大数据基础知识论文库-095-Avro
hadoop 毕业论文
hadoop 毕业论文Hadoop技术在大数据处理中的应用摘要：随着社会信息的不断发展，数据的规模越来越庞大，传统数据处理方法已经无法满足这样的需求，这时候大数据处理技术应运而生。
而Hadoop作为大数据领域中的重要技术之一，受到了越来越多的关注。
本文主要介绍了Hadoop的概念、工作原理及其在大数据处理中的应用，也探讨了Hadoop在未来的发展方向。
关键词：Hadoop；大数据处理；MapReduce；分布式文件系统一、引言随着科技和信息技术的迅速发展，我们产生的数据越来越多，数据量大，类型多，处理难度大。
在过去，大数据处理主要采用的是传统的关系型数据库方法，这种方式已经无法满足当今信息日益增长的需求，于是大数据处理技术应运而生。
随着大数据处理技术的逐渐成熟，颇受市场的青睐和社会的重视。
而Hadoop就是大数据处理技术中的一项重要技术，速度快、可扩展性好、可靠性高等特点受到了广泛关注。
本文将主要介绍Hadoop的基本概念，工作原理及其在大数据处理中的应用。
二、Hadoop的基本概念Hadoop是一个开源的分布式计算平台，可以有效地处理大数据，同时它也是一种分布式文件系统，可以在廉价商用计算机上实现分布式存储和计算。
它由Apache基金会开发和维护，其最初的设计目的是为了解决大规模数据集的计算问题。
Hadoop通常被分成两个主要的部分：Hadoop分布式文件系统(HDFS)和MapReduce。
1、Hadoop分布式文件系统(HDFS)HDFS是Hadoop的分布式文件系统，是一种设计用来在廉价硬件上存储大量数据的算法。
HDFS的设计架构采取了主从式的方式，通常被称为一个“NameNode+DataNode”的结构。
- NameNode: 管理文件系统的命名空间，维护文件系统中每个文件和目录的元数据信息；- DataNode：存储数据的节点。
在HDFS中，文件通常被分成若干个数据块进行存储，一个文件可以划分成很多数据块，并分发到不同的DataNode上，DataNode会在本地磁盘上存储这些数据块。
大数据论文3000字范文(精选5篇)
大数据论文3000字范文(精选5篇)第一篇:大数据论文3000字当人们还在津津乐道云计算、物联网等主题时, “大数据”一词已逐渐成为IT网络通信领域热门词汇。
争夺大数据发展先机俨然成为世界各国高度重视的问题, 其中不乏IBM、EMC.甲骨文、微软等在内的巨头厂商的强势介入, 纷纷跑马圈地, 它们投入巨额资金争相抢占该领域的主动权、话语权。
大数据时代的来临, 除了推动现有的信息技术产业的创新, 其对我们生产生活的方式也将产生重大影响。
从个人视角来看, 不管是日常工作中遇到的海量邮件或是从网上获取的社交、购物、娱乐、学习、理财等信息, 还是生活中最常见的手机存储, 大数据已经渗透到我们日常生活的方方面面, 极大地方便了我们的生活;对企业而言, 互联网公司已开始采用大数据来冲击传统行业, 精准营销与大数据驱动的产品快速迭代, 促进企业商业模式创新;在社会公共服务方面, 教育、医疗、交通等行业在大数据的影响下, 出现了各种新的应用, 数据化、社交化的新媒体平台、智能交通与城市数字监管系统, 以及病历存储调用的医疗云等, 此外, 政府还可以通过大数据来高效完成信息采集, 这样可优化升级管理运营。
然而大数据在给我们展示前所未有的发展机遇的同时, 也给国家信息安全、信息技术、人才等方面带来了很大的挑战。
不久前, 斯诺登披露了美国国家安全局(NSA)一直进行信息监视活动、已收集数以百万计的全球人的信息数据的消息, 在全球范围内掀起轩然大波。
该事件对“大数据”的信息安全敲响了警钟。
大数据让大规模生产、分享和应用数据成为可能, 将信息存储和管理集中化, 我们在百度上面的记录, 无意识阅读的产品广告、旅游信息, 习惯去哪个商场进行采购等这些痕迹, 却不知所有的关系和活动在数据化之后都被一些组织或商家公司掌控, 这也使得我们一方面享受了“大数据”带来的诸多便利, 但另一方面无处不在的“第三只眼”却在时刻监控着我们的行动。
关于大数据的参考文献
关于大数据的参考文献以下是关于大数据的一些参考文献，这些文献涵盖了大数据的基本概念、技术、应用以及相关研究领域。
请注意，由于知识截至日期为2022年，可能有新的文献发表，建议查阅最新的学术数据库获取最新信息。
1.《大数据时代》作者：维克托·迈尔-舍恩伯格、肯尼思·库克斯著，李智译。
出版社：中信出版社，2014年。
2.《大数据驱动》作者：马克·范·雷尔、肖恩·吉福瑞、乔治·德雷皮译。
出版社：人民邮电出版社，2015年。
3.《大数据基础》作者：刘鑫、沈超、潘卫国编著。
出版社：清华大学出版社，2016年。
4.《Hadoop权威指南》作者：Tom White著，陈涛译。
出版社：机械工业出版社，2013年。
5.《大数据：互联网大规模数据管理与实时分析》作者：斯图尔特·赫哈特、乔·赖赫特、阿什拉夫·阿比瑞克著，侯旭翔译。
出版社：电子工业出版社，2014年。
6.《Spark快速大数据分析》作者：Holden Karau、Andy Konwinski、Patrick Wendell、Matei Zaharia著，贾晓义译。
出版社：电子工业出版社，2015年。
7.《大数据时代的商业价值》作者：维克托·迈尔-舍恩伯格著，朱正源、马小明译。
出版社：中国人民大学出版社，2016年。
8.《数据密集型应用系统设计》作者：Martin Kleppmann著，张宏译。
出版社：电子工业出版社，2018年。
9.《大数据：互联网金融大数据风控模型与实证》作者：李晓娟、程志强、陈令章著。
出版社：机械工业出版社，2017年。
10.《数据科学家讲数据科学》作者：杰夫·希尔曼著，林巍巍译。
出版社：中信出版社，2013年。
这些参考文献覆盖了大数据领域的多个方面，包括理论基础、技术实践、应用案例等。
你可以根据具体的兴趣和需求选择阅读。
大数据参考文献
大数据研究综述陶雪娇，胡晓峰，刘洋(国防大学信息作战与指挥训练教研部，北京100091)研究机构Gartne:的定义:大数据是指需要新处理模式才能具有更强的决策力、洞察发现力和流程优化能力的海量、高增长率和多样化的信息资产。
维基百科的定义:大数据指的是所涉及的资料量规模巨大到无法通过目前主流软件工具，在合理时间内达到撷取、管理、处理并整理成为帮助企业经营决策目的的资讯。
麦肯锡的定义:大数据是指无法在一定时间内用传统数据库软件工具对其内容进行采集、存储、管理和分析的赞据焦合。
数据挖掘的焦点集中在寻求数据挖掘过程中的可视化方法，使知识发现过程能够被用户理解，便于在知识发现过程中的人机交互;研究在网络环境卜的数据挖掘技术，特别是在Internet上建立数据挖掘和知识发现((DMKD)服务器，与数据库服务器配合，实现数据挖掘;加强对各种非结构化或半结构化数据的挖掘，如多媒体数据、文本数据和图像数据等。
5.1数据量的成倍增长挑战数据存储能力大数据及其潜在的商业价值要求使用专门的数据库技术和专用的数据存储设备，传统的数据库追求高度的数据一致性和容错性，缺乏较强的扩展性和较好的系统可用性，小能有效存储视频、音频等非结构化和半结构化的数据。
目前，数据存储能力的增长远远赶小上数据的增长，设计最合理的分层存储架构成为信息系统的关键。
5.2数据类型的多样性挑战数据挖掘能力数据类型的多样化，对传统的数据分析平台发出了挑战。
从数据库的观点看，挖掘算法的有效性和可伸缩性是实现数据挖掘的关键，而现有的算法往往适合常驻内存的小数据集，大型数据库中的数据可能无法同时导入内存，随着数据规模的小断增大，算法的效率逐渐成为数据分析流程的瓶颈。
要想彻底改变被动局面，需要对现有架构、组织体系、资源配置和权力结构进行重组。
5.3对大数据的处理速度挑战数据处理的时效性随着数据规模的小断增大，分析处理的时间相应地越来越长，而大数据条件对信息处理的时效性要求越来越高。
大数据的经典的四种算法
大数据的经典的四种算法大数据经典的四种算法一、Apriori算法Apriori算法是一种经典的关联规则挖掘算法，用于发现数据集中的频繁项集和关联规则。
它的基本思想是通过迭代的方式，从单个项开始，不断增加项的数量，直到不能再生成频繁项集为止。
Apriori算法的核心是使用Apriori原理，即如果一个项集是频繁的，则它的所有子集也一定是频繁的。
这个原理可以帮助减少候选项集的数量，提高算法的效率。
Apriori算法的输入是一个事务数据库，输出是频繁项集和关联规则。
二、K-means算法K-means算法是一种聚类算法，用于将数据集划分成K个不同的类别。
它的基本思想是通过迭代的方式，不断调整类别中心，使得每个样本点都属于距离最近的类别中心。
K-means算法的核心是使用欧氏距离来度量样本点与类别中心的距离。
算法的输入是一个数据集和预设的类别数量K，输出是每个样本点所属的类别。
三、决策树算法决策树算法是一种分类和回归算法，用于根据数据集中的特征属性，构建一棵树形结构，用于预测目标属性的取值。
它的基本思想是通过递归的方式，将数据集分割成更小的子集，直到子集中的样本点都属于同一类别或达到停止条件。
决策树算法的核心是选择最佳的划分属性和划分点。
算法的输入是一个数据集，输出是一个决策树模型。
四、朴素贝叶斯算法朴素贝叶斯算法是一种基于贝叶斯定理的分类算法，用于根据数据集中的特征属性，预测目标属性的取值。
它的基本思想是假设特征属性之间相互独立，通过计算后验概率来进行分类。
朴素贝叶斯算法的核心是使用贝叶斯定理和条件独立性假设。
算法的输入是一个数据集，输出是一个分类模型。
五、支持向量机算法支持向量机算法是一种用于分类和回归的机器学习算法，用于找到一个超平面，将不同类别的样本点分开。
它的基本思想是找到一个最优的超平面，使得离它最近的样本点到超平面的距离最大化。
支持向量机算法的核心是通过求解凸二次规划问题来确定超平面。
算法的输入是一个数据集，输出是一个分类或回归模型。
大数据基础知识论文库-074--druid
DruidA Real-time Analytical Data StoreFangjin Y angMetamarkets Group,Inc. fangjin@Eric Tschetterecheddar@Xavier LéautéMetamarkets Group,Inc.xavier@Nelson Ray ncray86@Gian MerlinoMetamarkets Group,Inc.gian@Deep GanguliMetamarkets Group,Inc.deep@ABSTRACTDruid is an open source1data store designed for real-time exploratory analytics on large data sets.The system combines a column-oriented storage layout,a distributed,shared-nothing architecture,and an advanced indexing structure to allow for the arbitrary exploration of billion-row tables with sub-second latencies.In this paper,we describe Druid’s architecture,and detail how it supports fast aggre-gations,flexiblefilters,and low latency data ingestion.Categories and Subject DescriptorsH.2.4[Database Management]:Systems—Distributed databasesKeywordsdistributed;real-time;fault-tolerant;highly available;open source; analytics;column-oriented;OLAP1.INTRODUCTIONIn recent years,the proliferation of internet technology has cre-ated a surge in machine-generated events.Individually,these events contain minimal useful information and are of low value.Given the time and resources required to extract meaning from large collec-tions of events,many companies were willing to discard this data in-stead.Although infrastructure has been built to handle event-based data(e.g.IBM’s Netezza[37],HP’s Vertica[5],and EMC’s Green-plum[29]),they are largely sold at high price points and are only targeted towards those companies who can afford the offering.A few years ago,Google introduced MapReduce[11]as their mechanism of leveraging commodity hardware to index the inter-net and analyze logs.The Hadoop[36]project soon followed and was largely patterned after the insights that came out of the original MapReduce paper.Hadoop is currently deployed in many orga-nizations to store and analyze large amounts of log data.Hadoop has contributed much to helping companies convert their low-value 1http://druid.io/https:///metamx/druidPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on thefirst page.Copyrights for components of this work owned by others than the author(s)must be honored.Abstracting with credit is permitted.To copy otherwise,or republish,to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.Request permissions from permissions@.SIGMOD’14,June22–27,2014,Snowbird,UT,USA.Copyright is held by the owner/author(s).Publication rights licensed to ACM.ACM978-1-4503-2376-5/14/06...$15.00./10.1145/2588555.2595631.event streams into high-value aggregates for a variety of applica-tions such as business intelligence and A-B testing.As with many great systems,Hadoop has opened our eyes to a new space of problems.Specifically,Hadoop excels at storing and providing access to large amounts of data,however,it does not make any performance guarantees around how quickly that data can be accessed.Furthermore,although Hadoop is a highly available system,performance degrades under heavy concurrent stly, while Hadoop works well for storing data,it is not optimized for in-gesting data and making that data immediately readable.Early on in the development of the Metamarkets product,we ran into each of these issues and came to the realization that Hadoop is a great back-office,batch processing,and data warehousing sys-tem.However,as a company that has product-level guarantees around query performance and data availability in a highly concur-rent environment(1000+users),Hadoop wasn’t going to meet our needs.We explored different solutions in the space,and after trying both Relational Database Management Systems and NoSQL archi-tectures,we came to the conclusion that there was nothing in the open source world that could be fully leveraged for our require-ments.We ended up creating Druid,an open source,distributed, column-oriented,real-time analytical data store.In many ways, Druid shares similarities with other OLAP systems[30,35,22],in-teractive query systems[28],main-memory databases[14],as well as widely known distributed data stores[7,12,23].The distribution and query model also borrow ideas from current generation search infrastructure[25,3,4].This paper describes the architecture of Druid,explores the vari-ous design decisions made in creating an always-on production sys-tem that powers a hosted service,and attempts to help inform any-one who faces a similar problem about a potential method of solving it.Druid is deployed in production at several technology compa-nies2.The structure of the paper is as follows:wefirst describe the problem in Section2.Next,we detail system architecture from the point of view of how dataflows through the system in Section 3.We then discuss how and why data gets converted into a binary format in Section4.We briefly describe the query API in Section5 and present performance results in stly,we leave off with our lessons from running Druid in production in Section7,and related work in Section8.2.PROBLEM DEFINITIONDruid was originally designed to solve problems around ingest-ing and exploring large quantities of transactional events(log data). This form of timeseries data is commonly found in OLAP work-2http://druid.io/druid.htmlTimestamp Page Username Gender City Characters Added Characters Removed 2011-01-01T01:00:00Z Justin Bieber Boxer Male San Francisco1800252011-01-01T01:00:00Z Justin Bieber Reach Male Waterloo2912422011-01-01T02:00:00Z Ke$ha Helz Male Calgary1953172011-01-01T02:00:00Z Ke$ha Xeno Male Taiyuan3194170Table1:Sample Druid data for edits that have occurred on Wikipedia.flows and the nature of the data tends to be very append heavy.For example,consider the data shown in Table1.Table1contains data for edits that have occurred on Wikipedia.Each time a user edits a page in Wikipedia,an event is generated that contains metadata about the edit.This metadata is comprised of3distinct compo-nents.First,there is a timestamp column indicating when the edit was made.Next,there are a set dimension columns indicating var-ious attributes about the edit such as the page that was edited,the user who made the edit,and the location of the user.Finally,there are a set of metric columns that contain values(usually numeric) that can be aggregated,such as the number of characters added or removed in an edit.Our goal is to rapidly compute drill-downs and aggregates over this data.We want to answer questions like“How many edits were made on the page Justin Bieber from males in San Francisco?”and “What is the average number of characters that were added by peo-ple from Calgary over the span of a month?”.We also want queries over any arbitrary combination of dimensions to return with sub-second latencies.The need for Druid was facilitated by the fact that existing open source Relational Database Management Systems(RDBMS)and NoSQL key/value stores were unable to provide a low latency data ingestion and query platform for interactive applications[40].In the early days of Metamarkets,we were focused on building a hosted dashboard that would allow users to arbitrarily explore and visualize event streams.The data store powering the dashboard needed to return queries fast enough that the data visualizations built on top of it could provide users with an interactive experience.In addition to the query latency needs,the system had to be multi-tenant and highly available.The Metamarkets product is used in a highly concurrent environment.Downtime is costly and many busi-nesses cannot afford to wait if a system is unavailable in the face of software upgrades or network failure.Downtime for startups,who often lack proper internal operations management,can determine business success or failure.Finally,another challenge that Metamarkets faced in its early days was to allow users and alerting systems to be able to make business decisions in“real-time”.The time from when an event is created to when that event is queryable determines how fast inter-ested parties are able to react to potentially catastrophic situations in their systems.Popular open source data warehousing systems such as Hadoop were unable to provide the sub-second data ingestion latencies we required.The problems of data exploration,ingestion,and availability span multiple industries.Since Druid was open sourced in October2012, it been deployed as a video,network monitoring,operations mon-itoring,and online advertising analytics platform at multiple com-panies.3.ARCHITECTUREA Druid cluster consists of different types of nodes and each node type is designed to perform a specific set of things.We believe this design separates concerns and simplifies the complexity of the overall system.The different node types operate fairly independent of each other and there is minimal interaction among them.Hence, intra-cluster communication failures have minimal impact on data availability.To solve complex data analysis problems,the different node types come together to form a fully working system.The name Druid comes from the Druid class in many role-playing games:it is a shape-shifter,capable of taking on many different forms to fulfill various different roles in a group.The composition of andflow of data in a Druid cluster are shown in Figure1.3.1Real-time NodesReal-time nodes encapsulate the functionality to ingest and query event streams.Events indexed via these nodes are immediately available for querying.The nodes are only concerned with events for some small time range and periodically hand off immutable batches of events they have collected over this small time range to other nodes in the Druid cluster that are specialized in dealing with batches of immutable events.Real-time nodes leverage Zookeeper [19]for coordination with the rest of the Druid cluster.The nodes announce their online state and the data they serve in Zookeeper. Real-time nodes maintain an in-memory index buffer for all in-coming events.These indexes are incrementally populated as events are ingested and the indexes are also directly queryable.Druid be-haves as a row store for queries on events that exist in this JVM heap-based buffer.To avoid heap overflow problems,real-time nodes persist their in-memory indexes to disk either periodically or after some maximum row limit is reached.This persist process converts data stored in the in-memory buffer to a column oriented storage format described in Section4.Each persisted index is im-mutable and real-time nodes load persisted indexes into off-heap memory such that they can still be queried.This process is de-scribed in detail in[33]and is illustrated in Figure2.On a periodic basis,each real-time node will schedule a back-ground task that searches for all locally persisted indexes.The task merges these indexes together and builds an immutable block of data that contains all the events that have been ingested by a real-time node for some span of time.We refer to this block of data as a“segment”.During the handoff stage,a real-time node uploads this segment to a permanent backup storage,typically a distributed file system such as S3[12]or HDFS[36],which Druid refers to as “deep storage”.The ingest,persist,merge,and handoff steps are fluid;there is no data loss during any of the processes.Figure3illustrates the operations of a real-time node.The node starts at13:37and will only accept events for the current hour or the next hour.When events are ingested,the node announces that it is serving a segment of data for an interval from13:00to14:00.Every 10minutes(the persist period is configurable),the node willflush and persist its in-memory buffer to disk.Near the end of the hour, the node will likely see events for14:00to15:00.When this occurs, the node prepares to serve data for the next hour and creates a new in-memory index.The node then announces that it is also serving a segment from14:00to15:00.The node does not immediately merge persisted indexes from13:00to14:00,instead it waits for a configurable window period for straggling events from13:00toimmutable segment and hands the segment off.Once this segment is loaded and queryable somewhere else in the Druid cluster,the real-time nodeflushes all information about the data it collected for 13:00to14:00and unannounces it is serving this data.3.1.1Availability and ScalabilityReal-time nodes are a consumer of data and require a correspond-ing producer to provide the data monly,for data dura-bility purposes,a message bus such as Kafka[21]sits between the producer and the real-time node as shown in Figure4.Real-time nodes ingest data by reading events from the message bus.The time from event creation to event consumption is ordinarily on the order of hundreds of milliseconds.The purpose of the message bus in Figure4is two-fold.First,the message bus acts as a buffer for incoming events.A message bus such as Kafka maintains positional offsets indicating how far a con-sumer(a real-time node)has read in an event stream.Consumers can programmatically update these offsets.Real-time nodes update the nodes.The nodes have no knowledge of one another and are operationally simple;they only know how to load,drop,and serve immutable segments.Similar to real-time nodes,historical nodes announce their on-line state and the data they are serving in Zookeeper.Instructions to load and drop segments are sent over Zookeeper and contain infor-mation about where the segment is located in deep storage and how to decompress and process the segment.Before a historical node downloads a particular segment from deep storage,itfirst checks a local cache that maintains information about what segments already exist on the node.If information about a segment is not present in the cache,the historical node will proceed to download the segment from deep storage.This process is shown in Figure5.Once pro-cessing is complete,the segment is announced in Zookeeper.At this point,the segment is queryable.The local cache also allows for historical nodes to be quickly updated and restarted.On startup, the node examines its cache and immediately serves whatever data itfinds.Figure5:Historical nodes download immutable segments from deep storage.Segments must be loaded in memory before they can be queried.data,however,because the queries are served over HTTP,histori-cal nodes are still able to respond to query requests for the data they are currently serving.This means that Zookeeper outages do not impact current data availability on historical nodes.3.3Broker NodesBroker nodes act as query routers to historical and real-time nodes. Broker nodes understand the metadata published in Zookeeper about what segments are queryable and where those segments are located. Broker nodes route incoming queries such that the queries hit the right historical or real-time nodes.Broker nodes also merge partial results from historical and real-time nodes before returning afinal consolidated result to the caller.3.3.1CachingBroker nodes contain a cache with a LRU[31,20]invalidation strategy.The cache can use local heap memory or an external dis-tributed key/value store such as Memcached[16].Each time a bro-ker node receives a query,itfirst maps the query to a set of seg-ments.Results for certain segments may already exist in the cache and there is no need to recompute them.For any results that do not exist in the cache,the broker node will forward the query to the correct historical and real-time nodes.Once historical nodes return their results,the broker will cache these results on a per segment ba-sis for future use.This process is illustrated in Figure6.Real-time data is never cached and hence requests for real-time data will al-ways be forwarded to real-time nodes.Real-time data is perpetually changing and caching the results is unreliable.The cache also acts as an additional level of data durability.In the event that all historical nodes fail,it is still possible to query results if those results already exist in the cache.3.3.2AvailabilityIn the event of a total Zookeeper outage,data is still queryable. If broker nodes are unable to communicate to Zookeeper,they use their last known view of the cluster and continue to forward queries to real-time and historical nodes.Broker nodes make the assump-tion that the structure of the cluster is the same as it was before the outage.In practice,this availability model has allowed our Druid cluster to continue serving queries for a significant period of time while we diagnosed Zookeeper outages.3.4Coordinator NodesDruid coordinator nodes are primarily in charge of data manage-ment and distribution on historical nodes.The coordinator nodes tell historical nodes to load new data,drop outdated data,replicate data,and move data to load balance.Druid uses a multi-version concurrency control swapping protocol for managing immutable segments in order to maintain stable views.If any immutable seg-ment contains data that is wholly obsoleted by newer segments,the outdated segment is dropped from the cluster.Coordinator nodes undergo a leader-election process that determines a single node that runs the coordinator functionality.The remaining coordinator nodes act as redundant backups.A coordinator node runs periodically to determine the current state of the cluster.It makes decisions by comparing the expected state of the cluster with the actual state of the cluster at the time of the run.As with all Druid nodes,coordinator nodes maintain a Zookeeper connection for current cluster information.Coordinator nodes also maintain a connection to a MySQL database that con-tains additional operational parameters and configurations.One of the key pieces of information located in the MySQL database is a table that contains a list of all segments that should be served by historical nodes.This table can be updated by any service that cre-ates segments,for example,real-time nodes.The MySQL database also contains a rule table that governs how segments are created, destroyed,and replicated in the cluster.3.4.1RulesRules govern how historical segments are loaded and dropped from the cluster.Rules indicate how segments should be assigned to different historical node tiers and how many replicates of a segment should exist in each tier.Rules may also indicate when segments should be dropped entirely from the cluster.Rules are usually set for a period of time.For example,a user may use rules to load the most recent one month’s worth of segments into a“hot”cluster,the most recent one year’s worth of segments into a“cold”cluster,and drop any segments that are older.The coordinator nodes load a set of rules from a rule table in the MySQL database.Rules may be specific to a certain data source and/or a default set of rules may be configured.The coordinator node will cycle through all available segments and match each seg-ment with thefirst rule that applies to it.3.4.2Load BalancingIn a typical production environment,queries often hit dozens or even hundreds of segments.Since each historical node has limited resources,segments must be distributed among the cluster to en-sure that the cluster load is not too imbalanced.Determining opti-mal load distribution requires some knowledge about query patterns and speeds.Typically,queries cover recent segments spanning con-tiguous time intervals for a single data source.On average,queries that access smaller segments are faster.These query patterns suggest replicating recent historical seg-ments at a higher rate,spreading out large segments that are close in time to different historical nodes,and co-locating segments from different data sources.To optimally distribute and balance seg-ments among the cluster,we developed a cost-based optimization procedure that takes into account the segment data source,recency, and size.The exact details of the algorithm are beyond the scope of this paper and may be discussed in future literature.3.4.3ReplicationCoordinator nodes may tell different historical nodes to load a copy of the same segment.The number of replicates in each tier of the historical compute cluster is fully configurable.Setups that require high levels of fault tolerance can be configured to have a high number of replicas.Replicated segments are treated the same as the originals and follow the same load distribution algorithm.By replicating segments,single historical node failures are transparent in the Druid cluster.We use this property for software upgrades. We can seamlessly take a historical node offline,update it,bring it back up,and repeat the process for every historical node in a cluster. Over the last two years,we have never taken downtime in our Druid cluster for software upgrades.3.4.4AvailabilityDruid coordinator nodes have Zookeeper and MySQL as external dependencies.Coordinator nodes rely on Zookeeper to determine what historical nodes already exist in the cluster.If Zookeeper be-comes unavailable,the coordinator will no longer be able to send instructions to assign,balance,and drop segments.However,these operations do not affect data availability at all.The design principle for responding to MySQL and Zookeeper failures is the same:if an external dependency responsible for co-ordination fails,the cluster maintains the status quo.Druid uses MySQL to store operational management information and segment metadata information about what segments should exist in the clus-ter.If MySQL goes down,this information becomes unavailable to coordinator nodes.However,this does not mean data itself is un-available.If coordinator nodes cannot communicate to MySQL, they will cease to assign new segments and drop outdated ones. Broker,historical,and real-time nodes are still queryable during MySQL outages.4.STORAGE FORMATData tables in Druid(called data sources)are collections of times-tamped events and partitioned into a set of segments,where each segment is typically5–10million rows.Formally,we define a seg-ment as a collection of rows of data that span some period of time. Segments represent the fundamental storage unit in Druid and repli-cation and distribution are done at a segment level.with results computed on historical and real-time nodes.performance degradations [1].Druid has multiple column types to represent various data for-mats.Depending on the column type,different compression meth-ods are used to reduce the cost of storing a column in memory and on disk.In the example given in Table 1,the page,user,gender,and city columns only contain strings.Storing strings directly is unnecessarily costly and string columns can be dictionary encoded instead.Dictionary encoding is a common method to compress data and has been used in other data stores such as PowerDrill [17].In the example in Table 1,we can map each page to a unique integer identifier.Justin Bieber ->0Ke$ha ->1This mapping allows us to represent the page column as an in-teger array where the array indices correspond to the rows of theoriginal data set.For the page column,we can represent the unique pages as follows:[0,0,1,1]The resulting integer array lends itself very well to compression methods.Generic compression algorithms on top of encodings are extremely common in column-stores.Druid uses the LZF [24]com-pression algorithm.Similar compression methods can be applied to numeric columns.For example,the characters added and characters removed columns in Table 1can also be expressed as individual arrays.Characters Added ->[1800,2912,1953,3194]Characters Removed ->[25,42,17,170]In this case,we compress the raw values as opposed to their dic-tionary representations.Figure 7:Integer array size versus Concise set size.Indices for Filtering Datamany real world OLAP workflows,queries are issued for the results of some set of metrics where some set of di-specifications are met.An example query is:“How many edits were done by users in San Francisco who are also This query is filtering the Wikipedia data set in Table 1based a Boolean expression of dimension values.In many real world sets,dimension columns contain strings and metric columns contain numeric values.Druid creates additional lookup indices for string columns such that only those rows that pertain to a particular query filter are ever scanned.Let us consider the page column in Table 1.For each unique page in Table 1,we can form some representation indicating in which table rows a particular page is seen.We can store this information in a binary array where the array indices represent our rows.If a particular page is seen in a certain row,that array index is marked as 1.For example:Justin Bieber ->rows [0,1]->[1][1][0][0]Ke$ha ->rows [2,3]->[0][0][1][1]Justin Bieber is seen in rows 0and 1.This mapping of col-umn values to row indices forms an inverted index [39].To know which rows contain Justin Bieber or Ke$ha ,we can OR together the two arrays.[0][1][0][1]OR [1][0][1][0]=[1][1][1][1]This approach of performing Boolean operations on large bitmap sets is commonly used in search engines.Bitmap indices for OLAP workloads is described in detail in [32].Bitmap compression al-gorithms are a well-defined area of research [2,44,42]and often utilize run-length encoding.Druid opted to use the Concise algo-rithm [10].Figure 7illustrates the number of bytes using Concise compression versus using an integer array.The results were gen-erated on a cc2.8xlarge system with a single thread,2G heap,512m young gen,and a forced GC between each run.The data set is a single day’s worth of data collected from the Twitter garden hose [41]data stream.The data set contains 2,272,295rows and12dimensions of varying cardinality.As an additional comparison, we also resorted the data set rows to maximize compression.In the unsorted case,the total Concise size was53,451,144bytes and the total integer array size was127,248,520bytes.Overall, Concise compressed sets are about42%smaller than integer ar-rays.In the sorted case,the total Concise compressed size was 43,832,884bytes and the total integer array size was127,248,520 bytes.What is interesting to note is that after sorting,global com-pression only increased minimally.4.2Storage EngineDruid’s persistence components allows for different storage en-gines to be plugged in,similar to Dynamo[12].These storage en-gines may store data in an entirely in-memory structure such as the JVM heap or in memory-mapped structures.The ability to swap storage engines allows for Druid to be configured depending on a particular application’s specifications.An in-memory storage en-gine may be operationally more expensive than a memory-mapped storage engine but could be a better alternative if performance is critical.By default,a memory-mapped storage engine is used. When using a memory-mapped storage engine,Druid relies on the operating system to page segments in and out of memory.Given that segments can only be scanned if they are loaded in memory,a memory-mapped storage engine allows recent segments to retain in memory whereas segments that are never queried are paged out. The main drawback with using the memory-mapped storage engine is when a query requires more segments to be paged into memory than a given node has capacity for.In this case,query performance will suffer from the cost of paging segments in and out of memory.5.QUERY APIDruid has its own query language and accepts queries as POST requests.Broker,historical,and real-time nodes all share the same query API.The body of the POST request is a JSON object containing key-value pairs specifying various query parameters.A typical query will contain the data source name,the granularity of the result data, time range of interest,the type of request,and the metrics to ag-gregate over.The result will also be a JSON object containing the aggregated metrics over the time period.Most query types will also support afilter set.Afilter set is a Boolean expression of dimension name and value pairs.Any num-ber and combination of dimensions and values may be specified. When afilter set is provided,only the subset of the data that per-tains to thefilter set will be scanned.The ability to handle complex nestedfilter sets is what enables Druid to drill into data at any depth. The exact query syntax depends on the query type and the infor-mation requested.A sample count query over a week of data is asfollows:{"queryType":"timeseries", "dataSource":"wikipedia","intervals":"2013-01-01/2013-01-08","filter":{"type":"selector","dimension":"page","value":"Ke$ha"},"granularity":"day","aggregations":[{"type":"count","name":"rows"}]}The query shown above will return a count of the number of rows in the Wikipedia data source from2013-01-01to2013-01-08,fil-tered for only those rows where the value of the“page”dimension is equal to“Ke$ha”.The results will be bucketed by day and will be a JSON array of the following form:[{"timestamp":"2012-01-01T00:00:00.000Z","result":{"rows":393298}},{"timestamp":"2012-01-02T00:00:00.000Z","result":{"rows":382932}},...{"timestamp":"2012-01-07T00:00:00.000Z","result":{"rows":1337}}]Druid supports many types of aggregations including sums on floating-point and integer types,minimums,maximums,and com-plex aggregations such as cardinality estimation and approximate quantile estimation.The results of aggregations can be combined in mathematical expressions to form other aggregations.It is be-yond the scope of this paper to fully describe the query API but more information can be found online3.As of this writing,a join query for Druid is not yet implemented. This has been a function of engineering resource allocation and use case decisions more than a decision driven by technical merit.In-deed,Druid’s storage format would allow for the implementation of joins(there is no loss offidelity for columns included as dimen-sions)and the implementation of them has been a conversation that we have every few months.To date,we have made the choice that the implementation cost is not worth the investment for our organi-zation.The reasons for this decision are generally two-fold.1.Scaling join queries has been,in our professional experience,a constant bottleneck of working with distributed databases.2.The incremental gains in functionality are perceived to beof less value than the anticipated problems with managinghighly concurrent,join-heavy workloads.A join query is essentially the merging of two or more streams of data based on a shared set of keys.The primary high-level strate-gies for join queries we are aware of are a hash-based strategy or a sorted-merge strategy.The hash-based strategy requires that all but one data set be available as something that looks like a hash table, a lookup operation is then performed on this hash table for every row in the“primary”stream.The sorted-merge strategy assumes that each stream is sorted by the join key and thus allows for the in-cremental joining of the streams.Each of these strategies,however, requires the materialization of some number of the streams either in sorted order or in a hash table form.When all sides of the join are significantly large tables(>1bil-lion records),materializing the pre-join streams requires complex distributed memory management.The complexity of the memory management is only amplified by the fact that we are targeting highly concurrent,multitenant workloads.This is,as far as we are aware, an active academic research problem that we would be willing to help resolve in a scalable manner.6.PERFORMANCEDruid runs in production at several organizations,and to demon-strate its performance,we have chosen to share some real world numbers for the main production cluster running at Metamarkets as of early2014.For comparison with other databases we also include results from synthetic workloads on TPC-H data.3http://druid.io/docs/latest/Querying.html。
大数据应用的参考文献
大数据应用的参考文献关于大数据应用的参考文献可能会涉及多个方面，包括大数据技术、应用案例、数据分析方法等。
以下是一些建议，但请注意，这只是一个初步的列表，具体的参考文献可能取决于您感兴趣的具体主题：* 书籍：* "Big Data: A Revolution That Will Transform How We Live, Work, and Think" by Viktor Mayer-Schönberger and Kenneth Cukier.* "Hadoop: The Definitive Guide" by Tom White.* "Data Science for Business" by Foster Provost and Tom Fawcett.* 期刊论文：* Chen, M., Mao, S., and Liu, Y. (2014). "Big Data: A Survey." Mobile Networks and Applications, 19(2), 171-209.* Manyika, J., Chui, M., Brown, B., et al. (2011). "Big Data: The Next Frontier for Innovation, Competition, and Productivity." McKinsey Global Institute.* 技术报告和白皮书：* Dean, J., and Ghemawat, S. (2008). "MapReduce: Simplified Data Processing on Large Clusters." Google, Inc.* EMC. (2012). "Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data."* 会议论文：* Kambatla, K., Kollias, G., Kumar, V., and Grama, A. (2014). "Trends in Big Data Analytics." Journal of King Saud University -Computer and Information Sciences and Engineering.* 在线资源和指南：* Apache Hadoop Documentation: Hadoop Documentation.* Kaggle Datasets: Kaggle Datasets.确保在引用这些文献时查阅最新版本，以获取最准确和最新的信息。
大数据中常见的文件存储格式以及hadoop中支持的压缩算法 -回复
大数据中常见的文件存储格式以及hadoop中支持的压缩算法-回复大数据中常见的文件存储格式以及Hadoop中支持的压缩算法大数据时代的到来使得数据的处理和存储变得更加复杂和繁多。
为了高效地处理大规模数据，大数据系统通常需要选择合适的文件存储格式以及压缩算法。
在Hadoop生态系统中，也提供了一些常见的文件存储格式和支持的压缩算法。
本文将一步一步地回答这个问题，来帮助读者更好地了解大数据中常见的文件存储格式以及Hadoop中支持的压缩算法。
首先，我们来了解一些常见的文件存储格式。
在大数据领域，一般有以下几种常见的文件存储格式：1. 文本文件（Text File）：文本文件是一种最基本的文件存储格式，它以文本的形式存储数据，每一行为一个记录。
这种存储格式具有良好的兼容性和可读性，但是由于数据以文本的形式存储，所以在存储和传输过程中会占用较大的空间。
2. 序列文件（Sequence File）：序列文件是一种二进制文件存储格式，它将多个键-值对组织在一个文件中。
序列文件具有紧凑的存储结构，可以高效地进行数据的读写操作。
在Hadoop中，序列文件是最常见的文件格式之一，常用于MapReduce和Spark等大数据处理框架中。
3. Avro文件（Avro File）：Avro是一种数据序列化系统，它定义了一种二进制数据格式和通信协议。
Avro文件采用了类似于序列文件的存储结构，但是它还支持动态模式和数据压缩等特性。
Avro文件可以很好地适应数据的变化和演化，所以在大数据领域中得到了广泛应用。
4. 列式存储（Columnar Storage）：列式存储是一种以列为单位存储数据的方式。
它将同一列的数据存储在一起，这样可以提高数据的压缩比和查询效率。
在大数据领域中，列式存储已经成为一种常见的存储方式，如Apache Parquet和Apache ORC等。
接下来，我们来了解一些Hadoop中支持的压缩算法。
在Hadoop中，提供了一些常见的压缩算法，用于对文件进行压缩和解压缩。
大数据文献综述范文docx(一)
大数据文献综述范文docx（一）引言概述：本文旨在综述大数据领域的相关文献，通过对现有研究成果的整理和分析，归纳出目前大数据领域的研究热点和发展趋势，为进一步的研究提供参考和借鉴。
正文：一、大数据的定义与特征1. 大数据的概念及演变2. 大数据的四个基本特征：3V（Volume、Velocity、Variety）+ Value3. 大数据与传统数据的差异与联系4. 大数据对经济、社会、科学等领域的影响二、大数据的采集与存储1. 大数据采集的主要方法：传感器网络、物联网等2. 大数据存储的常用技术：分布式文件系统、NoSQL数据库等3. 大数据采集和存储过程中面临的挑战及解决方案4. 大数据隐私与安全保护的技术与方法三、大数据的分析与挖掘1. 大数据分析的基本流程与方法：数据清洗、数据集成、数据挖掘、模型建立、结果验证等2. 大数据分析常用的算法和技术：关联规则挖掘、聚类分析、分类与预测等3. 大数据分析的应用领域与案例研究4. 大数据分析在决策支持中的作用与价值四、大数据的可视化与交互1. 大数据可视化的基本原理及方法2. 大数据可视化工具的比较与选择3. 大数据可视化的应用案例与效果评估4. 大数据可视化的交互技术与方法五、大数据的发展趋势与挑战1. 大数据发展趋势：云计算、边缘计算、人工智能等技术的融合与应用2. 大数据面临的挑战：数据质量、隐私与安全、算法效率等问题3. 大数据发展的政策与法律环境4. 大数据发展的前景与应用展望总结：通过对大数据领域相关文献的综述，可以发现大数据在经济、社会和科学领域的重要作用和潜在价值。
同时，大数据采集、存储、分析与可视化面临许多挑战和难题，需要我们进一步研究和探索。
随着技术的不断发展和应用的深入推广，大数据必将在各个领域中发挥更大的作用，为社会进步和经济发展提供有力支持。
大数据中常见的文件存储格式以及hadoop中支持的压缩算法
大数据中常见的文件存储格式以及hadoop中支持的压缩算法摘要：1.大数据中的文件存储格式a.文本格式b.二进制格式c.列式存储格式d.对象存储格式2.Hadoop 中的文件存储格式a.HDFSb.Hivec.Impala3.Hadoop 支持的压缩算法a.Gzipb.Snappyc.LZOd.Parquet正文：随着大数据技术的发展，数据存储和处理能力不断提高，文件存储格式和压缩算法的选择对于数据处理效率至关重要。
本文将介绍大数据中常见的文件存储格式以及Hadoop 中支持的压缩算法。
一、大数据中的文件存储格式1.文本格式：文本格式是一种常见的数据存储格式，适用于存储结构化或半结构化的数据。
常见的文本格式包括CSV（逗号分隔值）和JSON （JavaScript 对象表示法）。
文本格式具有易于阅读和编写的优势，但不适用于存储大型数据集。
2.二进制格式：二进制格式适用于存储结构化数据，如数据库中的数据。
它可以有效地存储数据，并快速进行数据检索和处理。
常见的二进制格式包括Protobuf 和Avro。
二进制格式具有存储效率高、数据处理速度快的优势，但阅读和编写较为困难。
3.列式存储格式：列式存储格式是一种适用于大数据处理的存储格式。
它将数据按照列进行存储，以提高数据压缩率和查询速度。
常见的列式存储格式包括Parquet 和ORC。
列式存储格式具有存储空间小、查询速度快的优势，但写入数据时需要对数据进行列式处理。
4.对象存储格式：对象存储格式是一种以对象为单位存储数据的格式。
每个对象都包含一个唯一的键和数据内容。
常见的对象存储格式包括JSON 和XML。
对象存储格式具有数据结构灵活、易于扩展的优势，但不适用于所有场景。
二、Hadoop 中的文件存储格式1.HDFS：HDFS（Hadoop 分布式文件系统）是Hadoop 中的基础文件存储系统。
它适用于存储大规模数据，并提供高可靠性和容错能力。
HDFS 支持多种文件存储格式，如文本格式、二进制格式和列式存储格式。
数据库设计毕业论文
数据库毕业论文目录摘要 (1)Abstract. (1)1 引言 (1)1.1 图书管理的现状 (2)1.2 现有图书管理系统的概述 (3)1.3 选题的目的、意义 (3)1.4 图书管理系统的可行性分析 (3)1.5 系统开发运行环境 (4)2 图书管理系统开发相关技术的介绍 (4)2.1 的介绍 (4)2.1.1 的优势介绍 (4)2.1.2 的特点 (5)2.2 SQL Server 2005 概述 (5)2.3 Web技术 (7)2.3.1 浏览器/服务器(Browser/Server)结构 (7)2.3.2 IIS服务器技术 (7)3 系统总体设计分析 (8)3.1 系统需求分析 (8)3.2 系统实现的目标 (8)3.3 系统功能模块设计 (8)3.4 系统功能结构图 (9)3.5 系统流程图 (11)4 数据总体结构设计 (12)4.1 数据库概念结构设计 (12)4.2 数据库逻辑结构设计 (13)4.3 图书管理系统的系统E-R图 (15)4.4 数据表设计 (16)5 图书管理系统详细设计 (18)5.1 系统流程分析 (18)5.2 主要模块的运行 (19)5.2.1 登陆界面 (19)5.2.2 图书信息管理模块 (19)5.2.3 图书借还信息模块 (21)5.3 系统开发的遇到的相关问题及解决 (21)5.3.1 图书管理系统索引 (21)5.3.2 如何验证输入的字符串 (22)5.3.3 自动计算图书归还日期 (23)5.3.4 系统登陆验证码的实现 (23)6 结论 (25)6.1 主要研究内容及成果 (26)6.2 今后进一步研究方向 (26)参考文献 (26)致谢 (27)学校图书管理系统的开发数理信息与工程学院计算机科学与技术金维律（05600114）摘要：图书管理系统是智能办公系统(IOA)的重要组成部分，因此，图书管理系统也以方便、快捷的优点正慢慢地进入人们的生活，将传统的图书管理方式彻底的解脱出来，提高效率，减轻工作人员以往繁忙的工作，减小出错的概率，使读者可以花更多的时间在选择书和看书上。
大数据导论（1）——“大数据”相关概念、5V特征、数据类型
⼤数据导论（1）——“⼤数据”相关概念、5V特征、数据类型在过去的⼗⼏年中，各个领域都出现了⼤规模的数据增长，⽽各类仪器、通信⼯具以及集成电路⾏业的发展也为海量数据的产⽣与存储提供了软件条件与硬件⽀持。
⼤数据，这⼀术语正是产⽣在全球数据爆炸式增长的背景下，⽤来形容庞⼤的数据集合。
由于⼤数据为挖掘隐藏价值提供了新的可能，如今⼯业界、研究界甚⾄政府部门等各⾏各业都对⼤数据这⼀研究领域密切关注。
尽管⽬前⼤数据的重要性已被社会各界认同，但⼤数据的定义却众说纷纭，Apache Hadoop组织、麦肯锡、国际数据公司等其他研究者都对⼤数据有不同的定义。
但⽆论是哪种定义都具有⼀定的狭义性。
因此，我们可以从⼤数据的“5V”特征对⼤数据进⾏识别。
同时，企业内部在思考如何构建数据集时，也可以从此特征⼊⼿。
以下就是⼤数据的“5V”特征图。
1. 容量（Volume）是指⼤规模的数据量，并且数据量呈持续增长趋势。
⽬前⼀般指超过10T规模的数据量，但未来随着技术的进步，符合⼤数据标准的数据集⼤⼩也会变化。
⼤规模的数据对象构成的集合，即称为“数据集”。
不同的数据集具有维度不同、稀疏性不同（有时⼀个数据记录的⼤部分特征属性都为0）、以及分辨率不同（分辨率过⾼，数据模式可能会淹没在噪声中；分辨率过低，模式⽆从显现）的特性。
因此数据集也具有不同的类型，常见的数据集类型包括：记录数据集（是记录的集合，即数据库中的数据集）、基于图形的数据集（数据对象本⾝⽤图形表⽰，且包含数据对象之间的联系）和有序数据集（数据集属性涉及时间及空间上的联系，存储时间序列数据、空间数据等）。
2. 速率（Velocity）即数据⽣成、流动速率快。
数据流动速率指指对数据采集、存储以及分析具有价值信息的速度。
因此也意味着数据的采集和分析等过程必须迅速及时。
3. 多样性（Variety）指是⼤数据包括多种不同格式和不同类型的数据。
数据来源包括⼈与系统交互时与机器⾃动⽣成，来源的多样性导致数据类型的多样性。
Hadoop中的数据压缩和编码技术解析
Hadoop中的数据压缩和编码技术解析数据处理和存储一直是大数据领域的核心问题。
Hadoop作为一个开源的分布式计算框架，提供了高可靠性和高扩展性的解决方案。
在Hadoop中，数据压缩和编码技术被广泛应用，以减少存储空间和提高数据传输效率。
本文将对Hadoop中的数据压缩和编码技术进行解析。
一、数据压缩技术数据压缩是将数据从原始格式转换为更紧凑的格式，以减少存储空间和提高数据传输效率。
在Hadoop中，常用的数据压缩技术包括LZO、Snappy和Gzip。
1. LZO压缩LZO是一种基于Lempel-Ziv算法的快速压缩算法，具有较高的压缩和解压缩速度。
它在Hadoop中被广泛使用，特别适合于大型数据集的处理。
LZO压缩算法通过将重复的数据块替换为指向先前出现的相同数据块的指针来实现数据压缩。
2. Snappy压缩Snappy是Google开发的一种高速压缩和解压缩库。
它具有比LZO更高的压缩速度和较低的压缩比。
Snappy压缩算法通过使用字典和短字节序列的替换来实现数据压缩。
3. Gzip压缩Gzip是一种基于DEFLATE算法的压缩算法，具有较高的压缩比和较低的压缩速度。
Gzip压缩算法通过使用动态霍夫曼编码来实现数据压缩。
二、数据编码技术数据编码是将数据转换为特定格式的过程，以便在存储和传输过程中更有效地使用空间和带宽。
在Hadoop中，常用的数据编码技术包括二进制编码和可变长度编码。
1. 二进制编码二进制编码是将数据转换为二进制格式的编码方式。
在Hadoop中，常用的二进制编码技术包括Avro和Protocol Buffers。
- Avro：Avro是一种基于JSON的数据序列化系统，支持动态数据类型和架构演化。
它通过使用二进制编码和紧凑的数据格式来提高数据传输效率。
- Protocol Buffers：Protocol Buffers是一种由Google开发的语言无关的数据序列化系统。
参考文献_大数据技术与应用基础_[共2页]
![参考文献_大数据技术与应用基础_[共2页]](https://img.taocdn.com/s3/m/031be71058f5f61fb63666cd.png)
参考文献
[1]黄宜华．深入理解大数据[M]．北京：机械工业出版社, 2014.
[2]张良均．Hadoop大数据分析与挖掘实战[M]．北京：机械工业出版社, 2015.
[3]陆嘉恒．Hadoop实战[M].北京：机械工业出版社, 2012.
[4]刘鹏．实战Hadoop：开启通向云计算的捷径[M]．北京：电子工业出版社, 2011.
[5]王晓华．MapReduce 2.0源码分析与编程实战[M]．北京：人民邮电出版社, 2014.
[6]乔治．HBase权威指南[M］．北京：人民邮电出版社，2013.
[7]Karou H．Spark快速大数据分析[M].王道远,译.北京：人民邮电出版社，2015.
[8]王铭坤，袁少光，朱永利，等．基于Storm的海量数据实时聚类[J]．计算机应用, 2014, 34(11):3078-3081.
[9] 赵刚．大数据—技术与应用实践指南[M].北京：电子工业出版社, 2013.。
大数据参考文献
大数据参考文献大数据研究综述陶雪娇，胡晓峰，刘洋(国防大学信息作战与指挥训练教研部，北京100091)研究机构Gartne:的定义:大数据是指需要新处理模式才能具有更强的决策力、洞察发现力和流程优化能力的海量、高增长率和多样化的信息资产。
维基百科的定义:大数据指的是所涉及的资料量规模巨大到无法通过目前主流软件工具，在合理时间内达到撷取、管理、处理并整理成为帮助企业经营决策目的的资讯。
麦肯锡的定义:大数据是指无法在一定时间内用传统数据库软件工具对其内容进行采集、存储、管理和分析的赞据焦合。
数据挖掘的焦点集中在寻求数据挖掘过程中的可视化方法，使知识发现过程能够被用户理解，便于在知识发现过程中的人机交互;研究在网络环境卜的数据挖掘技术，特别是在Internet上建立数据挖掘和知识发现((DMKD)服务器，与数据库服务器配合，实现数据挖掘;加强对各种非结构化或半结构化数据的挖掘，如多媒体数据、文本数据和图像数据等。
5.1数据量的成倍增长挑战数据存储能力大数据及其潜在的商业价值要求使用专门的数据库技术和专用的数据存储设备，传统的数据库追求高度的数据一致性和容错性，缺乏较强的扩展性和较好的系统可用性，小能有效存储视频、音频等非结构化和半结构化的数据。
目前，数据存储能力的增长远远赶小上数据的增长，设计最合理的分层存储架构成为信息系统的关键。
5.2数据类型的多样性挑战数据挖掘能力数据类型的多样化，对传统的数据分析平台发出了挑战。
从数据库的观点看，挖掘算法的有效性和可伸缩性是实现数据挖掘的关键，而现有的算法往往适合常驻内存的小数据集，大型数据库中的数据可能无法同时导入内存，随着数据规模的小断增大，算法的效率逐渐成为数据分析流程的瓶颈。
要想彻底改变被动局面，需要对现有架构、组织体系、资源配置和权力结构进行重组。
5.3对大数据的处理速度挑战数据处理的时效性随着数据规模的小断增大，分析处理的时间相应地越来越长，而大数据条件对信息处理的时效性要求越来越高。
avro and parquet viewer原理
avro and parquet viewer原理Avro 和Parquet 是两种常用的数据存储格式，特别是在大数据领域。
它们各自都有一些特点和优势，使得它们在特定的场景中很有用。
下面我将简要介绍Avro 和Parquet 的原理。
AvroAvro 是由Apache 开发的一种数据序列化系统。
它允许你定义数据的模式（schema），然后将数据序列化为二进制格式。
Avro 的主要特点包括：模式演化：Avro 支持模式的演化，这意味着你可以在不破坏向后兼容性的情况下更新你的数据模式。
跨语言支持：Avro 支持多种编程语言，包括Java、C#、Python、Ruby 等。
高效的二进制格式：Avro 的二进制格式是紧凑的，并且为序列化和反序列化提供了快速的性能。
Avro 的数据存储通常涉及到Avro 文件，这些文件包含了序列化的数据和与之关联的模式。
要查看Avro 文件的内容，你需要一个能够解析Avro 格式的工具，这样的工具会读取文件，解析出数据，并根据模式将数据转换为人类可读的格式。
ParquetParquet 是一种列式存储格式，特别适用于分析型查询。
它优化了对于Hadoop 和其他大数据处理框架的读写性能。
Parquet 的主要特点包括：列式存储：Parquet 以列为单位存储数据，这有助于分析型查询更快地读取所需的数据列，而不需要读取整个行。
高效的压缩和编码：Parquet 支持多种压缩和编码技术，以减少存储空间的占用并提高查询性能。
支持多种数据类型：Parquet 支持多种数据类型，包括基本类型、复杂类型和嵌套类型。
要查看Parquet 文件的内容，你需要一个能够解析Parquet 格式的工具。
这样的工具会读取文件，解析出列数据，并将数据以表格的形式展示，这样你就可以查看和分析数据了。
总结Avro 和Parquet 的主要区别在于它们的存储方式和用途。
Avro 更适合作为通用的数据序列化格式，而Parquet 则更适合用于分析型查询的列式存储。
avro语法
avro语法1. 什么是avro语法Avro是一种数据序列化系统，用于将数据进行编码和解码。
它采用了一种紧凑的二进制格式，并具有快速、高效的性能。
Avro语法是用于定义数据结构的一种语法，类似于JSON或XML。
它提供了一种简单而灵活的方式来描述数据模型，并且可以轻松地进行扩展和演化。
2. Avro数据模型Avro数据模型由三个主要部分组成：记录（record）、枚举（enum）和联合（union）。
以下是每个部分的详细介绍：2.1 记录（record）记录是Avro中最常用的数据结构，类似于其他编程语言中的类或结构体。
它由一组字段组成，每个字段都有一个名称和一个类型。
以下是一个记录的示例：{"type": "record","name": "Person","fields": [{"name": "name", "type": "string"},{"name": "age", "type": "int"}]}在上面的示例中，我们定义了一个名为Person的记录，它有两个字段：name和age。
name的类型是string，age的类型是int。
2.2 枚举（enum）枚举用于定义一组可能的取值。
它类似于其他编程语言中的枚举类型。
以下是一个枚举的示例：{"type": "enum","name": "Color","symbols": ["RED", "GREEN", "BLUE"]}在上面的示例中，我们定义了一个名为Color的枚举，它有三个可能的取值：RED、GREEN、BLUE。
大数据中常见的文件存储格式以及hadoop中支持的压缩算法
大数据中常见的文件存储格式以及Hadoop中支持的压缩算法一、引言在大数据领域，文件存储格式和压缩算法是非常重要的技术，对数据的存储和处理效率有着直接的影响。
本文将介绍大数据中常见的文件存储格式，以及Hadoop中支持的压缩算法。
二、文件存储格式2.1 文本文件（Text File）文本文件是最常见的文件存储格式之一，它以纯文本的形式存储数据。
文本文件具有可读性好、易于处理的特点，但由于没有压缩，文件大小较大。
2.2 序列文件（Sequence File）序列文件是Hadoop中一种常见的文件存储格式，它将数据按照序列化的方式存储，可以高效地进行读写操作。
序列文件支持多种数据类型，并且可以进行压缩，以减小文件大小。
2.3 AvroAvro是一种数据序列化系统，它定义了一种数据格式以及一种通信协议。
Avro文件使用二进制格式存储数据，具有高效的压缩和快速的读写速度。
Avro文件还可以定义数据的模式，使得数据的结构更加清晰。
2.4 ParquetParquet是一种列式存储格式，它将数据按照列存储，可以高效地进行数据压缩和查询。
Parquet文件适用于大规模数据分析，可以提供更高的查询性能和存储效率。
2.5 ORCORC（Optimized Row Columnar）是一种优化的行列存储格式，它将数据按照行和列存储，可以高效地进行数据压缩和查询。
ORC文件适用于大规模数据分析，可以提供更高的查询性能和存储效率。
三、Hadoop中支持的压缩算法3.1 GzipGzip是一种常见的压缩算法，它使用DEFLATE算法对数据进行压缩。
Gzip压缩算法可以在保证一定压缩比的情况下，提供较快的压缩和解压缩速度。
在Hadoop中，Gzip是一种常用的压缩算法。
3.2 SnappySnappy是一种高速压缩算法，它在保证一定压缩比的情况下，提供非常快速的压缩和解压缩速度。
Snappy压缩算法适用于对速度要求较高的场景，如实时数据处理。
hadoop的基本概念及在大数据领域的应用。 -回复
hadoop的基本概念及在大数据领域的应用。
-回复Hadoop的基本概念及在大数据领域的应用随着互联网的蓬勃发展和移动设备的广泛普及，数据的规模和种类呈现出爆发式增长的趋势。
为了能够处理这些大规模的数据集，需要一种分布式计算框架来存储和处理海量的数据。
Hadoop作为大数据领域中最为常见的分布式计算框架，其基本概念和应用方式备受关注。
Hadoop最初是由Apache软件基金会开发的，它是一个开源的分布式文件系统和分布式计算框架，在处理大规模数据时表现出色。
Hadoop的主要特点是可靠性、扩展性和容错性。
Hadoop的核心组成部分是HDFS（Hadoop Distributed File System）和MapReduce。
HDFS是一种分布式文件系统，它能够将大规模的数据分散存储在多个硬件节点上，并展现为一个统一的文件系统。
HDFS的优势在于其高容错性和高可扩展性，通过数据的冗余备份和数据块在集群中的分布，可以有效地处理节点故障和数据丢失问题。
MapReduce是Hadoop的分布式计算框架，它的设计思想是将计算任务划分为更小的子任务，并在集群中的多个节点上进行并行处理。
MapReduce采用了“映射（Map）”和“归约（Reduce）”的编程模型。
在Map阶段，原始数据被分割成适合处理的小块，并由多个节点并行进行数据处理。
而Reduce阶段则是对Map阶段的结果进行合并和归约，最终得到一个汇总的结果。
除了HDFS和MapReduce，Hadoop还包括一些其他的相关工具和组件，如YARN（Yet Another Resource Negotiator）和HBase。
YARN是Hadoop的资源管理系统，它负责将计算任务分配给集群中的不同节点，并进行资源的调度和管理。
HBase则是Hadoop中的分布式数据库，它可以提供实时的读写功能，并具备高可靠性和可扩展性。
Hadoop在大数据领域有着广泛的应用。