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搜索[计算机基础]，共10007篇文章
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HBase基础

Hbase简介 HBase-Hadoop Database,是一个高可用性,面向列,可伸缩的分布式存储系统,利用HBase技术可在廉价的PC Server上搭建起大规模 结构化存储集群.HBase是Google Bigtable的开源实现，类似Google Bigtable利用GFS作为其文件存储系统，HBase利用Hadoop HDFS作为其文件存储系统；Google运行MapReduce来处理Bigtable中的海量数据，HBase同样利用Hadoop MapReduce来处理HBase中的海量数据；Google Bigtable利用 Chubby作为协同服务，HBase利用Zookeeper作为对应. 上图描述了Hadoop EcoSystem中的各层系统，其中HBase位于结构化存储层，Hadoop HDFS为HBase提供了高可靠性的底层存储支持，Hadoop MapReduce为HBase提供了高性能的计算能力，Zookeeper为HBase提供了稳定服务和failover机制。此外，Pig和Hive还为HBase提供了高层语言支持，使得在HBase上进行数据统计处理变的非常简单。 Sqoop则为HBase提供了方便的RDBMS数据导入功能，使得传统数据库数据向HBase中迁移变的非常方便。 HBase访问接口 1. Native Java API，最常规和高效的访问方式，适合Hadoop MapReduce Job并行批处理HBase表数据2. HBase Shell，HBase的命令行工具，最简单的接口，适合HBase管理使用3. Thrift Gateway，利用Thrift序列化技术，支持C++，PHP，Python等多种语言，适合其他异构系统在线访问HBase表数据4. REST Gateway，支持REST 风格的Http API访问HBase, 解除了语言限制5. Pig，可以使用Pig Latin流式编程语言来操作HBase中的数据，和Hive类似，本质最终也是编译成MapReduce Job来处理HBase表数据，适合做数据统计6. Hive，当前Hive的Release版本尚没有加入对HBase的支持，但在下一个版本Hive 0.7.0中将会支持HBase，可以使用类似SQL语言来访问HBase HBase数据模型 Ø Row Key: 行键，Table的主键，Table中的记录按照Row Key排序Ø Timestamp: 时间戳，每次数据操作对应的时间戳，可以看作是数据的version numberØ Column Family：列簇，Table在水平方向有一个或者多个Column Family组成，一个Column Family中可以由任意多个Column组成，即Column Family支持动态扩展，无需预先定义Column的数量以及类型，所有Column均以二进制格式存储，用户需要自行进行类型转换。 Table & Region 当Table随着记录数不断增加而变大后，会逐渐分裂成多份splits，成为regions，一个region由[startkey,endkey)表示，不同的region会被Master分配给相应的RegionServer进行管理：HBase中有两张特殊的Table，-ROOT-和.META.Ø .META.：记录了用户表的Region信息，.META.可以有多个regoinØ -ROOT-：记录了.META.表的Region信息，-ROOT-只有一个regionØ Zookeeper中记录了-ROOT-表的locationClient访问用户数据之前需要首先访问zookeeper，然后访问-ROOT-表，接着访问.META.表，最后才能找到用户数据的位置去访问，中间需要多次网络操作，不过client端会做cache缓存。 MapReduce on HBase 在HBase系统上运行批处理运算，最方便和实用的模型依然是MapReduce，如下图：HBase Table和Region的关系，比较类似HDFS File和Block的关系，HBase提供了配套的TableInputFormat和TableOutputFormat API，可以方便的将HBase Table作为Hadoop MapReduce的Source和Sink，对于MapReduce Job应用开发人员来说，基本不需要关注HBase系统自身的细节。 HBase系统架构 ClientHBase Client使用HBase的RPC机制与HMaster和HRegionServer进行通信，对于管理类操作，Client与HMaster进行RPC；对于数据读写类操作，Client与HRegionServer进行RPCZookeeperZookeeper Quorum中除了存储了-ROOT-表的地址和HMaster的地址，HRegionServer也会把自己以Ephemeral方式注册到Zookeeper中，使得HMaster可以随时感知到各个HRegionServer的健康状态。此外，Zookeeper也避免了HMaster的单点问题，见下文描述HMasterHMaster没有单点问题，HBase中可以启动多个HMaster，通过Zookeeper的Master Election机制保证总有一个Master运行，HMaster在功能上主要负责Table和Region的管理工作：1. 管理用户对Table的增、删、改、查操作2. 管理HRegionServer的负载均衡，调整Region分布3. 在Region Split后，负责新Region的分配4. 在HRegionServer停机后，负责失效HRegionServer 上的Regions迁移 HRegionServer HRegionServer主要负责响应用户I/O请求，向HDFS文件系统中读写数据，是HBase中最核心的模块。HRegionServer内部管理了一系列HRegion对象，每个HRegion对应了Table中的一个Region，HRegion中由多个HStore组成。每个HStore对应了Table中的一个Column Family的存储，可以看出每个Column Family其实就是一个集中的存储单元，因此最好将具备共同IO特性的column放在一个Column Family中，这样最高效。HStore存储是HBase存储的核心了，其中由两部分组成，一部分是MemStore，一部分是StoreFiles。MemStore是Sorted Memory Buffer，用户写入的数据首先会放入MemStore，当MemStore满了以后会Flush成一个StoreFile（底层实现是HFile），当StoreFile文件数量增长到一定阈值，会触发Compact合并操作，将多个StoreFiles合并成一个StoreFile，合并过程中会进行版本合并和数据删除，因此可以看出HBase其实只有增加数据，所有的更新和删除操作都是在后续的compact过程中进行的，这使得用户的写操作只要进入内存中就可以立即返回，保证了HBase I/O的高性能。当StoreFiles Compact后，会逐步形成越来越大的StoreFile，当单个StoreFile大小超过一定阈值后，会触发Split操作，同时把当前Region Split成2个Region，父Region会下线，新Split出的2个孩子Region会被HMaster分配到相应的HRegionServer上，使得原先1个Region的压力得以分流到2个Region上。下图描述了Compaction和Split的过程：在理解了上述HStore的基本原理后，还必须了解一下HLog的功能，因为上述的HStore在系统正常工作的前提下是没有问题的，但是在分布式系统环境中，无法避免系统出错或者宕机，因此一旦HRegionServer意外退出，MemStore中的内存数据将会丢失，这就需要引入HLog了。每个HRegionServer中都有一个HLog对象，HLog是一个实现Write Ahead Log的类，在每次用户操作写入MemStore的同时，也会写一份数据到HLog文件中（HLog文件格式见后续），HLog文件定期会滚动出新的，并删除旧的文件（已持久化到StoreFile中的数据）。当HRegionServer意外终止后，HMaster会通过Zookeeper感知到，HMaster首先会处理遗留的 HLog文件，将其中不同Region的Log数据进行拆分，分别放到相应region的目录下，然后再将失效的region重新分配，领取 到这些region的HRegionServer在Load Region的过程中，会发现有历史HLog需要处理，因此会Replay HLog中的数据到MemStore中，然后flush到StoreFiles，完成数据恢复。 HBase存储格式 HBase中的所有数据文件都存储在Hadoop HDFS文件系统上，主要包括上述提出的两种文件类型：1. HFile， HBase中KeyValue数据的存储格式，HFile是Hadoop的二进制格式文件，实际上StoreFile就是对HFile做了轻量级包装，即StoreFile底层就是HFile2. HLog File，HBase中WAL（Write Ahead Log） 的存储格式，物理上是Hadoop的Sequence FileHFile下图是HFile的存储格式：首先HFile文件是不定长的，长度固定的只有其中的两块：Trailer和FileInfo。正如图中所示的，Trailer中有指针指向其他数据块的起始点。File Info中记录了文件的一些Meta信息，例如：AVG_KEY_LEN, AVG_VALUE_LEN, LAST_KEY, COMPARATOR, MAX_SEQ_ID_KEY等。Data Index和Meta Index块记录了每个Data块和Meta块的起始点。Data Block是HBase I/O的基本单元，为了提高效率，HRegionServer中有基于LRU的Block Cache机制。每个Data块的大小可以在创建一个Table的时候通过参数指定，大号的Block有利于顺序Scan，小号Block利于随机查询。每个Data块除了开头的Magic以外就是一个个KeyValue对拼接而成, Magic内容就是一些随机数字，目的是防止数据损坏。后面会详细介绍每个KeyValue对的内部构造。HFile里面的每个KeyValue对就是一个简单的byte数组。但是这个byte数组里面包含了很多项，并且有固定的结构。我们来看看里面的具体结构：开始是两个固定长度的数值，分别表示Key的长度和Value的长度。紧接着是Key，开始是固定长度的数值，表示RowKey的长度，紧接着是RowKey，然后是固定长度的数值，表示Family的长度，然后是Family，接着是Qualifier，然后是两个固定长度的数值，表示Time Stamp和Key Type（Put/Delete）。Value部分没有这么复杂的结构，就是纯粹的二进制数据了。 HLogFile 上图中示意了HLog文件的结构，其实HLog文件就是一个普通的Hadoop Sequence File，Sequence File 的Key是HLogKey对象，HLogKey中记录了写入数据的归属信息，除了table和region名字外，同时还包括sequence number和timestamp，timestamp是“写入时间”，sequence number的起始值为0，或者是最近一次存入文件系统中sequence number。HLog Sequece File的Value是HBase的KeyValue对象，即对应HFile中的KeyValue，可参见上文描述。
2017-10-27
优秀的个人博客,低调大师

网络基础

基本概念 在网络编程中，有几个必须掌握的基本概念： 客户端（Client）：移动应用（iOS、android 等应用）； 服务器（Server）：为客户端提供服务、提供数据、提供资源的机器； 请求（Request）：客户端向服务器索取数据的一种行为； 响应（Response）：服务器对客户端的请求做出的反应，一般指返回数据给客户端； 基本概念 服务器 按照软件开发阶段来分，服务器可以大致分为 2 种； 远程服务器 别名：外网服务器、正式服务器； 使用阶段：应用上线后使用的服务器； 使用人群：供全体用户使用； 速度：取决于服务器的性能、用户的网速； 本地服务器 别名：内网服务器、测试服务器； 使用阶段：应用处于开发、测试阶段使用的服务器； 使用人群：仅供公司内部的开发人员、测试人员使用； 速度：由于是局域网，所以速度飞快，有助于提高开发测试效率；
2017-10-11
优秀的个人博客,低调大师

NLTK基础基础教程学习笔记（十四）

对于文本分类，最简单的定义就是基于文本内容来对其进行分类。通常情况算法是根据数字/变量特征来写的。先从https://archive.ics.uci.edu/ml/datasets/SMS+SPam+Collection中下载数据集接下来先要进行数据清理，标识化处理，以及词干提取来对SMS数据进行清理，使其内容更加简单一点： import nltk from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer import csv def preprocessing(text): # text=text.decode("utf8") tokens=[word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] stop=stopwords.words('english') tokens=[token for token in tokens if token not in stop] tokens=[word for word in tokens if len(word)>=3] tokens=[word.lower() for word in tokens] lmtzr=WordNetLemmatizer() tokens=[lmtzr.lemmatize(word)for word in tokens] preprocessed_text=' '.join(tokens) return preprocessed_text 接下来分别创建两个列表分别存储清理后SMS的内容和标签 smsdata=open('SMSSpamCollection',encoding='UTF-8') smsdata_data=[] sms_labels=[] csv_reader=csv.reader(smsdata,delimiter='\t') for line in csv_reader: sms_labels.append(line[0]) smsdata_data.append(preprocessing(line[1])) smsdata.close() 接下来介绍取样操作，其思想就是机器学习中的将训练集分为两部分，一部分训练集，一部分验证集，一种防止过拟合的方法。下面介绍了一个对语料库进行7，3分的操作： import sklearn import numpy as np trainset_size=int(round(len(smsdata_data)*0.70)) print('The training set size for this classifier is '+str(trainset_size)+'\n') x_train=np.array([''.join(el) for el in smsdata_data[0:trainset_size]]) y_train=np.array([el for el in sms_labels[0:trainset_size]]) x_test=np.array([''.join(el)for el in smsdata_data[trainset_size+1:len(smsdata_data)]]) y_test=np.array([el for el in sms_labels[trainset_size+1:len(smsdata_data)]]) print(x_train) print(y_train) 结果： [ 'jurong point crazy.. available bugis great world buffet ... cine got amore wat ...' 'lar ... joking wif oni ...' 'free entry wkly comp win cup final tkts 21st may 2005 text 87121 receive entry question std txt rate apply 08452810075over18' ..., 'tell call 09066358152 claim £5000 prize enter mobile personal detail prompt careful' "thank you 've wonderful" 'otherwise part time job na-tuition..'] ['ham' 'ham' 'spam' ..., 'spam' 'ham' 'ham'] 下面介绍了：词汇文档矩阵（term-document matrix），就是将文本转换为矩阵，文本文档也可以用bow（bag of word）表示如果要用python生成一个类似词汇矩阵，要用到sklearn中的向量化器： from sklearn.feature_extraction.text import CountVectorizer sms_exp=[ ] for line in csv_reader: sms_exp.append(preprocessing(line[1])) vectorizer=CountVectorizer(min_df=1) X_exp=vectorizer.fit_transform(sms_exp) print("||".join(vectorizer.get_feature_names())) print(X_exp.toarray()) 结果： 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[[0 0 0 ..., 0 0 0] [0 0 0 ..., 0 0 0] [0 0 0 ..., 0 0 0] ..., [0 0 0 ..., 0 0 0] [0 0 0 ..., 0 0 0] [0 0 0 ..., 0 0 0]] 用计算向量会遇到一些问题：即较长文档所获得的平均计数值会高于较短文档；一个较好的解决方法是用文档中每一个单词出现的次数除以该文档中单词总数就可以了，这个特征值叫做tf（term frequencies）tf之上还有另一个改进，对于语料库中文字中出现的词汇进行降维加权，可以减少语料库中某一小部分中出现的信息，这种方法叫做tf-idf（term frequency-inverse document frequency） from sklearn.feature_extraction.text import TfidfVectorizer vectorizer=TfidfVectorizer(min_df=2,ngram_range=(1,2),stop_words='english',strip_accents='unicode',norm='l2') X_train=vectorizer.fit_transform(x_train) X_test=vectorizer.fit_transform(x_test) print(x_train) print(x_test) 结果： [ 'jurong point crazy.. available bugis great world buffet ... cine got amore wat ...' 'lar ... joking wif oni ...' 'free entry wkly comp win cup final tkts 21st may 2005 text 87121 receive entry question std txt rate apply 08452810075over18' ..., 'tell call 09066358152 claim £5000 prize enter mobile personal detail prompt careful' "thank you 've wonderful" 'otherwise part time job na-tuition..'] ['okie' 'that depends how would like treated' 'right brah see later' ..., 'pity mood ... suggestion' 'the guy bitching acted like interested buying something else next week gave free' 'rofl it true name'] 得到一个矩阵格式的文本X_train和X_test
2018-02-10
优秀的个人博客,低调大师

【计算机视觉处理三】图像基本处理

图像基本处理 1、图像切片 在前面我们了解到opencv中的图像实际上就是一个ndarray数组，我们对ndarray数组进行操作就是对图像进行操作。我们先来看一下切片查找，这是我们非常常用的一个操作。 （1）一维数组的切片 我们来看看切片的语法，对于一维的数组我们可以通过下面的操作获取第0个到第4个元素： array[0:5] 从上面可以知道我们的切片操作是左闭右开的。上面的切片操作我们可以简写一下： array[:5] 如果我们没有设置第一个值，则表示从头开始切片。当然我们还可以省略第二个值，这时含义就是取到最后一个元素，比如下面的操作： array[3:] 我们用一个实际的例子来看看切片操作： import numpy as np# 创建一个一维的ndarray数组，数据为[0, 1, 2, 3, 4, 5, 6, 7]array = np.array([0, 1, 2, 3, 4, 5, 6, 7])# 取0到4个元素print(array[0:5])print(array[:5])# 取第3个到最后一个元素print(array[3:]) 输出内容如下： [0 1 2 3 4][0 1 2 3 4][3 4 5 6 7] 我们可以把切片操作总结为： # 左闭右开array[start: end-1] 当我们以第0个开始，获取以最后一个结尾的话，对应的值是可以省略的。 （2）二维数组的切片 在图像处理中，我们更关注二维数组的切片。它的语法和一维数组很相似。为了方便理解，我们直接使用图片来进行切片，比如下面这张图片： 二维数组切片的语法如下： array[start:end-1, start:end-1] 现在我们需要明确一点，左边部分是对高的截取，右边部分是对宽的截取。那现在我要截取图片的左半部分的操作应该如下： import cv2# 读取图片img = cv2.imread('xyql.jpg')# 获取图片的宽，并除2width = img.shape[1]//2# 对图片进行切片，截取左半部分left = img[:, :width]# 显示图像三步骤cv2.imshow('left', left)cv2.waitKey(0)cv2.destroyAllWindows() 其中切片的代码是： left = img[:, :width] 左边的是截取高，我们需要全部截取，因此两个值都可以省略。右边我们只需要截取左半部分，因此左边的值可以省略，右边的值则是我们前面计算到的宽度。下面是效果图： 这里需要注意一点，彩色图像其实是三维的，但是我们没有操作第三个维度。 2、图片区域替换 既然我们知道如何切片，那我们就可以对指定区域进行替换。不过需要注意替换和被替换的区域形状要相同，即shape属性要一样。比如我要把图片左上角100*100的区域替换成白色，那我可以进行如下操作： import cv2import numpy as npimg = cv2.imread('1.jpg')# 创建一个100*100的白色图像replace_img = np.ones((100, 100), dtype=np.uint8)*255# 将图片左上角100*100区域替换img[:100, :100] = replace_img 运行后会发现报了如下错误： img[:100, :100] = replace_imgValueError: could not broadcast input array from shape (100,100) into shape (100,100,3) 它的意思是不能将（100， 100）的图像转换成（100， 100， 3）的图像，也就是形状不匹配。在替换时我们需要特别注意这一点，我们将上面的代码进行一些修改： import cv2import numpy as npimg = cv2.imread('xyql.jpg')replace_img = np.ones((100, 100, 3), dtype=np.uint8)*255img[:100, :100] = replace_img *这样我们就可以成功替换了，效果如下： 其实用上面的操作我们可以实现一个“双胞胎”效果，下面我们就来看看吧。 3、实现“双胞胎”效果 我们先准备两张图片，保证相机在没有移动的情况下拍两张图片，比如下面两张： 这是我刚拍的两张图片，因为相机没有移动，所以背景应该是一样的。我们可以通过下面的代码把两个梦幻合并到一张图片上： import cv2# 读取两张图片img1 = cv2.imread('mh1.JPG')img2 = cv2.imread('mh2.JPG')# 求出宽的中间值mid = img1.shape[1]//2# 把mh2的右半边替换到mh1的右半部分img1[:, mid:] = img2[:, mid:]# 将拼接后的图片保存到本地cv2.imwrite('result.jpg', img1) 上面代码非常简单，我们只需要关注下面这句： img1[:, mid:] = img2[:, mid:] img1和img2的shape都是一样的，截取的区域也是一样的，所以进行替换没有上面问题。最后我们通过： cv2.imwrite('result.jpg', img1) 对图片进行保存。因为我们是直接对img1进行操作，因此我们直接保存img1就好了。下面是我们的效果图： 这里需要多说一句，我只有一个梦幻。 4、numpy生成数组 在上一篇中我们使用下面的代码生成了一个数组： im = np.zeros((3, 3, 1), dtype=np.uint8) 对于数组numpy来说我们是生成一个数组，但是对opencv来说是生成一张图片。下面我们来看看numpy生成数组的一些操作。 （1）np.ones 我们可以通过numpy的ones函数生成一个元素全为1的数组，比如下面的代码： np.ones((100, 100), dtype=np.uint8) ones接收两个参数，第一个参数是数组的shape，第二关则是数组元素的类型，其中np.uint8表示无符号的8位整型，即范围在（0-255）之间。我们可以用opencv显示一下上面的图片： import cv2import numpy as np# 生成一个100*100的图片，每个元素的值都为1img = np.ones((100, 100), dtype=np.uint8)cv2.imwrite('result.jpg', img) 效果图如下： 因为1很接近0，所以图片的颜色接近于黑色。我们可以通过下面的操作对每个元素进行操作： import cv2import numpy as npimg = np.ones((100, 100), dtype=np.uint8) * 127cv2.imwrite('result.jpg', img) 我们生成元素为1的数组后，如何进行乘127的操作，这样就可以对每个元素进行乘127操作。这时候数组的每个元素都是127，下面是显示效果： 当然我们的图片是二维的，对opencv来说是一个灰度图。如果想要生成一个彩色图像，我们可以生成一个三维的图像，后续我们会继续讲解。 （2）np.zeros np.zeros和ones没有上面区别，只是它元素的内容是0。我们来简单看一下： import numpy as npimg = np.zeros((5, 5), dtype=np.uint8)print(img) 为了方便看，我们直接生成一个简单的数组，输出结果如下： [[0 0 0 0 0] [0 0 0 0 0] [0 0 0 0 0] [0 0 0 0 0] [0 0 0 0 0]] 其它的都不再细说了。 本文分享自微信公众号 - ZackSock（ZackSock）。如有侵权，请联系 support@oschina.cn 删除。本文参与“OSC源创计划”，欢迎正在阅读的你也加入，一起分享。
2020-12-05
优秀的个人博客,低调大师

必读的计算机视觉开创性论文

从ILSVRC中可以看出，近几年图像分类神经网络架构的错误率以惊人的幅度下降 深度学习已经存在了几十年，Yann Lecun在1998年就发表了一篇关于卷积神经网络（CNN）的论文。但是直到十年前，深度学习才开始真正的发展并慢慢成为人工智能研究的主要焦点领域。这些转变主要是因为处理能力（即 GPU）的增强、大量可用性的数据（即Imagenet数据集）以及新的算法和技术。2012年，AlexNet（一种大型深度卷积神经网络），赢得了年度ImageNet大规模视觉识别挑战赛（ILSVRC）。 从此以后，CNN的变体开始在ILSVRC中称霸，并超过人类精确度的水平。 作为人类，我们很容易理解图像的内容。例如，在观看电影时，我们认知一个东西是矮人后，可以轻松的去识别其他的矮人。然而，对于一台机器来说，这项任务极具挑战性，因为它在这幅图中看到的是
2018-04-12

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