Serverless is a hot topic in the software architecture world and also one of the points of contention. Serverless let us run our code without provisioning or managing servers. We don't have to think about servers at all. Things like elasticity or resilience might not longer be our problem anymore. On the other hand, we have to embrace a little bit different approach how to design our applications. We also have to give up a lot of control we might want and the most importantly we have to use technology which just might not be ready. In this talk, I’d like to discuss if it is worth to use serverless in our applications, what are the advantages and disadvantages of this approach. Secondly, I'd like to describe various use cases we were considering serverless and what was the result. And finally, I’d like to talk about how Scala fits into this. This talk should be interesting for everyone who is considering using serverless or just heard this word somewhere and would like to learn more. The talk is a little bit more focused on AWS but the understanding of the concepts I’m going to talk about should be beneficial even if you prefer a different service provider.

2. Petr Zapletal
@petr_zapletal
@cakesolutions
REAL WORLD SERVERLESS

3. WHAT IF I TOLD YOU THAT...

4. THERE ARE SERVERS IN #SERVERLESS?
WHAT IF I TOLD YOU THAT...

5. BACKEND-AS-A-SERVICE

6. SERVERLESS
=
Backend-as-a-Service + Functions-as-a-Service
WHAT IS #SERVERLESS?

7. FUNCTIONS AS A SERVICE
Host Instance FaaS Platform
Process
Operation1
Operation2
Operation1
Operation2

8. PLATFORMS

9. PLATFORMS

10. ● Message Bus
● HTTP Requests
● Time events
● Storage actions
● Lambdas
● IoT
● Logs
● ...
TRIGGERING LAMBDAS

11. PROS

12. No Servers to Manage
PROS

13. PROS
Auto-scaling &
Implicit High Availability

14. PROS
Fast Development &
Prototyping

15. PROS
Cost Based on
Precise Usage

16. PROS
Integration with
a Platform Provider

17. CONS

18. State Management
CONS

19. CONS
Cold Starts

20. CONS
Resource Limitations

21. CONS
Vendor Lock-In

22. CONS
Testing

23. CONS
Bleeding Edge
Technology

24. Akka
Stream
SERVERLESS & {JVM, Java, Scala}

25. Akka
Stream
AWS Lambda and Scala
trait AwsLambda {
final def handler(event: KinesisEvent, context: Context): Unit = {
val logger = context.getLogger()
val records = event.getRecords().asScala.toList.flatMap { record => ... }
lazy val aurora = AuroraConfig(...)
NonEmptyList.fromList(records).foreach { nel =>
val log = new LambdaLogging(logger)
val tx = DoobieAurora.physical(aurora)
λ(nel)(log, tx).unsafeRunSync()
}
}
def λ(records: NonEmptyList[String])(implicit L: Logging[IO], T: Transactor[IO]): IO[Unit]
}

26. Akka
Stream
AWS Lambda and Scala
trait AwsLambda {
final def handler(event: KinesisEvent, context: Context): Unit = {
val logger = context.getLogger()
val records = event.getRecords().asScala.toList.flatMap { record => ... }
lazy val aurora = AuroraConfig(...)
NonEmptyList.fromList(records).foreach { nel =>
val log = new LambdaLogging(logger)
val tx = DoobieAurora.physical(aurora)
λ(nel)(log, tx).unsafeRunSync()
}
}
def λ(records: NonEmptyList[String])(implicit L: Logging[IO], T: Transactor[IO]): IO[Unit]
}

27. Akka
Stream
AWS Lambda and Scala
trait AwsLambda {
final def handler(event: KinesisEvent, context: Context): Unit = {
val logger = context.getLogger()
val records = event.getRecords().asScala.toList.flatMap { record => ... }
lazy val aurora = AuroraConfig(...)
NonEmptyList.fromList(records).foreach { nel =>
val log = new LambdaLogging(logger)
val tx = DoobieAurora.physical(aurora)
λ(nel)(log, tx).unsafeRunSync()
}
}
def λ(records: NonEmptyList[String])(implicit L: Logging[IO], T: Transactor[IO]): IO[Unit]
}

28. Akka
Stream
AWS Lambda and Scala
trait AwsLambda {
final def handler(event: KinesisEvent, context: Context): Unit = {
val logger = context.getLogger()
val records = event.getRecords().asScala.toList.flatMap { record => ... }
lazy val aurora = AuroraConfig(...)
NonEmptyList.fromList(records).foreach { nel =>
val log = new LambdaLogging(logger)
val tx = DoobieAurora.physical(aurora)
λ(nel)(log, tx).unsafeRunSync()
}
}
def λ(records: NonEmptyList[String])(implicit L: Logging[IO], T: Transactor[IO]): IO[Unit]
}

29. Akka
Stream
AWS Lambda and Scala
trait AwsLambda {
final def handler(event: KinesisEvent, context: Context): Unit = {
val logger = context.getLogger()
val records = event.getRecords().asScala.toList.flatMap { record => ... }
lazy val aurora = AuroraConfig(...)
NonEmptyList.fromList(records).foreach { nel =>
val log = new LambdaLogging(logger)
val tx = DoobieAurora.physical(aurora)
λ(nel)(log, tx).unsafeRunSync()
}
}
def λ(records: NonEmptyList[String])(implicit L: Logging[IO], T: Transactor[IO]): IO[Unit]
}

30. Akka
Stream
AWS Lambda and Scala
trait AwsLambda {
final def handler(event: KinesisEvent, context: Context): Unit = {
val logger = context.getLogger()
val records = event.getRecords().asScala.toList.flatMap { record => ... }
lazy val aurora = AuroraConfig(...)
NonEmptyList.fromList(records).foreach { nel =>
val log = new LambdaLogging(logger)
val tx = DoobieAurora.physical(aurora)
λ(nel)(log, tx).unsafeRunSync()
}
}
def λ(records: NonEmptyList[String])(implicit L: Logging[IO], T: Transactor[IO]): IO[Unit]
}

31. Akka
Stream
CONSIDERING SERVERLESS

32. Akka
Stream
CONSIDERING SERVERLESS
Startups

33. Akka
Stream
CONSIDERING SERVERLESS
Latency Telorant
Applications

34. Akka
Stream
CONSIDERING SERVERLESS
Connecting
Components Together

35. Akka
Stream
CONSIDERING SERVERLESS
Occasionally
Executed Tasks

36. Akka
Stream
CONSIDERING SERVERLESS
CDN Jobs

37. MAKING LIFE EASIER

38. Akka
Stream
OUR CHOICES
Various AWS Plumbing

39. Akka
Stream
OUR CHOICES
Various AWS Plumbing

40. Stateless Backend for UI/UX
OUR CHOICES

41. Akka
Stream
OUR CHOICES
Stateless Backend for UI/UX

42. Very High Burst Stateful App
OUR CHOICES

43. Very High Burst Stateful App
OUR CHOICES

44. Occasionally executed Python scripts
OUR CHOICES

45. Occasionally executed Python scripts
OUR CHOICES

46. Akka
Stream
SUMMARY
Embrace the
Hybrid Architecture

47. MANCHESTER LONDON NEW YORK
QUESTIONS

48. MANCHESTER LONDON NEW YORK
@petr_zapletal @cakesolutions 347 708 1518 petrz@cakesolutions.net
We are hiring
http://www.cakesolutions.net/careers

49. References
● https://medium.com/theburningmonk-com/all-my-posts-on-serverless-aws-lambda-43c17a147f91
● http://highscalability.com/blog/2017/11/6/birth-of-the-nearcloud-serverless-crdts-edge-is-the-new-next.html
● https://blog.symphonia.io/
● https://read.acloud.guru/does-coding-language-memory-or-package-size-affect-cold-starts-of-aws-lambda-a15e26d1
2c76
● https://www.slideshare.net/theburningmonk/serverless-in-production-an-experience-report-codemotion-milan
● https://blog.acolyer.org/2017/10/19/serverless-computing-economic-and-architectural-impact/
● https://www.contino.io/insights/why-aws-lambda-stands-out-from-the-competition
● https://drive.google.com/file/d/0BydCX7M9ba9qSXZKdTBITU1VaG8/view
● https://aws.amazon.com/lambda/
● https://serverless.com/
● https://github.com/lambci/docker-lambda
● https://github.com/awslabs/serverless-application-model

50. Images
● https://imgflip.com/memegenerator/Jackie-Chan-WTF
● http://knowyourmeme.com/photos/1234929-thinking-face-emoji-%F0%9F%A4%94