reactive streams 1.0.0 and why you should care (webinar)

Reactive Streams 1.0.0 and why you should care

Dr. Roland Kuhn @rolandkuhn — Akka Tech Lead

Reactive Streams 1.0.0

Philosophical Background

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“You cannot step twice into the same stream. For as you are stepping in, other waters are ever flowing on to you.”

— Heraclitus

What is a Stream?

• ephemeral flow of data

• possibly unbounded in size

• focused on describing transformation

• can be formed into processing networks

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Reactive Traits

The Problem:

Getting Data across an Asynchronous Boundary

without running OutOfMemory

Enter Reactive Streams

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“Reactive Streams is an initiative to provide a standard for asynchronous stream processing with non-blocking back pressure on the JVM.”

— reactive-streams.org

Participants

• Engineers from (among others): • Netflix

• Pivotal

• Red Hat

• Typesafe

• Individuals like Doug Lea and Todd Montgomery

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Motivation

• all participants face the same basic problem

• all are building tools for their community

• a common solution benefits everybody

• interoperability to make best use of efforts

• proposal to include in JDK9 (j.u.c.Flow)

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Recipe for Success

• minimal interfaces

• rigorous specification of semantics

• full TCK for verification of implementation

• complete freedom for many idiomatic APIs

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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

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Publisher Subscriber

data

demand

Dynamic Push–Pull

• “push” behavior when consumer is faster

• “pull” behavior when producer is faster

• switches automatically between these

• batching demand allows batching data

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Publisher Subscriber

data

demand

Back-Pressure is Contagious

• C is slow

• B must slow down

• A must slow down

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CA B

Back-Pressure can be Propagated

• TCP for example has it built-in

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CA B

netw

ork

host

s

The Meat

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trait Publisher[T] { def subscribe(sub: Subscriber[T]): Unit } trait Subscription { def request(n: Int): Unit def cancel(): Unit } trait Subscriber[T] { def onSubscribe(s: Subscription): Unit def onNext(e: T): Unit def onError(t: Throwable): Unit def onComplete(): Unit }

How does it Connect?

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SubscriberPublisher

subscribe

onSubscribeSubscription

How does it Flow?

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SubscriberPublisher

request

onNextElements

request

onNextElements

request

How does it Complete?

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SubscriberPublisher

request

onNextElements

onComplete

How does it Fail?

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SubscriberPublisher

request

onNextElements

onError☠

Reactive Streams

• asynchronous non-blocking data flow

• asynchronous non-blocking demand flow

• minimal coordination and contention

• message passing allows for distribution acrossapplications, nodes, CPUs, threads, actors, …

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http://reactive-­‐streams.org/

What is it good for?

Streams are Ubiquitous

• Streaming SIMD Extensions (SSE) — Intel, 1999

• ingesting, transforming and emitting data

• requests & responses flowing through a system

• streams are graphical and intuitive

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Moving Bulk Data over the Network

• content streaming (e.g. video or audio)

• data storage / backup

• data replication between data centers

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Mobile Devices

• streams of updates from the server

• streams of user commands from the client

• bad connection quality—need for back-pressure

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Internet of Things

• ingesting large numbers of low-rate streams

• conflating or extrapolating data

• aggregating many streams

• streaming towards the fleet of devices

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Realtime Data Analysis

• real-time business intelligence

• complex event processing

• temporal correlations (e.g. credit card fraud)

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The Ecosystem

RxJava

• «An API for asynchronous programming with observable streams» — reactivex.io

• inspiration for Reactive Streams interfaces

• RxJavaReactiveStreams provides RS compliant wrapper for Observable

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io.reactivex:rxjava-reactive-streams:1.0.0

Project Reactor

• «a foundational library for building reactive fast-data applications on the JVM» — projectreactor.io

• Stream directly implements Publisher and offers a rich set of transformations

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io.projectreactor:reactor-stream:2.0.3.RELEASE

Akka Streams

• compose any kind of stream processing topology from immutable and reusable blueprints

• creates RS compliant Publisher/Subscriber when materializing a flow graph into Actors

• high-level flow graph DSL

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com.typesafe.akka:akka-stream-experimental:1.0-RC3

ratpack

• «Ratpack is a set of Java libraries that facilitate fast, efficient, evolvable and well tested HTTP applications.» — ratpack.io

• accepts Publishers of streams to be sent to the client • chunked responses, server-sent events, websockets

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io.ratpack:ratpack-core:0.9.17

vert.x 3.0

• «Vert.x is a lightweight, high performance application platform for the JVM that's designed for modern mobile, web, and enterprise applications.» — vertx.io

• Reactive Streams Integration Module(https://github.com/vert-x3/vertx-reactive-streams)

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io.vertx:vertx-reactive-streams:3.0.0-milestone6

Slick 3.0

• «Functional Relational Mapping for Scala» — slick.typesafe.com

• database result sets can be streamed, returning a Publisher instead of a collection

• no streaming inserts yet

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com.typesafe.slick:slick_2.11:3.0.0

Kafka

• «Apache Kafka is publish-subscribe messaging rethought as a distributed commit log.» — kafka.apache.org

• Reactive Kafka (by Softwaremill):consumer/producer are RS Subscriber/Publisher

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com.softwaremill:reactive-kafka_2.11:0.6.0

AMQP

• «To become the standard protocol for interoperability between all messaging middleware» — amqp.org

• https://github.com/ScalaConsultants/reactive-rabbit

• fully asynchronous API

• consumer/publisher are RS Subscriber/Publisher

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io.scalac:reactive-rabbit_2.11:1.0.0

MongoDB

• «The official MongoDB Reactive Streams Java Driver, providing asynchronous stream processing with non-blocking back pressure for MongoDB.» —http://mongodb.github.io/mongo-java-driver-reactivestreams/

• all queries return Publishers

• no streaming inserts yet

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org.mongodb:mongodb-driver-reactivestreams:1.0.1

… and more are coming (TBC)

• Cassandra

• Spark

• Riak

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Reactive Streams in the Typesafe Reactive Platform

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O/S-level network stack

Java NIO (JDK)

Akka IO

Akka HTTP Core

Akka HTTP

application level

Akka Streams Database Driver

Slick 3.0

Play Framework ReactiveStreamsIntegration: preview available

Streaming Database Results to the Client

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import DefaultJsonProtocol._implicit val denormOrderFormat = jsonFormat5(DenormalizedOrder.apply)

val db = Database.forConfig("reportingDB")

private def getFromDb(userId: Int): Publisher[DenormalizedOrder] = db.stream(denormalizedOrders.filter(_.userId === userId).result)

Http().bindAndHandle( (get & path("orders" / IntNumber)) { userId => val pub = Source(getFromDb(userId)) .transform(() => new ToJsonArray) complete(HttpEntity.Chunked.fromData(`application/json`, pub)) }, "localhost", 8080)

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