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Flowlib: Another Scala Dataflow Library

Flowlib is a compact library for asynchronous programming within a single JVM.

  • Compared to futures and promises, flowlib has a message passing model and allows arbitrary message processing graphs. There is a DSL to describe these graphs and operators to split and join message flows in sum or product style. The degree of concurrency is tune-able at each processing node.

  • Compared to Akka actors, message flows are statically typed and many of the details of flow control that would be manually programmed are automatic. On the other hand, flowlib is confined to a single JVM instance.

  • Compared to Spark, Storm or Akka clusters, flowlib is for smaller asynchronous systems. There are many of these!

  • Compared to Functional Reactive Programming, there is no global synchronization of messages flows (signals and behaviours in FRP) nor any other glitch suppression strategy.

Important Caveats:

  • This an experimental library for now. I hope to have something well-proven soon.
  • This page is in draft form until I test my examples and post more complete example code.
  • The source is on github but the repository will likely be renamed to remove the reference to actors.
  • Updated 29/12/2013 to reflect API changes: a type parameter for Action; Supervisor parameter for run(); (p: Process) * (n: Int) == p*n no longer holds.

Without Further Ado: A Graph

Here is a toy processing graph in the flowlib DSL:


trait SampleGraph extends SampleDecls { 

  val N = 2

  def graph =
    InitialData :- urls :-> Fetcher :- raw :-> Splitter -: ( 
  	  urls ->: Dedup -: urls ->: Fetcher & 
  	  text ->: Filer*N -: metrics ->: Reporter )

Uppercase identifiers such as Fetcher by convention are processes. Lowercase identifiers such as raw are labels for message flows.

The connecting operator :- attaches a flow label to a process forming a projection and :-> connects this to a target process.

These have right associative equivalents ->: and -: respectively, which are useful to express fan-out as opposed to fan-in structures.

The & operator combines graphs or projections. More about the representation of graphs and projections later.

The * operator multiplies a process. It indicates how many parallel instances should be executed.

We left out the declarations of the identifiers. Let's put them in a separate trait without committing to the types just yet.

trait SampleDecls { 
  type Text
  type Address
  type Metric 
  val raw, text = label[Text]
  val urls = label[Address]
  val metrics = label[Metric]
  val Fetcher, Splitter, Dedup, Filer, Reporter, Supervisor: Process


On to the definition of processes. Lets take Dedup as an example:

trait SampleProcesses  { 
  def dedup[Message](flow: Label[Message]) = new Process {

    def description = "remove duplicate messages"
    def action = loop(Set.empty)
    private def loop(seen: Set[Message]): Action[Nothing] =
      input(flow) { message => 
      	if(seen contains message) loop(seen)
      	else output(flow, message) { loop(seen + message) }

  // more process definitions here ...

This defines a dedup method that will create a process with a given message type and flow label. The parameters make dedup potentially usable in different positions within a graph or in different graphs.

The process is constructed in continuation passing style as follows:

  • The action member is the process entry point.

  • The input method returns an Action that will be dispatched when a message is available on the port labelled flow.

  • The function message => ... is a continuation that is invoked when the input action is dispatched. It returns a new Action to be dispatched.

  • The output method returns an Action that will be dispatched when the message can be delivered on the output port labelled flow.

  • The passed block is a continuation that is invoked when the output action is dispatched.

  • Action has a type parameter, in this case Nothing, which indicates that this series of actions loops indefinitely.

Any other type U indicates that a stop(u: U) action may be encountered which terminates a series of actions.

A Note About Process State

The dedup process must keep track of the messages already seen which it does using an immutable Set[Message].
Successive versions of this set are passed from continuation to continuation.

It might be tempting to use a mutable set here and make it a member of Process. That would be a common actor programming style but it is not suitable for processes.

The library assumes Process is immutable - it may not have var members or mutable members. Among other things, this enables the * operator and allows a given graph to be run more than once.

Putting it Together

To complete the example we need to bring the graph and the process definitions together:

object Sample extends SampleGraph with SampleProcesses  {

  type Address =
  val Dedup = dedup(urls)

  // commit the remaining types and processes here ...

  val procmap = run(graph, Supervisor)

  println(s"Started ${procmap.size} processes!")

This fills in the Address type and creates a Dedup process whose input and output will be given the urls label. (The remaining types and processes are omitted for brevity.)

The run(graph, Supervisor) method puts everything in motion. A network of sites connected by communication channels is created that mirrors the passed graph. The corresponding process from the graph is executed at each site.

The given Supervisor process is also executed. It is connected to the prefined errors port of all the other processes and receives messages on errors or process termination.

The run method returns a map of processes to the sites at which they are executing.


A typed actor library. There are better ideas now, see instead.






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