微信自研生产级paxos类库PhxPaxos实现原理介绍

http://mp.weixin.qq.com/s?__biz=MzI4NDMyNTU2Mw==&mid=2247483695&idx=1&sn=91ea422913fc62579e020e941d1d059e#rd

如果优化multi-paxos？

paxos made simple的最后一节实现状态机如何理解

《Paxos Made Simple》

《Paxos Made live》

《The Chubby lock service for loosely-coupled distributed systems》

Paxos Made Practical

https://zhuanlan.zhihu.com/p/21438357?refer=lynncui

https://docs.google.com/viewer?url=https%3A%2F%2Fraft.github.io%2Fraft.pdf

https://ramcloud.stanford.edu/~ongaro/thesis.pdf

Paxos 我还着重推荐阅读微信后端团队写的系列博客，包括他们开源的 phxpaxos 实现，基本上将所有问题都讨论到了，并且通俗易懂。

但是 Raft 真的好理解多了，我读的是《In Search of an Understandable Consensus Algorithm》，论文写到这么详细的步骤，你不想理解都难。毕竟 Raft 号称就是一个 Understandable Consensus Algorithm。无论从任何角度，都推荐阅读这一篇论文。

首先能理解 paxos 的一些难点，其次是了解 Raft 的实现，加深对 Etcd 等系统的理解。这篇论文还有一个 250 多页的加强版《CONSENSUS: BRIDGING THEORY AND PRACTICE》，教你一行一行写出一个 Raft 实现，

最后，我还读了《Building Consistent Transactions with Inconsistent Replication》，包括作者的演讲，作者也开放了源码。

关于 TAPIR 的解读推荐两篇博客:Building Consistent Transactions with Inconsistent Replication和Paper review: Building Consistent Transactions with Inconsistent Replication (SOSP’15)。 TAPIR 的源码只包含了 normal case 的处理，恢复之类的过程都是没有的，对于 recovery 的一些疑问，可以参考 A FEW WORDS ABOUT INCONSISTENT REPLICATION (IR)，

A Distributed Systems Reading List

Introduction

I often argue that the toughest thing about distributed systems is changing the way you think. The below is a collection of material I've found useful for motivating these changes.

Thought Provokers

Ramblings that make you think about the way you design. Not everything can be solved with big servers, databases and transactions.

Harvest, Yield and Scalable Tolerant Systems - Real world applications of CAP from Brewer et al
On Designing and Deploying Internet Scale Services - James Hamilton
The Perils of Good Abstractions - Building the perfect API/interface is difficult
Chaotic Perspectives - Large scale systems are everything developers dislike - unpredictable, unordered and parallel
Data on the Outside versus Data on the Inside - Pat Helland
Memories, Guesses and Apologies - Pat Helland
SOA and Newton's Universe - Pat Helland
Building on Quicksand - Pat Helland
Why Distributed Computing? - Jim Waldo
A Note on Distributed Computing - Waldo, Wollrath et al
Stevey's Google Platforms Rant - Yegge's SOA platform experience

Latency

Latency Exists, Cope! - Commentary on coping with latency and it's architectural impacts
Latency - the new web performance bottleneck - not at all new (see Patterson), but noteworthy
The Tail At Scale - the latencychallenges inherent of dealing with latency in large scale systems

Amazon

Somewhat about the technology but more interesting is the culture and organization they've created to work with it.

A Conversation with Werner Vogels - Coverage of Amazon's transition to a service-based architecture
Discipline and Focus - Additional coverage of Amazon's transition to a service-based architecture
Vogels on Scalability
SOA creates order out of chaos @ Amazon

Google

Current "rocket science" in distributed systems.

MapReduce
Chubby Lock Manager
Google File System
BigTable
Data Management for Internet-Scale Single-Sign-On
Dremel: Interactive Analysis of Web-Scale Datasets
Large-scale Incremental Processing Using Distributed Transactions and Notifications
Megastore: Providing Scalable, Highly Available Storage for Interactive Services - Smart design for low latency Paxos implementation across datacentres.
Spanner - Google's scalable, multi-version, globally-distributed, and synchronously-replicated database.
Photon - Fault-tolerant and Scalable Joining of Continuous Data Streams. Joins are tough especially with time-skew, high availability and distribution.
Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing - Data warehousing system that stores critical measurement data related to Google's Internet advertising business.

Consistency Models

Key to building systems that suit their environments is finding the right tradeoff between consistency and availability.

CAP Conjecture - Consistency, Availability, Parition Tolerance cannot all be satisfied at once
Consistency, Availability, and Convergence - Proves the upper bound for consistency possible in a typical system
CAP Twelve Years Later: How the "Rules" Have Changed - Eric Brewer expands on the original tradeoff description
Consistency and Availability - Vogels
Eventual Consistency - Vogels
Avoiding Two-Phase Commit - Two phase commit avoidance approaches
2PC or not 2PC, Wherefore Art Thou XA? - Two phase commit isn't a silver bullet
Life Beyond Distributed Transactions - Helland
If you have too much data, then 'good enough' is good enough - NoSQL, Future of data theory - Pat Helland
Starbucks doesn't do two phase commit - Asynchronous mechanisms at work
You Can't Sacrifice Partition Tolerance - Additional CAP commentary
Optimistic Replication - Relaxed consistency approaches for data replication

Theory

Papers that describe various important elements of distributed systems design.

Distributed Computing Economics - Jim Gray
Rules of Thumb in Data Engineering - Jim Gray and Prashant Shenoy
Fallacies of Distributed Computing - Peter Deutsch
Impossibility of distributed consensus with one faulty process - also known as FLP [access requires account and/or payment, a free version can be found here]
Unreliable Failure Detectors for Reliable Distributed Systems. A method for handling the challenges of FLP
Lamport Clocks - How do you establish a global view of time when each computer's clock is independent
The Byzantine Generals Problem
Lazy Replication: Exploiting the Semantics of Distributed Services
Scalable Agreement - Towards Ordering as a Service
Scalable Eventually Consistent Counters over Unreliable Networks - Scalable counting is tough in an unreliable world

Languages and Tools

Issues of distributed systems construction with specific technologies.

Programming Distributed Erlang Applications: Pitfalls and Recipes - Building reliable distributed applications isn't as simple as merely choosing Erlang and OTP.

Infrastructure

Principles of Robust Timing over the Internet - Managing clocks is essential for even basics such as debugging

Storage

Paxos Consensus

Understanding this algorithm is the challenge. I would suggest reading "Paxos Made Simple" before the other papers and again afterward.

The Part-Time Parliament - Leslie Lamport
Paxos Made Simple - Leslie Lamport
Paxos Made Live - An Engineering Perspective - Chandra et al
Revisiting the Paxos Algorithm - Lynch et al
How to build a highly available system with consensus - Butler Lampson
Reconfiguring a State Machine - Lamport et al - changing cluster membership
Implementing Fault-Tolerant Services Using the State Machine Approach: a Tutorial - Fred Schneider

Other Consensus Papers

Mencius: Building Efficient Replicated State Machines for WANs - consensus algorithm for wide-area network

Gossip Protocols (Epidemic Behaviours)

P2P

Chord: A Scalable Peer-to-peer Lookup Protocol for Internet Applications
Kademlia: A Peer-to-peer Information System Based on the XOR Metric
Pastry: Scalable, decentralized object location and routing for large-scale peer-to-peer systems
PAST: A large-scale, persistent peer-to-peer storage utility - storage system atop Pastry
SCRIBE: A large-scale and decentralised application-level multicast infrastructure - wide area messaging atop Pastry

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Distributed Systems

General Papers
Topics

External Papers

Other Hosted Papers

Topics

Datastores

Physics

“On the Electrodynamics of Moving Bodies” (1905) — Einstein

By solving the asymmetries that arise in Maxwell’s equations, Einstein’s 1905 paper set the stage for current distributed systems work by demonstrating that there is no absolute frame of reference and by providing an upper bound on the speed of communication.

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