The document discusses Reactive Streams, which provide an approach for efficiently handling large data streams across distributed systems. It defines Reactive Streams as asynchronous data sources that may have no defined start or end and are retrieved on demand. The document describes how Reactive Streams use back-pressure to regulate data flow between components and support operations like splitting, merging, filtering and transforming streams. It concludes that Reactive Streams enable effective resource usage and flexibility for distributed applications.

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Reactive Streams
By Dorin Cobzac

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• Define the Problem
• Describe the Solution
• Conclude the Findings

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• Define the Problem

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Every Minute:
• Facebook users share nearly 2.5 million pieces of content.
• Twitter users tweet nearly 300,000 times.
• Instagram users post nearly 220,000 new photos.
• Apple users download nearly 50,000 apps.
• Email users send over 200 million messages.
HUGE amounts of Data

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• … applications
• … network nodes
• … CPUs
• … threads
• … actors (actor systems)
Distributed between …

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Distribution is trending

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How to handling HUGE data streams
across distributed systems?

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Solutions?

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• Making usual calls and wait for responses
• Push to a Queue and risk losing messages
• Use distributed queues

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OR

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Go Reactive

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• Define the Problem

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• Describe the Solution

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What is a Stream?
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… but seriously, what is a Stream?

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• … may not have a start
• … may not have an end
• … doesn’t have a size
• … its elements are retrieved/computed “on-the-spot”
A Stream is a Data source that …
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Reactive Stream basics?
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Data Flow: Fast Producer
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Data Flow: Slow Producer
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Data Flow: Self-regulating
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Automatically switches between being consumer and producer driven

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Data Flow: Asynchronous
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The communication between components is asynchronous!

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Data Flow: Back-pressure
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Data Flow: Back-pressure
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Stream Operations: Split
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Stream Operations: Merge
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Other Stream Operations
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• Filter certain elements from a stream
• Transform one or more elements in a stream
• Match certain elements based on some complex logic and/or order

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Reactive Streams working together
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• Describe the Solution

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• Conclude the Findings

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Reactive Streams: Advantages
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• Effective resource consumption
• Flexibility
• Inter-operability

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Reactive Streams: Caveat
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Go fully Reactive, or you’re not getting the full benefits

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Reactive Streams: Relevance
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Internet of Things is here
Are you ready?

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Thank you!

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Reactive Stream Flavors
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1. Reactive Streams Specification v.1
- Released on 30 April 2015
- Contributions from Pivotal, Redhat, TypeSafe, Netflix
- Includes a Technology Compatibility Kit
- Only on JVM ecosystem (for now)
2. Reactive Extensions API
- Multiple implementations (including .Net and Ruby)
- A Netflix Original Production
- Reactive Streams spec v.0.9 (kind-of)
- Interops through RxJava Reactive Streams project

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public interface Publisher<T> {
public void subscribe(Subscriber<? super T> s);
}
public interface Subscriber<T> {
public void onSubscribe(Subscription s);
public void onNext(T t);
public void onError(Throwable t);
public void onComplete();
}
public interface Subscription {
public void request(long n);
public void cancel();
}
public interface Processor<T, R> extends Subscriber<T>, Publisher<R>
Reactive Streams Specification v.1

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Reactive Streams Specification v.1
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Reactive Extensions API (short)
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public class Observable<T> {
public static Observable<T> create(OnSubscriber<T> s);
public Subscription<T> subscribe(Observer<? super T> subscriber);
}
public interface Observer<T> {
public void onNext(T t);
public void onError(Throwable t);
public void onComplete();
}