欢迎查看Eetal的第七篇博客–Cacheable注解
SpringBoot的Cacheable注解 开启缓存
注解于方法上
形如
@Cacheable(“user”)
public User getById(int id) {
System.out.println(“come in with id : “+id);
return userDao.getOne(id);
}
则此方法的代理对象会具备缓存
意思即，除了同一类中的方法对此方法的调用，其他方法的调用，结果都会存储一个user名称的缓存，
根据方法参数优先查找该缓存，具备对应缓存则不会执行下面的方法
注解于类上则应用于所有方法
至于为什么同类无效，时候因为spring的Cache是通过Aop代理对象来实现的
所以如果是同类中方法调用，在Aop生成代理对象时，其实只是加载代码，也可以看做一个小漏洞吧
个人主页: yangyitao.top
欢迎查看Eetal的第六篇博客–Centos7搭建Elasticsearch服务器
centos7 64位
elasticsearch 6.3.0
之前一开始用的32位，后来报错
elasticsearch6.3版本依赖的X-pack插件不支持32位
所以用的64位linux
elasticsearch和solr都是基于lucene开发的搜索引擎
lucene是用java开发的
所以java环境是必须的
而且elasticsearch6.3.0版本依赖的java版本应该是1.8+的
下载对应linux版本的jdk
http://www.oracle.com/technetwork/java/javase/downloads/index.html
window发送文件到linux可以使用xshell，securityCRT等SSH连接工具
解压文件，-C指定解压目录1
$ tar zxf jdk-xxx.tar.gz -C /usr
1 | $ vi /etc/profile |
在末尾添加1
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6JAVA_HOME=/usr/jdk1.8.0_171
PATH=$JAVA_HOME/bin:$PATH
CLASSPATH=$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
export JAVA_HOME
export PATH
export CLASSPATH
生效环境配置1
$ source /etc/profile
输入1
$ java
可以看到相关命令提示就是安装成功了
下载elasticsearch
https://www.elastic.co/downloads/elasticsearch
解压文件1
$ tar zxf elasticsearch-xxx.tar.gz -C /usr
这一步坑比较多
进入解压后的文件夹的bin目录
运行elasticsearch1
$ ./elasticsearch
咔~报错一大堆
首先,出于安全考虑，elasticsearch设置了不能以root用户运行
ok,创建一个新用户elasticsearch并配置密码1
2$ useradd elasticsearch
$ passwd elasticsearch
并指定这个用户加入一个es用户角色群组1
2$ groupadd es
$ usermod -a -G es elasticsearch
切换到新创建的elasticsearch用户1
$ su elasticsearch
在运行一遍~1
$ ./elasticsearch
咔~报错更多了
提示access denied
权限不够(~~)
没办法，很大一部分文件权限都是root才有
所以我们应该给es群组授权
切换到root1
$ su root
指定解压的elasticsearch的文件夹的拥有者及群组1
$ chown -R elasticsearch:es elasticsearch-6.3.0/
切回elasticsearch用户并运行1
2$ su elasticsearch
$ ./elasticsearch
咔~
这一步因系统而异了，因为涉及到linux的一些默认配置
比如单个文件最大大小
最多线程数量这些
如果报错1
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5bootstrap checks failed
max file descriptors [4096] for elasticsearch process likely too low, increase to at least [65536]
max number of threads [1024] for user [elasticsearch] likely too low, increase to at least [4096]
max virtual memory areas vm.max_map_count [65530] likely too low, increase to at least [262144]
切换到root用户
修改linux默认配置1
2$ su root
$ vi /etc/security/limits.conf
在末尾添加1
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7@es soft nofile 65536
@es hard nofile 131072
@es soft nproc 4096
@es hard nproc 4096
第一个参数可以是用户名也可以是一个 @群组名
分别代表对一个用户授权或者对一个群组授权
接下来1
$ vi /etc/sysctl.conf
在末尾添加1
vm.max_map_count=655360
生效配置1
$ sysctl -p
至此，elasticsearch可以正常运行
运行elasticsearch服务后，我们打开浏览器
访问http://es服务器ip:9200
还是访问不了
1.linux防火墙
先开启linux防火墙
ctrl+c取消运行elasticsearch服务后1
2
3$ su root
$ firewall-cmd --zone=public --add-port=9200/tcp --permanent
$ firewall-cmd --reload
–permanent参数代表永久开放，没有该参数则重启机器后端口不开放
此时运行elasticsearch
还是访问不了
2.修改elasticsearch.yml中的配置
出于安全起见
elasticsearch默认只允许本机localhost访问
修改elasticsearch的配置文件，允许别的ip访问1
$ vi config/elasticsearch.yml
在末尾增加1
network.host: 0.0.0.0
0.0.0.0代表所有ip都可以访问
文件中还可以设置自定义日志、数据目录，此处不再啰嗦
至此，重启elasticsearch1
$ ./elasticsearch
打开浏览器访问
http://es服务器ip:9200
可以看到返回的elasticsearch相关信息
完成部署
个人主页: yangyitao.top
欢迎查看Eetal的第五篇博客–Python网络爬虫并输出到excel
笔者用的是python3.6.5以及PyCharm不得不说，JetBrains做的IDE都很不错，无论是这款PyCharm还是IntelliJ
在形参处的名称提示太方便了
笔者学java的，本次写这个爬虫纯粹为了交作业
学过java的URL类的基本都明白
所为爬虫无非就是建立一个与某个网站的连接
通过该连接获取输入流，读取网站内容
实质上就是一个socket的输入输出操作，根据http状态码以及请求头里的信息，验证是否发送完毕（一般是200），结束连接
本次使用的爬取类库是python3.6的一个标准库
urllib不依赖任何第三方库,无需安装
通过以下代码1
req=urllib.request.Request(url,header)
可以得到一个HttpRequest对象
再通过以下代码，发出该请求并得到一个HttpResponse对象1
res=urllib.request.urlopen(req,context)
以上代码中，如果是http:开头的网站header和context都可以省略，则采用默认参数
而对于https开头的网站，因为要进行证书验证
所以要创建一个带ssl证书的context并传入1
context = ssl._create_unverified_context()
通过以下方式创建header来指定模拟爬虫的浏览器信息
并在网站支持国际化时指定语言为中文1
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7header = {
'Accept': 'text/html, application/xhtml+xml, */*',
'Accept-Language': 'zh-CN',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.181 Safari/537.36',
'DNT': '1',
'Connection': 'Keep-Alive',
}
通过得到的response对象的read方法会返回页面内容的byte数组
而我们需要的是字符串
所以使用decode方法，对byte数组编码并转换成字符串
而网络传输的数据格式是json，所以使用json模块的load方法将得到的数据转换为json对象
总结以上几步，我们封装一下爬取资源的方法1
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6def getUrlJson(inUrl):
req = urllib.request.Request(url=inUrl, headers=webheader)
webPage = urllib.request.urlopen(req,context=context)
data = webPage.read().decode('utf-8')
data = json.loads(data)
return data
通过调用该函数1
data = getUrlJson(Url)
这样就得到了网页的内容
有个bug，有时候爬取一些网站会提示页面中存在0x8b这个值不能编码为utf-8中的字符
笔者上网百度了很久，包括在stackOverflow上查看相关问题
发现没有真正能解决的，像（注释掉Accept-encode,不注释对压缩giz进行处理，都不能解决）
然后在PyCharm中进行运行居然又不报这个错了…
也希望知道这个bug解决方法的，联系笔者QQ1183609515，谢谢
本次爬取的是选股宝这个股票网站上面所有股票的信息
作为一个股票网站，实时更新是必须的，所以肯定是个动态网站
选股宝https://xuangubao.cn/dingpan/
爬取下来页面内容后，输出查看1
print(data)
结果是这个1
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<span><span class="ivu-date-picker-header-label">2018年</span> <span class="ivu-date-picker-header-label">6月</span></span>
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<span> 日 </span>
<span> 一 </span>
<span> 二 </span>
<span> 三 </span>
<span> 四 </span>
<span> 五 </span>
<span> 六 </span>
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<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled ivu-date-picker-cells-cell-prev-month"><em>27</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-prev-month"><em>28</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-prev-month"><em>29</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-prev-month"><em>30</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-prev-month"><em>31</em></span>
<span class="ivu-date-picker-cells-cell"><em>1</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>2</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>3</em></span>
<span class="ivu-date-picker-cells-cell"><em>4</em></span>
<span class="ivu-date-picker-cells-cell"><em>5</em></span>
<span class="ivu-date-picker-cells-cell"><em>6</em></span>
<span class="ivu-date-picker-cells-cell"><em>7</em></span>
<span class="ivu-date-picker-cells-cell"><em>8</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>9</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>10</em></span>
<span class="ivu-date-picker-cells-cell"><em>11</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-selected ivu-date-picker-cells-cell-today"><em>12</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>13</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>14</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>15</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>16</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>17</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>18</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>19</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>20</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>21</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>22</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>23</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>24</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>25</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>26</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>27</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>28</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>29</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled"><em>30</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled ivu-date-picker-cells-cell-next-month"><em>1</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled ivu-date-picker-cells-cell-next-month"><em>2</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled ivu-date-picker-cells-cell-next-month"><em>3</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled ivu-date-picker-cells-cell-next-month"><em>4</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled ivu-date-picker-cells-cell-next-month"><em>5</em></span>
<span class="ivu-date-picker-cells-cell ivu-date-picker-cells-cell-disabled ivu-date-picker-cells-cell-next-month"><em>6</em></span>
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涨跌对比
<span id="fluctuation-title-number" data-v-2209365c><span class="-market-color--red" data-v-2209365c>0</span>：<span class="-market-color--green" data-v-2209365c>0</span></span>
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<span id="fluctuation-title-number" data-v-3bde4c92>0 : 0</span>
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<span id="behavior_kline-title-number" data-v-b41f1032>0%</span>
</div>
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<p data-v-b41f1032>0.00%</p>
<p data-v-b41f1032>0.00%</p>
<p data-v-b41f1032>0.00%</p>
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<span id="behavior_kline-title-number" data-v-dc69ee62>0%</span>）
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与我们在浏览器里看到的页面不同，body中只有空标签
这是因为选股宝为了实时更新页面数据
内容都是通过js定时器定时发送ajax请求以后渲染数据到页面中
在浏览器中打开开发者模式(F12)查看刚刚爬取的网站的网络请求数据
发现有这个请求
这个url获取到的是所有的股票id
还有另一个重要的url
这个url可以根据id获取到对应的股票信息
根据这个:
1 | queryProdCodeUrl='https://wows-api.wallstreetcn.com/v3/aioria/plates/summary/field?ids=all&fields=plate_id,stocks' |
1 | queryInfoUrl='https://wows-api.wallstreetcn.com/real?fields=prod_name,px_change,last_px,px_change_rate,trade_status,circulation_value,pe_rate,market_value,turnover_ratio' |
至此，就完成了爬区全股宝上的股票信息并输出
这里要用到第三方的库
xlwt
这是一个python向excel文件输出内容的库，不依赖其他第三方库
安装后
使用命令1
2xls = xlwt.Workbook()
sheet = xls.add_sheet('sample')
就创建了一个包含一个sample名字的sheet的xls文件对象
使用命令1
sheet.write(row, col, data)
就可以将data的内容输出到该对象的row行从来列的格子
最后调用命令1
xls.save(yourSavePath)
就可以把xls文件输出到给定目录
综上，我们只需要
在原有代码里修改下1
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23total=0
xls = xlwt.Workbook()
sheet = xls.add_sheet('sample')
for item in prodData['data']['items']:
for iitem in prodData['data']['items'][item][1]['items']:
total=total+1
infoUrl = queryInfoUrl + '&en_prod_code=' + iitem[0]
snapshot = getUrlJson(infoUrl)['data']['snapshot']
snapshot = zip(snapshot['fields'],snapshot[iitem[0]])
col=0
for info in snapshot:
#print(info[0],':',info[1],end=' ')
sheet.write(total, col, info[1])
col=col+1
infoUrl = queryInfoUrl + '&en_prod_code=' + iitem[0]
snapshot = getUrlJson(infoUrl)['data']['snapshot']
snapshot = zip(snapshot['fields'],snapshot[iitem[0]])
col=0
for info in snapshot:
sheet.write(0, col, info[0])
col=col+1
xls.save(yourSavePath)
最后一段代码是把每个field的名字写入excel的第一行
最终查看目录文件
去除第一行的field名称
总共12924条股票信息
运行时间差不多一小时，才输出完毕
个人主页: yangyitao.top
欢迎查看Eetal的第四篇博客–快速图片服务器fastdfs集群版的搭建
笔者之前电脑空间拮据
搭过一次，只能使用一台电脑不同端口来当服务器
搭建的单机版fastdfs
加了1T的硬盘，刚好有空
今天顺便来把真·集群版的也完成一遍
参考：http://www.oschina.net/p/fastdfs
FastDFS 是一个开源的分布式文件系统
它对文件进行管理，功能包括：
文件存储、文件同步、文件访问（文件上传、文件下载）等
解决了大容量存储和负载均衡的问题。
特别适合以文件为载体的在线服务，如相册网站、视频网站等等
FastDFS 服务端有两个角色：跟踪器（tracker）和存储器（storage）
跟踪器主要做调度工作，在访问上起负载均衡的作用
存储节点存储文件，完成文件管理的所有功能：存储、同步和提供存取接口
本次的搭建环境为，在虚拟机下的搭建
使用的镜像为centos6.9
首先创建6台虚拟机
2个跟踪者集群，两个group，每个group两个存储者集群
规定好每台虚拟机ip，后面配置静态ip
接下来，给每台虚拟机绑定静态ip
不然每次重启都会重新分配ip
使用命令1
$ ifconfig
查看当前ip
在实际生产环境中，应该只开放用到的端口
因为此处是在本地的虚拟机
所以为了方便，笔者直接关闭每台虚拟机防火墙
使用命令1
$ service iptables stop
此时，提示only use by root，需要root权限
于是我们先开启root权限1
$ su root
然后输入当前用户的密码，验证通过后即具备root权限
这时候使用刚刚的命令关闭防火墙就没问题了
接下来，禁止防火墙开机自启动
使用命令1
$ chkconfig iptables off
至此，虚拟机环境的准备工作就完成了
接下来就是搭建fastdfs了
后续用到的东西，笔者通过百度云分享在这里了
fastdfs相关
链接：https://pan.baidu.com/s/1mNVVmdA8luxzHkPUostgqw 密码：nt0f
nginx相关
链接：https://pan.baidu.com/s/1vVEarFoFJvtoDe1xZROH_A 密码：05wp
perl相关
链接：https://pan.baidu.com/s/1iEeunTzU_voTjLSxx3ALyg 密码：2koi
在每台tracker里：
使用命令1
$ mkdir /opt/fastdfs_tracker
创建/opt/fastdfs_tracker目录，作为tracker的base_path
在每台storager里：1
2$ mkdir /opt/fastdfs_storage_info
$ mkdir /opt/fastdfs_storage_data
创建/opt/fastdfs_storage_info目录，作为存储storage间同步文件的存储目录
创建/opt/fastdfs_storage_data目录，作为storage的存储目录
上传压缩包到虚拟机有很多种方式
笔者采用较方便的，直接挂载文件夹
挂载后，虚拟机中的/mnt/hgfs/下，就会具备挂载到的本机目录
如果没有hgfs目录，手动创建一下，重新启用共享就好了
进入挂载目录（此处以挂载到桌面文件夹为例）1
$ cd /mnt/hgfs/桌面/
解压命令1
$ tar zxf libfastcommonV1.0.7.tar.gz -C /
-C / 参数代表指定解压到根目录，此处C必须大写
进入解压后的目录
执行make命令1
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3$ cd /libfastcommon-1.0.7/
$ ./make.sh
$ ./make.sh install
根据安装信息，发现是在/usr/lib64,但fastDFS的主程序的lib目录是/usr/local/lib
所以要建立软连接1
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4$ ln -s /usr/lib64/libfastcommon.so /usr/local/lib/libfastcommon.so
$ ln -s /usr/lib64/libfastcommon.so /usr/lib/libfastcommon.so
$ ln -s /usr/lib64/libfdfsclient.so /usr/local/lib/libfdfsclient.so
$ ln -s /usr/lib64/libfdfsclient.so /usr/lib/libfdfsclient.so
如果报缺乏gcc和perl环境的错误
先安装gcc和perl
使用命令1
2$ yum -y install gcc
$ yum -y install gcc-c++
安装gcc
接着安装perl
和刚刚一样，先解压perl的压缩包1
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6$ tar zxf perl-5.20.2.tar.gz -C /
$ makedir /usr/local/perl
$ ./Configure-des-Dprefix=/usr/local/perl-Dusethreads=Uversiononly
$ make
$ make install
$ perl -version
然后就可以使用刚刚的两个命令安装libfastcommon，并创建软连接了
进入挂载目录（此处以挂载到桌面文件夹为例）1
$ cd /mnt/hgfs/桌面/
解压命令1
$ tar zxf FastDFS_v5.05.tar.gz -C /
-C / 参数代表指定解压到根目录，此处C必须大写
进入解压后的目录
执行make命令1
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3$ cd /FastDFS/
$ ./make.sh
$ ./make.sh install
至此，所有fastdfs服务机必备的步骤就完成，接下来就是分别对tracker和storage配置了
以下操作对于两台tracker进行
复制并编辑配置文件1
2
3$ cd /etc/fdfs
$ cp tracker.conf.sample trakcer.conf
$ vi trakcer.conf
修改如下几个选项（#及后面内容不用输入进去）1
2
3disabled=false #启用配置文件
port=22122 #使用端口
base_path=/opt/fastdfs_tracker #数据和日志文件存储目录
按Esc
输入wq，回车保持并退出
启动tracker1
$ /usr/bin/fdfs_trackerd /etc/fdfs/tracker.conf restart
修改用户自定义开机脚本，配置开机自动启动tracker1
$ vi /etc/rc.d/rc.local
在文件末尾加上开启tracker的命令
查看端口1
$ netstat -untlp|grep 22122
有显示就是启动成功了
至此，完成了tracker的搭建
tracker的nginx负载均衡我们搭完storage再搭建
因为涉及到storage的缓存
以下操作对于四台storage进行
不同的group对group_name进行修改
同一个group的storage的port必须使用一样的
复制并编辑配置文件1
2
3$ cd /etc/fdfs
$ cp storage.conf.sample storage.conf
$ vi storage.conf
修改如下几个选项（#及后面内容不用输入进去）1
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15disabled=false #启用配置文件
group_name=group1 #组名（第一组为 group1，第二组为 group2）
port=23001 #storage 的端口号,同一个组的 storage 端口号必须相同
base_path=/opt/fastdfs_storage_info #设置 storage 的日志目录
store_path0=/opt/fastdfs_storage_data #存储路径
store_path_count=1 #存储路径个数，需要和 store_path 个数匹配
tracker_server=192.168.156.151:22122 #tracker 服务器的 IP 地址和端口
tracker_server=192.168.156.152:22122 #多个 tracker 直接添加多条配置
按Esc
输入wq，回车保持并退出
启动storage1
$ /usr/bin/fdfs_storaged /etc/fdfs/storage.conf restart
修改用户自定义开机脚本，配置开机自动启动tracker1
$ vi /etc/rc.d/rc.local
在文件末尾加上开启storage的命令
查看端口1
$ netstat -untlp|grep 23001
有显示就是启动成功了
至此，完成了storage的搭建
完成所有tracker和storage的启动后
可以在任意storage机器使用命令1
/usr/bin/fdfs_monitor /etc/fdfs/storage.conf
查看整个fastdfs系统的信息
复制，修改任意tracker机器里的client.conf配置文件1
2
3$ cd /etc/fdfs
$ cp client.conf.sample client.conf
$ vi client.conf
改动base_path和配置tracker_server1
2
3base_path=/opt/fastdfs_storage_info
tracker_server=192.168.156.151:22122
tracker_server=192.168.156.152:22122
上传命令(最后一个参数代表要上传的文件，此处为了方便使用client.conf上传测试)1
$ /usr/bin/fdfs_upload_file /etc/fdfs/client.conf /etc/fdfs/client.conf
如果有返回一个以group开头的字符串则是上传成功
至此，fastdfs的搭建我们已经完成近一半
此步骤每台storage都要安装
storage安装nginx的目的是作为服务器提供http方式访问文件，并解决不同storage之间同步延迟的问题
先创建一个安装nginx的目录1
2$ mkdir /usr/local/nginx
$ cd /usr/local/nginx
解压安装包修改配置1
2
3
4$ tar zxf /mnt/hgfs/桌面/fastdfs-nginx-module_v1.16.tar.gz -C .
$ tar zxf /mnt/hgfs/桌面/nginx-1.8.0.tar.gz -C .
$ cd fastdfs-nginx-module
$ vi src/config
修改CORE_INCS的值1
CORE_INCS="$CORE_INCS /usr/include/fastdfs /usr/include/fastcommon/"
1 | $ yum install gcc gcc-c++ make automake autoconf libtool pcre pcre-devel zlib zlib-devel openssl openssl-devel |
进入nginx目录并安装module1
2
3$ cd /usr/local/nginx/nginx-1.8.0
$ ./configure --prefix=/usr/local/nginx --add-module=/usr/local/nginx/fastdfs-nginx-module/src
$ make && make install
1 | $ cd /usr/local/nginx |
在listen 80那个server里面添加1
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3
4location ~/group1/M00{
root /opt/fastdfs_storage_data/data;
ngx_fastdfs_module;
}
不同的group对应不同的路径
比如对于group2里的storage
location就是 ~/group2/M00
1 | $ cp /usr/local/nginx/fastdfs-nginx-module/src/mod_fastdfs.conf /etc/fdfs |
修改对应内容，此处以group1为例1
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41connect_timeout=10
base_path=/opt/fastdfs_storage_info
tracker_server=192.168.156.151:22122
tracker_server=192.168.156.152:22122
storage_server_port=23001
group_name=group1
url_have_group_name = true
store_path0=/opt/fastdfs_storage_data
group_count = 2
[group1]
group_name=group1
storage_server_port=23001
store_path_count=2
store_path0=/opt/fastdfs_storage_data
store_path1=/opt/fastdfs_storage_data
[group2]
group_name=group2
storage_server_port=23002
store_path_count=2
store_path0=/opt/fastdfs_storage_data
store_path1=/opt/fastdfs_storage_data
复制fastdfs提供的配置文件1
2$ cd /FastDFS/conf
$ cp http.conf mime.types /etc/fdfs/
建立软连接，
将storage文件存储目录链接到实际存放数据的目录1
$ ln -s /opt/fastdfs_storage_data/data /opt/fastdfs_storage_data/data/M00
启动nginx1
$ /usr/local/nginx/sbin/nginx
因为我们用的是80端口
在浏览器输入http://storage的ip地址
能访问到nginx的界面则代表storage配合nginx成功了
将启动nginx命令写入开机文件1
$ vi /etc/rc.d/rc.local
tracker安装nginx主要目的是负载均衡，反向代理以及缓存服务
以下步骤对于所有tracker都是必须
创建缓存目录1
2$ mkdir -p /opt/cache/nginx/proxy_cache
$ mkdir -p /opt/cache/nginx/proxy_cache/tmp
创建安装nginx的目录1
2$ mkdir /usr/local/nginx
$ cd /usr/local/nginx
解压安装包修改配置1
2$ tar zxf /mnt/hgfs/桌面/nginx-1.8.0.tar.gz -C .
$ tar zxf /mnt/hgfs/桌面/ngx_cache_purge-2.3.tar.gz -C .
1 | $ yum install gcc gcc-c++ make automake autoconf libtool pcre pcre-devel zlib zlib-devel openssl openssl-devel |
安装nginx1
2
3$ cd /usr/local/nginx/nginx-1.8.0
$ ./configure --prefix=/usr/local/nginx --add-module=/usr/local/nginx/ngx_cache_purge-2.3
$ make && make install
###配置nginx1
$ vi /usr/local/nginx/conf/nginx.conf
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events {
worker_connections 1024;
use epoll;
}
http{
tcp_nopush on;
#gzip on;
#设置缓存
server_names_hash_bucket_size 128;
client_header_buffer_size 32k;
large_client_header_buffers 4 32k;
client_max_body_size 300m;
proxy_redirect off;
proxy_set_header Host $http_host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_connect_timeout 90;
proxy_send_timeout 90;
proxy_read_timeout 90;
proxy_buffer_size 16k;
proxy_buffers 4 64k;
proxy_busy_buffers_size 128k;
proxy_temp_file_write_size 128k;
#设置缓存存储路径、存储方式、分配内存大小、磁盘最大空间、缓存期限
proxy_cache_path /opt/cache/nginx/proxy_cache levels=1:2
keys_zone=http-cache:200m max_size=1g inactive=30d;
proxy_temp_path /opt/cache/nginx/proxy_cache/tmp;
#设置 group1 的服务器
upstream fdfs_group1 {
server 192.168.156.153 weight=1 max_fails=2 fail_timeout=30s;
server 192.168.156.154 weight=1 max_fails=2 fail_timeout=30s;
}
#设置 group2 的服务器
upstream fdfs_group2 {
server 192.168.156.155 weight=1 max_fails=2 fail_timeout=30s;
server 192.168.156.156 weight=1 max_fails=2 fail_timeout=30s;
}
server {
listen 80;
server_name localhost;
#设置 group 的负载均衡参数
location /group1/M00 {
proxy_next_upstream http_502 http_504 error timeout invalid_header;
proxy_cache http-cache;
proxy_cache_valid 200 304 12h;
proxy_cache_key $uri$is_args$args;
proxy_pass http://fdfs_group1;
expires 30d;
}
location /group2/M00 {
proxy_next_upstream http_502 http_504 error timeout invalid_header;
proxy_cache http-cache;
proxy_cache_valid 200 304 12h;
proxy_cache_key $uri$is_args$args;
proxy_pass http://fdfs_group2;
expires 30d;
}
#设置清除缓存的访问权限
location ~/purge(/.*) {
allow 127.0.0.1;
allow 192.168.1.0/24;
deny all;
proxy_cache_purge http-cache $1$is_args$args;
}
}
}
启动nginx1
$ /usr/local/nginx/sbin/nginx
因为我们用的是80端口
在浏览器输入http://storage的ip地址
能访问到nginx的界面则代表storage配合nginx成功了
将启动nginx命令写入开机文件1
$ vi /etc/rc.d/rc.local
首先，访问每台服务器的nginx首页
每台服务器的nginx都可以访问
OK!!!
如果有某一台访问不了
查看nginx目录下log目录里的日志文件
根据提示查错
复制，修改任意tracker机器里的client.conf配置文件1
2
3$ cd /etc/fdfs
$ cp client.conf.sample client.conf
$ vi client.conf
改动base_path和配置tracker_server1
2
3base_path=/opt/fastdfs_storage_info
tracker_server=192.168.156.151:22122
tracker_server=192.168.156.152:22122
上传命令(最后一个参数代表要上传的文件，此处为了方便使用client.conf上传测试)1
$ /usr/bin/fdfs_upload_file /etc/fdfs/client.conf /etc/fdfs/client.conf
上传后得到一个返回的文件名1
group2/M00/00/00/wKicm1saiXGALps_AAAF4Qywnmo39.conf
这时候我们在浏览器访问任意一台tracker的ip加上这个文件名
http://trackerip/group2/M00/00/00/wKicm1saiXGALps_AAAF4Qywnmo39.conf
都可以下载该文件
nginx会自动根据group选择不同的upstream，并根据负载均衡计算
跳转到某个storage服务器，看浏览器地址变化就明白了
至此，我们完成了采用Epoll进行负载均衡的fastdfs的搭建
在每台tracker上
修改nginx的配置文件1
$ vi /usr/local/nginx/conf/nginx.conf
修改内容1
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events {
worker_connections 1024;
#use epoll;
}
http{
#tcp_nopush on;
#gzip on;
keepalive_timeout 65;
# Tracker Proxy
upstream fastdfs_tracker {
server 192.168.156.151 weight=1 max_fails=2 fail_timeout=30s;
server 192.168.156.152 weight=1 max_fails=2 fail_timeout=30s;
}
location /dfs {
root html;
index index.html index.htm;
proxy_pass http://fastdfs_tracker/;
proxy_set_header Host $http_host;
proxy_set_header Cookie $http_cookie;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme; client_max_body_size 300m;
}
}
这个时候，下载文件的路径就不再是trackerip+返回的文件名了
而是通过这个trackerip/dfs/+返回的文件名
这时候由keepalived程序计算
判断访问哪边的tracker，实现高可用
keepalived就是通过定时互相发送心跳，定时检查主从服务机的健康状态
判断服务机是否还活着，当主服务器死了，就由从服务器接手
配置完毕，重启nginx1
$ /usr/local/nginx/sbin/nginx -s reload
个人主页: yangyitao.top
欢迎查看Eetal的第三篇博客–Docker下mongodb的安装与使用
windows下可以下载使用Docker for windows,但是最新版对于win10版本有限制，像笔者的windows由于版本号低于最低要求版本，所以装的是linux下的
linux下建议使用centos7，因为默认就具备Docker插件，
centos7下
$ yum install docker
安装docker
首先，运行docker
$ service docker start
启动docker
$ docker pull mongo:版本号
此处的版本号可以自行选择，笔者pull的最新版，3.6
$ docker run -p localport:containerPort --name -d containerName mongo:版本号
此处 -p localport:containerPort 代表将要创建的docker容器使用的ContainerPort端口映射到本机的localport端口
像mongodb默认就是 -p 27017:27017
多个端口就写多次-p如:-p 80:80 -p 8080:8090
–name containerName表示为该容器指定一个名字，没有该参数docker会自行指定一个容器的名字
运行成功后，会返回一个容器ID，通过docker ps命令可以查看正在运行的容器的相关信息
$ docker exec -it containerName/containerID /bin/bash
通过容器名或者容器ID进入其容器命令行
进入容器的bin目录下
运行命令
$ mongo
开启shell后，即可对mongodb进行操作
至此，我们已经完成Docker下安装mongodb的操作
如果是在shell中，使用命令
$ use admin
只有进入admin数据库才能操作关闭mongodb
接着调用
$ db.shutdownServer()
关闭mongolian并会自动退出容器
docker run命令是新创建一个容器
想要重启容器使用命令
$ docker restart containerName/containerID
个人主页: yangyitao.top
欢迎查看Eetal的第二篇博客–JpaRepository02
org.hibernate.LazyInitializationException: could not initialize proxy - no Session
at org.hibernate.proxy.AbstractLazyInitializer.initialize(AbstractLazyInitializer.java:155)
at org.hibernate.proxy.AbstractLazyInitializer.getImplementation(AbstractLazyInitializer.java:268)
at org.hibernate.proxy.pojo.javassist.JavassistLazyInitializer.invoke(JavassistLazyInitializer.java:73)
at com.ws.dataobject.ProductInfo_$$_jvst26a_2.getProductStatus(ProductInfo_$$_jvst26a_2.java)
at com.ws.service.impl.ProductInfoServiceImpl.offSale(ProductInfoServiceImpl.java:102)
at com.ws.service.impl.ProductInfoServiceImpl$$FastClassBySpringCGLIB$$f6542ae8.invoke(
at org.springframework.cglib.proxy.MethodProxy.invoke(MethodProxy.java:204)
at org.springframework.aop.framework.CglibAopProxy$DynamicAdvisedInterceptor.intercept(CglibAopProxy.java:685)
……
正解是因为在新版本(2.0)的SpriingBoot中,Jpa已经不支持getOne方法，只能使用findById(id).get()获取
而且笔者是在SpringTest环境中测试，一开始还以为是上下文没有装配对应的Bean…
个人主页: yangyitao.top
欢迎查看Eetal的第一篇博客–JpaRepository01
笔者用的是1.5的springBoot版本，结果教材是2.0的版本，出现了这个问题，主要是SpringBoot1.x的版本要手动配置多几个注解开启一些支持
在SpringBoot1.x中，Repository除了继承JpaRepository并标注Repository注解，还要开启EnableJpaRepositories注解，才能自动创建实现类并供给注入
在SpringBoot1.x中，Entity除了标明Entity注解（此处有坑，所有Entity中使用的具备多个选择的注解，都是选javax包下的，特别是Id注解，选错了会报缺乏id域的错误，很难查）
个人主页: yangyitao.top