zookeeper一般用于distributed locking,并不适合用于distributed storage,由于zookeeper的每一个node。也叫做znode的存储容量限制是1M。

zookeeper里的角色主要有client,leader和learner。当中learner也包含observer和follower。

client为请求的发起方,follower为请求的接收方,同一时候也会返回结果。參与投票过程

leader负责投票的发起和决策,更新系统状态

observer不參加投票。仅仅同步leader状态。它可接受client连接并将写请求转发给leader。observer是为了扩展系统。提高吞吐速度。

zookeeper的架构本身和传统的文件系统(file system)非常相似,不一样的是对于zookeeper每一个node上都能存1M数据

zookeeper主要用于存储协调信息(coordination data),比如status information, configuration, location information

因为zookeeper是in-memory storage,所以zookeeper能够实现high throughput和low latency.

參考文献: zookeeper官方wiki,以下摘抄一段overview

ZooKeeper allows distributed processes to coordinate with each other through a shared hierarchical name space of data registers (we call these registers znodes), much like a file system. Unlike normal file systems ZooKeeper provides its clients with high throughput,
low latency, highly available, strictly ordered access to the znodes. The performance aspects of ZooKeeper allow it to be used in large distributed systems. The reliability aspects prevent it from becoming the single point of failure in big systems. Its strict
ordering allows sophisticated synchronization primitives to be implemented at the client.

The name space provided by ZooKeeper is much like that of a standard file system. A name is a sequence of path elements separated by a slash ("/"). Every znode in ZooKeeper's name space is identified by a path. And every znode has a parent whose path is a prefix
of the znode with one less element; the exception to this rule is root ("/") which has no parent. Also, exactly like standard file systems, a znode cannot be deleted if it has any children.

The main differences between ZooKeeper and standard file systems are that every znode can have data associated with it (every file can also be a directory and vice-versa) and znodes are limited to the amount of data that they can have. ZooKeeper was designed
to store coordination data: status information, configuration, location information, etc. This kind of meta-information is usually measured in kilobytes, if not bytes. ZooKeeper has a built-in sanity check of 1M, to prevent it from being used as a large data
store, but in general it is used to store much smaller pieces of data.

The service itself is replicated over a set of machines that comprise the service. These machines maintain an in-memory image of the data tree along with a transaction logs and snapshots in a persistent store. Because the data is kept in-memory, ZooKeeper is
able to get very high throughput and low latency numbers. The downside to an in-memory database is that the size of the database that ZooKeeper can manage is limited by memory. This limitation is further reason to keep the amount of data stored in znodes small.

The servers that make up the ZooKeeper service must all know about each other. As long as a majority of the servers are available, the ZooKeeper service will be available. Clients must also know the list of servers. The clients create a handle to the ZooKeeper
service using this list of servers.

Clients only connect to a single ZooKeeper server. The client maintains a TCP connection through which it sends requests, gets responses, gets watch events, and sends heartbeats. If the TCP connection to the server breaks, the client will connect to a different
server. When a client first connects to the ZooKeeper service, the first ZooKeeper server will setup a session for the client. If the client needs to connect to another server, this session will get reestablished with the new server.

Read requests sent by a ZooKeeper client are processed locally at the ZooKeeper server to which the client is connected. If the read request registers a watch on a znode, that watch is also tracked locally at the ZooKeeper server. Write requests are forwarded
to other ZooKeeper servers and go through consensus before a response is generated. Sync requests are also forwarded to another server, but do not actually go through consensus. Thus, the throughput of read requests scales with the number of servers and the
throughput of write requests decreases with the number of servers.

Order is very important to ZooKeeper; almost bordering on obsessive–compulsive disorder. All updates are totally ordered. ZooKeeper actually stamps each update with a number that reflects this order. We call this number the zxid (ZooKeeper Transaction Id).
Each update will have a unique zxid. Reads (and watches) are ordered with respect to updates. Read responses will be stamped with the last zxid processed by the server that services the read.

zookeeper工作原理解析的更多相关文章

  1. Zookeeper 3、Zookeeper工作原理(详细)

    1.Zookeeper的角色 » 领导者(leader),负责进行投票的发起和决议,更新系统状态 » 学习者(learner),包括跟随者(follower)和观察者(observer),follow ...

  2. Zookeeper 3、Zookeeper工作原理(转)

    1.Zookeeper的角色 » 领导者(leader),负责进行投票的发起和决议,更新系统状态 » 学习者(learner),包括跟随者(follower)和观察者(observer),follow ...

  3. zookeeper工作原理、安装配置、工具命令简介

    1.Zookeeper简介 Zookeeper 是分布式服务框架,主要是用来解决分布式应用中经常遇到的一些数据管理问题,如:统一命名服务.状态同步服务.集群管理.分布式应用配置项的管理等等. 2.zo ...

  4. [转载] zookeeper工作原理、安装配置、工具命令简介

    转载自http://www.cnblogs.com/kunpengit/p/4045334.html 1 Zookeeper简介Zookeeper 是分布式服务框架,主要是用来解决分布式应用中经常遇到 ...

  5. 分布式协调服务ZooKeeper工作原理

    分布式协调服务ZooKeeper工作原理 原创 2016-02-19 杜亦舒 性能与架构 性能与架构 性能与架构 微信号 yogoup 功能介绍 网站性能提升与架构设计 大数据处理框架Hadoop.R ...

  6. jdk线程池ThreadPoolExecutor工作原理解析(自己动手实现线程池)(一)

    jdk线程池ThreadPoolExecutor工作原理解析(自己动手实现线程池)(一) 线程池介绍 在日常开发中经常会遇到需要使用其它线程将大量任务异步处理的场景(异步化以及提升系统的吞吐量),而在 ...

  7. Servlet 工作原理解析

    转自:http://www.ibm.com/developerworks/cn/java/j-lo-servlet/ Web 技术成为当今主流的互联网 Web 应用技术之一,而 Servlet 是 J ...

  8. Zookeeper工作原理一

    ZooKeeper是一个分布式的,开放源码的分布式应用程序协调服务,它包含一个简单的原语集,分布式应用程序可以基于它实现同步服务,配置维护和命名服务等.Zookeeper是hadoop的一个子项目,其 ...

  9. Zookeeper工作原理

    ZooKeeper是一个分布式的,开放源码的分布式应用程序协调服务,它包含一个简单的原语集,分布式应用程序可以基于它实现同步服务,配置维护和命名服务等.Zookeeper是hadoop的一个子项目,其 ...

随机推荐

  1. phpcms功能列表

    上1 站点首页 就是前台首页 会员中心 跳到会员中心页面 搜索 新闻,图片等文档搜索 锁屏 锁住账号 Phpcms 官网 授权 官网查询 支持论坛 官网论坛 帮助 官网帮助 上2 我的面板 个人信息 ...

  2. 报错:无法将类型"System.Data.EntityState"隐式转换为"System.Data.Entity.EntityState"

    报错:无法将类型"System.Data.EntityState"隐式转换为"System.Data.Entity.EntityState".   出错语句停留 ...

  3. duplicate symbol _main in: / linker command failed with exit code 1

    duplicate symbol _main in: /Users/mb467/Library/Developer/Xcode/DerivedData/barChartDemo-gevlnavnpan ...

  4. NSPredicate 的使用(持续更新)

    NSPredicate 谓词工具一般用于过滤数组数据,也可用来过滤CoreData查询出的数据. 1). 支持keypath 2). 支持正则表达式 在使用之前先新建3个类 Teacher Info ...

  5. [翻译] Haneke(处理图片缓存问题)

    Haneke https://github.com/hpique/Haneke A lightweight zero-config image cache for iOS. 轻量级0配置图片缓存. H ...

  6. iptables配置实践

    前言 在大企业中防火墙角色主要交给硬件来支持,效果自然没话说只是需要增加一点点成本,但对于大多数个人或者互联网公司来说选择系统自带的iptables或者第三方云防火墙似乎是更加合适的选择,通过一些合理 ...

  7. [JQuery] jQuery选择器ID、CLASS、标签获取对象值、属性、设置css样式

    reference : http://www.suyunyou.com/aid1657.html jQuery是继prototype之后又一个优秀的Javascrīpt框架.它是轻量级的js库(压缩后 ...

  8. mysqld_safe脚本执行的基本流程

    mysqld_safe脚本执行的基本流程:1.查找basedir和ledir.2.查找datadir和my.cnf.3.对my.cnf做一些检查,具体检查哪些选项请看附件中的注释.4.解析my.cnf ...

  9. GNU GRUB

    Introduction GNU GRUB is a Multiboot boot loader. It was derived from GRUB, the GRand Unified Bootlo ...

  10. Ubuntu双系统安装

    原文链接: http://www.jianshu.com/p/2eebd6ad284d   作者 Volcanoo 2016.01.31 00:07 字数 1737 阅读 141859评论 161喜欢 ...