Recently I was working on some code using GHC Haskell’s STM (short for Software Transactional Memory) functionality. STM gives a programmer mutable variables (so-called TVars), functions to work on such variables in some context (e.g., writeTVar :: TVar a -> a -> STM ()), and a way to run a program working with such variables transactionally (atomically :: STM a -> IO a). The transactional aspect is what makes STM so powerful: when multiple threads of execution mutate the same TVars concurrently, a transaction will be automatically aborted and restarted by the runtime upon conflicts. As such, we can construct programs working with one or more mutable variables, without the need for explicit locking.

The above only scratches the surface of what’s possible with STM. To learn more, see chapter 28 of Real World Haskell.

STM makes writing concurrent code with shared mutable state easier, but still leaves room for mistakes. Code with mutable state, especially with concurrent access to shared mutable state, is more difficult to test than pure computations. Using DejaFu, we can write tests of concurrent code which will simulate various schedulings of application threads and validate the result, e.g., by ensuring a program yields the same value no matter the order in which threads were scheduled, or by ensuring some invariants were maintained at every observable point in time. In this post, I want to show two patterns I adopted when writing concurrent code, using STM, with the ability to test it using DejaFu. If you’re not familiar with the latter, you can find more information on its website.

First, some boilerplate so this post can be executed as a Literal Haskell script:

{- cabal: 
  , base ^>=
  , concurrency ^>=
  , exceptions ^>=0.10.7
  , tasty ^>=1.4.3
  , tasty-dejafu ^>=
build-tool-depends: markdown-unlit:markdown-unlit
ghc-options: -threaded -pgmL markdown-unlit -Wall -Werror
default-language: Haskell2010

To test code using DejaFu, it can’t be written using, e.g., readTVar :: TVar a -> STM a, but instead needs to use the abstractions provided by the concurrency package, e.g., readTVar :: MonadSTM stm => TVar stm a -> stm a. Notice how the TVar type is parametrized by the monad in which it’ll be used. This allows for use in the traditional STM monad (which is an instance of MonadSTM), as well as in emulations of it provided by DejaFu.

Furthermore, we’ll use the exceptions package to throw exceptions while checking invariants, and the tasty test framework, including DejaFu integration for it brought by tasty-dejafu to define and run some tests.

All good Haskell code starts with some LANGUAGE pragmas and some imports, so let’s get those out of the way:

{-# LANGUAGE QuantifiedConstraints #-}
{-# LANGUAGE RankNTypes #-}
{-# LANGUAGE StandaloneDeriving #-}
{-# LANGUAGE UndecidableInstances #-}

import Control.Concurrent.Classy.Async (concurrently_, wait, withAsync)
import Control.Concurrent.Classy.STM (TVar, readTVar, modifyTVar')
import Control.Monad (unless)
import Control.Monad.Catch (Exception(..), MonadThrow, throwM)
import Control.Monad.Conc.Class (MonadConc, STM, atomically, newTVarConc)
import Control.Monad.STM.Class (MonadSTM)
import Data.Functor.Identity (Identity(..))
import Test.Tasty (defaultMain, testGroup)
import Test.Tasty.DejaFu (Basic, Program, WithSetup, inspectTVar, registerInvariant, testAuto, withSetup)

State and Mutations

In the (admittedly contrived) example used throughout this post, our program keeps track of two Int values, and provides two actions to mutate them: one to add an Int which will add the given value to the first mutable variable and substract it from the second, and another to substract an Int which will, as you may have guessed, substract the given value from the first mutable variable and add it to the second. Hence, there’s an invariant that should be maintained at all times: the sum of both mutable variables should be 0.

State Type

In regular STM code, we’d use a datatype like the following to maintain the state:

data Store = Store
  { storeA :: TVar Int,
    storeB :: TVar Int

This works, but as mentioned before, we need to use the types provided by the concurrency package to be able to test our code using DejaFu. Hence, we need to use its TVar stm a type (family) instead:

data Store stm = Store
  { storeA :: TVar stm Int,
    storeB :: TVar stm Int

This goes in the right direction, and indeed, we could provide implementations for add and sub and test those. However, there are still some shortcomings:

  • When checking invariants, writing code doing so in a pure style (modulo throwing exceptions) simplifies the implementation a lot, instead of littering the validation code with readTVar calls and monadic binds. This, in turn, makes testing of the invariants validation code easier as well.

  • DejaFu can be used to validate a concurrent computation always yields the same result, which imposes an Eq constraint on the result type. We often want to check the shared mutable state is the same no matter which scheduling of mutators happened, but a data type with TVars inside can’t have an Eq instance.

So, we build Store such that it’s paramterized over a cell type which, for the mutable version of Store will be some TVar, whilst cell can also be Identity in which case the resulting type is immutable:

data Store' cell = Store'
  { storeA :: cell Int,
    storeB :: cell Int

deriving instance (forall a. Show a => Show (cell a)) => Show (Store' cell)
deriving instance (forall a. Eq a => Eq (cell a)) => Eq (Store' cell)

type FrozenStore = Store' Identity
type Store stm = Store' (TVar stm)

As in the above, we can derive a Show and Eq instance for Store' cell as long as there’s a Show and Eq instance for cell a, for all possible as. This is not the case for Store (e.g., there’s no Show instance for TVar stm a), but it is for FrozenStore.


We can now define a function to create a new Store, and implement add and sub:

newStoreConc :: MonadConc m => m (Store (STM m))
newStoreConc =
    <$> newTVarConc 0
    <*> newTVarConc 0

addSTM :: MonadSTM stm => Int -> Store stm -> stm ()
addSTM i store = do
  modifyTVar' (storeA store) (\a -> a + i)
  modifyTVar' (storeB store) (\b -> b - i)

add :: MonadConc m => Int -> Store (STM m) -> m ()
add i store = atomically (addSTM i store)

subSTM :: MonadSTM stm => Int -> Store stm -> stm ()
subSTM i store = do
  modifyTVar' (storeA store) (\a -> a - i)
  modifyTVar' (storeB store) (\b -> b - i)

sub :: MonadConc m => Int -> Store (STM m) -> m ()
sub i store = atomically (subSTM i store)

(Did you spot the bug?)

If the types in the code above are a bit daunting, when specialized to IO we’d get

import Control.Concurrent.STM (STM, TVar)

type Store = Store' TVar

-- | 'newStoreIO', so you want.
newStoreConc :: IO Store

addSTM :: Int -> Store -> STM ()
add :: Int -> Store -> IO ()

subSTM :: Int -> Store -> STM ()
sub :: Int -> Store -> IO ()


The code above works on the mutable version of Store'. However, we went through this cell hassle in order to get an immutable version of a Store' for use in invariant checks and consistency tests. The following functions can be defined to turn a Store stm into an stm FrozenStore:

snapshotWith ::
  Applicative m =>
  (forall a. cell a -> m a) ->
  Store' cell ->
  m FrozenStore
snapshotWith readCell store =
    <$> (Identity <$> readCell (storeA store))
    <*> (Identity <$> readCell (storeB store))

snapshotSTM :: MonadSTM stm => Store stm -> stm FrozenStore
snapshotSTM = snapshotWith readTVar

snapshot :: MonadConc m => Store (STM m) -> m FrozenStore
snapshot = atomically . snapshotSTM

The snapshotWith function is where the magic happens. It takes an action which, in some Applicative context, can read the value of a cell, and can then turn a Store' cell into a FrozenStore (where cell is Identity) within said context. snapshotSTM is a utility to work with a MonadSTM stm => Store stm (i.e., snapshotting with readTVar), and snapshot wraps snapshotSTM to run in a MonadConc.

We need snapshotWith, and not only snapshotSTM, for use in DejaFu’s Invariant monad later.


Finally, we can define checkInvariants, which ensures invariants are met in a FrozenStore. If not, it throws an InvariantViolation exception:

newtype InvariantViolation = InvariantViolation String
  deriving (Show)

instance Exception InvariantViolation where
  displayException (InvariantViolation msg) = "Invariant violation: " <> msg

invariantViolation :: MonadThrow m => String -> m a
invariantViolation = throwM . InvariantViolation

checkInvariants :: MonadThrow m => FrozenStore -> m ()
checkInvariants store = do
  let Identity a = storeA store
      Identity b = storeB store

  unless (a + b == 0) $
    invariantViolation ("a + b /= 0, a = " <> show a <> ", b = " <> show b)

There are many ways to implement checkInvariants, and the above is only one of them. Some alternatives include

  • Making checkInvariants pure, returning, e.g., Either String () or a Bool value, and wrapping it later to turn a non-successful result into an exception (as required by DejaFu).

  • Using the Validation monad to capture all invariant violations instead of only the first one, and either returning them or throwing them at the end.

Concurrent Access

With the above code in place, here’s some code using a Store, mutating it concurrently in various ways:

-- | Add two values to a `Store`, concurrently.
concurrentAdd :: MonadConc m => Int -> Int -> Store (STM m) -> m ()
concurrentAdd i j store =
    (add i store)
    (add j store)

-- | Add two values to a `Store`, concurrently. With a bug.
brokenConcurrentAdd :: MonadConc m => Int -> Int -> Store (STM m) -> m ()
brokenConcurrentAdd i j store =
  withAsync (add i store) $ \a ->
    withAsync (add j store) $ \_ ->
      wait a

-- | Add and substract a value from a `Store`, concurrently.
concurrentAddAndSub :: MonadConc m => Int -> Int -> Store (STM m) -> m ()
concurrentAddAndSub i j store =
    (add i store)
    (sub j store)


Finally, we can write some tests for the above code. First, withStore is a helper to create a Store' (within the appropriate DejaFu MonadConc environment), and register an invariant check. The latter will create a FrozenStore from the mutable one using snapshotWith inspectTVar. inspectTVar is a function in DejaFu’s Invariant monad which allows to peek inside the contents of a TVar in the model:

withStore ::
  Monad m =>
  (Store (STM (Program Basic m)) -> Program Basic m a) ->
  Program (WithSetup (Store (STM (Program Basic m)))) m a
withStore = withSetup $ do
  store <- newStoreConc

  registerInvariant $
    checkInvariants =<< snapshotWith inspectTVar store

  pure store

Then, the tests. Each test runs the default DejaFu validations calling one of the concurrent functions on a provisioned Store', and returns a snapshot of the store. DejaFu will then check whether every interleaving of concurrent actions results in the same value/state.

main :: IO ()
main = defaultMain $ testGroup "store-app" [
  testAuto "concurrentAdd" $ withStore $ \store -> do
    concurrentAdd 10 20 store

    snapshot store,

  testAuto "brokenConcurrentAdd" $ withStore $ \store -> do
    brokenConcurrentAdd 10 20 store

    snapshot store,

  testAuto "concurrentAddAndSub" $ withStore $ \store -> do
    concurrentAddAndSub 10 20 store

    snapshot store

The first test succeeds, as desired:

    Never Deadlocks:   OK (0.13s)
    No Exceptions:     OK (0.02s)
    Consistent Result: OK (0.02s)

The second one, however, fails, as expected:

    Never Deadlocks:   OK
    No Exceptions:     OK
    Consistent Result: FAIL
        Store' {storeA = Identity 30, storeB = Identity (-30)} S0-----------S1---------S0---S2---------S0-------------

        Store' {storeA = Identity 10, storeB = Identity (-10)} S0-----------S1---------S0---S2--P0---S2---S0-----------

      Use -p '/brokenConcurrentAdd.Consistent Result/' to rerun this test only.

Indeed, brokenConcurrentAdd fails to wait for the inner Async. Hence, there are two possible interleavings: one where the second add succeeds before the thread running it is killed (the first interleaving above, where storeA is 30), or another one where the thread is killed before its add action has completed, and the snapshotted state has 10 in storeA (the second interleaving above).

The last test also fails, in this case not because some concurrency bug as above, but because the invariants we expect our state to adhere to are violated:

    Never Deadlocks:   OK
    No Exceptions:     OK
    Consistent Result: FAIL
        [invariant failure] S0-------S1--------S2---

        [invariant failure] S0-------S2---

      Use -p '/concurrentAddAndSub.Consistent Result/' to rerun this test only.

2 out of 9 tests failed (0.19s)

Indeed, once the second thread (which runs sub j store) ran, no matter whether the first thread (running add i store) ran before or not, the a + b == 0 invariant no longer holds. Indeed, there’s a bug in the subSTM function: instead of substracting i from storeB, it should add it.

Sadly, at this point tasty-dejafu won’t show which exception was raised while checking invariants, and only displays invariant failure. I opened an issue to maybe improve this.


STM allows for safe concurrent access to shared mutable state with fine-grained locking. However, when multiple variables are at play, defining invariants between their values and ensuring these invariants aren’t breached in some interleaving of concurrent mutations is important. The DejaFu library can help to achieve this, so building your code on the concurrency abstractions from the start allows to write all kinds of tests later.

Future Work

  • The trick used above to use Identity as the cell type allows for immutable versions of the data type bundling all variables. However, the Identity wrapper is a bit of a burden when dissecting FrozenStore. It’d be nice if cell a could become a plain a removing the need to unpack the Identity constructor. I have a hunch this might be possible by using some closed type family, but didn’t pursue this further yet.

  • Implementing snapshotWith is quite boring. I imagine it’s possible to have a generic version of it using GHC.Generics.

  • Using the concurrency abstraction brings a MonadConc or MonadSTM constraint and hence a dictionary lookup at runtime. I’d love to see some benchmarks where a library is defined in terms of MonadConc and MonadSTM, but every function is INLINEABLE and/or SPECIALIZEd to Control.Concurrent.STM.STM and IO so there’s, ideally, no performance hit for regular application consumers of the library vs. using plain STM and IO in the library code. [Update: here’s the answer]