Parallel & async processing using tpl dataflow
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Parallel & async processing using tpl dataflow
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Parallel & async processing using tpl dataflow
- 1. PARALLEL & ASYNC PROCESSING USING TPL DATAFLOW Petru Rebeja
- 3. AGENDA • What is Dataflow? • When to use it? • How to use it? • Q&A
- 4. THE BIG PICTURE CLR Thread Pool Tasks PLINQ Parallel Loops Concurrent Collections Dataflow
- 5. DATAFLOW BENEFITS • Effortless use of multi-threading • Performance boost via painless optimization • Development focus is on the ‘what’ rather than ‘how’
- 6. DATAFLOW USAGES High throughput, low-latency scenarios Robotics Manufacturing Imaging Biology Oil & Gas Finance
- 7. PROGRAMMING MODEL • Actor-based programming • In-process message passing • Components (blocks) for creating data processing pipelines
- 13. 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
- 14. 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
- 15. 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
- 16. 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
- 17. CREATING CUSTOM BLOCKS The easy way: DataflowBlock.Encapsulate<TInput, TOutput>( target, source)
- 18. CREATING CUSTOM BLOCKS The hard(core) way: class CustomBlock: IPropagatorBlock<TInput, TOutput> { }
- 19. CREATING CUSTOM BLOCKS Either way you choose, don’t forget to: • Propagate completion • Pool for cancellation
- 20. REFERENCES & FURTHER READING Dataflow (Task Parallel Library) http://msdn.microsoft.com/en-us/library/hh228603(v=vs.110).aspx Stephen Toub TPL Dataflow Tour http://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 Dataflow http://channel9.msdn.com/Shows/Going+Deep/Stephen-Toub-Inside-TPL- Dataflow Alexey Kursov Pipeline TPL Dataflow Usage examples https://www.youtube.com/watch?v=AI9KxgDF43k https://www.youtube.com/watch?v=AI9KxgDF43k Richard Blewett, Andrew Clymer Pro Asynchronous Programming with .NET APRESS 2013 ISBN: 978-1430259206 AKKA.NET http://getakka.net/
- 21. QUESTIONS?
- 22. THANK YOU! Petru.Rebeja@gmail.com Parallel & Async Processing using TPL Dataflow
Notas del editor
- When discussing about how to use Dataflow we’ll touch the following points of interest: - programming model (what are the entities exposed by Dataflow?) - configuring the behavior of the entities (parallelism, completion, error handling) - although Dataflow removes the need for dealing with concurrent scenarios there are cases when concurrency is inevitable and developers must properly deal with concurrency pitfalls - whenever the functionality of built-in blocks isn’t enough, Dataflow offers the possibility to create custom blocks
- .NET Framework 4.0 comes with three APIs for Parallel Programming: Tasks (lower level), PLINQ and Parallel (upper level). The Dataflow library is a natural extension of the TPL library that allows developers to create data-processing pipelines in their applications. The Dataflow library provides a framework for creating blocks that perform a specific function asynchronously. These blocks can be composed together to form a pipeline where data flows into one end of the pipeline and some result or results come out from the other end. This is great when data can be processed at different rates or when parallel processing can efficiently spread work out across multiple CPU cores.
- Dataflow is a paradigm shift but when the developers overcome the discomfort of the paradigm shift they will benefit from the high expressivity of the code.