This document discusses GPars Remoting, which allows for remote concurrency and parallelism using GPars. GPars already enables concurrency on a single JVM using actors, dataflow, data parallelism and CSP. Thanks to a Google Summer of Code student, GPars can now also realize remote objects using Netty to enable distributed concurrent applications. The document discusses how this capability could be applied to services, microservices and big data problems, and compares local vs remote concurrency models. Code examples are promised but not included.

1. Copyright © 2014–2015 Russel Winder 1
GPars Remoting
Dr Russel Winder
russel@winder.org.uk
@russel_winder
http://www.russel.org.uk

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This is a sales pitch…

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This is a sales pitch…✘

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This is breaking news…

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This is breaking news…✘

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It's new…

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It's new…✘

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It's improved…

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It's improved…✔

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It's…

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Thanks to GSoC 2014,
Rafał Sławik
built on a partial implementation by
Alex Tkachman.

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Remote Objects

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GPars already has single JVM
concurrency and parallelism.
You can use it from Java
as well as Groovy.

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GPars now has remote objects.
Realized by using Netty.

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Actors
Independent processes
communicating via
asynchronous exchange
of messages.
Dataflow
Operators connected by
channels with activity
triggered by arrival of
data on the channels.
Data Parallelism
Transform a sequence to
another sequence where all
individual actions happen
at the same time.
CSP
Operators connected by
channels with activity
triggered by arrival of
data on the channels.

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Active Objects
An object that is actually
an actor but looks like a
full service object.
Agents
A wrapper for some
shared mutable state.
Software Transactional Memory
Wrappers for mutable values that uses transactions
rather than locks.
Fork/Join
An toolkit for tree structured
concurrency and parallelism.

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Actors
Independent processes
communicating via
asynchronous exchange
of messages.
Dataflow
Operators connected by
channels with activity
triggered by arrival of
data on the channels.
Data Parallelism
Transform a sequence to
another sequence where all
individual actions happen
at the same time.
CSP
Operators connected by
channels with activity
triggered by arrival of
data on the channels.

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Data Parallelism
Transform a sequence to
another sequence where all
individual actions happen
at the same time.

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Dataflow
Operators connected by
channels with activity
triggered by arrival of
data on the channels.

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Code

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CSP
Sequential processes
communicating by
sending messages on
channels.

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Actors
Independent processes
communicating via
asynchronous exchange
of messages.

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Code

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Actors
Independent processes
communicating via
asynchronous exchange
of messages.
Dataflow
Operators connected by
channels with activity
triggered by arrival of
data on the channels.
Data Parallelism
Transform a sequence to
another sequence where
all individual actions
happen at the same time.
CSP
Operators connected by
channels with activity
triggered by arrival of
data on the channels.

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But isn't this just clustering, and
isn't this standard stuff?

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Elephant in the Room…

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Hadoop

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GPars can compete in
“Big Data”, but will it?

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Service Oriented Architecture

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Services

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Micro-Services

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Micro-Services
Passé
✘

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Pico-Services

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No need for HTTP, Web,
port 80, HTML, CSS…

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Actors
Active Objects
Agents
Dataflow Operators
Local
Remote

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NUMA

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Non-Uniform Memory Architecture

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Local concurrency/parallelism
Remote concurrency/parallelism

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A fun demo…

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GPars Remoting
Dr Russel Winder
russel@winder.org.uk
@russel_winder
http://www.russel.org.uk