parallel & async processing using tpl dataflow

PARALLEL & ASYNCPROCESSING USING TPL DATAFLOW

Petru Rebeja

AGENDA

• What is Dataflow?

• When to use it?

• How to use it?

• Q&A

THE BIG PICTURE

CLR Thread Pool

Tasks

PLINQ Parallel Loops

Concurrent Collections

Dataflow

DATAFLOW BENEFITS

• Effortless use of multi-threading

• Performance boost via painless optimization

• Development focus is on the ‘what’ rather than ‘how’

DATAFLOW USAGES

High throughput, low-latency scenarios

Robotics

Manufacturing

Imaging Biology

Oil & Gas

Finance

PROGRAMMING MODEL

• Actor-based programming

• In-process message passing

• Components (blocks) for creating data processing pipelines

ARCHITECTURE

IDataflowBlock

ISourceBlock<TOutput> ITargetBlock<TInput>

IPropagatorBlock<Tinput,Toutput>

COMPOSITION

Source

Target

Propagator

OptionalTransform

BUFFERING BLOCKS

BufferBlock<T>

BroadcastBlock<T>

WriteOnceBlock<T>

EXECUTION BLOCKS

ActionBlock<T>

TransformBlock<T,V>

TransformManyBlock<T,V>

GROUPING BLOCKS

BatchBlock<T>

JoinBlock<T1,T2,…>

BatchedJoinBlock<T1,T2>

BEHAVIOR CONFIGURATION OPTIONS

• BufferBlock<T>

• BroadcastBlock<T>

• WriteOnceBlock<T>

DataflowBlockOptions

• ActionBlock<T>

• TransformBlock<TIn, TOut>

• TransformManyBlock<TIn, TOut>

ExecutionDataflowBlockOptions

• BatchBlock<T>

• JoinBlock<T1, T2[, T3]>

• BatchedJoinBlock<T1, T2>

GroupingDataflowBlockOptions

COMPLETION & CANCELLATION

• To know when a block completes await block.Completion

or add a continuation task to it

• To propagate completion from source to target, set

DataflowLinkOptions.PropagateCompletion when

linking

• Set DataflowBlockOptions.CancellationToken to

enable cancellation

ERROR HANDLING

• If the exception does not affect the integrity of the

pipeline – use a try/catch inside the block

• Otherwise, handle errors outside of the pipeline by

• Adding a continuation to block.Completion

• Propagating errors through the pipeline

DEALING WITH CONCURRENCY

• Rule of thumb: avoid shared state whenever possible.

• Use ConcurrentExclusiveSchedulerPair to perform

updates on shared state

• Be aware of the caveats with

ConcurrentExclusiveSchedulerPair

CREATING CUSTOM BLOCKS

The easy way:

DataflowBlock.Encapsulate<TInput, TOutput>(

target, source)

CREATING CUSTOM BLOCKS

The hard(core) way:

class CustomBlock:

IPropagatorBlock<TInput, TOutput>

{

}

CREATING CUSTOM BLOCKS

Either way you choose, don’t forget to:

• Propagate completion

• Pool for cancellation

REFERENCES & FURTHER READING

Dataflow (Task Parallel Library) http://msdn.microsoft.com/en-us/library/hh228603(v=vs.110).aspx

Stephen Toub

TPL Dataflow Tourhttp://channel9.msdn.com/posts/TPL-Dataflow-Tour

Joseph Albahari

The Future of .NET Parallel Programming

http://channel9.msdn.com/events/TechEd/Australia/Tech-Ed-Australia-2011/DEV308

Stephen Toub

Inside TPL Dataflowhttp://channel9.msdn.com/Shows/Going+Deep/Stephen-Toub-Inside-TPL-Dataflow

Alexey Kursov

Pipeline TPL Dataflow Usage exampleshttps://www.youtube.com/watch?v=AI9KxgDF43khttps://www.youtube.com/watch?v=AI9KxgDF43k

Richard Blewett, Andrew Clymer

Pro Asynchronous Programming with .NET

APRESS 2013ISBN: 978-1430259206

AKKA.NET http://getakka.net/

QUESTIONS?

THANK YOU!

Petru.Rebeja@gmail.com

Parallel & Async Processing using TPL Dataflow