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Funflow is a library and tools to compose and run computational workflows. A workflow is a computation built up of multiple steps. These steps are then wired together into a larger computation. Outputs from previous steps can form inputs to subsequent steps.

For more in-depth context, see the Funflow announcement.


  • Funflow allows for seamless composition of multiple types of computation:

    • Pure functions (of type a -> b)
    • IO actions (of type a -> IO b)
    • External computations. External computations are executed outside of Funflow. They allow Funflow to invoke computations from any language.
    • User-defined effects. Funflow is extensible with user-defined effects. By specifying different interpreters for such effects, you can easily test Funflow in a mock environment.
  • Funflow is designed to be integrated into your application. Flows can be executed inside your Haskell program, even where they involve external computations which are run by other processes. Funflow's support for user-defined effects lets you extend the grammar of a workflow with your own domain specific applications.

  • Funflow provides very powerful caching for steps.

    • Funflow's caching is based around the content store. This stores all artifacts according to the hash of the inputs and computations which produced them. Funflow uses this to know when exactly a computation must be rerun and when previous results can be re-used. [2]

    • The content store also acts as a CAS system. This means that, if multiple inputs produce the same output, that output will be stored only once on disk (and subsequent computations will not be rerun).

      This is particularly useful for build systems, where if you change your input in such a way that the ouptut is not altered, there is no need to run the rest of the computation again.

    • Content store caching is used by default for all external steps, and can be enabled for internal computations by providing suitable serialisation/deserialisation functions.

  • Failure handling. In a long-running workflow, failures are inevitable. Funflow has support for handling failures inside workflows, and for resuming workflows from the last successful point once some external error has been corrected.

  • Sequential and parallel execution. Funflow executes sections of workflows in parallel where this is possible, and handles sequential execution where tasks have dependencies.

  • Task distribution. External steps can be serialised and run remotely. Funflow includes a number of central coordinators which handle distributing steps among multiple machines:

    • An in-memory coordinator, useful for single-process computation.
    • A Redis based coordinator.
    • A SQLite coordinator, which uses a SQLite database as a shared location for task distribution.
  • Support for distributing complete workflows, rather than just individual steps. The funflow-jobs package provides support for distributing full workflows using Redis. This has some extra constraints compared to distributing external tasks, since Haskell functions are in general not serialisable.

  • Safe and powerful workflow composition. Funflow takes advantage of Haskell's type system to ensure flows are composed in a safe manner. And since the composition of flows results in a flow, it's easy to include one complex workflow as part of another.

Defining a workflow

Here's an example of a simple workflow, defined from basic Haskell functions:

-- | A flow which doubles each element of a list, and then reverses the list
doubleAndReverse :: SimpleFlow [Int] [Int]
doubleAndReverse = mapA (step (*2)) >>> step reverse
The step function takes a Haskell function of type a -> b and lifts it into a SimpleFlow a b. [1]
The mapA combinator applies a flow over a list.
>>> is an arrow combinator which composes two flows sequentially.

You can also use Haskell's arrow notation to compose more complex flows:

-- | Flow which asks for the user's name, then their favourite number.
--   The favourite number will be cached, such that if the user comes back,
--   they will not be asked again.
favouriteNumber = proc () -> do
  name <- stepIO (\_ -> putStrLn "Hi, what's your name?" >> const getLine) -< ()
  fnum <- stepIO' (def { cache = defaultCacherWithIdent 172652 })
                  (\_ -> putStrLn "What's your favourite number?" >> const getLine) -< name
  returnA -< (name, fnum)

In this flow, you can also see the use of Funflow's caching.

stepIO lifts an IO action into a flow.
stepIO' is a more complex variant of stepIO that allow for configuring things such as the caching properties.

Running a workflow

To run a workflow, use one of the options from Control.Funflow.Exec.Simple . The easiest place to start is with withSimpleLocalRunner. This takes the absolute path to a directory used to host the content store. It returns a function which can be used to run your workflow.

Here's an example of a complete pipeline, along with a runner:

{-# LANGUAGE Arrows #-}
{-# LANGUAGE QuasiQuotes #-}
import Control.Arrow
import Control.Funflow
import Control.Funflow.Exec.Simple (withSimpleLocalRunner)
import Data.Default
import Path

-- | Flow which asks for the user's name, then their favourite number.
--   The favourite number will be cached, such that if the user comes back,
--   they will not be asked again.
favouriteNumber :: SimpleFlow () (String, String)
favouriteNumber = proc () -> do
  name <- stepIO (\_ -> putStrLn "Hi, what's your name?" >> getLine) -< ()
  -- We enable caching for this step. The default cacher uses 'Store' instances
  -- to provide serialisation/deserialisation. The ident is used to ensure that
  -- multiple different steps with the same input do not resolve to the same
  -- cache item.
  fnum <- stepIO' (def { cache = defaultCacherWithIdent 172652 })
                  (\_ -> putStrLn "What's your favourite number?" >> getLine) -< name
  returnA -< (name, fnum)

-- | Runs the 'favourite number' flow in IO.
runFavouriteNumber :: IO ()
runFavouriteNumber = do
  -- Use /tmp/funflow as the path to our content store.
  res <- withSimpleLocalRunner [absdir|/tmp/funflow|] $ \run ->
    -- 'run' takes the flow and the initial input. Since this flow has an input type of '()',
    -- this is what we provide.
    run favouriteNumber ()
  case res of
    Left err -> putStrLn $ "Something went wrong: " ++ show err
    Right (name, num) -> putStrLn $ "Hi, " ++ name ++ ", your favourite number is " ++ num

Defining external tasks

Use external tasks to run steps outside of the Haskell process. Fundamentally, an external task will resolve to a command-line call to another application. While this could be done with stepIO, but using external tasks has a few big advantages:

  • External tasks fit in naturally with the content store framework. Both the inputs to the task and the actual definition of the task are used to determine the resultant hash, so that if, say, a script changes, the results will be recomputed. This is hard to achieve with stepIO.
  • External tasks are naturally distributable. When running in a production setting, you're likely to want to distribute tasks among multiple machines. This is not, in general, achievable with IO computations.
  • IO steps are opaque to inspection. External tasks, on the other hand, can be visualised in the workflow graph, and their stdout/stderr streams are captured automatically.

Funflow's current approach to external tasks is heavily based on Docker. Using Docker allows tasks to be self-contained, and adds minimal requirements to the system being used to host Funflow instances (they just need to have docker running).

To use a docker container as an external step, define a function of type a -> Docker.Config, where a is the input type to the flow. At its core, this means:

  • Specifying the docker image (and optionally, image ID) to use. For example, nixos/nix:1.11.14.
  • Specifying the path to the command which will be run within the container.
  • Specifying which inputs (from the content store) are to be mounted within the container.

Here's an example:

myDockerStep :: SimpleFlow CS.Item CS.Item
myDockerStep = docker $ \input -> Docker.Config
  { -- Set the docker image to use for this step
    Docker.image = "nixos/nix"
    -- Optionally, you can define a specific tag to use, to fix the version.
  , Docker.optImageID = Just "1.11.14"
    -- Define how the inputs are mounted into the container. We can either
    -- have a single input, which will be mounted at /input/, or multiple
    -- inputs, which will be mounted as subdirectories inside /input/.
  , Docker.input = Docker.SingleInput input
    -- Command to run inside the container. It's best to use an absolute
    -- path here.
  , Docker.command = "/root/"
    -- Additional arguments to pass to the script being run.
  , Docker.args =
      , "--input_dir", "/input/"
      , "--output_dir", "/output/"

A CS.Item refers to an item within the content store. You can use putInStore, getFromStore, copyFileToStore and similar tools to add and fetch files from the store. This lets you interleave internal and external computations. Here's an example of a more complex flow using both internal and external computation:

-- | This flow takes a string which is assumed to be the source code
--   for a 'C' function. It writes this to a file, then uses two external
--   steps to compile and run the function. The resulting 'stdout' is read
--   in and presented to the user.
compileAndRunC :: SimpleFlow String String
compileAndRunC = proc csrc -> do
    cInput <- writeString -< (csrc, [relfile|out.c|])
    scriptInput <- writeExecutableString -< (compileScript, [relfile||])
    compiled <- compileDocker -< (cInput, scriptInput)
    result <- runDocker -< compiled
    readString_ -< result
    compileScript =
      " #!/usr/bin/env nix-shell \n\
      \ #! nix-shell -i bash -p gcc \n\
      \ gcc -o $2 $1 "

    compileDocker = docker $ \(cInput, scriptInput) -> Docker.Config
      { Docker.image = "nixos/nix"
      , Docker.optImageID = Just "1.11.14"
      , Docker.input = Docker.MultiInput
        $ Map.fromList [ ("script", CS.contentItem scriptInput)
                      , ("data", CS.contentItem cInput)
      , Docker.command = "/input/script/"
      , Docker.args = ["/input/data/out.c", "/output/out"]
    runDocker = docker $ \input -> Docker.Config
      { Docker.image = "nixos/nix"
      , Docker.optImageID = Just "1.11.14"
      , Docker.input = Docker.SingleInput input
      , Docker.command = "bash -c"
      , Docker.args = ["\"/input/out > /output/out\""]

Running on multiple machines

To run on multiple machines, you need to use one of the distributable coordinators - either the Redis coordinator or the SQLite coordinator. To do this, you need to:

  1. Start some executors pointed at the coordinator. An executor is a process which reads tasks from the coordinator and executes them.
  2. Run your flow using that coordinator.

The simplest way to run an executor is to use the bundled ffexecutord executable. This can work with either the Redis or SQLite coordinators.

Here's an example of initialising an executor using /tmp/funflow as the content store directory, and /tmp/coordinator.db as our coordinating database:

ffexecutord sqlite /tmp/funflow /tmp/coordinator.db

You then need to run the flow, pointing at this coordinator. To do so, you'll need a slightly more complex function from Control.Funflow.Exec.Simple: runSimpleFlow. You need to give this the correct parameters for the SQLite coordinator:

CS.withStore [absdir|/tmp/funflow|] $ \store -> do
  runFlow SQLite [absfile|/tmp/coordinator.db|] store runNoEffect 123123 flow input

A couple of the parameters here may be confusing:

This is used to handle any user-defined effects in the flow. Since there are none here, you can use runNoEffect.
This is a random integer used in helping to determine the hashes for caching internal steps. It's needed because there might be parts of the environment which Funflow is unaware of but which have an impact on the results of computations, and so should form part of the cache.

User-defined effects

Funflow allows you to extend the possible steps in a flow with your own user-defined effects. Suppose for example you are working on a flow which talks to a REST service offering details of your record collection. Then you might define the following grammar for interacting with it:

-- | Example grammar for dealing with your record collection.
data RecordCollectionAction a b where
  Insert :: DatabaseAction Record ()
  Select :: DatabaseAction Ix (Maybe Record)
  Delete :: DatabaseAction Ix ()

As with external actions, you will note that this is all possible using stepIO. But as with external actions, there are some benefits to defining your own effects:

  • By using effects, you can choose whether details need to be provided at workflow construction or execution time. In the above example, you can define a workflow without knowing where exactly the record collection is being hosted. This is only needed when actually interpreting the workflow.
  • Using effects makes it very easy to test your workflow in a mock environment, by changing the interpreter for your effects.
  • IO actions are opaque to inspection, and so hard to visualise. Providing your own effects, on the other hand, lets you fully visualise what's happening in a workflow.

So far, all of our examples have used the type SimpleFlow a b. SimpleFlow is a type alias for the fully general type Flow:

-- | A workflow taking input of type 'a' and producing output of type 'b'.
--   This workflow may include user-defined effects of type 'eff' and
--   raise exceptions of type 'ex'.
type Flow eff ex a b
type SimpleFlow = Flow NoEffect SomeException

To include the RecordCollectionAction, you can define a new type for your flow:

type MyFlow = Flow RecordCollectionAction SomeException

To run the flow, you must also provide an interpreter for your effects. This is a function of type forall a b. eff a b -> AsyncA IO a b. Here's an example of an interpreter for the RecordCollectionAction type which just logs what's happening:

runRecordCollectionAction :: RecordCollectionAction a b -> AsyncA IO a b
runRecordCollectionAction Insert = AsyncA $ \rec -> putStrLn $ "Inserting " ++ show rec
runRecordCollectionAction Select = AsyncA $ \ix -> do
  putStrLn $ "Selecting " ++ show ix
  -- Fail to find anything  in this mock interpreter
  return Nothing
runRecordCollectionAction Delete = AsyncA $ \ix -> putStrLn $ "Deleting " ++ show ix

Having defined the interpreter, you can use it in place of runNoEffect, as in the example above:

CS.withStore [absdir|/tmp/funflow|] $ \store -> do
  runFlow SQLite [absfile|/tmp/coordinator.db|] store runRecordCollectionAction 123123 flow input
[1]Technically, it lifts it to the more general type Flow eff ex a b, but that full generality is not needed here.
[2]This is heavily inspired by the nix package manager.