depfunc is a package that enables concurrent resolution of dependent actions.
Image you have a network of actions that should be evaluated, by this depends on that, and that depends on another.
This package allows the construction of such a graph and a way to evaluate the result.
It seemed like a good idea at the time.
Imagine applesauce.
To produce applesauce, you need apples, sugar, and a can. To make a can, you need metal. You also might want to perform some kind of QA on the canning process.
Your manufacturing process may look something like this:
| [apples] [sugar] [metal]
| \ / /
| \ / /
| \ / [qa] [can]
| \ \ / /
↓ [applesauce]
You could create your graph like this:
graph := depfunc.NewGraph()
graph.AddAction("apples", growApples)
graph.AddAction("sugars", growSugar)
graph.AddAction("metals", recycleMetal)
graph.AddAction("cans", formCans)
graph.AddAction("applesauce", canApplesauce)
graph.AddAction("qa", qa)
graph.LinkDependency("metals", "cans")
graph.LinkDependency("cans", "applesauce")
graph.LinkDependency("apples", "applesauce")
graph.LinkDependency("sugars", "applesauce")
graph.LinkDependency("qa", "applesauce")Great! Now every season you can run it like so:
ctx, err := graph.Resolve(context.Background(), &factory{})
checkError(err)
select {
<-ctx.Done():
fmt.Println("Hooray! Applesauce!")
}And your factory will be full of various products.
Cool, but how long did it take?
stats := depfunc.NewStatistics()
graph.Resolve(context.Background(), &factory{}, stats.Recorder())
checkError(err)
select {
<-ctx.Done():
fmt.Printf("Hooray! Applesauce! Applesauce waited %v to produce, and took %v to actual can it up.\n", stats.Wait(), stats.Action())
}
// prints: Hooray! Applesauce! Applesauce waited 337.254578ms to produce, and took 312.194456ms to actual can it up.