38 skills found · Page 1 of 2
giltene / PauselessHashMapA java.util.HashMap compatible map that won't stall puts or gets when resizing
akshayavb99 / CodingNinjas Java DSAContains the solutions for the programming questions in the CodingNinjas Java+DSA course
mikvor / HashmapTestHashMap performance tests from java-performance.info
xiaoxinxing66 / Sivan Cache🚀 推出 SivanCacheFramework！一款轻量级、高效的 缓存框架，让 缓存操作 变得不再复杂，提升数据处理速度，为开发者节约宝贵时间。
cfelde / BinaryOffheapHashMapA hashmap implementation for Java that stores map entries off-heap
trivago / Fastutil Concurrent WrapperSet of concurrent wrappers around fastutil primitive maps
bnyeggen / LashDisk-backed hashmaps for Java
tangcr / Redis Redis是什么 Redis是一个NOSQL，NOSQL有许多种，它们分为： 列存储，如：Hbase、Cassandra这种 文档存储，如：MongoDB（首推） key-value存储，如：Berkeley DB、MemcacheDB、Redis，其中Redis最强 图存储，这块基本不用，有：Neo4j、Versant XML存储，如：Berkeley DB Xml还有XBASE，ORACLE很早已经支持这种存储方式了 光知道这些NOSQL的名词是没有用的，关键在于要知道在哪种场景下选用哪种NOSQL才是我们真正要去掌握的。 我们这边说Redis就拿Redis说事吧，它能干什么呢？ Redis基础应用场景 web间session共享，即多个war工程共享一个session 分布式缓存，因为redis为键值对，而且它提供了丰富的adapter可以支持到C、.net、java客户端，因此对于异质平台间进行数据交换起到了作用，因此它可以用作大型系统的分布式缓存，并且其setnx的锁常被用于”秒杀“，”抢红包“这种电商活动场景中。 安装Redis 我本来想在这儿写”Redis上的‘坑‘“，最后我还是觉得把它放到后面章节中去写吧，因为中国人的思维是先有感性再有理性的一种逆向思维，其实这点很像美国人，因此中国人在世界上是最聪明的民族之一，所以我们还是先从动手搭一个Redis的环境来说起吧，老规矩，红色加粗很重要。 一定要使用Linux来布署Redis，请不要偷懒使用Redis 2.8.1 for windows那个版本，如果你使用了这个版本你将无法跟上这一系列教程的步伐。因为Redis为GCC+这样的东西开发出来的，它天生就是运行在LINUX/Unix环境下的，而那个windows版的Redis是一个”烟“割版，而且是一个unofficial的版本，非官方授权的哈。 先从Docker开始 如果已经有Linux/Unix环境的同协们可以直接跳过这一章。 我们这边要开始变态了，因为我们要真正开始踏上SOA、PAAS、互联网的脚步了。 如果对于没有Linux/Unix环境的用户来说，我在这边推荐使用docker，即boot2docker windows版来安装，它下载后是一个这样的文件 安装前把你的网络连接中的IPV6协议前的勾去掉 双击它，在安装时记得选择Virtual-Box选项，因为docker本为linux/unix下之物，因此为了在windows下使用docker，boot2docker内嵌了一个virtualbox来虚拟docker的环境。 装完后它会在你的桌面上生成一个蓝色的图标，双击它，它会打开一个绿色的字，黑色的背景像matrix电影里的那种命令行窗口，这就是Docker。 装完后运行： [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker@boot2docker:~$ docker run hello-world 看到下面这些提示 [plain] view plain copy 在CODE上查看代码片派生到我的代码片 Hello from Docker. This message shows that your installation appears to be working correctly. To generate this message, Docker took the following steps: 1. The Docker client contacted the Docker daemon. 2. The Docker daemon pulled the “hello-world” image from the Docker Hub. (Assuming it was not already locally available.) 3. The Docker daemon created a new container from that image which runs the executable that produces the output you are currently reading. 4. The Docker daemon streamed that output to the Docker client, which sent it to your terminal. To try something more ambitious, you can run an Ubuntu container with: $ docker run -it ubuntu bash For more examples and ideas, visit: http://docs.docker.com/userguide/ 说明你的Docker安装成功了。 在Docker中安装unix环境 有了Docker我们就用Docker虚拟一个Ubuntu（UNIX）环境吧，在这边我们使用的是Ubuntu14。 ubuntu14请下载这个包：戳: 下载Ubuntu14包 下载后直接在docker下运行下面这条命令： [plain] view plain copy 在CODE上查看代码片派生到我的代码片 cat ubuntu-14.04-x86_64.tar.gz |docker import - ubuntu:ubuntu14 这个过程会很快，完成后查看自己的image： 成功导入了ubuntu，这样我们就可以在Docker中运行出一个自己的ubuntu了。 [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker run -i -t ubuntu:ubuntu14 /bin/bash 以上运行后，进入了该ubuntu的bash环境。 注：如果上述命令出错，可以使用下面这条命令： [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker run -i -t ubuntu:ubuntu14 //bin/bash 两个 “/” 哈 如果你能看到类似于root@ubuntu14_这样的命令行界面说明你的ubuntu14也已经安装成功了，下面我们就要在这个docker->ubuntu14中安装和布署我们的Redis了，这个过程和在Linux下一样。 在ubuntu14下先安装SSHD，以便于我们使用WINSCP这样的SFTP工具来管理我们的ubuntu14中的文件系统 在ubuntu14中安装SSHD 第一步： [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker run -t -i ubuntu/mk:v1 /bin/bash 进入我们的ubuntu环境，这边的ubuntu/mk就是我本机的docker中ubuntu14 container（容器）的名字，如果按照上面的延续此处可以替换成ubuntu:ubuntu14这个名字吧。 第二步： 升级一下你的apt-get，它就是一个命令行IE下载工具，如果你不update，那么你apt-get的源、内核都为旧的，因此为了升级apt-get请键入下面的命令 [plain] view plain copy 在CODE上查看代码片派生到我的代码片 apt-get update 这个过程很快（依赖于你的网络环境） 第三步： 下载和安装openssh组件 [plain] view plain copy 在CODE上查看代码片派生到我的代码片 apt-get install openssh-server openssh-client 第四步： 修改你的root密码 [plain] view plain copy 在CODE上查看代码片派生到我的代码片 passwd 键入两次你的root密码，我这边都为6个小写的a 第五步： 退出容器，并保存以上修改，如果docker在退出后你接着退出docker环境或者是关机那么刚才的4步全部不生效，你一定要commit它才能生效，为此： 你先要知道你刚才用docker run命令运行的ubuntu14的容器的ID，你可以使用 [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker ps -a 来查到你latest的一次容器的ID，它是一组16进制一样的编码如：1edfb9aabde8890，有了这个container id我们就可以commit我们刚才装的openssh的环境了 commit刚才在容器中所做的修改 [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker commit 1edfb9aabde8890 ubuntu:ssh 第六步： 运行带有openssh的ubuntu14以便于我们使用winscp这样的SFTP工具连入我们的ubuntu14中去，依次输入下面的命令： [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker kill $(docker ps -q) 杀掉正在运行的所有的container的进程 [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker rm $(docker ps -a -q) 删除所有在进程中的容器，以上2步又被称为docker大扫除 Docker是这样的机制的，它可以开启多个容器，每个容器带着一堆的image（镜像），要删一个镜像必须先停止这个镜像所在的容器，再把这个镜像删除，因此我们使用上面这两条命令对于Docker来一个大扫除。 接着我们先查一下我们目前手头有的镜像 [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker images 你会看到一个images列表，里面有我们的ubuntu:14，有我们的ubuntu:ssh也有一个hello-world，我们把ubuntu:14这个镜像删了吧（为了保持干净哈） 每个image也它自己的id，即image id，因此你用docker images命令查到该镜像的id后可以使用： [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker rmi imageid 这条命令把一个不用的镜像给删了。 接下去我们要启动我们的ubuntu14:ssh了，可以使用下面这条命令： [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker -d -p 122:22 ubuntu:ssh //usr/sbin/sshd -D 这条命令的意思为： -d即把我们的image启动在后台进程，它将会是一个daemon进程，而不会像刚才我们使用-t一样，一旦exit后该image进程也自动退出了 -p为端口映射，什么意思呢，这边要说一下docker的端口映射问题。我们知道docker安装后它会利用virtualbox中的vhost only的nat机制来建立一个虚拟的IP 可以打开我们的virtualbox中在菜单”全局->设定->网络”中进行查找 所以我们可以知道一旦boot2docker环境运行后它的地址为192.168.56.*这个段，一般为192.168.56.101这个地址，你可以在boot2docker启动后直接使用winscp边入这个docker环境。 地址：192.168.56.101 端口：22 用户名：docker 密码：tcuser 以上为默认值，具体地址按照你的virtualbox中在boot2docker安装时自动给出的设置来做参考。 而， 我们在这个docker中安装了一个ubuntu14:ssh的image，然后用后台进程的方式打开了这个ubuntu14:ssh，因此它自己也有一个IP（可能是172也可能是169段），具体不得而知，一般来说它是每次启动镜像后自己变换的（可以使用动态网络域名绑定docker中镜像的ip来达到域名不变的目的-集群环境下有用）。 我们都知道ssh是以端口22来进行TCP连接的，因此我们把ubuntu14的IP上的22端口映射到了我们的docker主机192.168.56.101上的122端口。 参数//usr/sbin/sshd -D代表该镜像启动会的entrypoint即启动后再启动一个什么命令，在最后的-D（大写的D）告诉docker这是一个启动文件 于是，一旦该命令发出后，显示image启动的提示后（启动后你会得到一个image id）你就可以直接打开你的winscp使用： 地址：192.168.56.101 端口：122 （此处是122，不是22，因为我们把image的22端口映射到了192.168.56.101-docker主机上的122端口了） 用户名：root 密码：aaaaaa 即可以连入我们的ubuntu14环境了，如果此时你安装了putty还可以使用putty+winscp直接进入ubuntu14的命令行环境中去，于是你就有ubuntu14的试验环境了。 在ubuntu14下安装redis 网上很多在ubuntu14下安装redis的教程都不对的，大家看了要上当的，原因在于如下，请各位看完： 网上的redis环境搭建直接使用的是apt-get update完后用wget https://github.com/ijonas/dotfiles/raw/master/etc/init.d/redis-server 这样的方式来安装的，这样装固然方便，可是也因为方便所以取到的redis不是最新的redis版本，一般为2.8.x版或者是redis3.0.rc，这依赖于你的unit/linux所连接的wget库 redis为c写成，它的2.4-2.8版都为不稳定版或者是缺少功能或者是有bug，而这些bug在你如果真正使用redis作为网站生产环境时将会因为这些bug而无法面对峰涌而来的巨大并发，因此当有这样的redis运行了一段时间后你的生产环境会面临着巨大的压力 还是redis不够新不够稳定的原因，由于在redis3前redis还不支持集群、主备高可用方案的功能，因此不得不依靠于繁杂的打补丁式的如：linux/unix-keepalive或者是haproxy这种系统级层面然后写一堆的复杂脚本去维护你的redis集群，还要用外部手段（Linux/Unix Shell脚本）去维护多个redis节点间的缓存数据同步。。。这这这。。。不复合我们的网站扩容、增量、运维和面对巨大用户（万级并发-最高支持百万用户如：新浪微博、微信）的场景 因此，我在这边推荐大家使用下面我将要使用的“下载源码包结合你本机的Linux/Unix内核进行实时编译”的安装过程。 第一步：下载redis目前最稳定版本也是功能最完善，集群支持最好并加入了sentinel（哨兵-高可用）功能的redis3.0.7版即redis-stable版，为此我们需要获取redis-stable版 redis官方下载连接 就是用的这个redis-stable.tar.gz包，这是我在写博客时目前最新最稳定版本，修复了大量的BUG和完善了功能。 第二步： 下载后我们把该包上传到我们的docker中的ubuntu14中，我们把它放在/opt目录下 然后我们使用tar -zxvf redis-stable.tar.gz对它进行解压 解压后它就会生成一个redis-stable目录，进入该目录 cd redis-stable 别急，我们先一会编译和安装它 第三步：编译安装redis 我们先输入gcc -v 这个命令来查看我们的gcc版本，如果它低于4.2以下那么你在编译redis3.0.7时一定会碰到大量的出错信息，如前面所述，redis为gcc写成，最新的redis需要gcc4.2-5这个版本才能进行编译，而一般去年或者之前装的linux/unix 的 gcc都为4.0以下或者甚至是3.x版。 升级GCC先 [plain] view plain copy 在CODE上查看代码片派生到我的代码片 apt-get install build-essential 因此apt-get update显得很重要，要不然你获取的gcc也将不是最新的版本，目前我的gcc为5.3.1为这周刚做的升级。 升级后我们开始编译redis3.0.7了，为此我们需要在redis-stable目录下 键入如下命令： [plain] view plain copy 在CODE上查看代码片派生到我的代码片 make PREFIX=/usr/local/redis1 install 我们告知我们的GCC把redis-stable编译并同时安装在/usr/local/redis1目录下 这个过程很快，可能只有10秒钟时间（依据你的机器来说，建议使用>=8gb, 4核CPU的PC机），然后我们就可以看到everything ok了。我们进入/usr/local/redis1就可以看到我们刚才安装的redis3.0.7稳定版了。 我们进入我们的redis目录 cd /usr/local/redis1/bin 在此目录下我们即可以运行我们的redis server了，不过请别急，在启动前我们需要对redis进行一些配置。 我的博客面对的是“全栈式”工程师的，架构师只是成为全栈式工程师中的一个起点，如果你不会搭环境那么你就不能接触到最新的技术，因此这就是许多程序员工作了近5年，7年结果发觉也只会一个SSH的主要原因。 Redis3配置要领 使用winscp通过122连入docker下的ubuntu14，进行redis的配置。 我们需要编辑的文件为/usr/local/redis1/bin/redis.conf这个文件 [plain] view plain copy 在CODE上查看代码片派生到我的代码片 daemonize yes # When running daemonized, Redis writes a pid file in /var/run/redis.pid by # default. You can specify a custom pid file location here. pidfile "/var/run/redis/redis1.pid" # Accept connections on the specified port, default is 6379. # If port 0 is specified Redis will not listen on a TCP socket. port 7001 我们把: daemonize设为yes，使得redis以后台进程的方式来运行，你可以认为为“server”模式，如果redis以server模式运行的话它会生成一个pid文件 ，因此我们把它的路径放在/var/run/redis目录中，并命名它为redis1.pid文件 ，为此你需要在/var/run目录下建立redis这个目录 端口号我们把它设为7001，这样好辩识，因为将来我们会进一步做redis集群，所以我们的redis都为redis1, redis2, redis3那么我们的端口号也为7001, 7002, 7003。。。这样来延续。那么很多同协这时要问了，“为什么我们不把它命名成master, slave1, slave2这样的名字呢？”，理由很简单，无论是现在的hadoop还是zookeeper它们的集群是跨机房的，多个master间也有MASTER-SLAVE模式互为备份，因为一些大型网站不仅仅只有一个IDC机房，它们一般都会有2个，3个IDC机房，或者是在同一个IDC机房中有“跨机柜”的布署来形成超大规模集群，就和ALI的TAOBAO网一样，它在北美都有机房，因此当你需要在LOCAL NATIVE建一个IDC机房，在北美再做一个机房，你不要想把一个MASTER设在中国，SLAVE设到美国去，而是多地甚至是多机柜都有MASTER，一旦一个MASTER宕机了，这种集群会通过一个叫“选举策略”选出一个节点把这个节点作为当前“群”的MASTER，因此我们的命名才会是redis1, redis2, redis3...这样来命名的。 此处把原来的： [plain] view plain copy 在CODE上查看代码片派生到我的代码片 save 900 1 save 300 10 save 60 10000 中的300 10 和60 10000注释掉。这边代表的是： redis以每900秒写一次、300秒写10次，60秒内写1万次这样的策略把缓存放入一个叫.rdb的磁盘文件中，这点和ehcache或者是memcache很像，以便于redis在重启时可以从本地持久化文件中找出关机前的数据记录。 如果按照默认的话，此三个策略会轮流起效，在大并发环境中，这样的写策略将会对我们的性能造成巨大的影响，因此我们这边只保留900秒写1次这条策略，这边有人会问，如果你这样会有数据丢失怎么办。。。别急，这个问题我们后面会解答，这涉及到redis的“正确”使用，如果它只是一个缓存，我相信5分钟内缓存的丢失此时程序直接访问数据库也不会有太大问题，又要保证数据完整性又要保证性能这本身是一个矛与盾的问题，除非你钱多了烧那我会给出你一个烧钱的配置策略，连新浪都不会这么烧钱，呵呵。 dbfilename，此处我们维持redis原有的缓存磁盘文件的原名 dir "/usr/local/redis1/data"为rdb文件所在的目录 这边大家要注意的是一个是只能写文件名，另一个地方只能写目录名。 为此我们需要在/usr/local/redis1下建立 data目录。 把此处的appendonly设为no，这样我们就关闭了Redis的AOF功能。 AOF 持久化记录服务器执行的所有写操作命令，并在服务器启动时，通过重新执行这些命令来还原数据集。AOF是redis在集群或者是高可用环境下的一个同步策略，它会不断的以APPEND的模式把redis的缓存中的数据从一个节点写给另一个节点，它对于数据的完整性保证是要高于rdb模式的。 RDB 是一个非常紧凑（compact）的文件，它保存了 Redis 在某个时间点上的数据集。 这种文件非常适合用于进行备份： 比如说，你可以在最近的 24 小时内，每小时备份一次 RDB 文件，并且在每个月的每一天，也备份一个 RDB 文件。 这样的话，即使遇上问题，也可以随时将数据集还原到不同的版本。RDB 非常适用于灾难恢复（disaster recovery）：它只有一个文件，并且内容都非常紧凑，可以（在加密后）将它传送到别的数据中心如阿里的mysql异地机房间使用FTP传binlog的做法。 按照官方的说法，启用AOF功能，可以在redis高可用环境中如果发生了故障客户的数据不会有高于2秒内的历史数据丢失，它换来的代价为高昂的I/O开销，有些开发者为了追求缓存中的数据100%的正确有时会碰到因为redis在AOF频繁刷新时整个环境如死机一的情况，并且你会看到恶梦一般的”Asynchronous AOF fsync is taking too long “警告信息，这是因为redis它是单线程的，它在进行I/O操作时会阻塞住所有的操作，包括登录。。。这个很可怕，不过这个BUG/ISSUE已经在最新redis中进行了优化，它启用了另一根进程来进行AOF刷新，包括优化了RDB持久化功能，这也是为什么我让大家一定一定要用最新最稳定版的redis的原因。 一般默认情况下redis内的rdb和AOF功能同为开启， 如果RDB的数据不实时，同时使用两者时服务器重启也只会找AOF文件。 因为RDB文件只用作后备用途，建议只在Slave上持久化RDB文件，而且只要15分钟备份一次就够了，所以我只保留save 900 1这条规则。 如果Enalbe AOF： 好处是在最恶劣情况下也只会丢失不超过两秒数据，启动脚本较简单只load自己的AOF文件就可以了。 代价一是带来了持续的IO，二是AOF rewrite的最后将rewrite过程中产生的新数据写到新文件造成的阻塞几乎是不可避免的。只要硬盘许可，应该尽量减少AOF rewrite的频率，AOF重写的基础大小默认值64M太小了，可以设到5G以上。默认超过原大小100%大小时重写，这边可以设定一个适当的数值。 如果不Enable AOF ，仅靠Master-Slave Replication 实现高可用性也可以。能省掉极大的IO也减少了rewrite时带来的系统波动。代价是如果Master/Slave同时倒掉（那你的网站基本也就歇了），会丢失十几分钟的数据，启动脚本也要比较两个Master/Slave中的RDB文件，载入较新的那个。新浪微博就选用了这种架构。 最后我们不要忘了设一个redis的log文件，在此我们把它设到了/var/log/redis目录，为此我们需要在/var/log目录下建立一个redis目录。 好了，保存后我们来启动我们的redis吧。 我们使用以下这条命令来启动我们的redis server。 然后我们在我们的windows机上装一个windows版的redis 2.8.1 for windows（只用它来作为redis的client端） 然后我们在windows环境下使用： redis-cli -p 7001 -h 192.168.56.101 咦，没反映，连不上，哈哈。。。。。。 那是肯定连不上的，因为： 我们刚才在用docker启动ubuntu14时使用docker -d -p 122:22 ubuntu:ssh //usr/sbin/sshd -D来启动的，这边我们并未把redis服务的7001端口映射到192.168.56.101这台docker主机上，怎么可以通过windows主机（可能windows的ip为169.188.xx.xx）来访问docker内的进程服务呢？对吧，为此我们：先把刚才做了这么多的更改docker commit成一个新的image如：redis:basic吧。 然后我们对docker进行一次大扫除，然后我们启动redis:basic这个image并使用以下命令： [plain] view plain copy 在CODE上查看代码片派生到我的代码片 docker -d -p 122:22 -p 7001:7001 redis:basic //usr/sbin/sshd -D 看，此处我们可以使用多个-p来作docker内容器的多端口映射策略（它其实使用的就是iptables命令）。 好了，用putty连入这个image的进程并启动redis服务，然后我们拿windows中的redis-cli命令来连。 如果在linux环境下还是没有连通（可能的哦），那是因为你没有禁用linux下的防火墙，我们可以使用iptables -F来禁用linux的防火墙或者使用： vi /etc/selinux/config 然后把 SELINUX=enforcing 这句用”#“注释掉 增加一句： SELINUX=disabled #增加 这样每次启动后linux都不会有iptables的困扰了（这是在本机环境下这么干哦，如果你是生产环境请自行加iptables策略以允许redis服务端口可以被访问）。 看到下面这个PONG即代表你的redis服务已经在网络环境中起效了。 下面我们要开始使用Java客户端来连我们的Redis Service了。 使用Spring Data + JEDIS来连接Redis Service Spring+Session+Redis pom.xml 在此我们需要使用spring data和jedis，下面给出相关的maven配置 [html] view plain copy 在CODE上查看代码片派生到我的代码片 <dependencies> <!-- poi start --> <dependency> <groupId>org.apache.poi</groupId> <artifactId>poi</artifactId> <version>${poi_version}</version> </dependency> <dependency> <groupId>org.apache.poi</groupId> <artifactId>poi-ooxml-schemas</artifactId> <version>${poi_version}</version> </dependency> <dependency> <groupId>org.apache.poi</groupId> <artifactId>poi-scratchpad</artifactId> <version>${poi_version}</version> </dependency> <dependency> <groupId>org.apache.poi</groupId> <artifactId>poi-ooxml</artifactId> <version>${poi_version}</version> </dependency> <!-- poi end --> <!-- active mq start --> <dependency> <groupId>org.apache.activemq</groupId> <artifactId>activemq-all</artifactId> <version>5.8.0</version> </dependency> <dependency> <groupId>org.apache.activemq</groupId> <artifactId>activemq-pool</artifactId> <version>${activemq_version}</version> </dependency> <dependency> <groupId>org.apache.xbean</groupId> <artifactId>xbean-spring</artifactId> <version>3.16</version> </dependency> <!-- active mq end --> <!-- servlet start --> <dependency> <groupId>javax.servlet</groupId> <artifactId>servlet-api</artifactId> <version>${javax.servlet-api.version}</version> <scope>provided</scope> </dependency> <dependency> <groupId>javax.servlet.jsp</groupId> <artifactId>jsp-api</artifactId> <version>2.1</version> <scope>provided</scope> </dependency> <dependency> <groupId>javax.servlet</groupId> <artifactId>jstl</artifactId> <version>1.2</version> </dependency> <!-- servlet end --> <!-- redis start --> <dependency> <groupId>redis.clients</groupId> <artifactId>jedis</artifactId> <version>2.7.2</version> </dependency> <dependency> <groupId>org.redisson</groupId> <artifactId>redisson</artifactId> <version>1.0.2</version> </dependency> <!-- redis end --> <dependency> <groupId>org.slf4j</groupId> <artifactId>jcl-over-slf4j</artifactId> <version>${slf4j.version}</version> </dependency> <dependency> <groupId>org.slf4j</groupId> <artifactId>slf4j-log4j12</artifactId> <version>${slf4j.version}</version> </dependency> <!-- spring conf start --> <dependency> <groupId>org.springframework.data</groupId> <artifactId>spring-data-redis</artifactId> <version>1.6.2.RELEASE</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-webmvc</artifactId> <version>${spring.version}</version> <exclusions> <exclusion> <groupId>commons-logging</groupId> <artifactId>commons-logging</artifactId> </exclusion> </exclusions> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-tx</artifactId> <version>${spring.version}</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-aop</artifactId> <version>${spring.version}</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-context-support</artifactId> <version>${spring.version}</version> </dependency> <dependency> <groupId>org.springframework.data</groupId> <artifactId>spring-data-redis</artifactId> <version>1.6.2.RELEASE</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-orm</artifactId> <version>${spring.version}</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-jms</artifactId> <version>${spring.version}</version> </dependency> <dependency> <groupId>org.springframework.session</groupId> <artifactId>spring-session</artifactId> <version>${spring.session.version}</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-core</artifactId> <version>${spring.version}</version> </dependency> <!-- spring conf end --> </dependencies> redis-config.xml [html] view plain copy 在CODE上查看代码片派生到我的代码片 <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:p="http://www.springframework.org/schema/p" xmlns:context="http://www.springframework.org/schema/context" xmlns:jee="http://www.springframework.org/schema/jee" xmlns:tx="http://www.springframework.org/schema/tx" xmlns:aop="http://www.springframework.org/schema/aop" xsi:schemaLocation=" http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd"> <context:property-placeholder location="classpath:/spring/redis.properties" /> <context:component-scan base-package="org.sky.redis"> </context:component-scan> <bean id="jedisConnectionFactory" class="org.springframework.data.redis.connection.jedis.JedisConnectionFactory"> <property name="hostName" value="${redis.host.ip}" /> <property name="port" value="${redis.host.port}" /> <property name="poolConfig" ref="jedisPoolConfig" /> </bean> <bean id="jedisPoolConfig" class="redis.clients.jedis.JedisPoolConfig"> <property name="maxTotal" value="${redis.maxTotal}" /> <property name="maxIdle" value="${redis.maxIdle}" /> <property name="maxWaitMillis" value="${redis.maxWait}" /> <property name="testOnBorrow" value="${redis.testOnBorrow}" /> <property name="testOnReturn" value="${redis.testOnReturn}" /> </bean> <bean id="redisTemplate" class="org.springframework.data.redis.core.StringRedisTemplate"> <property name="connectionFactory" ref="jedisConnectionFactory" /> </bean> <!--将session放入redis --> <bean id="redisHttpSessionConfiguration" class="org.springframework.session.data.redis.config.annotation.web.http.RedisHttpSessionConfiguration"> <property name="maxInactiveIntervalInSeconds" value="1800" /> </bean> <bean id="customExceptionHandler" class="sample.MyHandlerExceptionResolver" /> </beans> redis.properties [plain] view plain copy 在CODE上查看代码片派生到我的代码片 redis.host.ip=192.168.0.101 redis.host.port=6379 redis.maxTotal=1000 redis.maxIdle=100 redis.maxWait=2000 redis.testOnBorrow=false redis.testOnReturn=true web.xml [html] view plain copy 在CODE上查看代码片派生到我的代码片 <?xml version="1.0" encoding="UTF-8"?> <web-app version="2.5" xmlns="http://java.sun.com/xml/ns/javaee" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://java.sun.com/xml/ns/javaee http://java.sun.com/xml/ns/javaee/web-app_2_5.xsd"> <!-- - Location of the XML file that defines the root application context - Applied by ContextLoaderListener. --> <!-- tag::context-param[] --> <context-param> <param-name>contextConfigLocation</param-name> <param-value> classpath:/spring/redis-conf.xml </param-value> </context-param> <!-- end::context-param[] --> <!-- tag::springSessionRepositoryFilter[] --> <filter> <filter-name>springSessionRepositoryFilter</filter-name> <filter-class>org.springframework.web.filter.DelegatingFilterProxy</filter-class> </filter> <filter-mapping> <filter-name>springSessionRepositoryFilter</filter-name> <url-pattern>/*</url-pattern> </filter-mapping> <session-config> <session-timeout>30</session-timeout> </session-config> <!-- end::springSessionRepositoryFilter[] --> <filter> <filter-name>encodingFilter</filter-name> <filter-class>org.springframework.web.filter.CharacterEncodingFilter</filter-class> <init-param> <param-name>encoding</param-name> <param-value>UTF-8</param-value> </init-param> <init-param> <param-name>forceEncoding</param-name> <param-value>true</param-value> </init-param> </filter> <filter-mapping> <filter-name>encodingFilter</filter-name> <url-pattern>/*</url-pattern> </filter-mapping> <servlet> <servlet-name>dispatcher</servlet-name> <servlet-class>org.springframework.web.servlet.DispatcherServlet</servlet-class> <init-param> <param-name>contextConfigLocation</param-name> <param-value>classpath:/spring/spring-mvc.xml</param-value> </init-param> <load-on-startup>1</load-on-startup> </servlet> <servlet-mapping> <servlet-name>dispatcher</servlet-name> <url-pattern>/</url-pattern> </servlet-mapping> <!-- - Loads the root application context of this web app at startup. - The application context is then available via - WebApplicationContextUtils.getWebApplicationContext(servletContext). --> <!-- tag::listeners[] --> <listener> <listener-class>org.springframework.web.context.ContextLoaderListener</listener-class> </listener> <!-- end::listeners[] --> <servlet> <servlet-name>sessionServlet</servlet-name> <servlet-class>sample.SessionServlet</servlet-class> </servlet> <servlet-mapping> <servlet-name>sessionServlet</servlet-name> <url-pattern>/servlet/session</url-pattern> </servlet-mapping> <welcome-file-list> <welcome-file>index.jsp</welcome-file> </welcome-file-list> </web-app> 这边主要是一个： [html] view plain copy 在CODE上查看代码片派生到我的代码片 <filter> <filter-name>springSessionRepositoryFilter</filter-name> <filter-class>org.springframework.web.filter.DelegatingFilterProxy</filter-class> </filter> <filter-mapping> <filter-name>springSessionRepositoryFilter</filter-name> <url-pattern>/*</url-pattern> </filter-mapping> <session-config> <session-timeout>30</session-timeout> </session-config> 这个filter一定要写在一切filter之前 SessionController [java] view plain copy 在CODE上查看代码片派生到我的代码片 package sample; import org.springframework.session.data.redis.config.annotation.web.http.EnableRedisHttpSession; import org.springframework.stereotype.Controller; import org.springframework.ui.Model; import org.springframework.web.bind.annotation.RequestMapping; import javax.servlet.http.HttpServletRequest; import javax.servlet.http.HttpSession; /** * Created by mk on 15/1/7. */ @Controller @EnableRedisHttpSession public class SessionController { @RequestMapping("/mySession") public String index(final Model model, final HttpServletRequest request) { if (request.getSession().getAttribute("testSession") == null) { System.out.println("session is null"); request.getSession().setAttribute("testSession", "yeah"); } else { System.out.println("not null"); } return "showSession"; } } showSession.jsp文件 [html] view plain copy 在CODE上查看代码片派生到我的代码片 <%@ page language="java" contentType="text/html; charset=utf-8" pageEncoding="utf-8"%> <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"> <html> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> <title>showSession</title> </head> <body> <% String sessionValue=(String)session.getAttribute("testSession"); %> <h1>Session Value From Servlet is: <%=sessionValue%></h1> </body> </html> 测试 保证我们的redise-server是启动的，然后我们启动起这个web工程后使用： http://localhost:8080/webpoc/mySession访问一下这个controller 此时我们使用redis客户端工具连入查看spring session是否已经进入到了redis中去。 在redis客户端工具连入后我们可以在redis console中使用keys *来查看存入的key，LOOK，spring的session存入了redis中去了。 再来看我们的eclipse后台，由于我们是第一次访问这个controller，因此这个session为空，因此它显示如下： 我们在IE中再次访问该controller 由于之前的session已经存在于redis了，因此当用户在1800秒（30分钟）内再次访问controller，它会从session中获取该session的key testSession的值，因此eclipse后台打印为not null。 SpringRedisTemplate + Redis 讲过了spring session+redis我们来讲使用spring data框架提供的redisTemplate来访问redis service吧。说实话，spring这个东西真强，什么都可以集成，cassandra, jms, jdbc...jpa...bla...bla...bla...Spring集成Barack Hussein Obama? LOL :) pom.xml 不用列了，上面有了 redis-conf.xml 不用列了，上面有了 web.xml 也不用列了，上面也有了 SentinelController.java 我们就先用这个名字吧，后面我们会用它来做我们的redis sentinel（哨兵）的高可用（HA）集群测试 [java] view plain copy 在CODE上查看代码片派生到我的代码片 package sample; import java.util.HashMap; import java.util.Map; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.context.ApplicationContext; import org.springframework.context.support.ClassPathXmlApplicationContext; import org.springframework.data.redis.core.BoundHashOperations; import org.springframework.data.redis.core.StringRedisTemplate; import org.springframework.stereotype.Controller; import org.springframework.ui.Model; import org.springframework.web.bind.annotation.ExceptionHandler; import org.springframework.web.bind.annotation.RequestMapping; import redis.clients.jedis.Jedis; import redis.clients.jedis.JedisSentinelPool; import util.CountCreater; import javax.servlet.http.HttpServletRequest; import javax.servlet.http.HttpSession; /** * Created by xin on 15/1/7. */ @Controller public class SentinelController { @Autowired private StringRedisTemplate redisTemplate; @RequestMapping("/sentinelTest") public String sentinelTest(final Model model, final HttpServletRequest request, final String action) { return "sentinelTest"; } @ExceptionHandler(value = { java.lang.Exception.class }) @RequestMapping("/setValueToRedis") public String setValueToRedis(final Model model, final HttpServletRequest request, final String action) throws Exception { CountCreater.setCount(); String key = String.valueOf(CountCreater.getCount()); Map mapValue = new HashMap(); for (int i = 0; i < 1000; i++) { mapValue.put(String.valueOf(i), String.valueOf(i)); } try { BoundHashOperations<String, String, String> boundHashOperations = redisTemplate .boundHashOps(key); boundHashOperations.putAll(mapValue); System.out.println("put key into redis"); } catch (Exception e) { e.printStackTrace(); throw new Exception(e); } return "sentinelTest"; } } 打开IE，输入：http://localhost:8080/webpoc/setValueToRedis 观察我们的后台 然后使用redis client连入后进行查看 看。。。这个值key=1的，就是我们通过spring的redisTemplate存入进去的值，即使用下面这段代码进行存入的值： [java] view plain copy 在CODE上查看代码片派生到我的代码片 for (int i = 0; i < 1000; i++) { mapValue.put(String.valueOf(i), String.valueOf(i)); } try { BoundHashOperations<String, String, String> boundHashOperations = redisTemplate.boundHashOps(key); boundHashOperations.putAll(mapValue); 如何你要存入一个简单的如key=test value=hello，你可以这样使用你的redisTemplate [java] view plain copy 在CODE上查看代码片派生到我的代码片 redisTemplate.execute(new RedisCallback<Object>() { @Override public Object doInRedis(RedisConnection connection) throws DataAccessException { connection.set( redisTemplate.getStringSerializer().serialize( "test"), redisTemplate .getStringSerializer() .serialize("hello")); return null; } }); 是不是很方便的哈？结束第一天的教程，明天开始搭建redis集群。
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dyneth02 / Java Collections Framework ExampleJava OOP project demonstrating Collection Framework and Generics for SLIIT coursework. Includes implementations using PriorityQueue for book management and HashMap for inventory tracking with add, update, and remove operations.
akhilsharma12297 / Data Structures And AlgorithmsPopulated with various Data Structures and algorithms. This repository is written In Java and consists of various Data Structures (Stack, Queue, LinkedList, Trees, Graph, Heap, HashMap, Tries) implementing them in all their variations and types along with various working and operations. Also included are Various set of top algorithms like kadanas, FloydWarshal, Prims, Kruskal, Moore Voting System, Djikrats, BelmanFord, Moris loop Algo, KMP, Z, Suduko, etc.
noahjonesx / MarkovModelMarkov Text Generation Problem Description The Infinite Monkey Theorem1 (IFT) says that if a monkey hits keys at random on a typewriter it will almost surely, given an infinite amount of time, produce a chosen text (like the Declaration of Independence, Hamlet, or a script for ... Planet of the Apes). The probability of this actually happening is, of course, very small but the IFT claims that it is still possible. Some people have tested this hypotheis in software and, after billions and billions of simulated years, one virtual monkey was able to type out a sequence of 19 letters that can be found in Shakespeare’s The Two Gentlemen of Verona. (See the April 9, 2007 edition of The New Yorker if you’re interested; but, hypothesis testing with real monkeys2 is far more entertaining.) The IFT might lead to some interesting conversations with Rust Cohle, but the practical applications are few. It does, however, bring up the idea of automated text generation, and there the ideas and applications are not only interesting but also important. Claude Shannon essentially founded the field of information theory with the publication of his landmark paper A Mathematical Theory of Computation3 in 1948. Shannon described a method for using Markov chains to produce a reasonable imitation of a known text with sometimes startling results. For example, here is a sample of text generated from a Markov model of the script for the 1967 movie Planet of the Apes. "PLANET OF THE APES" Screenplay by Michael Wilson Based on Novel By Pierre Boulle DISSOLVE TO: 138 EXT. GROVE OF FRUIT TREES - ESTABLISHING SHOT - DAY Zira run back to the front of Taylor. The President, I believe the prosecutor's charge of this man. ZIRA Well, whoever owned them was in pretty bad shape. He picks up two of the strain. You got what you wanted, kid. How does it taste? Silence. Taylor and cuffs him. Over this we HEAR from a distance is a crude horse-drawn wagon is silhouetted-against the trunks and branches of great trees and bushes on the horse's rump. Taylor lifts his right arm to ward off the blow, and the room and lands at the feet of Cornelius and Lucius are sorting out equipment falls to his knees, buries his head silently at the Ranch). DISSOLVE TO: 197 INT. CAGES - CLOSE SHOT - FEATURING LANDON - FROM TAYLOR'S VOICE (o.s.) I've got a fine veternary surgeons under my direction? ZIRA Taylor! ZIRA There is a small lake, looking like a politician. TAYLOR Dodge takes a pen and notebook from the half-open door of a guard room. Taylor bursts suddenly confronted by his 1https://en.wikipedia.org/wiki/Infinite_monkey_theorem2https://web.archive.org/web/20130120215600/http://www.vivaria.net/experiments/notes/publication/NOTES_ EN.pdf3http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6773024 1 original pursuer (the dismounted cop coming up with a cigar butt and places it in the drawer beside them. TAYLOR What's the best there is a. loud RAP at the doll was found beside the building. Zira waits at the third table. TAYLOR Good question. Is he a man? CORNELIUS (impatiently. DODGE Blessed are the vegetation. These SHOTS are INTERCUT with: 94 WHAT THE ASTRONAUTS They examine the remnants of the cage. ZIRA (plunging on) Their speech organs are adequate. The flaw lies not in anatomy but in the back of his left sleeve. TAYLOR (taking off his shirt. 80 DODGE AND LANDON You don't sound happy in your work. GALEN (defensively) Gorilla hunter stands over a dead man, one fo Besides a few spelling errors and some rather odd things that make you wonder about the author, this passage is surprisingly human-like. This is a simple example of natural language generation, a sub-area of natural language processing—a very active area of research in computer science. The particular approach we’re using in this assignment was famously implemented as the fictitious Mark V. Shaney4 and the Emacs command Disassociated Press5. Approach So, here’s the basic idea: Imagine taking a book (say, Tom Sawyer) and determining the probability with which each character occurs. You would probably find that spaces are the most common, that the character ‘e’ is fairly common, and that the character ‘q’ is rather uncommon. After completing this “level 0” analysis, you would be able to produce random Tom Sawyer text based on character probabilities. It wouldn’t have much in common with the real thing, but at least the characters would tend to occur in the proper propor- tion. In fact, here’s an example of what you might produce: Level 0 rla bsht eS ststofo hhfosdsdewno oe wee h .mr ae irii ela iad o r te u t mnyto onmalysnce, ifu en c fDwn oee iteo Now imagine doing a slightly more sophisticated level 1 analysis by determining the probability with which each character follows every other character. You would probably discover that ‘h’ follows ‘t’ more frequently than ‘x’ does, and you would probably discover that a space follows ‘.’ more frequently than ‘,’ does. You could now produce some randomly generated Tom Sawyer text by picking a character to begin with and then always choosing the next character based on the previous one and the probabilities revealed by the analysis. Here’s an example: Level 1 "Shand tucthiney m?" le ollds mind Theybooure He, he s whit Pereg lenigabo Jodind alllld ashanthe ainofevids tre lin-p asto oun theanthadomoere Now imagine doing a level k analysis by determining the probability with which each character follows every possible sequence of characters of length k (kgrams). A level 5 analysis of Tom Sawyer for example, would reveal that ‘r’ follows “Sawye” more frequently than any other character. After a level k analysis, you would be able to produce random Tom Sawyer by always choosing the next character based on the previous k characters (a kgram) and the probabilities revealed by the analysis. 4https://en.wikipedia.org/wiki/Mark_V._Shaney5https://en.wikipedia.org/wiki/Dissociated_press Page 2 of 5 At only a moderate level of analysis (say, levels 5-7), the randomly generated text begins to take on many of the characteristics of the source text. It probably won’t make complete sense, but you’ll be able to tell that it was derived from Tom Sawyer as opposed to, say, The Sound and the Fury. Here are some more examples of text that is generated from increasing levels of analysis of Tom Sawyer. (These “levels of analysis” are called order K Markov models.) K = 2 "Yess been." for gothin, Tome oso; ing, in to weliss of an’te cle - armit. Papper a comeasione, and smomenty, fropeck hinticer, sid, a was Tom, be suck tied. He sis tred a youck to themen K = 4 en themself, Mr. Welshman, but him awoke, the balmy shore. I’ll give him that he couple overy because in the slated snufflindeed structure’s kind was rath. She said that the wound the door a fever eyes that WITH him. K = 6 people had eaten, leaving. Come - didn’t stand it better judgment; His hands and bury it again, tramped herself! She’d never would be. He found her spite of anything the one was a prime feature sunset, and hit upon that of the forever. K = 8 look-a-here - I told you before, Joe. I’ve heard a pin drop. The stillness was complete, how- ever, this is awful crime, beyond the village was sufficient. He would be a good enough to get that night, Tom and Becky. K = 10 you understanding that they don’t come around in the cave should get the word "beauteous" was over-fondled, and that together" and decided that he might as we used to do - it’s nobby fun. I’ll learn you." To create an order K Markov model of a given source text, you would need to identify all kgrams in the source text and associate with each kgram all the individual characters that follow it. This association or mapping must also capture the frequency with which a given character follows a given kgram. For example, suppose that k = 2 and the sample text is: agggcagcgggcg The Markov model would have to represent all the character strings of length two (2-grams) in the source text, and associate with them the characters that follow them, and in the correct proportion. The following table shows one way of representing this information. kgram Characters that follow ag gc gg gcgc gc agg ca g cg g Once you have created an order K Markov model of a given source text, you can generate new text based on this model as follows. Page 3 of 5 1. Randomly pick k consecutive characters that appear in the sample text and use them as the initial kgram. 2. Append the kgram to the output text being generated. 3. Repeat the following steps until the output text is sufficiently long. (a) Select a character c that appears in the sample text based on the probability of that character following the current kgram. (b) Append this character to the output text. (c) Update the kgram by removing its first character and adding the character just chosen (c) as its last character. If this process encounters a situation in which there are no characters to choose from (which can happen if the only occurrence of the current kgram is at the exact end of the source), simply pick a new kgram at random and continue. As an example, suppose that k = 2 and the sample text is that from above: agggcagcgggcg Here are four different output text strings of length 10 that could have been the result of the process described above, using the first two characters (’ag’) as the initial kgram. agcggcagcg aggcaggcgg agggcaggcg agcggcggca For another example, suppose that k = 2 and the sample text is: the three pirates charted that course the other day Here is how the first three characters of new text might be generated: •A two-character sequence is chosen at random to become the initial kgram. Let’s suppose that “th” is chosen. So, kgram = th and output = th. •The first character must be chosen based on the probability that it follows the kgram (currently “th”) in the source. The source contains five occurrences of “th”. Three times it is followed by ’e’, once it is followed by ’r’, and once it is followed by ’a’. Thus, the next character must be chosen so that there is a 3/5 chance that an ’e’ will be chosen, a 1/5 chance that an ’r’ will be chosen, and a 1/5 chance that an ’a’ will be chosen. Let’s suppose that we choose an ’e’ this time. So, kgram = he and output = the. •The next character must be chosen based on the probability that it follows the kgram (currently “he”) in the source. The source contains three occurrences of “he”. Twice it is followed by a space and once it is followed by ’r’. Thus, the next character must be chosen so that there is a 2/3 chance that a space will be chosen and a 1/3 chance that an ’r’ will be chosen. Let’s suppose that we choose an ’r’ this time. So, kgram = er and output = ther. •The next character must be chosen based on the probability that it follows the kgram (currently “er”) in the source. The source contains only one occurrence of “er”, and it is followed by a space. Thus, the next character must be a space. So, kgram = r_ and output = ther_, where ’_’ represents a blank space. Page 4 of 5 Implementation Details You are provided with two Java files that you must use to develop your solution: MarkovModel.java and TextGenerator.java. The constructors of MarkovModel build the order-k model of the source text. You are required to represent the model with the provided HashMap field. The main method of TextGenerator must process the following three command line arguments (in the args array): •A non-negative integer k •A non-negative integer length. •The name of an input file source that contains more than k characters. Your program must validate the command line arguments by making sure that k and length are non- negative and that source contains at least k characters and can be opened for reading. If any of the command line arguments are invalid, your program must write an informative error message to System.out and terminate. If there are not enough command line arguments, your program must write an informative error message to System.out and terminate. With valid command line arguments, your program must use the methods of the MarkovModel class to create an order k Markov model of the sample text, select the initial kgram, and make each character selection. You must implement the MarkovModel methods according to description of the Markov modeling process in the section above. A few sample texts have been provided, but Project Gutenberg (http://www.gutenberg.org) maintains a large collection of public domain literary works that you can use as source texts for fun and practice. Acknowledgments This assignment is based on the ideas of many people, Jon Bentley and Owen Astrachan in particular.
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TejasKoshti / Java Console Shopping SystemThis is a simple Java console application for an online shopping system. It allows users to log in, buy products, cancel products, display all products, and log out. The application uses a HashMap to store user credentials and a separate Business Logic (BL) class to handle the buying and cancelling of products.
jmcamposdev / Java Book Exercises📚 Welcome to the ☕️ code exercise repository for the Paraninfo Programming book by Alfonso Jiménez Martín and Francisco Manuel Pérez Montes (ed. Paraninfo)! 👨💻 Improve your coding skills with carefully written and commented exercises 📝, ranging from simple to complex.
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hpmmp / Java CollectionsJava的ArrayList、LinkedList、HashMap、TreeMap、LinkedHashMap、HashSet、TreeSet相关源码分析，及相关问题和应用总结。
