blob: 27705ea3669fba8b7551c6f46587a2aa81bf4097 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
|
---
title: "Yet Another Page on Readings in Distributed Systems"
description: "My own list of links, articles, papers, etc. I enjoyed reading
about distributed systems"
tags:
- "Distributed Systems"
- "Readings"
date: "2015-05-08"
updated: "2015-05-12"
categories:
- "Distributed Systems"
slug: "readings-in-distributed-systems"
---
> "Distributed systems are hard."
-Everyone.
This page is dedicated to general discussion of distributed systems, references
to general overviews and the like. Distributed systems are difficult and even
the well established ones aren't [bulletproof][1]. How can we make this better?
As SysAdmins? As Developers? First we can attempt to understand some of the
issues related to designing and implementing distributed systems. Then we can
throw all that out and figure out what *really* happens to distributed systems.
## Recommended Reading ##
### General ###
* [Fallacies of Distributed Computing][2]
* [CAP Theorem][3]
- [LYSEFGG: Distribunomicon: My other cap is a theorem][4]
- For a more entertaining introduction to CAP, Hebert's ''Learn You Some
Erlang for Great Good'' has a really good subsection on the topic that
includes the zombie apocalypse and some introduction to how a blend
between AP and CP systems can be achieved.
- [CAP Theorem Proof][5]
- [You can't sacrifice partition tolerance][6]
* [Consistency Model][7]
- [List of Consistency Models][8]
- [Linearizability][9]
- [Linearizability versus Serializability][10]
- [Eventual Consistency][11]
* [Paxos][12]
- [Understanding Paxos (Part 1)][13]
- [Lessons learned from implementing Paxos (2013)][34]
* [Vector Clock][14]
* [Split-Brain][15]
* [Network Partitions][16]
* [Distributed Systems and the End of the API][17]
* [The Log][18]: What every software engineer should know about real time
data's unifying abstraction
The [Jepsen][19] "Call me maybe" articles are really good, well written essays
on topics and technologies related to distributed systems.
Introductory post to the "Call me maybe" series:
* [Call me maybe][20]
Here are some personal recommendations:
* [The Network is Reliable][1]
* [Strong Consistency Models][21]
* [Asynchronous Replication with Failover][22]
Really anything from Ferd Herbert is good. Particularly, the first and last
chapters of [Erlang In Anger][30] which includes longer essays from his blog
posts.
* [Queues Don't Fix Overload][31]
* [It's About the Guarantees][32]
* [Lessons Learned while Working on Large-Scale Server Software][33]
### General Networking ###
* [TCP incast][29]
### Hadoop ecosystem ###
This link is more specific to HDFS and is a rather limited experiment but
nonetheless a good read to further understand partition issues that can arise
in Hadoop systems:
* [Partition Tolerance in HDFS][23]
More links from the [Jepsen essays][19]:
* [Call me maybe: Zookeeper][24]
* [Call me maybe: Kafka][25]
* [Call me maybe: Cassandra][26]
### Databases ###
- [Wikipedia ACID][27]
- [Call me maybe: Postgres][28]
[1]: http://aphyr.com/posts/288-the-network-is-reliable
[2]: http://en.wikipedia.org/wiki/Fallacies_of_Distributed_Computing
[3]: http://en.wikipedia.org/wiki/CAP_theorem
[4]: http://learnyousomeerlang.com/distribunomicon#my-other-cap-is-a-theorem
[5]: http://lpd.epfl.ch/sgilbert/pubs/BrewersConjecture-SigAct.pdf
[6]: http://codahale.com/you-cant-sacrifice-partition-tolerance/
[7]: http://en.wikipedia.org/wiki/Consistency_model
[8]: http://en.wikipedia.org/wiki/Category:Consistency_models
[9]: http://en.wikipedia.org/wiki/Linearizability
[10]: http://www.bailis.org/blog/linearizability-versus-serializability/
[11]: http://en.wikipedia.org/wiki/Eventual_consistency
[12]: http://en.wikipedia.org/wiki/Paxos_(computer_science)
[13]: http://distributedthoughts.wordpress.com/2013/09/22/understanding-paxos-part-1/
[14]: http://en.wikipedia.org/wiki/Vector_clock
[15]: http://en.wikipedia.org/wiki/Split-brain_(computing)
[16]: http://en.wikipedia.org/wiki/Network_partitioning
[17]: https://speakerdeck.com/cemerick/distributed-systems-and-the-end-of-the-api
[18]: http://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying
[19]: http://aphyr.com/tags/jepsen
[20]: http://aphyr.com/posts/281-call-me-maybe
[21]: http://aphyr.com/posts/313-strong-consistency-models
[22]: http://aphyr.com/posts/287-asynchronous-replication-with-failover
[23]: https://www.growse.com/2014/07/18/partition-tolerance-and-hadoop-part-1-hdfs/
[24]: http://aphyr.com/posts/291-call-me-maybe-zookeeper
[25]: http://aphyr.com/posts/293-call-me-maybe-kafka
[26]: http://aphyr.com/posts/294-call-me-maybe-cassandra
[27]: http://en.wikipedia.org/wiki/ACID
[28]: http://aphyr.com/posts/282-call-me-maybe-postgres
[29]: http://www.snookles.com/slf-blog/2012/01/05/tcp-incast-what-is-it/
[30]: http://www.erlang-in-anger.com/
[31]: http://ferd.ca/queues-don-t-fix-overload.html
[32]: http://ferd.ca/it-s-about-the-guarantees.html
[33]: http://ferd.ca/lessons-learned-while-working-on-large-scale-server-software.html
[34]: http://blog.willportnoy.com/2012/06/lessons-learned-from-paxos.html
|
