Reactive Programming with RxJavafor Faster Big Data

Michael Nitschinger

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New challenges need new solutions

Modern applications need to React to user load

React to failure

Be responsive under load and failure

Decoupled, event-driven architectures are the foundation.

Resources need to be utilized as best as possible. Waiting for IO is bad.

Why Reactive?

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Detour: Java Client Architecture

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Detour: Smart Batching

Source: http://mechanical-sympathy.blogspot.co.at/2011/10/smart-batching.html4

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Detour: Smart Batching

Source: http://mechanical-sympathy.blogspot.co.at/2011/10/smart-batching.html5

RxJava

A Gentle Introduction

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Java implementation for Reactive Extensions https://github.com/ReactiveX

A library to compose asynchronous and event-driven programs through observable sequences.

RxJava: Introduction

single multiple

sync T Iterable<T>

async Future<T> Observable<T>

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Observables are the duals of Iterables

They describe both Latency and Error side effects.

RxJava: Introduction

event Iterable<T> (pull) Observable<T> (push)

data retrieval T next() onNext(T)

error discovery throws Exception onError(Exception)

completion returns onCompleted()

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Consuming Observables

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The Observer subscribes and receives events.

A cold Observablestarts when subscribed.

onNext can be called0..N times

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RxJava: Creating Observables

just

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RxJava: Creating Observables

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RxJava: Creating Observables

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RxJava: Creating Observables

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RxJava: Creating Observables

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RxJava: Creating Observables

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RxJava: Transforming Observables

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RxJava: Transforming Observables

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RxJava: Transforming Observables

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RxJava: Transforming Observables

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RxJava: Transforming Observables

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RxJava: Transforming Observables

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RxJava: Transforming Observables

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RxJava: Transforming Observables

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RxJava: Transforming Observables

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RxJava: Filtering Observables

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RxJava: Filtering Observables

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RxJava: Filtering Observables

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RxJava: Filtering Observables

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Error Handling

with RxJava

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Resiliency is key

Things will go wrong, so better plan for it

Do not aim for QA, aim for production

Do not trust integration points

Treat the Database (SDK) as an integration point

Reacting to Failure

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Timeouts

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The network is unreliable

Servers fail

The SDK contains bugs

Always specify timeouts and deal with them!

The synchronous wrapper defines them for you all the time.

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Timeouts: Simple

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Timeouts: Synchronous API

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Timeouts: Complex Example

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Coordinated Retry

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Fail fast Don’t let your system get stuck

Shed load with backpressure

Retry immediately won’t help

either linear or exponential back-off necessary

have a strategy if retry also doesn’t work (Fallbacks!)

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Coordinated Retry: Fallback

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Coordinated Retry with Delay

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More Patterns

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Circuit Breaker (https://github.com/Netflix/Hystrix)

Closed if everything okay

Opens once the integration point breaks

Fail fast if open

Rechecking with half-open

Bulkheads Contain errors into manageable compartments

Don’t let your ship sink!

Thank you.