Reactive Streams 1.0.0 is now live, and so are our implementations in Akka Streams 1.0 and Slick 3.0. Reactive Streams is an engineering collaboration between heavy hitters in the area of streaming data on the JVM. With the Reactive Streams Special Interest Group, we set out to standardize a common ground for achieving statically-typed, high-performance, low latency, asynchronous streams of data with built-in non-blocking back pressure—with the goal of creating a vibrant ecosystem of interoperating implementations, and with a vision of one day making it into a future version of Java. Akka (recent winner of “Most Innovative Open Source Tech in 2015”) is a toolkit for building message-driven applications. With Akka Streams 1.0, Akka has incorporated a graphical DSL for composing data streams, an execution model that decouples the stream’s staged computation—it’s “blueprint”—from its execution (allowing for actor-based, single-threaded and fully distributed and clustered execution), type safe stream composition, an implementation of the Reactive Streaming specification that enables back-pressure, and more than 20 predefined stream “processing stages” that provide common streaming transformations that developers can tap into (for splitting streams, transforming streams, merging streams, and more). Slick​ is a relational database query and access library for Scala that enables loose-coupling, minimal configuration requirements and abstraction of the complexities of connecting with relational databases. With Slick 3.0, Slick now supports the Reactive Streams API for providing asynchronous stream processing with non-blocking back-pressure. Slick 3.0 also allows elegant mapping across multiple data types, static verification and type inference for embedded SQL statements, compile-time error discovery, and JDBC support for interoperability with all existing drivers.

1. A Deeper Look Into Reactive
Streams with Akka Streams 1.0
and Slick 3.0
Endre Varga, @drewhk — Akka Team

2. Outline
• Reactive Streams
• Slick 3
• Akka Streams
• Akka HTTP
• Bringing it all together
2

3. Reactive Streams 1.0.0

4. Participants
• Engineers from
• Netflix
• Pivotal
• Red Hat
• Typesafe
• Individuals like Doug Lea and Todd Montgomery
4

5. Recipe for Success
• minimal interfaces
• rigorous specification of semantics
• full TCK for verification of implementation
• complete freedom for many idiomatic APIs
5

6. Supply and Demand
• data items flow downstream
• demand flows upstream
• data items flow only when there is demand
• recipient is in control of incoming data rate
• data in flight is bounded by signaled demand
6
Publisher Subscriber
data
demand

7. Dynamic Push–Pull
• “push” behavior when consumer is faster
• “pull” behavior when producer is faster
• switches automatically between these
• batching demand allows batching data
7
Publisher Subscriber
data
demand

8. Back-Pressure is Contagious
• C is slow
• B must slow down
• A must slow down
8
CA B

9. • TCP for example has it built-in
Back-Pressure can be Propagated
9
CA B
networkhosts

10. Reactive Streams
• asynchronous non-blocking data flow
• asynchronous non-blocking demand flow
• minimal coordination and contention
• message passing allows for distribution
• across
applications, nodes, CPUs, threads, actors
10

11. Slick 3.0
The Reactive Database Layer

12. Slick 3.0
• Reactive API for accessing databases
• Provides internal management for blocking
JDBC access
• Reactive frameworks/libraries can be used together
with ordinary blocking database drivers
• without sacrificing scalability and efficiency
12

13. Slick and Reactive Streams
• On top of the usual database operations, a
Reactive Streams implemenation for queries is
implemented
• Query results can now directly streamed in a non-
blocking way
• Allows integration with other Reactive Streams
compliant libraries
• (Akka Streams and Http, and many others)
13

14. Akka Streams

15. Declaring a Stream Topology
15

16. Declaring a Stream Topology
16

17. Declaring a Stream Topology
17

18. Declaring a Stream Topology
18

19. Declaring a Stream Topology
19

20. Declaring a Stream Topology
20

21. Declaring a Stream Topology
21

22. API Design
• goals:
• supreme compositionality
• exhaustive model of bounded stream processing
• consequences:
• immutable and reusable stream blueprints
• explicit materialization step
22
http://doc.akka.io/docs/akka-stream-and-http-experimental/1.0-RC2/stream-design.html

23. Materialization
• Akka Streams separate the what from the how
• declarative Source/Flow/Sink DSL to create blueprint
• FlowMaterializer turns this into running Actors
• this allows alternative materialization strategies
• optimization
• verification / validation
• cluster deployment
• only Akka Actors for now, but more to come!
23

24. Akka Streams in Java 8 / Scala
24
Typesafe Activator: http://typesafe.com/get-started

25. Akka HTTP

26. Why do we add an HTTP module?
• Akka is about building distributed applications
• distribution implies integration
• between internal (sub)systems
➜ akka-cluster based on akka-remote
• with external systems or between microservices
➜ akka-http (lingua franca of the internet)
26

27. Akka HTTP—The Origins: Spray.IO
• Fully embeddable HTTP stack based on Actors
• focused on HTTP integration (toolkit)
• server- and client-side
• seamlessly integrated with Akka
27

28. Spray.IO Features
• immutable, case class-based HTTP model
• fast, lightweight HTTP client and server
• powerful DSL for server-side API definition
• fully async & non-blocking, actor-friendly,
modular, testable
• based entirely on Scala & Akka Actors
28

29. Spray.IO Weaknesses
• some unintuitive corner-cases in routing DSL
• deep implicit argument chains in some cases hard
to debug
• missing features, foremost websocket support
• no Java API
• handling of chunked requests is clunky,
incomplete
• dealing with large message entities can be difficult
29

30. Proxying Large Responses
30

31. Akka HTTP is Spray 2.0
• addressing the weaknesses, polishing the
features
• Java API
• simplified module structure
• core improvement: fully stream-based
31

32. HTTP Stream Topology
32

33. Stream Pipelines
33
TCP
SSL/TL
S
HTTP App
Requests
Responses
optional

34. Bringing it all together

35. The Application Stack
35
O/S-level network stack
Java NIO (JDK)
Akka IO
Akka HTTP Core
Akka HTTP
application level
Akka Streams Database
Driver
Slick 3.0

36. 36
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Delivered on-site for Akka, Spark, Scala and Play
Help is just a click away. Get in touch with
Typesafe about our training courses.
• Intro Workshop to Apache Spark
• Fast Track & Advanced Scala
• Fast Track to Akka with Java or Scala
• Fast Track to Play with Java or Scala
• Advanced Akka with Java or Scala
Ask us about local trainings available by 24
Typesafe partners in 14 countries around the
world.
CONTACT US Learn more about on-site training

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