Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Building Adaptive Systems
Search
Chris Keathley
May 28, 2020
Programming
32
1.9k
Building Adaptive Systems
Chris Keathley
May 28, 2020
Tweet
Share
More Decks by Chris Keathley
See All by Chris Keathley
Solid code isn't flexible
keathley
3
900
Contracts for building reliable systems
keathley
5
640
Kafka, the hard parts
keathley
2
1.3k
Building Resilient Elixir Systems
keathley
6
1.8k
Consistent, Distributed Elixir
keathley
5
1.3k
Telling stories with data visualization
keathley
0
480
Easing into continuous deployment
keathley
1
250
Leveling up your git skills
keathley
0
600
Generative Testing in Elixir
keathley
0
390
Other Decks in Programming
See All in Programming
Goのmultiple errorsについて (2024年4月版)
syumai
4
1.3k
Exploring Type-Informed Lint Rules in Rust based TypeScript Linters
unvalley
3
600
JavaScript Closure
asoluka
0
1.9k
ソースコードを美しくたもつために ~コードレビューの認知限界を突破し、年間400リリースを達成する~
kotauchisunsun
1
150
Criando a Woovi em uma semana
daniloab
0
120
WebGLで始める コンピュータグラフィックス入門
heller77
0
370
Sheets API使ってみた
toshi0383
2
180
Documentation testsの恩恵 / Documentation testing benefits
ssssota
1
530
SIMD Parallel Programming with the Vector API
josepaumard
0
250
RustでAWS Lambda functionをいい感じに書く
taiki45
2
140
TypeScriptのパフォーマンス改善
yajihum
12
4.8k
Direct Style Effect Systems The Print[A] ExampleA Comprehension Aid
philipschwarz
PRO
0
400
Featured
See All Featured
Put a Button on it: Removing Barriers to Going Fast.
kastner
58
3.1k
Optimizing for Happiness
mojombo
370
69k
Robots, Beer and Maslow
schacon
PRO
155
7.9k
Fight the Zombie Pattern Library - RWD Summit 2016
marcelosomers
228
16k
Web development in the modern age
philhawksworth
203
10k
"I'm Feeling Lucky" - Building Great Search Experiences for Today's Users (#IAC19)
danielanewman
221
21k
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
20
1.8k
Faster Mobile Websites
deanohume
300
30k
Designing with Data
zakiwarfel
96
4.8k
The Cult of Friendly URLs
andyhume
74
5.7k
Atom: Resistance is Futile
akmur
260
25k
Let's Do A Bunch of Simple Stuff to Make Websites Faster
chriscoyier
501
140k
Transcript
Chris Keathley / @ChrisKeathley /
[email protected]
Building Adaptive Systems
Server Server
Server Server I have a request
Server Server
Server Server
Server Server No Problem!
Server Server
Server Server Thanks!
Server Server
Server Server I have a request
Server Server
Server Server
Server Server I’m a little busy
Server Server I’m a little busy I have more requests!
Server Server I’m a little busy I have more requests!
Server Server I’m a little busy I have more requests!
Server Server I’m a little busy I have more requests!
Server Server I’m a little busy I have more requests!
Server Server I’m a little busy I have more requests!
Server Server I’m a little busy I have more requests!
Server Server I’m a little busy I have more requests!
Server Server I don’t feel so good
Server
Server Welp
Server Welp
All services have objectives
A resilient service should be able to withstand a 10x
traffic spike and continue to meet those objectives
Lets Talk About… Queues Overload Mitigation Adaptive Concurrency
Lets Talk About… Queues Overload Mitigation Adaptive Concurrency
What causes overload?
What causes overload? Server Queue
What causes overload? Server Queue Processing Time Arrival Rate >
Little’s Law Elements in the queue = Arrival Rate *
Processing Time
Little’s Law Server 1 requests = 10 rps * 100
ms 100ms
Little’s Law Server 1 requests = 10 rps * 100
ms 100ms
Little’s Law Server 1 requests = 10 rps * 100
ms 100ms
Little’s Law Server 2 requests = 10 rps * 200
ms 200ms
Little’s Law Server 2 requests = 10 rps * 200
ms 200ms
Little’s Law Server 2 requests = 10 rps * 200
ms 200ms
Little’s Law Server 2 requests = 10 rps * 200
ms 200ms
Little’s Law Server 2 requests = 10 rps * 200
ms 200ms
Little’s Law Server 2 requests = 10 rps * 200
ms 200ms BEAM Processes
Little’s Law Server 2 requests = 10 rps * 200
ms 200ms BEAM Processes CPU Pressure
Little’s Law Server 3 requests = 10 rps * 300
ms 300ms BEAM Processes CPU Pressure
Little’s Law Server 30 requests = 10 rps * 3000
ms 3000ms BEAM Processes CPU Pressure
Little’s Law Server 30 requests = 10 rps * ∞
ms ∞ BEAM Processes CPU Pressure
Little’s Law 30 requests = 10 rps * ∞ ms
Little’s Law ∞ requests = 10 rps * ∞ ms
Little’s Law ∞ requests = 10 rps * ∞ ms
This is bad
Lets Talk About… Queues Overload Mitigation Adaptive Concurrency
Lets Talk About… Queues Overload Mitigation Adaptive Concurrency
Overload Arrival Rate > Processing Time
Overload Arrival Rate > Processing Time We need to get
these under control
Load Shedding Server Queue Server
Load Shedding Server Queue Server Drop requests
Load Shedding Server Queue Server Drop requests Stop sending
Autoscaling
Autoscaling
Autoscaling Server DB Server
Autoscaling Server DB Server Requests start queueing
Autoscaling Server DB Server Server
Autoscaling Server DB Server Server Now its worse
Autoscaling needs to be in response to load shedding
Circuit Breakers
Circuit Breakers
Circuit Breakers Server Server
Circuit Breakers Server Server
Circuit Breakers Server Server Shut off traffic
Circuit Breakers Server Server
Circuit Breakers Server Server I’m not quite dead yet
Circuit Breakers are your last line of defense
Lets Talk About… Queues Overload Mitigation Adaptive Concurrency
Lets Talk About… Queues Overload Mitigation Adaptive Concurrency
We want to allow as many requests as we can
actually handle
None
Adaptive Limits Time Concurrency
Adaptive Limits Actual limit Time Concurrency
Adaptive Limits Actual limit Dynamic Discovery Time Concurrency
Load Shedding Server Server
Load Shedding Server Server Are we at the limit?
Load Shedding Server Server Am I still healthy?
Load Shedding Server Server
Load Shedding Server Server Update Limits
Adaptive Limits Time Concurrency Increased latency
Latency Successful vs. Failed requests Signals for Adjusting Limits
Additive Increase Multiplicative Decrease Success state: limit + 1 Backoff
state: limit * 0.95 Time Concurrency
Prior Art/Alternatives https://github.com/ferd/pobox/ https://github.com/fishcakez/sbroker/ https://github.com/heroku/canal_lock https://github.com/jlouis/safetyvalve https://github.com/jlouis/fuse
Regulator https://github.com/keathley/regulator
Regulator.install(:service, [ limit: {Regulator.Limit.AIMD, [timeout: 500]} ]) Regulator.ask(:service, fn ->
{:ok, Finch.request(:get, "https://keathley.io")} end) Regulator
Conclusion
Queues are everywhere
Those queues need to be bounded to avoid overload
If your system is dynamic, your solution will also need
to be dynamic
Go and build awesome stuff
Thanks Chris Keathley / @ChrisKeathley /
[email protected]