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
Concurrency Basics for Elixir
Search
Maciej Kaszubowski
August 02, 2018
Programming
0
120
Concurrency Basics for Elixir
Slides from internal presentation at
https://appunite.com
Maciej Kaszubowski
August 02, 2018
Tweet
Share
More Decks by Maciej Kaszubowski
See All by Maciej Kaszubowski
Error-free Elixir
mkaszubowski
0
330
Modular Design in Elixir (ElixirConf EU 2019)
mkaszubowski
2
720
The Big Ball of Nouns
mkaszubowski
0
97
Modular Design in Elixir
mkaszubowski
1
380
Our three years with Elixir
mkaszubowski
0
240
Distributed Elixir
mkaszubowski
0
150
Software Architecture
mkaszubowski
0
130
Let it crash - fault tolerance in Elixir/OTP
mkaszubowski
0
470
CRDTs - The science behind Phoenix Presence
mkaszubowski
2
260
Other Decks in Programming
See All in Programming
[SRE NEXT] 複雑なシステムにおけるUser Journey SLOの導入
yakenji
0
150
チームのテスト力を総合的に鍛えて品質、スピード、レジリエンスを共立させる/Testing approach that improves quality, speed, and resilience
goyoki
5
1.1k
A full stack side project webapp all in Kotlin (KotlinConf 2025)
dankim
0
150
NPOでのDevinの活用
codeforeveryone
0
900
GPUを計算資源として使おう!
primenumber
1
250
スタートアップの急成長を支えるプラットフォームエンジニアリングと組織戦略
sutochin26
1
7.3k
Hack Claude Code with Claude Code
choplin
7
2.5k
AIと”コードの評価関数”を共有する / Share the "code evaluation function" with AI
euglena1215
1
180
Agentic Coding: The Future of Software Development with Agents
mitsuhiko
0
130
코딩 에이전트 체크리스트: Claude Code ver.
nacyot
0
930
What's new in AppKit on macOS 26
1024jp
0
150
“いい感じ“な定量評価を求めて - Four Keysとアウトカムの間の探求 -
nealle
2
12k
Featured
See All Featured
Imperfection Machines: The Place of Print at Facebook
scottboms
267
13k
Build The Right Thing And Hit Your Dates
maggiecrowley
37
2.8k
Building Flexible Design Systems
yeseniaperezcruz
328
39k
Why Our Code Smells
bkeepers
PRO
337
57k
Testing 201, or: Great Expectations
jmmastey
43
7.6k
For a Future-Friendly Web
brad_frost
179
9.8k
Become a Pro
speakerdeck
PRO
29
5.4k
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
29
9.6k
Into the Great Unknown - MozCon
thekraken
40
1.9k
Building Applications with DynamoDB
mza
95
6.5k
Evolution of real-time – Irina Nazarova, EuRuKo, 2024
irinanazarova
8
830
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
656
60k
Transcript
Concurrency basics For Elixir-based Systems
None
So, what’s concurrency?
Sequential Execution (3 functions, 1 thread)
Sequential Execution (3 functions, 1 thread) Concurrent Execution (3 functions,
3 threads)
Sequential Execution (3 functions, 1 thread) Concurrent Execution (3 functions,
3 threads) Preemptive scheduling
Where’s the benefit?
Req1 Req2 Req3 Resp Sequential Execution time Waiting time
Req1 Req2 Req3 Resp Req1 Resp Req2 Req3 Sequential Concurrent
Execution time Waiting time
CPU bound Re Re Re Res Re Res Re Re
I/O bound
Concurrent or Parallel What’s the difference?
Concurrent Execution (3 functions, 3 threads)
Concurrent Execution (3 functions, 3 threads) Parallel Execution (3 functions,
3 threads, 2 cores) core 1 core 2
root@kingschat-api-c8f8d6b76-4j65j:/app# nproc 12 root@tahmeel-api-prod-b5979bdc6-q5wz6:/# nproc 1 How many cores?
Concurrent Execution (3 functions, 3 threads) Parallel Execution (3 functions,
3 threads, 2 cores) core 1 core 2 (by default) One erlang scheduler per core
:observer_cli.start()
None
Req1 Req2 Req3 Resp Req1 Resp Req2 Req3 Sequential Concurrent
Execution time Waiting time Req1 Resp Req2 Req3 Parallel
Sequential execution
Phoenix Request Req 1
Phoenix Request Resp
Phoenix Request Req 2
Phoenix Request Resp
Phoenix Request Req 3
Phoenix Request Resp
Concurrent execution
Phoenix Request
Phoenix Request Task 1 Task 2 Task 3
Phoenix Request Task 1 Task 2 Task 3 Req 1
Req 2 Req 3
Phoenix Request Task 1 Task 2 Task 3 Resp Resp
Resp
Phoenix Request Task 1 Task 2 Task 3
R1 APP Server DB Server (3 cores) R2 R1 R2
Time Execution time Waiting time
R1 APP Server DB Server (3 cores) Send resp R2
R3 R1 R2 R3 Time Execution time Waiting time
How much can we gain?
Amdahl’s Law
Amdahl’s Law
Amdahl’s Law in a nutshell The more synchronisation, the less
benefit from multiple cores
R1 APP Server Send resp R2 R3 R1 R2 R3
Time Execution time Waiting time Almost 100% parallel (almost no synchronisation) DB Server (3 cores)
But…
R1 APP Server Send resp R2 R3 R1 R2 R3
Time Execution time Waiting time This is not constant DB Server (3 cores)
R1 APP Server Send resp R2 R3 R1 R2 R3
Time Execution time Waiting time This is not infinite DB Server (3 cores)
R1 APP Server R2 R3 R1 R2 R3 R4 Time
Execution time Waiting time DB Server (3 cores)
R1 APP Server R2 R3 R1 R2 R3 R4 Time
Execution time Waiting time DB Server (3 cores)
R1 APP Server R2 R3 R1 R2 R3 R4 R4
Time Execution time Waiting time DB Server (3 cores)
R1 APP Server R2 R3 R1 R2 R3 R4 R4
Time Execution time Waiting time DB Server (3 cores)
R1 APP Server R2 R3 R1 R2 R3 R4 R4
Time Execution time Waiting time DB Server (3 cores)
R1 APP Server Send resp R2 R3 R1 R2 R3
R4 R4 Time Execution time Waiting time DB Server (3 cores)
R1 APP Server R2 R3 R1 R2 R3 R4 R4
Time Execution time Waiting time R5 R6 R7 R5 R6 R7 DB Server (3 cores)
Phoenix Request Task 1 Task 2 Task 3 Req 1
Req 2 Req 3 Remember this?
This isn’t exactly true
None
Connection pool (Prevents from overworking the DB)
Pool Manager (Blocks until a free worker is available)
None
Pool Manager (Blocks until a free worker is available)
None
It gets worse
Pool Manager Mailbox Has to be synchronised
Pool Manager Message Passing Is just copying data in shared
memory
Pool Manager Remember semaphores?
Logger Metrics Sentry
Network stack
Network stack
Network stack
Network stack Sentry Metrics
OS Threads (Garbage Collection) Data Bus Virtual Machines Memory characteristics
(e.g. processor caches) … Other synchronisation points
That’s hard
That’s REALLY hard
That’s REALLY hard Seriously, people spend their entire careers on
this
So, what to do?
Measure
Measure Measure
Measure Measure Measure
Measure ON PRODUCTION
Measure ON PRODUCTION You WILL get false results on staging/locally
Measure Entire system You WILL get false results for single
functions
Measure ONLY IF YOU HAVE TRAFFIC
“premature optimization is the root of all evil”
If something takes X ms, it will always take X
ms.
Async execution cannot “remove” this time It can only hide
it
BACK PRESSURE
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer Stop
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer
Producent Consumer Consumer OK, give me more
Producent Consumer Consumer
None
Back pressure
Thanks!