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
41
2.5k
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
4
1k
Contracts for building reliable systems
keathley
5
850
Kafka, the hard parts
keathley
2
1.6k
Building Resilient Elixir Systems
keathley
6
2.2k
Consistent, Distributed Elixir
keathley
5
1.5k
Telling stories with data visualization
keathley
0
600
Easing into continuous deployment
keathley
2
360
Leveling up your git skills
keathley
0
730
Generative Testing in Elixir
keathley
0
500
Other Decks in Programming
See All in Programming
Rollupのビルド時間高速化によるプレビュー表示速度改善とバンドラとASTを駆使したプロダクト開発の難しさ
plaidtech
PRO
1
160
Day0 初心者向けワークショップ実践!ソフトウェアテストの第一歩
satohiroyuki
0
830
生成AIを使ったQAアプリケーションの作成 - ハンズオン補足資料
oracle4engineer
PRO
3
180
Compose Hot Reload is here, stop re-launching your apps! (Android Makers 2025)
zsmb
1
470
趣味全開のAITuber開発
kokushin
0
190
Devinのメモリ活用の学びを自社サービスにどう組み込むか?
itarutomy
0
2k
custom_lintで始めるチームルール管理
akaboshinit
0
200
Bedrock×MCPで社内ブログ執筆文化を育てたい!
har1101
6
780
サービスレベルを管理してアジャイルを加速しよう!! / slm-accelerate-agility
tomoyakitaura
1
140
PHPのガベージコレクションを深掘りしよう
rinchoku
0
260
Do Dumb Things
mitsuhiko
0
410
AIコーディングワークフローの試行 〜AIエージェント×ワークフローでの自動化を目指して〜
rkaga
2
3.2k
Featured
See All Featured
A designer walks into a library…
pauljervisheath
205
24k
Six Lessons from altMBA
skipperchong
27
3.7k
Site-Speed That Sticks
csswizardry
4
470
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
46
2.4k
4 Signs Your Business is Dying
shpigford
183
22k
KATA
mclloyd
29
14k
ピンチをチャンスに:未来をつくるプロダクトロードマップ #pmconf2020
aki_iinuma
118
51k
Code Review Best Practice
trishagee
67
18k
Practical Orchestrator
shlominoach
186
10k
The Pragmatic Product Professional
lauravandoore
33
6.5k
Rails Girls Zürich Keynote
gr2m
94
13k
The Language of Interfaces
destraynor
157
24k
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]