[Déjà Fu is] A martial art in which the user's limbs move in time as well as space, […] It is best described as "the feeling that you have been kicked in the head this way before"
-- Terry Pratchett, Thief of Time
Have you ever written a concurrent Haskell program and then, Heaven forbid, wanted to test it? Testing concurrency is normally a hard problem, because of the nondeterminism of scheduling: you can run your program ten times and get ten different results if you're unlucky.
There is a solution! Through these libraries, you can write concurrent programs and test them deterministically. By abstracting out the actual implementation of concurrency through a typeclass, an alternative implementation can be used for testing, allowing the systematic exploration of the possible results of your program.
Table of Contents
- Concurrent Programming
|async-dejafu [docs] [hackage]||0.1.3.0||Authors||Run MonadConc operations asynchronously and wait for their results.|
|concurrency [docs] [hackage]||220.127.116.11||Authors||Typeclasses, functions, and data types for concurrency and STM.|
|dejafu [docs] [hackage]||0.5.0.0||Testers||Systematic testing for Haskell concurrency.|
|hunit-dejafu [docs] [hackage]||0.4.0.0||Testers||Deja Fu support for the HUnit test framework.|
|tasty-dejafu [docs] [hackage]||0.4.0.0||Testers||Deja Fu support for the Tasty test framework.|
Each package has its own README in its subdirectory.
There is also dejafu-tests, the test suite for dejafu. This is in a separate package due to Cabal being bad with test suite transitive dependencies.
You should read Parallel and Concurrent Programming in Haskell, by Simon Marlow. It's very good, and the API of the async-dejafu and concurrency packages is intentionally kept very similar to the standard packages, so all the knowledge transfers.
The wonderful async package by Simon Marlow greatly eases the difficulty of writing programs which merely need to perform some asynchronous IO. The async-dejafu package provides an almost-total reimplementation of async, using the abstractions provided by concurrency.
For example, assuming a suitable
getURL function, to fetch the
contents of two web pages at the same time:
withAsync (getURL url1) $ \a1 -> do withAsync (getURL url2) $ \a2 -> do page1 <- wait a1 page2 <- wait a2 -- ...
withAsync function starts an operation in a separate thread, and
kills it if the inner action finishes before it completes.
Another example, this time waiting for any of a number of web pages to download, and cancelling the others as soon as one completes:
let download url = do res <- getURL url pure (url, res) downloads <- mapM (async . download) urls (url, res) <- waitAnyCancel downloads printf "%s was first (%d bytes)\n" url (B.length res)
async function starts an operation in another thread but, unlike
withAsync takes no inner action to execute: the programmer needs to
make sure the computation is waited for or cancelled as appropriate.
Threads and MVars
The fundamental unit of concurrency is the thread, and the most basic
communication mechanism is the
main = do var <- newEmptyMVar fork $ putMVar var 'x' fork $ putMVar var 'y' r <- takeMVar m print r
fork function starts executing a
MonadConc action in a
separate thread, and
putMVar are used to communicate
newEmptyMVar just makes an
MVar with nothing in it). This
will either print
'y', depending on which of the two
On top of the simple
MVar, we can build more complicated concurrent
data structures, like channels. A collection of these are provided in
the concurrency package.
If a thread attempts to read from an
MVar which is never written to,
or write to an
MVar which is never read from, it blocks.
Software Transactional Memory
Software transactional memory (STM) simplifies stateful concurrent
programming by allowing complex atomic state operations. Whereas only
MVar can be modified atomically at a time, any number of
can be. STM is normally provided by the stm package, but the
concurrency package exposes it directly.
For example, we can swap the values of two variables, and read them in another thread:
main = do var1 <- newTVar 'x' var2 <- newTVar 'y' fork . atomically $ do a <- readTVar var1 b <- readTVar var2 writeTVar var2 a writeTVar var1 b a <- atomically $ readTVar var1 b <- atomically $ readTVar var2 print (a, b)
Even though the reads and writes appear to be done in multiple steps
inside the forked thread, the entire transaction is executed in a
single step, by the
atomically function. This means that the main
thread will observe the values
('x', 'y') or
('y', 'x'), it can
('x', 'x') as naive
MVar implementation would.
Relaxed Memory and CRefs
There is a third type of communication primitive, the
CRef (known in
normal Haskell as the
IORef). These do not impose synchronisation,
and so the behaviour of concurrent reads and writes depends on the
memory model of the underlying processor.
crefs = do r1 <- newCRef False r2 <- newCRef False x <- spawn $ writeCRef r1 True >> readCRef r2 y <- spawn $ writeCRef r2 True >> readCRef r1 (,) <$> readMVar x <*> readMVar y
spawn forks a thread and gives an
MVar which can be read from
to get the return value. Under a sequentially consistent memory model,
there are three possible results:
(True, False), and
(False, True). Under the relaxed memory model of modern processors,
(False, False) is also possible. Relaxed memory models
allow for reads and writes to be re-ordered between threads.
For testing, three memory models are supported (with the default being TSO):
Sequential Consistency: A program behaves as a simple interleaving of the actions in different threads. When a
CRefis written to, that write is immediately visible to all threads.
Total Store Order (TSO): Each thread has a write buffer. A thread sees its writes immediately, but other threads will only see writes when they are committed, which may happen later. Writes are committed in the same order that they are created.
Partial Store Order (PSO): Each
CRefhas a write buffer. A thread sees its writes immediately, but other threads will only see writes when they are committed, which may happen later. Writes to different
CRefsare not necessarily committed in the same order that they are created.
If you just write your concurrent program using the
MonadSTM typeclasses (maybe with
MonadIO if you need
well), then it is testable with dejafu!
Testing is similar to unit testing, the programmer produces a self-contained monadic action to execute. It is run under many schedules, and the results gathered into a list. This is a little different from normal unit testing, where you just return "true" or "false", here you have many results, and need to decide if the collection is valid or not.
For the simple cases, where you just want to check something is
deterministic and doesn't deadlock, there is a handy
function. For example:
example = do a <- newEmptyMVar b <- newEmptyMVar c <- newMVar 0 let lock m = putMVar m () let unlock = takeMVar j1 <- spawn $ lock a >> lock b >> modifyMVar_ c (return . succ) >> unlock b >> unlock a j2 <- spawn $ lock b >> lock a >> modifyMVar_ c (return . pred) >> unlock a >> unlock b takeMVar j1 takeMVar j2 takeMVar c
The correct result is 0, as it starts out as 0 and is incremented and decremented by threads 1 and 2, respectively. However, note the order of acquisition of the locks in the two threads. If thread 2 pre-empts thread 1 between the acquisition of the locks (or if thread 1 pre-empts thread 2), a deadlock situation will arise, as thread 1 will have lock a and be waiting on b, and thread 2 will have b and be waiting on a.
Here is what
autocheck has to say about it:
> autocheck example [fail] Never Deadlocks (checked: 5) [deadlock] S0------------S1-P2--S1- [pass] No Exceptions (checked: 12) [fail] Consistent Result (checked: 11) 0 S0------------S2-----------------S1-----------------S0---- [deadlock] S0------------S1-P2--S1- False
It identifies the deadlock, and also the possible results the computation can produce, and displays a simplified trace leading to each failing outcome. The traces contain thread numbers, which the programmer can give a thread a name when forking. It also returns false as there are test failures.
Note that if your test case does
IO will be executed a lot
of times. It needs to be deterministic enough to not invalidate the
results of testing. That may seem a burden, but it's a requirement of
any form of testing.
As a general rule of thumb, to convert some existing code to work with dejafu:
- Depend on "concurrency".
MonadConc m => m a
MonadSTM stm => stm a
- Parameterise all the types by the monad:
CRef m, etc
- Fix the type errors.
Bug reports, pull requests, and comments are very welcome!
Feel free to contact me on GitHub, through IRC (#haskell on freenode), or email (email@example.com).
These libraries wouldn't be possible without prior research, which I mention in the documentation. Haddock comments get the full citation, whereas in-line comments just get the shortened name:
[BPOR] Bounded partial-order reduction, K. Coons, M. Musuvathi, and K. McKinley (2013) http://research.microsoft.com/pubs/202164/bpor-oopsla-2013.pdf
[RDPOR] Dynamic Partial Order Reduction for Relaxed Memory Models, N. Zhang, M. Kusano, and C. Wang (2015) http://www.faculty.ece.vt.edu/chaowang/pubDOC/ZhangKW15.pdf
[Empirical] Concurrency Testing Using Schedule Bounding: an Empirical Study, P. Thompson, A. Donaldson, and A. Betts (2014) http://www.doc.ic.ac.uk/~afd/homepages/papers/pdfs/2014/PPoPP.pdf
[RMMVerification] On the Verification of Programs on Relaxed Memory Models, A. Linden (2014) https://orbi.ulg.ac.be/bitstream/2268/158670/1/thesis.pdf
There are also a couple of papers on dejafu itself:
Déjà Fu: A Concurrency Testing Library for Haskell, M. Walker and C. Runciman (2015) https://www.barrucadu.co.uk/publications/dejafu-hs15.pdf
This details dejafu-0.1, and was presented at the 2015 Haskell Symposium.
Déjà Fu: A Concurrency Testing Library for Haskell, M. Walker and C. Runciman (2016) https://www.barrucadu.co.uk/publications/YCS-2016-503.pdf
This is a more in-depth technical report, written between the dejafu-0.2 and dejafu-0.3 releases.