统执行效率更高，满足用户的需求，由于模块之间相互独立，能够满足系统功能的扩展需求，不会影响系统基本功能的实现，能够适应系统的不断变化和发展，对设计功能强大的网上应用程序具有理论与现实意义，再结合基本网页设计对系统进行布局和美化，最后提供给用户界面简洁，功能强大的搜索引擎引用。

对于此系统的研究和设计，能够将所学知识应用到实际操作中，深刻理解整个开发流程。

参考文献
[1] /nutch/NutchTutorial
[2] /solr/4_2_0/tutorial.html
[3] /nutch/OldHadoopTutorial
[4] /
[5] 李晓明闫宏飞王继民．搜索引擎—原理、技术与系统．科学出版社，2004
[6] 易剑（Hadoop 技术论坛）．Hadoop开发者入门专刊．
[8] Rafał Kuć．Apache Solr 3.1 Cookbook．Packt Publishing Ltd，2011
[9] 董宇．一个 Java 搜索引擎的实现
/developerworks/cn/java/j-lo-dyse1/index.html，2010
[10] 杨尚川．Nutch相关框架安装使用最佳指南．
/281032878?ptlang=2052#!app=2&via=QZ.HashRefresh&po s=1362131478。