From b64bfe2701aa3b749bc88e8d6e23f1390f4fd5c3 Mon Sep 17 00:00:00 2001 From: xuesong Date: Thu, 12 Nov 2020 16:31:52 +0800 Subject: [PATCH 01/23] Add new file --- Assignment6.md | 254 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 254 insertions(+) create mode 100644 Assignment6.md diff --git a/Assignment6.md b/Assignment6.md new file mode 100644 index 0000000..f7c0d63 --- /dev/null +++ b/Assignment6.md @@ -0,0 +1,254 @@ +# Assignment 5 指南 + +## `******注意,实验结束请立即删除云主机和数据仓库,节省费用******` + +## `******注意2,实验未结束且短期内不会继续实验,也请删除云主机和数据仓库。下次实验时重新创建******` + + + +## 实验内容 + +- 创建Greenplum数据仓库实例: `实验步骤 一)` +- 登录数据仓库,并练习建表、插入、查找等基本操作:`实验步骤 二)` +- 使用MADlib完成简单的机器学习任务: `实验步骤 三)` + +## 实验要求(仔细看) + +- 完成所有步骤,并在实验报告([模板下载](file/assignment5/学号-实验五.docx))中完成穿插在本指南中的作业1~作业4(只需要截图)。实验报告转成“学号-实验四.pdf”,并上传至http://113.31.104.68:3389/index.htm +- 实验报告上传deadline: `11月13日` + +## 使用UCloud产品 + +云数据仓库UDW、云主机uhost、私有网络vpc、基础网络unet + +## 需要权限 + +云数据仓库UDW、云主机uhost、基础网络unet + + +## 基础知识(本实验详细知识会在数据仓库相关课程中学习,这里我们仅简单熟悉一下相关操作,你也可以去Google上Baidu一下相关知识 :ghost: :ghost: :ghost:) + + +`数据仓库:` data warehouse(缩写DW),是用于报告和数据分析的数据管理系统。 + +`和SQL数据库的区别:` 传统的关系型数据库(如实验四中的MySQL)主要应用于基本的、日常的事务处理(Transaction Processing),例如银行交易,淘宝购物,订购车票等。而数据仓库系统主要应用于生产数据的整理和分析(Analytical Processing),通过扩展数据分析和可视化等工具,支持业务决策,例如流量分析、用户画像、统计建模等。 + +举个简单例子,淘宝前端(网页端、app端)每天的交易事务,都是用关系型数据库支持的。而每天的交易数据,会定时导入到后端的数据仓库(俗称ETL,Extract, Transform, Load),并根据特定业务逻辑重新整理,从而方便后端各个团队分析数据和做出业务决策。 + + + + + + +`UDW云数据仓库:` 是UCloud的DW产品,基于开源软件Greenplum(底层为PostgreSQL数据库)开发的大规模并发、完全托管的PB级数据仓库服务。支持MADlib、PostGIS等扩展工具,可以方便地支持机器学习、空间地理位置应用等数据分析任务。 + +`MADlib:` MADlib提供了一套基于SQL的机器学习、数据挖掘和统计算法,可以在数据库引擎内大规模运行。在Greenplum中安装了MADlib插件以后,可以直接在数据仓库内进行复杂计算和数据建模等工作。 + + +闲话少说,我们开始创建一个UDW玩玩。:ghost: :ghost: :ghost: + +## 实验步骤 + +### 一)创建Greenplum数据仓库实例 + +#### 1)在产品中选择云数据仓库UDW,然后点击新建数据仓库 + + + + +#### 2)使用默认配置,2个节点。将子网改成"DefaultNetwork",记住DB名称,端口,管理员用户名,设置管理员密码。选择按时付费,立即购买。 + + + + + + + + + + + + +#### 3)支付之后,数仓创建时间从几分钟到十几分钟不等,等状态显示“运行中”,则创建完毕。记住数仓的IP地址。 + + + + +### 二)登录数据仓库。Greenplum数据仓库的登录有很多方式,比如用PostgreSQL客户端登录,用Greenplum客户端登录,用JDBC(在Java中使用)或者ODBC(在C/C++中使用)访问,用psycopg2(在Python中使用)访问。本实验我们完成PostgreSQL客户端和psycopg2两种方式。其余方式同学们以后可以自行尝试。 + +#### 1)使用PostgreSQL客户端登录。创建一个最低配置的Centos云主机,选择按流量计费,20M带宽,云主机按小时付费。登录以后运行以下命令,安装PostgreSQL客户端psql。 +``` +sudo yum install postgresql.x86_64 -y +``` + +##### 1.1)安装完毕后运行以下命令登录数据仓库。将相关参数替换成你的数据仓库参数。登录后将看到如下界面(我们已经在dev库中)。 +``` +psql -h hostIP –U username -d database -p port +``` + + + + +## `**************作业1:请将登录命令和登录成功界面截图,并插入实验报告***************` + +##### 1.2)让我们运行几个SQL代码来实现建表,插入,查询等操作。 +> 复制以下sql代码,并在psql中运行(注意要包含最后的分号) +``` +CREATE TABLE regression ( + id int, + y int, + x1 int, + x2 int +); +``` + +> 运行后我们看到如下notice,提示建表时没有使用`DISTRIBUTED BY`语句,因此Greenplum默认使用id作为分布键。这是因为greenplum是一个分布式数仓,数据会分布在不同的节点上,因此建表的时候要用`DISTRIBUTED BY`语句说明按照哪一个属性(即所谓“分布键”)对数据进行划分。由于我们没有指定,系统默认使用了第一列作为分布键。 + + + + + + +> 接着让我们在regression表中插入一些值 +``` +INSERT INTO regression VALUES + (1, 5, 2, 3), + (2, 10, 7, 2), + (3, 6, 4, 1), + (4, 8, 3, 4); +``` + +> 让我们查询一下regression表中的数据。你应该看到如下输出。 +``` +SELECT * FROM regression; +``` + + + + + +##### 1.3)运行\q退出psql客户端。 + +## `**************作业2:请如上图一样将regression表的查询命令、输出结果和退出psql以后的界面截图,并插入实验报告***************` + + +#### 2)使用`Python`+`psycopg2`访问。很多时候我们需要在程序中访问数据仓库,比如用Python读取DW中的数据,然后进一步操作。我们可以通过使用`psycopg2`来实现访问。运行以下命令安装`psycopg2`package。 +``` +sudo yum install python3-devel -y +sudo yum install postgresql-libs -y +sudo yum install postgresql-devel -y +sudo yum install gcc -y +pip3 install --user psycopg2 +``` + +##### 2.1)安装完毕后运行python3,然后import psycopg2,若没有报错,则说明psycopg2安装成功。 + + + + +##### 2.2)quit()退出python3命令行,让我们运行几个Python代码来实现建表,插入,查询等操作。 + +> 建立createTable.py文件,并复制如下代码。其中username,password,hostIP,port都要替换成你的数据仓库参数。 +``` +import psycopg2 +conn = psycopg2.connect(database="dev", user="username", password="password", host="hostIP", port="port") +cur = conn.cursor() +cur.execute('CREATE TABLE COMPANY (ID INT PRIMARY KEY NOT NULL,\ + NAME TEXT NOT NULL,\ + AGE INT NOT NULL,\ + ADDRESS CHAR(10),\ + SALARY REAL);') +conn.commit() +conn.close() +``` + +> 运行createTable.py,如果没有报错,则说明建表成功。 +``` +python3 createTable.py +``` + +> 向COMPANY表中添加一些记录。建立insertTable.py文件,复制如下代码并运行。 +``` +import psycopg2 +conn = psycopg2.connect(database="dev", user="username", password="password", host="hostIP", port="port") +cur = conn.cursor() +cur.execute("INSERT INTO COMPANY VALUES (1, 'Paul', 32, 'California', 20000.00 )"); +cur.execute("INSERT INTO COMPANY VALUES (2, 'Allen', 25, 'Texas', 15000.00 )"); +cur.execute("INSERT INTO COMPANY VALUES (3, 'Eric', 35, 'Florida', 25000.00 )"); +conn.commit() +conn.close() +``` + +> 让我们查询一下刚刚建的表。建立selectTable.py文件,复制如下代码并运行。 +``` +import psycopg2 +conn = psycopg2.connect(database="dev", user="username", password="password", host="hostIP", port="port") +cur = conn.cursor() +cur.execute("SELECT id, name, address, salary from COMPANY order by id") +rows = cur.fetchall() +print ("ID NAME ADDRESS SALARY") +for row in rows: + print (row[0], "\t", row[1], "\t", row[2], "\t", row[3]) +conn.close() +``` + +> 你应该会看到如下输出。 + + + + + +## `**************作业3:请将运行selectTable.py的命令和输出结果截图,并插入实验报告***************` + + +### 三)使用MADlib完成简单的机器学习任务(线性回归) + +#### 1)前面我们说过,在Greenplum中使用MADlib插件可以直接在DW中建立机器学习模型。这里我们用二)中的regression表跑一下线性回归算法。请使用psql或者psycopg2登录数仓,并运行如下命令。 + +``` +SELECT madlib.linregr_train ( + 'regression', -- source table + 'regression_model', -- output model table + 'y', -- dependent variable + 'ARRAY[1, x1, x2]' -- independent variables +); +``` + +> 这里我们看到使用了MADlib的线性回归模型linregr_train来对regression表中的数据进行训练,模型输入变量为x1, x2以及偏置项,输出变量为y。训练好的模型保存在regression_model表中。 + +### 2)让我们看下模型表中有什么,运行以下命令 + +``` +SELECT * FROM regression_model; +``` + +> 可能输出比较乱,你可以运行以下命令,让表的结果竖起来。 + +``` +\x + +SELECT * FROM regression_model; +``` +> 这样是不是顺眼多了 :ghost: :ghost: :ghost: 我们看到了线性回归模型的参数以及标准差,p-value等指标的值 + + + + + +### 3)最后让我们用这个模型去做预测。我们直接在训练集上进行预测。你会看到如下输出,其中predict是预测结果,observation是原始的y值。 +``` +\x + +SELECT regression.*, + madlib.linregr_predict ( ARRAY[1, x1, x2], m.coef ) as predict, + y as observation +FROM regression, regression_model m; +``` + + + + + +### 4)运行\q退出psql客户端。 + +## `**************作业4:请如上图一样把预测结果和退出psql以后的界面一起截图,并插入实验报告***************` From 9b6bb33bac0572ec3bdf0727147c8102b0176eb5 Mon Sep 17 00:00:00 2001 From: xuesong Date: Thu, 12 Nov 2020 16:37:24 +0800 Subject: [PATCH 02/23] Update Assignment6.md --- Assignment6.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/Assignment6.md b/Assignment6.md index f7c0d63..68ca2b1 100644 --- a/Assignment6.md +++ b/Assignment6.md @@ -1,32 +1,32 @@ -# Assignment 5 指南 +# Assignment 6 指南 -## `******注意,实验结束请立即删除云主机和数据仓库,节省费用******` - -## `******注意2,实验未结束且短期内不会继续实验,也请删除云主机和数据仓库。下次实验时重新创建******` +## `******注意,实验结束请立即删除负载均衡器和所有云主机,节省费用******` +## `******注意2,实验未结束且短期内不会继续实验,也请删除负载均衡器和所有云主机。下次实验时重新创建******` +## `******提示:勤用保存镜像******` ## 实验内容 -- 创建Greenplum数据仓库实例: `实验步骤 一)` -- 登录数据仓库,并练习建表、插入、查找等基本操作:`实验步骤 二)` -- 使用MADlib完成简单的机器学习任务: `实验步骤 三)` +- 创建负载均衡器: `实验步骤 一)` +- 创建Flask APP服务器,并连接负载均衡器:`实验步骤 二)` +- 进行Flask APP服务器压力测试: `实验步骤 三)` -## 实验要求(仔细看) +## 实验要求 -- 完成所有步骤,并在实验报告([模板下载](file/assignment5/学号-实验五.docx))中完成穿插在本指南中的作业1~作业4(只需要截图)。实验报告转成“学号-实验四.pdf”,并上传至http://113.31.104.68:3389/index.htm -- 实验报告上传deadline: `11月13日` +- 完成所有步骤,并在实验报告([模板下载](file/assignment6/学号-实验六.docx))中完成穿插在本指南中的作业1~作业4(只需要截图)。实验报告转成“学号-实验六.pdf”,并上传至http://113.31.104.68:3389/index.htm +- 实验报告上传deadline: `11月20日` ## 使用UCloud产品 -云数据仓库UDW、云主机uhost、私有网络vpc、基础网络unet +云主机UHost、负载均衡ULB、私有网络VPC、基础网络UNet ## 需要权限 -云数据仓库UDW、云主机uhost、基础网络unet +云主机UHost、负载均衡ULB、基础网络UNet -## 基础知识(本实验详细知识会在数据仓库相关课程中学习,这里我们仅简单熟悉一下相关操作,你也可以去Google上Baidu一下相关知识 :ghost: :ghost: :ghost:) +## 基础知识 `数据仓库:` data warehouse(缩写DW),是用于报告和数据分析的数据管理系统。 From 052d937729ea7813812ba9c3e1de3b148f404899 Mon Sep 17 00:00:00 2001 From: xuesong Date: Thu, 12 Nov 2020 16:57:29 +0800 Subject: [PATCH 03/23] Update Assignment6.md --- Assignment6.md | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/Assignment6.md b/Assignment6.md index 68ca2b1..81d8ff1 100644 --- a/Assignment6.md +++ b/Assignment6.md @@ -29,20 +29,14 @@ ## 基础知识 -`数据仓库:` data warehouse(缩写DW),是用于报告和数据分析的数据管理系统。 - -`和SQL数据库的区别:` 传统的关系型数据库(如实验四中的MySQL)主要应用于基本的、日常的事务处理(Transaction Processing),例如银行交易,淘宝购物,订购车票等。而数据仓库系统主要应用于生产数据的整理和分析(Analytical Processing),通过扩展数据分析和可视化等工具,支持业务决策,例如流量分析、用户画像、统计建模等。 - -举个简单例子,淘宝前端(网页端、app端)每天的交易事务,都是用关系型数据库支持的。而每天的交易数据,会定时导入到后端的数据仓库(俗称ETL,Extract, Transform, Load),并根据特定业务逻辑重新整理,从而方便后端各个团队分析数据和做出业务决策。 +`负载均衡:` load balancing,是指将用户请求按照一定的规则,分流到提供相同服务的多个服务实例上,从而减轻单点服务器的压力,是保证服务高并发高可用的技术手段之一。负载均衡和服务实例的关系可以简化为下图。 -`UDW云数据仓库:` 是UCloud的DW产品,基于开源软件Greenplum(底层为PostgreSQL数据库)开发的大规模并发、完全托管的PB级数据仓库服务。支持MADlib、PostGIS等扩展工具,可以方便地支持机器学习、空间地理位置应用等数据分析任务。 - -`MADlib:` MADlib提供了一套基于SQL的机器学习、数据挖掘和统计算法,可以在数据库引擎内大规模运行。在Greenplum中安装了MADlib插件以后,可以直接在数据仓库内进行复杂计算和数据建模等工作。 +`压力测试:` 闲话少说,我们开始创建一个UDW玩玩。:ghost: :ghost: :ghost: From 10af9b7dd1abac3cbf7446a57d4ec898c8738eba Mon Sep 17 00:00:00 2001 From: xuesong Date: Thu, 12 Nov 2020 21:56:22 +0800 Subject: [PATCH 04/23] Add new directory --- img/assignment6/.gitkeep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 img/assignment6/.gitkeep diff --git a/img/assignment6/.gitkeep b/img/assignment6/.gitkeep new file mode 100644 index 0000000..e69de29 From 6a37269b4925942dd31c8143e41a2a274bae1367 Mon Sep 17 00:00:00 2001 From: xuesong Date: Thu, 12 Nov 2020 21:56:51 +0800 Subject: [PATCH 05/23] Upload New File --- img/assignment6/ass6-lb.jpeg | Bin 0 -> 35380 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 img/assignment6/ass6-lb.jpeg diff --git a/img/assignment6/ass6-lb.jpeg b/img/assignment6/ass6-lb.jpeg new file mode 100644 index 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21 deletions(-) diff --git a/Assignment6.md b/Assignment6.md index 81d8ff1..81ee073 100644 --- a/Assignment6.md +++ b/Assignment6.md @@ -9,8 +9,8 @@ ## 实验内容 - 创建负载均衡器: `实验步骤 一)` -- 创建Flask APP服务器,并连接负载均衡器:`实验步骤 二)` -- 进行Flask APP服务器压力测试: `实验步骤 三)` +- 创建Flask App服务实例,并连接负载均衡器:`实验步骤 二)` +- 进行服务器压力测试: `实验步骤 三)` ## 实验要求 @@ -29,43 +29,34 @@ ## 基础知识 -`负载均衡:` load balancing,是指将用户请求按照一定的规则,分流到提供相同服务的多个服务实例上,从而减轻单点服务器的压力,是保证服务高并发高可用的技术手段之一。负载均衡和服务实例的关系可以简化为下图。 +`负载均衡:` load balancing,是指将用户请求按照一定的规则,分流到提供相同服务的多个服务实例上,从而减轻单点服务器的压力,是保证服务高并发高可用的技术手段之一。用户、负载均衡器和服务器实例的关系可以简化为下图。这种架构的另一个好处是可以把服务器隐藏在局域网防火墙背后,因为外网直接访问的是负载均衡器。 - + +负载均衡的算法有很多,常见的如轮询,ip地址哈希,最小连接数等,应对不同的需求,感兴趣的同学可以去了解一下。 -`压力测试:` +`压力测试:` 测试一个系统的最大抗压能力,在强负载、高并发的情况下,测试系统所能承受的最大压力,预估系统的瓶颈。例如天猫双11之前,阿里内部会对整个淘宝平台进行全链路压测,预估应对峰值交易的能力。压力测试的工具有很多,今天我们练习使用非常简单的ApacheBench对http服务器进行压测。 -闲话少说,我们开始创建一个UDW玩玩。:ghost: :ghost: :ghost: +好,废话少说,咱们书归正传! ## 实验步骤 -### 一)创建Greenplum数据仓库实例 +### 一)使用ULB创建一个外网负载均衡器 -#### 1)在产品中选择云数据仓库UDW,然后点击新建数据仓库 - - - +#### 1)在产品->网络中选择负载均衡ULB,然后点击创建负载均衡。 -#### 2)使用默认配置,2个节点。将子网改成"DefaultNetwork",记住DB名称,端口,管理员用户名,设置管理员密码。选择按时付费,立即购买。 +#### 2)根据下图配置,选择按时付费。因为我们一会需要从外网访问Flask App,所以选择外网负载均衡。 - + - - - - - - - #### 3)支付之后,数仓创建时间从几分钟到十几分钟不等,等状态显示“运行中”,则创建完毕。记住数仓的IP地址。 - + ### 二)登录数据仓库。Greenplum数据仓库的登录有很多方式,比如用PostgreSQL客户端登录,用Greenplum客户端登录,用JDBC(在Java中使用)或者ODBC(在C/C++中使用)访问,用psycopg2(在Python中使用)访问。本实验我们完成PostgreSQL客户端和psycopg2两种方式。其余方式同学们以后可以自行尝试。 From c97ca294dcf94978c586a8bb12de3baedb56c945 Mon Sep 17 00:00:00 2001 From: xuesong Date: Thu, 12 Nov 2020 22:47:55 +0800 Subject: [PATCH 08/23] Delete ass6-createlb.png --- img/assignment6/ass6-createlb.png | Bin 177270 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 img/assignment6/ass6-createlb.png diff --git a/img/assignment6/ass6-createlb.png b/img/assignment6/ass6-createlb.png deleted file mode 100644 index 7d8be2900c43eb1ab62ed11a3f687e5e14c49e3d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 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z5DV1He!4>ncC88d;NvWP%Dz`mbqa=Y1qTs#%Ka<4M6djKHLYgfQ~v^eDNIh|??^PM zaS^jML$W2mAs%j@A26QQ0arGloD_%O)GD;9R4lm8!ox0(L|r>;_O literal 0 HcmV?d00001 From b4f57c40022c3eeff863c7d56ec25f222bc52e44 Mon Sep 17 00:00:00 2001 From: xuesong Date: Thu, 12 Nov 2020 23:50:58 +0800 Subject: [PATCH 16/23] Update Assignment6.md --- Assignment6.md | 59 ++++++++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 49 insertions(+), 10 deletions(-) diff --git a/Assignment6.md b/Assignment6.md index 81ee073..1b9a103 100644 --- a/Assignment6.md +++ b/Assignment6.md @@ -38,41 +38,80 @@ 负载均衡的算法有很多,常见的如轮询,ip地址哈希,最小连接数等,应对不同的需求,感兴趣的同学可以去了解一下。 -`压力测试:` 测试一个系统的最大抗压能力,在强负载、高并发的情况下,测试系统所能承受的最大压力,预估系统的瓶颈。例如天猫双11之前,阿里内部会对整个淘宝平台进行全链路压测,预估应对峰值交易的能力。压力测试的工具有很多,今天我们练习使用非常简单的ApacheBench对http服务器进行压测。 +`压力测试:` 测试一个系统的最大抗压能力,在强负载、高并发的情况下,测试系统所能承受的最大压力,预估系统的瓶颈。例如天猫双11之前,阿里内部会对整个淘宝平台进行全链路压测,预估应对峰值交易的能力。压力测试的工具有很多,今天我们练习使用非常简单的ApacheBench(ab)对http服务器进行压测。 好,废话少说,咱们书归正传! ## 实验步骤 -### 一)使用ULB创建一个外网负载均衡器 +### 一)使用ULB创建一个外网负载均衡器。 #### 1)在产品->网络中选择负载均衡ULB,然后点击创建负载均衡。 #### 2)根据下图配置,选择按时付费。因为我们一会需要从外网访问Flask App,所以选择外网负载均衡。 + +#### 3)创建完毕后,记住ULB的IP地址,点击详情->VServer管理->添加VServer。这里VServer就可以看成是一个负载均衡服务器,负责分流。取任意VServer名称,其他保持默认设置,点击确定。 -#### 3)支付之后,数仓创建时间从几分钟到十几分钟不等,等状态显示“运行中”,则创建完毕。记住数仓的IP地址。 - + -### 二)登录数据仓库。Greenplum数据仓库的登录有很多方式,比如用PostgreSQL客户端登录,用Greenplum客户端登录,用JDBC(在Java中使用)或者ODBC(在C/C++中使用)访问,用psycopg2(在Python中使用)访问。本实验我们完成PostgreSQL客户端和psycopg2两种方式。其余方式同学们以后可以自行尝试。 +#### 4)点击“服务节点”标签页,当前没有任务实际的节点提供服务。我们接下来创建一些服务节点。 + + +### 二)使用云主机创建Flask App服务实例,并连接负载均衡器。 + +#### 1)创建一个1核1G的云主机,绑定弹性IP,选择web型防火墙,按时付费(已经很熟练了吧 :dog: :dog:) + +#### 2)使用docker file创建一个Flask App的image(比如实验2中的hello world),并且暴露docker容器的5000端口(因为Flask开发模式默认端口就是5000)。创建完毕之后运行docker,将5000端口映射到云主机的80端口。 -#### 1)使用PostgreSQL客户端登录。创建一个最低配置的Centos云主机,选择按流量计费,20M带宽,云主机按小时付费。登录以后运行以下命令,安装PostgreSQL客户端psql。 ``` -sudo yum install postgresql.x86_64 -y +docker run -d --rm -p 80:5000 -name myflask luxuesong/myflaskapp ``` +请同学们复习一下docker的安装和使用,以及通过dockerfile创建镜像。如果你意外保存了实验二的镜像,可以从镜像创建云主机,然后直接启动并docker run。 + +#### 3)打开浏览器,输入 `http://你的云主机外网ip`,测试app是否正常运行。正常则进入下一步。 + + + + + +#### 4)将云主机连到负载均衡器。回到前面添加服务节点的界面,输入端口80,则所有可添加的节点都会出现(以内网ip形式)。将你的云主机对应的内网ip,转移到待添加节点,点击确定。这时候显示健康检查“失败”,不用管它,我们在浏览器中输入 `http://你的负载均衡器ip`,奇迹发生了!我们也能访问flask app!!并且这时候健康检查也变成“正常”。 + + + + + +#### 5)此时负载均衡器只连接了一个服务实例,等同于单机访问。`为步骤三做准备,请在这一步制作当前云主机的镜像`。 + + +### 三)使用ab进行http服务器压力测试 + +#### 1)使用步骤二)中制作的镜像,创建三个最低配置的云主机(`和之前一样,只是在云主机创建界面用“自制镜像”,并且不需要弹性ip`),可以选择三台主机一次性创建 + + + + + +#### 2)创建完毕后,使用有外网ip的那台云主机,逐个ssh到三台内网机器上(通过内网ip),然后每台机器直接启动docker服务,并运行flask app容器(因为已经全都安装并且build好了 :smiley_cat: :smiley_cat: :smiley_cat:) + + + + + + + + + ##### 1.1)安装完毕后运行以下命令登录数据仓库。将相关参数替换成你的数据仓库参数。登录后将看到如下界面(我们已经在dev库中)。 ``` psql -h hostIP –U username -d database -p port ``` - - - ## `**************作业1:请将登录命令和登录成功界面截图,并插入实验报告***************` From 897f6fc9b626d918a1473d36f276305822badc48 Mon Sep 17 00:00:00 2001 From: xuesong Date: Thu, 12 Nov 2020 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-87,6 +87,9 @@ docker run -d --rm -p 80:5000 -name myflask luxuesong/myflaskapp + +## `**************作业1:请将浏览器中通过负载均衡ip访问flask app的界面,以及负载均衡器中服务节点界面截图,并插入实验报告***************` + #### 5)此时负载均衡器只连接了一个服务实例,等同于单机访问。`为步骤三做准备,请在这一步制作当前云主机的镜像`。 @@ -108,171 +111,40 @@ docker run -d --rm -p 80:5000 -name myflask luxuesong/myflaskapp -##### 1.1)安装完毕后运行以下命令登录数据仓库。将相关参数替换成你的数据仓库参数。登录后将看到如下界面(我们已经在dev库中)。 -``` -psql -h hostIP –U username -d database -p port -``` - -## `**************作业1:请将登录命令和登录成功界面截图,并插入实验报告***************` - -##### 1.2)让我们运行几个SQL代码来实现建表,插入,查询等操作。 -> 复制以下sql代码,并在psql中运行(注意要包含最后的分号) -``` -CREATE TABLE regression ( - id int, - y int, - x1 int, - x2 int -); -``` - -> 运行后我们看到如下notice,提示建表时没有使用`DISTRIBUTED BY`语句,因此Greenplum默认使用id作为分布键。这是因为greenplum是一个分布式数仓,数据会分布在不同的节点上,因此建表的时候要用`DISTRIBUTED BY`语句说明按照哪一个属性(即所谓“分布键”)对数据进行划分。由于我们没有指定,系统默认使用了第一列作为分布键。 - - - - - - -> 接着让我们在regression表中插入一些值 -``` -INSERT INTO regression VALUES - (1, 5, 2, 3), - (2, 10, 7, 2), - (3, 6, 4, 1), - (4, 8, 3, 4); -``` - -> 让我们查询一下regression表中的数据。你应该看到如下输出。 -``` -SELECT * FROM regression; -``` - - - - - -##### 1.3)运行\q退出psql客户端。 - -## `**************作业2:请如上图一样将regression表的查询命令、输出结果和退出psql以后的界面截图,并插入实验报告***************` - - -#### 2)使用`Python`+`psycopg2`访问。很多时候我们需要在程序中访问数据仓库,比如用Python读取DW中的数据,然后进一步操作。我们可以通过使用`psycopg2`来实现访问。运行以下命令安装`psycopg2`package。 -``` -sudo yum install python3-devel -y -sudo yum install postgresql-libs -y -sudo yum install postgresql-devel -y -sudo yum install gcc -y -pip3 install --user psycopg2 -``` +#### 3)现在让我们把这三个云主机连接到负载均衡器,并且从负载均衡器中删除刚才的具有外网ip的节点。接下来我们要用外网ip节点做压测。首先我们禁用掉其中的两个节点,只保留一个节点在启用状态。如下图所示。 -##### 2.1)安装完毕后运行python3,然后import psycopg2,若没有报错,则说明psycopg2安装成功。 - + -##### 2.2)quit()退出python3命令行,让我们运行几个Python代码来实现建表,插入,查询等操作。 +## `**************作业2:请将连接到负载均衡的服务器节点截图,并插入实验报告***************` -> 建立createTable.py文件,并复制如下代码。其中username,password,hostIP,port都要替换成你的数据仓库参数。 +#### 4)登录具有外网ip的云主机,安装ApacheBench ``` -import psycopg2 -conn = psycopg2.connect(database="dev", user="username", password="password", host="hostIP", port="port") -cur = conn.cursor() -cur.execute('CREATE TABLE COMPANY (ID INT PRIMARY KEY NOT NULL,\ - NAME TEXT NOT NULL,\ - AGE INT NOT NULL,\ - ADDRESS CHAR(10),\ - SALARY REAL);') -conn.commit() -conn.close() +sudo yum -y install httpd ``` -> 运行createTable.py,如果没有报错,则说明建表成功。 +#### 5)运行压测命令。`-c`表示并发数,即同时发生的请求数,`-n`表示请求总数,`http://106.75.216.169/`替换成你的负载均衡器ip。所以这里对我们的flask app并发1000个请求,总共发出10000个请求。结果如下图所示。 ``` -python3 createTable.py +ab -c 1000 -n 10000 http://106.75.216.169/ ``` -> 向COMPANY表中添加一些记录。建立insertTable.py文件,复制如下代码并运行。 -``` -import psycopg2 -conn = psycopg2.connect(database="dev", user="username", password="password", host="hostIP", port="port") -cur = conn.cursor() -cur.execute("INSERT INTO COMPANY VALUES (1, 'Paul', 32, 'California', 20000.00 )"); -cur.execute("INSERT INTO COMPANY VALUES (2, 'Allen', 25, 'Texas', 15000.00 )"); -cur.execute("INSERT INTO COMPANY VALUES (3, 'Eric', 35, 'Florida', 25000.00 )"); -conn.commit() -conn.close() -``` - -> 让我们查询一下刚刚建的表。建立selectTable.py文件,复制如下代码并运行。 -``` -import psycopg2 -conn = psycopg2.connect(database="dev", user="username", password="password", host="hostIP", port="port") -cur = conn.cursor() -cur.execute("SELECT id, name, address, salary from COMPANY order by id") -rows = cur.fetchall() -print ("ID NAME ADDRESS SALARY") -for row in rows: - print (row[0], "\t", row[1], "\t", row[2], "\t", row[3]) -conn.close() -``` - -> 你应该会看到如下输出。 - - + -## `**************作业3:请将运行selectTable.py的命令和输出结果截图,并插入实验报告***************` - - -### 三)使用MADlib完成简单的机器学习任务(线性回归) - -#### 1)前面我们说过,在Greenplum中使用MADlib插件可以直接在DW中建立机器学习模型。这里我们用二)中的regression表跑一下线性回归算法。请使用psql或者psycopg2登录数仓,并运行如下命令。 - -``` -SELECT madlib.linregr_train ( - 'regression', -- source table - 'regression_model', -- output model table - 'y', -- dependent variable - 'ARRAY[1, x1, x2]' -- independent variables -); -``` - -> 这里我们看到使用了MADlib的线性回归模型linregr_train来对regression表中的数据进行训练,模型输入变量为x1, x2以及偏置项,输出变量为y。训练好的模型保存在regression_model表中。 +> 这里我们关注几个重要结果 -### 2)让我们看下模型表中有什么,运行以下命令 +> 1) Time taken for tests:压测总时间 -``` -SELECT * FROM regression_model; -``` - -> 可能输出比较乱,你可以运行以下命令,让表的结果竖起来。 - -``` -\x - -SELECT * FROM regression_model; -``` -> 这样是不是顺眼多了 :ghost: :ghost: :ghost: 我们看到了线性回归模型的参数以及标准差,p-value等指标的值 +> 2) Requests per second: 平均每秒处理请求数 - - - +> 3) Time per request: 平均每个请求处理时间 -### 3)最后让我们用这个模型去做预测。我们直接在训练集上进行预测。你会看到如下输出,其中predict是预测结果,observation是原始的y值。 -``` -\x +#### 6)接下来,请逐渐启用第2和第3个服务器节点,然后再运行上面的压测命令。 -SELECT regression.*, - madlib.linregr_predict ( ARRAY[1, x1, x2], m.coef ) as predict, - y as observation -FROM regression, regression_model m; -``` - - - - +## `**************作业3:请将启用一个节点、两个节点、三个节点时的压测结果分别截图,并插入实验报告***************` -### 4)运行\q退出psql客户端。 +注意,由于有网络因素干扰,每个实验你可以多进行几次压测,得到比较正常的结果再截图。 -## `**************作业4:请如上图一样把预测结果和退出psql以后的界面一起截图,并插入实验报告***************` +## `**************作业4:假设现在压测结果显示单台机器的Requests per second为30,一天中的请求峰值时间是2个小时,总共需要处理5千万个请求,请问需要至少准备多少台机器才可能平稳度过峰值请求?***************` From 970d63374c320d411294abeb8083c3fe37b0e995 Mon Sep 17 00:00:00 2001 From: xuesong Date: Fri, 13 Nov 2020 00:28:25 +0800 Subject: [PATCH 20/23] Add new directory --- file/assignment6/.gitkeep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 file/assignment6/.gitkeep diff --git a/file/assignment6/.gitkeep b/file/assignment6/.gitkeep new file mode 100644 index 0000000..e69de29 From 85349d8bcb26f998b9094f9b01842bf2f9baeb7c Mon Sep 17 00:00:00 2001 From: xuesong Date: Fri, 13 Nov 2020 00:29:22 +0800 Subject: [PATCH 21/23] Upload New File --- file/assignment6/学号-实验六.docx | Bin 0 -> 13820 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 file/assignment6/学号-实验六.docx diff --git a/file/assignment6/学号-实验六.docx b/file/assignment6/学号-实验六.docx new file mode 100644 index 0000000000000000000000000000000000000000..aef16deb02e13129c899cc23d7bf19c6889f854c GIT binary patch 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z_bdF@tlyvTe2|6zk^}q|{MTuXKcQe?Pb6Uf<0QwgEPoB^e`XOU` Date: Fri, 13 Nov 2020 00:30:28 +0800 Subject: [PATCH 22/23] Update Assignment6.md --- Assignment6.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Assignment6.md b/Assignment6.md index 902972c..a337cd4 100644 --- a/Assignment6.md +++ b/Assignment6.md @@ -14,7 +14,7 @@ ## 实验要求 -- 完成所有步骤,并在实验报告([模板下载](file/assignment6/学号-实验六.docx))中完成穿插在本指南中的作业1~作业4(只需要截图)。实验报告转成“学号-实验六.pdf”,并上传至http://113.31.104.68:3389/index.htm +- 完成所有步骤,并在实验报告([模板下载](file/assignment6/学号-实验六.docx))中完成穿插在本指南中的作业1~作业4)。实验报告转成“学号-实验六.pdf”,并上传至http://113.31.104.68:3389/index.htm - 实验报告上传deadline: `11月20日` ## 使用UCloud产品 From c2c17e48d44ca0d8009305fe84f9eef5480707b2 Mon Sep 17 00:00:00 2001 From: xuesong Date: Fri, 13 Nov 2020 00:39:53 +0800 Subject: [PATCH 23/23] Update Assignment6.md --- Assignment6.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/Assignment6.md b/Assignment6.md index a337cd4..e4893ef 100644 --- a/Assignment6.md +++ b/Assignment6.md @@ -60,7 +60,7 @@ -#### 4)点击“服务节点”标签页,当前没有任务实际的节点提供服务。我们接下来创建一些服务节点。 +#### 4)点击“服务节点”标签页,当前没有任何实际的节点提供服务。我们接下来创建一些服务节点。 ### 二)使用云主机创建Flask App服务实例,并连接负载均衡器。 @@ -111,13 +111,13 @@ docker run -d --rm -p 80:5000 -name myflask luxuesong/myflaskapp -#### 3)现在让我们把这三个云主机连接到负载均衡器,并且从负载均衡器中删除刚才的具有外网ip的节点。接下来我们要用外网ip节点做压测。首先我们禁用掉其中的两个节点,只保留一个节点在启用状态。如下图所示。 +#### 3)现在让我们把这三个云主机连接到负载均衡器,并且从负载均衡器中删除刚才的具有外网ip的节点(接下来我们要用外网ip节点做压测)。首先我们禁用掉其中的两个节点,只保留一个节点在启用状态。如下图所示。 -## `**************作业2:请将连接到负载均衡的服务器节点截图,并插入实验报告***************` +## `**************作业2:请将此时连接到负载均衡的服务节点(三个)截图,并插入实验报告***************` #### 4)登录具有外网ip的云主机,安装ApacheBench ```