tpl dataflow – зачем и для кого?

Control your data right way

TPL Dataflow crash course

Mikhail Veselov

Moscow

20 Апрель 2015

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History overview §  TPL Dataflow is another abstracGon level §  ReacGve Extensions & Flow sync §  TTB Flow Graph analog §  AAL à CCR à TDF

TPL Dataflow – crash course IntroducGon


Data

AcGon

Recycle

Cache

Thread Pool

Threads

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Main idea -‐ why Dataflow? §  Define your applicaGon dataflow §  Async I/O and CPU-‐oriented code, high-‐throughput & low-‐latency §  Random, unstructured data (compare with Parallel and PLINQ) §  Rx IObservable<T> support §  Easy start: ActionBlock<T> & BufferBlock<T>

TPL Dataflow – crash course IntroducGon


FIFO queue Action

Example: GZIP compressing simple schema

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Interfaces -‐ IDataflowBlock public interface IDataflowBlock { void Complete(); void Fault(Exception error); Task Completion { get; } } block.Completion.ContinueWith(t => { if (t.IsFaulted) ((IDataflowBlock)nextBlock).Fault(t.Exception); else nextBlock.Complete(); });

TPL Dataflow – crash course Fundamental Interfaces


§  IDataflowBlock – base interface, no abstract implementaGon

§  Task CompleGon staGc methods: §  WhenAll §  ContinueWith

§  AggregateException with previous block’s fault §  CancellationToken

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Interfaces -‐ ITargetBlock public interface ITargetBlock<in TInput> : IDataflowBlock { DataflowMessageStatus OfferMessage( DataflowMessageHeader messageHeader, TInput messageValue, ISourceBlock<TInput> source, bool consumeToAccept); }

§  DataflowMessageStatus: –  Accepted – OK, I’ll handle it –  Declined – NO, take it back –  Postponed – May Be, please, call back later J –  NotAvailable – Tried to consume with no luck –  DecliningPermanently – No, and don’t call me anymore L

§  bool consumeToAccept: –  Call ConsumeMessage synchroniously



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Interfaces -‐ ISourceBlock public interface ISourceBlock<out TOutput> : IDataflowBlock { IDisposable LinkTo(ITargetBlock<TOutput> target, bool unlinkAfterOne); bool ReserveMessage( // prepare DataflowMessageHeader messageHeader, ITargetBlock<TOutput> target); TOutput ConsumeMessage( // commit DataflowMessageHeader messageHeader, ITargetBlock<TOutput> target, out bool messageConsumed); void ReleaseReservation( //rollback DataflowMessageHeader messageHeader, ITargetBlock<TOutput> target); }

§  2-‐phase commit protocol (a.k.a. transacGon)



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Advanced Interfaces §  IPropagatorBlock<TInput, TOutput>

public interface IPropagatorBlock<in TInput, out TOutput> : ITargetBlock<TInput>, ISourceBlock<TOutput> { }

§  1-‐by-‐1 linking vs 1-‐by-‐n linking

§  IReceivableSourceBlock<TOutput> public interface IReceivableSourceBlock<TOutput> :

ISourceBlock<TOutput> { bool TryReceive(out TOutput item, Predicate<TOutput> filter); bool TryReceiveAll(out IList<TOutput> items); }

§  Easier data process



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Pure Buffering Blocks §  BufferBlock<T>

–  FIFO queue –  Producer/Consumer

BufferBlock<FacebookDTO> dataToProcess = new BufferBlock<FacebookDTO>(); dataToProcess.PostAsync(newVideo); dataToProcess.Post(newRepost); dataToProcess.SendAsync(newLike);

TPL Dataflow – crash course Built-‐in Dataflow Blocks


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Pure Buffering Blocks §  BroadcastBlock<T>

–  Current overwrite –  No receivers – drop it

var bb = new BroadcastBlock<ImageDTO>(i => i); var saveToDisk = new ActionBlock<ImageDTO>(item => item.Image.Save(item.Path)); var showInUi = new ActionBlock<ImageDTO>(item => imagePanel.AddImage(item.Image), new DataflowBlockOptions { TaskScheduler = TaskScheduler.FromCurrentSynchronizationContext() }); bb.LinkTo(saveToDisk); bb.LinkTo(showInUi);



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Pure Buffering Blocks §  WriteOnceBlock<T>

–  Singleton

writeOnce = new WriteOnceBlock<Lazy<Task<T>>>(i => i); writeOnce.Post(new Lazy<Task<T>>(() => Task.Run(amadeusConnectionFactory))); var lazyValue = await writeOnce.RecieveAsync(); var taskConnection = await lazyValue.Value;

var connection = taskConnection.Result;



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Executor Blocks §  ActionBlock<TInput> var chooser = new ActionBlock<PostDTO>(post => { Process(post); });

var threeMessageAtOnce = new DataflowBlockOptions { BoundedCapacity = 3, TaskScheduler = TaskScheduler.Current }; var threePerTask = new DataflowBlockOptions { MaxMessagesPerTask = 3 };



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Executor Blocks §  TransformBlock<TInput, TOutput>

–  Output ordering-‐safe queue

var gzipper = new TransformBlock<byte[], Task<byte[]>> (b => Task.Run(() => Compress(b)); var RSAEncryptor = new TransformBlock<byte[], byte[]>(z => RSA(z)); gzipper.LinkTo(RSAEncryptor);



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Executor Blocks §  TransformManyBlock<TInput, TOutput>

–  Produce zero or more items per 1 input message –  Output can be a Task

// .SelectMany() analog var tagCloudAggregator = new TransformManyBlock<TagFromPost[], TagFromPost> (arrayOfTags => arrayOfTags); var filteringTags = return new TransformManyBlock<T, T>(async tag => await filter(tag) ? new [] { tag } : Enumerable.Empty<T>()); tagCloudAggregator.LinkTo(filteringTags); // provide info to UI filteringTags.TryRecieveAll(out tagDataSource); tagCloudControl.Show(tagDataSource);



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Executor Blocks §  NullTarget<TInput> Recycle bin



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Joining Blocks §  BatchBlock<T>

–  accumulate and run var batch = new BatchBlock<T>(batchSize: Int32.MaxValue); new Timer(delegate { batch.TriggerBatch(); }).Change(1000, 1000);

var batch = new BatchBlock<T>(batchSize: 100);



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Joining Blocks §  JoinBlock<T1, T2, …>

–  make a Tuple –  StarvaGon problem

var throttle = new JoinBlock<SyntaxTree, Request>(); for (int i = 0; i < 10; ++i) throttle.Target1.Post(new SyntaxTree()); var processor = new TransformBlock<Tuple<SyntaxTree, Request>, SyntaxTree> (pair => { var request = pair.Item2; var resource = pair.Item1; request.ProcessWith(resource); return resource; }); throttle.LinkTo(processor); processor.LinkTo(throttle.Target1);



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Joining Blocks §  BatchedJoinBlock<T1, T2,…>

–  accumulate a Tuples and run try { batchedJoin.Target1.Post(DoWork()); batchedJoin.Target2.Post(default(T2)); } Catch (Exception e) { batchJoin.Target2.Post(e); batchJoin.Target1.Post(default(T1)); } // Item1 – results from Target1 // Item2 – results from Target12 await batchedJoin.RecieveAsync();



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ConfiguraKon OpKons – TPL support §  TaskScheduler & SynchronizationContext

–  TaskScheduler.Default is default –  TaskScheduler.Current is not a default –  ConcurrentExclusiveSchedulerPair

§  MaxDegreeOfParallelism –  ExecuGonDataflowBlockOpGons –  All operaGons are not concurrent by default

§  CancellationToken

TPL Dataflow – crash course Advanced topics


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ConfiguraKon OpKons – load balancing §  MaxMessagesPerTask

–  Blocks try to minimize number of Tasks §  MaxNumberOfGroups

–  Grouping blocks autocomplete §  Greedy

–  How to create batches and join §  BoundedCapacity

–  load balancing – queue size var taskSchedulerPair = new ConcurrentExclusiveSchedulerPair(); var readerActions = from checkBox in new[] { checkBox1, checkBox2, checkBox3 } select new ActionBlock<int>(milliseconds => { toggleCheckBox.Post(checkBox); Thread.Sleep(milliseconds); toggleCheckBox.Post(checkBox);

);}, new ExecutionDataflowBlockOptions { TaskScheduler = taskSchedulerPair.ConcurrentScheduler });



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StaKc extension methods – data process §  Choose

–  MulGple sources for an acGon –  There will be only one message processed

§  OutputAvailableAsync –  Analog of Stack.Peek operaGon

§  Post/SendAsync –  Always async data propagaGon –  You can postpone the message with SendAsync , not with Post

§  Receive(Async)



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StaKc extension methods – outer API §  Encapsulate (propagator block factory method) §  LinkTo

–  Filter the message propagaGon –  Link opGons –  Do not confuse with ISourceBlock<T> method

§  AsObsevable(er) –  Rx extension support, no holy war here

new DataflowLinkOptions { MaxMessages = 1, Append = false, PropagateCompletion = true

}



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Deep inside §  Implement your own block §  Advanced debug info with DebuggerDisplayAttribute

§  Chapter #4 in Concurrency in C# Cookbook by Stephen Cleary



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Gains When to use Losses

TPL Dataflow – crash course Ending


Gathering it up

§  Thread Safety §  Structured dataflow §  Async TPL-‐oriented §  Rx-‐Extension support §  CCR-‐oriented code

easy migraGon

§  Another abstracGon layer

§  Hard debug when dataflow is complicated

§  Too many generics

§  You have a random data which must be ordered

§  CPU and I/O operaGons §  You can parallelize work

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I am at your disposal in case of any questions or doubts


Mikhail Veselov

Moscow

[email protected]

+7 911 951 42 98

tpl dataflow – зачем и для кого?

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