This document discusses functional programming in serverless environments. It begins by defining serverless computing and Function as a Service (FaaS). It then explores why serverless is useful from the perspectives of costs, architecture, and operations. The document examines using different programming languages like F# within serverless and the importance of tooling and context. It describes experiments running programs on AWS Lambda to measure predictability and constraints. Finally it provides references for further reading on serverless computing topics.

1. Functional Programming
in Serverless World

2. ~ # whoami (afronski)
✓ Co-founder and Functional Aficionado
at Pattern Match
Erlang, Elixir, Node.js
AWS (3 certificates)
✓ Co-organizer of Functional Miners

4. What is
serverless?

6. There are
servers

7. There are
ops

8. read.acloud.guru/iaas-paas-serverless-the-next-big-deal-in-cloud-computing

9. Serverless
Back-end as a Service
(BaaS)
Function as a Service
(FaaS)

10. Incoming Event
Source Code Package Configuration
Function (Execution Unit)
RAM: 512 MB
RAM: 512 MB
0.5 GB
Real time: 120 ms
Bill time: 200 ms (buckets by 100 ms)
Price: 0.00001667 GB * seconds
Cost: 0.00001667 * 0.5 GB * 0.2 s =
0.000001667 USD =
1.7 μUSD
Response

11. Why
serverless?

12. www.doc.ic.ac.uk/~rbc/papers/fse-serverless-17.pdf
Gojko Adzic - Designing for the Serverless Age (GOTO 2017)

13. Costs

14. Architecture
[...] breaking the monolith into functions that could
be independently deployed, meant that they were
better able to split the team up to work on more
things in parallel, and to deploy each feature
separately.

15. Operations
[…] so their estimate is that moving to Lambda gave
an operational cost reduction of greater than 95% for
a comparable amount of compute.

17. Technology

20. Shims
Using Node.js process
API to spawn an
executable
Handling events
reported by subprocess
and passing results to
the AWS Lambda
handler
Sending input
arguments to the
spawned subprocess

22. Measurements

23. Test Scenarios
1. Two endpoints:
a. Echoing incoming body.
b. Sieve of Eranthoses (naive implementation).
2. Package Size.
3. Memory Usage (dependent on input argument).
4. Execution Time (dependent on input argument).
5. Startup Time (cold start, no work done).

24. Measure
predictability

27. "Situated" Programs (from Rich Hickey's keynote at Clojure/Conj 2017)

28. How long does AWS Lambda keep your idle functions around before a cold start? Serverless: Cold Start War

29. Constraints

30. Constraints
1. No knowledge about container and its reuse.
2. New approach requires new paths.
3. Limits everywhere!
4. Provider limitations.
5. VM optimizations required from the get go.
6. Beware the toolchain!

31. Summary

32. Safe and sane default:
F# on Azure or AWS
(.NET Core 2.0)

33. Tooling is King

34. Context is King

35. Thank you!
Questions?

36. References
1. Our company - Pattern Match and my talks.
2. Functional Miners meet-up (facebook, twitter, github, email, website).
3. Serverless Architecture, Serverless (Martin Fowler’s articles).
4. AWS Lambda, Google Cloud Functions, Azure Functions.
5. Container Reuse in AWS Lambda, Serverless: Cold Start War.
6. Running Executables in AWS Lambda.
7. Serverless Computing: Economic and Architectural Impact.
8. Why the JVM is a Good Choice for Serverless Computing.
9. Droplr Serverless Revolution.
10. Optimizing Enterprise Economics with Serverless.
11. Should I use AWS Lambda or EC2?
12. AWS Lambda support for .NET Core 2.0.
13. Repository with examples, scripts, and measurements.