I'm processing images using TPL Dataflow. I receive a processing request, read an image from a stream, apply several transformations, then write the resulting image to another stream:
Request -> Stream -> Image -> Image ... -> Stream
For that I use the blocks:
BufferBlock<Request>
TransformBlock<Request,Stream>
TransformBlock<Stream,Image>
TransformBlock<Image,Image>
TransformBlock<Image,Image>
...
writerBlock = new ActionBlock<Image>
The problem is the initial Request
is what contains some data necessary to create the resulting Stream
along with some additional info I need at that point. Do I have to pass the original Request
(or some other context object) down the line to the writerBlock
across all the other blocks like this:
TransformBlock<Request,Tuple<Request,Stream>>
TransformBlock<Tuple<Request,Stream>,Tuple<Request,Image>>
TransformBlock<Tuple<Request,Image>,Tuple<Request,Image>>
...
(which is ugly), or is there a way to link the first block to the last one (or, generalizing, to the ones that need the additional data)?
Yes, you pretty much need to do what you described, passing the additional data from every block to the next one.
But using a couple of helper methods, you can make this much simpler:
public static IPropagatorBlock<TInput, Tuple<TOutput, TInput>>
CreateExtendedSource<TInput, TOutput>(Func<TInput, TOutput> transform)
{
return new TransformBlock<TInput, Tuple<TOutput, TInput>>(
input => Tuple.Create(transform(input), input));
}
public static IPropagatorBlock<Tuple<TInput, TExtension>, Tuple<TOutput, TExtension>>
CreateExtendedTransform<TInput, TOutput, TExtension>(Func<TInput, TOutput> transform)
{
return new TransformBlock<Tuple<TInput, TExtension>, Tuple<TOutput, TExtension>>(
tuple => Tuple.Create(transform(tuple.Item1), tuple.Item2));
}
The signatures look daunting, but they are actually not that bad.
Also, you might want to add overloads that pass options to the created block, or overloads that take async delegates.
For example, if you wanted to perform some operations on a number using separate blocks, while passing the original number along the way, you could do something like:
var source = new BufferBlock<int>();
var divided = CreateExtendedSource<int, double>(i => i / 2.0);
var formatted = CreateExtendedTransform<double, string, int>(d => d.ToString("0.0"));
var writer = new ActionBlock<Tuple<string, int>>(tuple => Console.WriteLine(tuple));
source.LinkTo(divided);
divided.LinkTo(formatted);
formatted.LinkTo(writer);
for (int i = 0; i < 10; i++)
source.Post(i);
As you can see, your lambdas (except for the last one) deal only with the “current” value (int
, double
or string
, depending on the stage of the pipeline), the “original” value (always int
) is passed automatically. At any moment, you can use block created using the normal constructor to access both values (like the final ActionBlock
in the example).
(That BufferBlock
isn't actually necessary, but I added it to more closely match your design.)
I may be going over my head since I am only starting to play with TPL Dataflow.
But I believe you can accomplish that using a BroadcastBlock
as an intermediary between your source and your first target.
BroadcastBlock
can offer the message to many targets, so you use it to offer to your target, and also to a JoinBlock
, at the end that will merge the result with the original message.
source -> Broadcast ->-----------------------------------------> JoinBlock <source, result>
-> Transformation1 -> Transformation 'n' ->
For example:
var source = new BufferBlock<int>();
var transformation = new TransformBlock<int, int>(i => i * 100);
var broadCast = new BroadcastBlock<int>(null);
source.LinkTo(broadCast);
broadCast.LinkTo(transformation);
var jb = new JoinBlock<int, int>();
broadCast.LinkTo(jb.Target1);
transformation.LinkTo(jb.Target2);
jb.LinkTo(new ActionBlock<Tuple<int, int>>(
c => Console.WriteLine("Source:{0}, Target Result: {1}", c.Item1, c.Item2)));
source.Post(1);
source.Post(2);
source.Complete();
yields...
Source:1, Target Result: 100
Source:2, Target Result: 200
I am just not too sure about how it would behave in an asynchronous environment.
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