感谢eric的敦促，感谢shuai的感召，我尝试记录一点混乱的思考。
毋庸置疑，Internet和DNS是两个典型的成功的分布式系统。那么，分布式系统是不是就是计算机网络？1990年，Sun Microsystems公司提出网络即是计算机（The network is the computer.），后来google提出数据中心即是计算机，现在有人提出云即是计算机。这些都试图从抽象的概念上总结分布式系统呈现的一致视图。《分布式系统：原理与范型》一书中对分布式系统的定义就是从用户的角度描述的：
A distributed system is a collection of independent computers that appears to its users as a single coherent system.
其强调的的结构是这样的：
《分布式系统：概念与设计》一书中给出的定义则更强调了消息传递的本质：
A distributed system is one in which components located at networked computers communicate and coordinate their actions onlyby passing messages. This definition leads to the following especially significant characteristics of distributed systems:concurrency of components, lack of a global clock and independent failures ofcomponents。
那么分布式系统一个基本模型将是这样的：
《分布式计算：原理，算法，系统》一书给出的定义则更接近自然界的本质：
A distributed system is a collection of independent entities that cooperate to solve a problem that cannot be individually solved.
Distributed systems havebeen in existence since the start of the universe. From a school of fish to a flock of birds and entire ecosystems of microorganisms, there is communication among mobile intelligent agents in nature.
《失控：全人类的最终命运和结局》一书描述分布式系统已经超越了机器和软件的范围，着重描述人造和天生的混合以及人类社会，生物群落，自组织，涌现，超级有机体等概念。作者认为人类社会是由各种人和机器组成的大型分布式系统（第22.6节）。
书中有大量的新颖的观点，值得一读。
为什么安全性和活性是一致性算法的需求呢？因为一致性和活性是所有分布式算法正确性的的等价描述。LESLIE LAMPORT在其1977年的论文《Proving the Correctness of Multiprocess Programs》中第一次描述了安全性和活性，后来成为构建分布式系统的标准。
Correctness == Safety and liveness
在这里想稍微离开主题一下，是关于Leslie Lamport。Leslie Lamport，has been awarded the 2013 Association for Computing Machinery A.M. Turing Award，for “imposing clear, well-defined coherence on the seemingly chaotic behavior of distributed computing systems, in which several autonomous computers communicate with each other by passing messages”.他使分布式计算系统看起来混乱的行为变得清晰、定义明确且具有连贯性，在该系统下，多台自主计算机之间可以相互通信（参见CSDN报道）。个人认为：这是一个迟到的图灵奖，Leslie Lamport的基础理论和算法大量用于现在互联网公司的分布式系统中（google,hadoop,yahoo!,amzon,facebook等等），几乎所有涉及分布式一致性问题的系统都要引用他的paxos算法。Google的论文chubby中写道：Indeed, all working protocols for asynchronous consensus we have so far encountered have Paxos at their core.迄今为止，未见其他计算机大牛在分布式系统领域的贡献比他多。ACM的报道以《General Agreement》为题，明喻其分布式系统一致算法，暗语他的理论和算法已经成为分布式平台的基础设施，并最终得到世界的一致承认。他提出和解决了分布式系统大量基础问题，包括逻辑时钟（解决分布式系统时序问题，思想来源于爱因斯坦的相对论），安全性和活性（并发系统的正确性验证）问题，拜占庭将军问题（分布式系统在任意故障的环境下如何达成一致），paxos算法（解决分布式系统的一致性问题）等。 Leslie Lamport目前工作在微软，是微软第五位获得图灵奖的科学家。Leslie Lamport的文章都值得一读，参考他的HOME PAGE。
我查阅了最初的文章，未有耐心推敲枯燥的数学证明。在这里搜集一些有趣的例子分享一下：
一些有趣的描述：
Safety的通俗理解是：没有坏的事情发生。同时出现两个方向的红灯或绿灯。如果车钥匙扭到点火的位置，车子必须启动。程序能够运行。钟表能够滴答。如果一个用户发出访问临界区的请求，那么必须被通过。Liveness的通俗理解：美好的事情最终会发生。红灯最终会变绿。如果车钥匙扭到点火的位置，车子最终将会启动。程序最终将会终止。钟表会无限滴答。如果一个用户发出访问临界区的请求，那么最终请求会被通过。那么，Safety就是保证没有坏的事情发生，而Liveness就是保证好的事情最终都会发生。每一个分布式系统都会不同程度上保证这两个属性，一些强一点，而另一些弱一点。这是一种平衡。比如说，原子操作就是最大程度的保证了Safety，却丧失了Liveness。原子操作在多个操作同时到达时不会相互干扰，但是在网络分区的情况下无能为力。而今天的分布式系统中广泛采用最终一致性。最终一致性是在一段时间之后，所有的参与者都会对同一个值达成一致。那意味着美好的事情最终会发生。但是，最终一致性无法保证Safety，把最终一致性看作Liveness（最终）和Safety（一致性）的组合，那么我们可以提出这样的问题：系统中间状态的哪一个值最终被采纳？在所有参与者达成一致前，系统返回什么值呢？
看来，Safety和Liveness有那么一点普适意义的感觉，这对我们的系统设计有什么指导意义呢？
How to reach consensus/data consistency in distributed system that can tolerate non-malicious failures?
分布式系统书籍中，首推《分布式系统：概念与设计》。所以我更倾向于依赖消息传递而实现的分布式系统。因为这是一种share-nothing的结构，而消息传递本身是无状态的（类似于TCP协议的状态绑定是协议的使用，而不是协议本身的性质）。share-nothing便于线性扩展，无状态便于故障恢复（failover click click，failback，failsafe，failstop，failfast，failsilent）。对比一下RedHat 的GFS和NFS协议，可以深入理解一下无状态的概念。
GFS是基于共享存储的集群文件系统，而NFS准确来说只是一个兼容posix语义的文件共享系统。GFS中各个node是全对称（peer
to peer），但需要中心的锁服务器并发和协定，在文件数量很大和并发操作很多的情况下，特别node的故障和恢复的情况下，不出问题都很难。而NFS的实现是无状态的，且经过多年工业级检验，比较稳定。
NFS is a stateless protocol. This means that the file server stores no per-client information, and there are no NFS "connections". For example, NFS has no operation to
open a file, since this would require the server to store state information (that a file is open; what its file descriptor is; the next byte to read; etc). Instead, NFS supports a Lookup procedure, which converts a filename into a file handle. This file handle
is an unique, immutable identifier, usually an i-node number, or disk block address. NFS does have a Read procedure, but the client must specify a file handle and starting offset for every call to Read. Two identical calls to Read will yield the exact same
results. If the client wants to read further in the file, it must call Read with a larger offset.
Issues of state：
One of the design decisions made when designing a network filesystem is determining what part of the system will track the files that each client has open, information referred to generically as “state.” A server that does not record the status of files and
clients is said to be stateless; one that does is stateful. Both approaches have been used over the years, and both have benefits and drawbacks.
Stateful servers keep track of all open files across the network. This mode of operation introduces many layers of complexity (more than you might expect) and makes recovery in the event of a crash far more difficult. When the server returns from a hiatus,
a negotiation between the client and server must occur to reconcile the last known state of the connection. Statefulness allows clients to maintain more control over files and facilitates the management of files that are opened in read/write mode.
On a stateless server, each request is independent of the requests that have preceded it. If either the server or the client crashes, nothing is lost in the process. Under this design, it is painless for servers to crash or reboot, since no context is maintained.
However, it’s impossible for the server to know which clients have opened files for writing, so the server cannot manage concurrency.
在这样的系统中，进程实体和通信信道都可能发生故障。
迄今为止，除了超时机制外，未见更加有效的进程故障判断方法。在异步系统中，超时只能表明没有响应，其原因肯那个是崩溃了，也可以是消息还没有到达。
通信的故障发生在三个区域：发送方的缓冲区，通信信道和接收方的缓冲区。
这是可能出现的最坏的故障，此时可能发生任何类型错误，包括假冒的节点或者是被黑客攻陷的节点等。关于这个问题，Leslie Lamport在《The Byzantine Generals Problem》第一次描述了这个问题。非正式的来说，是这样的：
Zookeeper论文中说：To date, we have not observed faults in production that would have been prevented using a fully Byzantine fault-tolerant protocol.
基于所有的分布式系统书籍都会提到复制技术以及它的两个需求：性能和高可用性。也就是说，通过使用复制技术，可以提供就近的数据访问和故障时的切换。当然，复制也是产生一致性问题的根源。
A number of widely cited models exist for data replication, each having its own properties and performance:
1.Transactional replication. This is the model for replicating transactional data, for example a database or some other form of transactional storage structure. The one-copy serializability model is employed in this case, which defines legal outcomes of
a transaction on replicated data in accordance with the overall ACID properties that transactional systems seek to guarantee.
2.State machine replication. This model assumes that replicated process is a deterministic finite automaton and that atomic broadcast of every event is possible. It is based on a distributed computing problem called distributed consensus and has a great deal
in common with the transactional replication model. This is sometimes mistakenly used as synonym of active replication. State machine replication is usually implemented by a replicated log consisting of multiple subsequent rounds of the Paxos algorithm. This
was popularized by Google‘s Chubby system, and is the core behind the open-source Keyspace data store.[4][5]
3.Virtual synchrony. This computational model is used when a group of processes cooperate to replicate in-memory data or to coordinate actions. The model defines a distributed entity called a process group. A process can join a group, and is provided with a
checkpoint containing the current state of the data replicated by group members. Processes can then send multicasts to the group and will see incoming multicasts in the identical order. Membership changes are handled as a special multicast that delivers a
new membership view to the processes in the group.
文件系统的复制技术：
一般有文件复制和分块复制。
数据库系统复制：
语句复制
binlog复制
一致性问题源于70年代末的数据库系统：那个时候的目标是实现distribution transparency—that is, to the user of the system it appears as if there is only one system instead of a number of collaborating systems.而采用的策略是：Many systems during this time took the approach that it
was better to fail the complete system than to break this transparency。
90年代中期，互联网开始发展。此时人们认为可用性比一致性更重要，但也在思考是否应该在一致性和可用性之间平衡。
2000年，在Principles of Distributed Computing(PODC)会议上，Eric Brewer在其Towards Robust Distributed Systems论文中提出来CAP理论，阐述了data consistency, system availability, and tolerance to network partition分布式系统中的三个属性在任何时候都只能实现两个。两年后，猜想得到证明Seth Gilbert and Nancy Lynch：Gilbert,
S. and Lynch, N. Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant Web services. ACM SIGACT News 33, 2 (2002).其含义是一个不允许分区的系统可以通过事务协议实现数据一致性和可用性。这就要求客户机和服务器在同一个环境中，并且故障对于两者是整体性的，对于客户机就不会出现分区。但是在更大的分布式系统中，分区必然会出现，一致性和可用性就不能同时保证。这样就只有两个选择：放松一致性获得可用性和保证一致性牺牲可用性。系统的使用者（例如开发人员）需要知悉系统的行为。如果系统强调了一致性，开发者将会面临系统不可用的情况，例如无法写入，此时开发者需要处理写失败的情况（暂存本地还是一直挂起？）。如果系统强调了可用性，开发者要明确读取数据不一定是最新的，有大量的应用采用这样的模型，允许少量的陈旧数据而工作的很好。
在事务处理领域，一致性被定义为：ACID properties (atomicity, consistenconsistency,isolation, durability) 可以认为是一致性的另外一种类型或描述。
In ACID, consistency relates to the guarantee that when a transaction is finished the database is in a consistent state;
In ACID-based systems, this kind of consistency is often the responsibility of the developer writing the transaction but can be assisted by the database managing integrity constraints.
我们可以从客户端和服务器两个角度来看一致性问题。从客户端的角度也是从用户的角度，就是说你如何看待数据的更新？对于用户来说，服务器就是一个黑盒，但是必须提供数据的持久性和可用性。客户端的一致性特别强调用户多个无关的进程或者线程如何看待存储系统中数据对象，具体来说就是当一个更新操作完成之后，对于后续的进程有什么影响。比如：
我们熟悉的一致性保证是强一致性，因果一致性，单调读一致性，单调一致性。
从服务器的角度是从实现的角度，即更新的数据流是如何流过系统？系统为这些更新提供了哪些保证？
Classes of agents:
A node can act as more than one clients (usually 3).
A proposer selects a proposal number n and sends a prepare request with number n to majority of acceptors.
If an acceptor receives a prepare request with number n greater than that of any prepare request it saw, it responses YES to that request with a promise not to accept any more proposals numbered less than n and include the highest-numbered proposal (if any)
that it has accepted.
If the proposer receives a response YES to its prepare requests from a majority of acceptors, then it sends an accept request to each of those acceptors for a proposal numbered n with a values v which is the value of the highest-numbered proposal among the
responses.
If an acceptor receives an accept request for a proposal numbered n, it accepts the proposal unless it has already responded to a prepare request having a number greater than n.
A value is chosen at proposal number n iff majority of acceptor accept that value in phase 2 of the proposal number.
这样的结果是2对于2，平手。问题在于既要满足P1又要满足多数原则，则必然要求一个acceptor能够接受多个proposal。所以需要P2阶段。
有了全序的number，就能够保证P2。如果一致已经达成v，后续能够被接受的提案必须都有v。一个proposal必须至少被一个acceptor接受，所以P2a可以满足P2。
P2a . If a proposal with value v is chosen, then every higher-numbered proposal accepted by any acceptor has value v.
P2a 处理不了下面的情况：ABC已经达成一致，C没有收到任何的proposal，由于消息的异步，一致的proposal还没有通知到它。然后一个曾经断链或者关机的节点加入系统，并提出一个更higher-numbered带有新value的proposal给D，根据P1，D必须接受该proposal，与P2a矛盾。所以用P2b对P2a加强。
P2b . If a proposal with value v is chosen, then every higher-numbered proposalissued by any proposer has value v.
其实P2a是从acceptor端控制不接受不是已经达成一致的那个v，其实P2b是从proposer端控制不接受不是已经达成一致的那个v。
P2b不易实现，所以又有了P2c。P2c. For any v and n, if a proposal with value v and number n is issued,then there is a set S consisting of a majority of acceptors such thatEither (a) no acceptor in S has accepted any proposal numbered less than n, or (b) v is the value of the highest-numbered proposal amongAll proposals numbered less than n accepted by the acceptors in S.
多数派要么没有接受过，要么当前是the highest-numbered proposal。关键是偏序的number，每次都要去拿号。去遍询当前的状态。
论文《Enhanced Paxos Commit for Transactions on DHTs》讨论了paxos算法的一些本质含义，对于工程实现有很好的借鉴意义。比如多个角色的重合，固定acceptors数量（不能在运行时增加），acceptors作为分布式内存，一致性的达成是由learner来宣布等。
①proposer的初始化：选择任意一个目前为止最大的整数设置为序列号r，开始的时候，任意的的序列号都是可以的，唯一的限制是必须是在多个proposers中保证唯一（一种可行的方法是使用proposer的标识作为一部分）。如果恰好一个序列号比前一个序列号小，那么该序列号对应的propose会被认为是过时的而被忽略。
②acceptors的初始化：响应的序列号r = 0;已接受的序列号r = 0;选择的vaule为NULL。
③proposer发送带有序列号r的prepare(r)消息，广播给所有acceptors，然后开始超时计时。如果在超时值时间内没有收到大多数acceptors的应答消息，就是用更大的序列号和一个增大的超时值重新开始新一轮的prepare。
④如果acceptors收到的序列号是迄今为止最大的序列号，那么保存序列号并发送确认消息。
⑤proposer检查从acceptors那里获得的最新的vaule，如果仍旧为NULL，则proposer自己propose一个值；如果不为NULL，则接受最新的vaule。
⑥如果learner从多数acceptors那里获得valure，一致达成于value。
⑦对于过时的序列号，为了帮助proposer更快的（使用更大的序列号）发起新一轮的propose，acceptors用已经接受的序列号来响应proposer。
⑧如果proposer接受的value所对应的序列号过时，则acceptors发送拒绝的消息给proposer。
留待我补充。
原文地址：http://blog.csdn.net/three_man/article/details/35568421
