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ยท 16 min read

IOSim on Hackageโ€‹

The IOG Networking Team is pleased to announce that we published io-sim, io-classes, si-timers, strict-stm, strict-mvar and io-classes-mtl on Hackage. These are tools without which we could not imagine writing a complex distributed system like Cardano.

These packages support our goal of using the same code to run in production and simulation, what greatly increases the reliability and quality of the final system. io-sim and its ecosystem is designed to let write a simulation environment which provides provided things usually provided by an operating system like networking stack or disk IO and develop as well as implement & model complex applications/systems.

For developing a robust system one needs a proper testing framework which allows one to model the key characteristics of the system. To achieve this goal we needed to create an abstraction that captures the key aspects of the Haskell runtime and operating system environment for distributed systems. The Cardano network stack is a highly concurrent system, and as a network application, it needs to deal with time: there are all sorts of timeouts that guard resource usage: inactivity timeouts, message timeouts, or an application level TCP's WAIT_TIMEOUT among others. The tools which we provide permitted us to capture issues related to timing (which abound in network programming) which, in production, would be extremely rare (things like simultaneous TCP open or critical race conditions) and ensure that we can test (in the simulation) these scenarios. Recently we caught a bug in simultaneous TCP open when one side of the connection crashed - a corner case of a corner case, that's how effective is the combination of quickcheck style property-based testing & simulation!


io-classes allow testing production code under simulation where one can mock services usually provided by the operating system: socket API or disk IO (both will land on Hackage at some point too). Our design principle was to closely follow the base API but also provide extensions that are packaged as separate libraries: si-timers, strict-stm, io-classes-mtl.

io-classes support MVar's and in the future it will also support IOVars. The IO instance of MonadMVar is using GHC's MVar, while IOSim instance is based on a TVar specific to IOSim. MVars, whether native or simulated, provide fairness but don't provide compositionality (like TVars do via STM). Here's a complete list of currently supported APIs (more to come in the future, if you need more we are happy to accept contributions!). The list includes APIs present in core packages like: base, stm, async or time:

  • MVar API;
  • stm API: all monadic operations with support for TVars, TMVars, TQueues, TBQueues, TArrays, TSems, TChans;
  • fork API: forkIO, forkOn, forkWithUnmask, killThread, yield;
  • thread API: myThreadId, labelThread, threadStatus;
  • synchronous and asynchronous exceptions API: throw, throwTo, catch, catchJust, try, tryJust, handle, handleJust, catches; and the high-level bracket, finally, onException and bracketOnException;
  • masking API: mask, mask_, uninterruptibleMask, uninterruptibleMask_
  • masking state API: getMaskingState, interruptible, allowInterrupt
  • evaluate
  • async API;
  • event trace API: traceEventIO, traceEventMarkerIO;
  • ST support: stToIO;
  • time API: getCurrentTime, getMonotonicTimeNSec; (more in si-timers)
  • timeout API: registerDelay, timeout ; (more in si-timers)
  • delays: threadDelay; (more in si-timers)
  • although this does not strictly belong to this list, we also support MonadFix, which at times is useful for both IO and IOSim!

and non-standard APIs:

  • say (a print like function)
  • labelling TVars & friends (labelTVar)
  • tracing committed values to TVars and TMVars, inspection of TVars
  • race exploration of IOSimPOR

We plan to add support for IORefs.

There's even more but packaged in separate packages, keep reading!


The si-timers (SI) package was designed with two principles in mind:

  • provide API which is consistent with timers package and thus is using SI units (seconds) rather than Int represented milliseconds like base does;
  • provide a safe API for 32-bit systems.

On a 32-bit system an Int can represent up to ~35 minutes, which often is too small for longer delays.

si-timers also comes with a non-standard polyfill for timers API. The low level base timers API (e.g. registerTimeout & friends) is not available on all the systems (currently not available on Windows or GHCJS). Although GHC native timers are performant (when available), our polyfill is only good enough to develop sub-second timeouts. It's not performant enough on all platforms for sub-millisecond timeouts as it uses concurrency (on Windows or GHCJS) and thus relies on GHC scheduler. In the future, we are planning to release an implementation of timeout API which is performant across all platforms (which is part of network-mux library right now). There's also an interesting longer GHC project to provide performant native timeouts based on io-uring (Linux) and io-completion ports (Windows).


io-sim package provides a pure (free) monad that has instances for type classes defined in io-classes and si-timers. In particular, io-sim supports threads (via low-level forkIO as well as async package interface), deadlock detection, asynchronous exceptions, software transaction memory (STM), lifting ST computations, mfix, and various APIs that support tracing and inspection.

There are many distinctive features of io-sim. It supports time domains, which means you can emulate isolated services which only communicate through a simulation of some IPC interface. The time is discrete and only advances when all threads are blocked: either because a thread explicitly called threadDelay, or the action it runs blocked one an STM transaction. If a thread registered a timeout it will be unblocked at a specific time. Because the application controls time, this allows testing different interleavings using the usual QuickCheck techniques. Here we come to another distinctive feature of io-sim. John Hughes built an interpreter, called IOSimPOR, which dynamically detects races in a given time slot. The IOSimPOR interpreter is then able to execute different schedules which revert the order of evaluation of races and recursively discover & revert new races. The implementation follows a partial-order reduction algorithm to limit the schedule exploration. IOSimPOR is still considered an experimental feature, if you encounter a bug we'd really like to hear about it.


The strict-stm package provides strict versions of TVar, TMVar & friends. The StrictTVar also provides a way to embed invariants, which allows one to hook nothunks api and verify in a testing environment that one never commit thunks. strict-stm and nothunks were designed after a long development period of Cardano when besides correctness and asymptotic behaviour also performance started to be important. In a relatively short time, it allowed to squash many memory leaks allowing us to release the first version of the current Cardano implementation.


The package provides strict version of MVar. Like strict-stm it will support a way to embed invariants.


The io-classes contain instances only for ReaderT monad which are uncontroversial. Instances for other monad transformers are packaged in io-classes-mtl package. These are experimental, not properly tested, and also not extensively used by us at this stage. Some of the instances are also novel so please inspect the implementation to get familiar with them before you start relying on them.

As a design principle, we tried to be compatible with the exceptions package especially when it comes to MonadThrow instances of monad transformers.

For MonadSTM instances we also transform the associated STM monad, e.g. for StateT s IO the STM (StateT s IO) monad is StateT s (STM IO). This means you can interleave StateT operations with STM.

We don't provide MonadAsync instances for transformer stacks (except of ReaderT). One could follow lifted-async or be even more general and allow monoidal join of the asynchronously computed states. However, this seems to extravagant and might lead to subtle concurrency bugs.


There are many example usages of the io-sim which can be used for inspiration. Here we point to just a few which we developed as part of our testing efforts.

STM test suiteโ€‹

In the Test.Control.Monad.STM module we generate valid STM terms and evaluate them in both IO and IOSim. This follows the Composable memory transactions paper.

Terms of this mini-language are a GADT:

data Term (t :: Type) where

Return :: Expr t -> Term t
Throw :: Expr a -> Term t
Catch :: Term t -> Term t -> Term t
Retry :: Term t

ReadTVar :: Name (TyVar t) -> Term t
WriteTVar :: Name (TyVar t) -> Expr t -> Term TyUnit
NewTVar :: Expr t -> Term (TyVar t)

-- | This is the ordinary monad bind for STM terms.
Bind :: Term a -> Name a -> Term t -> Term t
OrElse :: Term t -> Term t -> Term t

-- | expressions which can appear in `Terms`
data Expr (t :: Type) where

ExprUnit :: Expr TyUnit
ExprInt :: Int -> Expr TyInt
ExprName :: Name t -> Expr t

Following the rules specified in the Composable Memory Transactions paper we have:

evalTerm :: Env -> Heap -> Allocs -> Term t -> (NfTerm t, Heap, Allocs)

-- | The heap is a mapping of 'Var's to their current values.
newtype Heap = Heap (Map VarId SomeValue)
deriving (Show, Semigroup, Monoid)

-- | The STM semantics uses two heaps, the other one is called the allocations.
type Allocs = Heap

-- | The normal form for a 'Term' after execution.
data NfTerm (t :: Type) where

NfReturn :: Value t -> NfTerm t
NfThrow :: Value a -> NfTerm t
NfRetry :: NfTerm t

See evalTerm.

Each term can be executed in STM m monad:

execTerm :: (MonadSTM m, MonadCatch (STM m))
=> ExecEnv m
-> Term t
-> STM m (ExecValue m t)

See execTerm.

execTerm and evalTerm all together give three ways of executing a Term:

  • via the spec (as implemented by evalTerm)
  • in IO (using stm package)
  • in IOSim (using the built-in stm support)

The last two are only possible because execTerm is written in terms of MonadSTM m and MonadCatch (STM m) are available from io-classes.

This gave us confidence that the most tricky operator orElse is implemented correctly.

The Test.Control.Monad.STM module also implements an Arbitrary instance (with a proper shrinker) of Termss. The heart of it is the genTerm function which generates arbitrary expressions of a given type.

Simulated Network Interfacesโ€‹

Cardano must run on various platforms and support different communication bearers, e.g. it communicates via TCP/IP between nodes, but it also exposes an IPC using UNIX sockets. That would be fine if we'd need to only support Linux and MacOS. However, many end users who are running a full wallet are using Windows machines, on which UNIX sockets are not well supported. Windows has its own named pipes API. Although similar, their interface is a bit different than the familiar Berkeley socket interface. However, it's possible to embrace both using the following Snocket API:

-- | Abstract communication interface that can be used by 'Socket' or a named pipe.
-- Snockets are polymorphic over monad, which is useful for testing and/or
-- simulations.
data Snocket m fd addr = Snocket {
getLocalAddr :: fd -> m addr
, getRemoteAddr :: fd -> m addr

, addrFamily :: addr -> AddressFamily addr

-- | Open a file descriptor (socket / named pipe). For named pipes, it is
-- using 'CreateNamedPipe' syscall, for Berkeley sockets 'socket' is used.
, open :: AddressFamily addr -> m fd

-- | A way to create 'fd' to pass to 'connect'. For named pipes, it will
-- use 'CreateFile' syscall. For Berkeley sockets it is the same as 'open'.
-- For named pipes, we need full 'addr' rather than just address family as
-- it is for sockets.
, openToConnect :: addr -> m fd

-- | `connect` is only needed for Berkeley sockets, for named pipes this is
-- no-op.
, connect :: fd -> addr -> m ()
, bind :: fd -> addr -> m ()
, listen :: fd -> m ()

, accept :: fd -> m (Accept m fd addr)

, close :: fd -> m ()

Snocket is parametrised by the monad in which it runs, file descriptor type, and address type. The difference between Berkeley sockets and named pipes lies in the accept call (which will not cover in this blog, see Accept).

There are four ways to construct a Snocket:

  • socketSnocket - which is using the Berkeley socket API (and thus supports both AF_INET, AF_INET6 and AF_UNIX families);
  • localSnocket - a local socket: using AF_UNIX on systems on which Berkeley sockets are available or named pipes on Windows;
  • withSnocket - an implementation of Snocket dedicated for IOSim simulations.

Snocket interface allows us to parametrise the Cardano diffusion layer (Ouroboros.Network.Diffusion.P2P.runM) and either run in IO with Snockets provided by the system calls and at the same time build IOSim simulations which run multiple Cardano diffusion layers. The withSnocket API allows to embed network errors, simulating network delays, TCP simultaneous open (which is not infrequent in the Cardano ecosystem). For an example we test that when accept call errors the application can recover (this turned out to be an issue once we discovered a bug in MacOS kernel, see ref). IOSim allowed us to simulate the buggy behaviour of MacOS and verify that our solution indeed fixes the problem. What is outstanding for the Ouroboros.Network.Diffusion.P2P.runM function is that its entirely described by classes from io-classes and si-timers packages, while it's responsible for:

  • maintaining bidirectional connections with remote peers (which turned out to be more complex than we initially anticipated, which involves a handful of states and transitions);
  • running multiplexed protocol applications: this includes making decisions like from which peer to download a block based on real-time data and executing such a decision via one of the protocols;
  • resolving DNS names;
  • provide safety guarantees (e.g. if one of the protocol errors the connection must be reset);
  • keeping remote nodes honest about their timeliness obligations (i.e. enforcing network timeouts).

Since the architecture is inherently concurrent: each connection multiplexes up to four concurrent mini-protocols in both directions (which makes at least 8 threads (even 16 if we count our current implementation of network protocol pipelining), not counting some other concurrent services. Being able to execute the whole diffusion layer in a deterministic simulation environment which supports race discovery helped us to discover and fix deadlocks and rare bugs which otherwise would be very difficult to debug in real-system.

One code base for production & testingโ€‹

To run the same codebase for production and testing we use one more trick. We use the contra-tracer library. The current implementation of Tracer is more complex (so that it's zero cost abstraction if tracing is not used), but for this blog post we can assume that the Tracer is defined as:

newtype Tracer m a = Tracer { withTracer :: a -> m () }

instance Contravariant (Tracer m) where
contramap f (Tracer g) = Tracer (g . f)

In testing, we can instantiate the tracer with something like Control.Monad.IOSim.traceM. With the help of Control.Monad.IOSim.selectTraceEventsDynamic one can recover the trace and build a quickcheck property that must be satisfied. In this way Tracer is our eye into the underlying state of the system.

In the production environment, a dedicated logging backend is behind the Tracer abstraction.

The contravariant nature of the Tracer is very helpful. If you are testing your component you might have access to the logs it's emitting, but in the real implementation the component might be embedded deep inside your stack and it's logs might as well be embedded into some larger data structure or even more importantly with more context. Contravariant tracer allows you to peal the onion as you pass the tracer deeper into the stack, adding the extra information on the way. Contravariant tracing allows for avoiding low-level components to have access to high-level tracing information, and make it possible to decouple and isolate components even if they depend on each other.

Some IOSim featuresโ€‹


io-sim not only allows one to run simulations but also gives access to detailed traces. With Control.Monad.IOSim.runSimTrace function you can get a trace that contains timed execution events including fork events, blocking / unblocking STM events, synchronous and asynchronous exceptions (including blocking information of throwTo), forking events, delay & timers events. The SimTrace can be pretty-printed with Control.Monad.IOSim.ppTrace. You also have control over the names of threads and TVars. And as we mentioned you can extract information logged by the code in simulation. From the SimTrace you can also extract the result computed by your simulation with Control.Monad.IOSim.traceResult. These three functions are useful for example to enhance test failure information (e.g. via the well known counterexample from the QuickCheck library).

You can use runSim or runSimOrThrow if you are not interested in the trace but you just want to get the result. Note that the SimTrace is very verbose, and thus it might include too much information to analyse simple problems, but it is indispensable when analysing concurrency bugs.

io-sim also allows inspecting values committed to TVars in an STM transaction. In the early days, we relied on tracing, but that can be rescheduled and thus reorder events. Control.Monad.Class.MonadSTM.MonadTraceSTM provides the API. IOSim attaches the callbacks to its TVars and executes them whenever an STM transaction is committed. The callbacks allow one to either log Strings (the same way as the say from Control.Monad.Class.MonadSay module does), or arbitrary data as traceM does.

io-sim's STM monad allows to log values with traceSTM. Since this function is not polymorphic over monad, it is usage is mostly limited to debugging stm transactions.


IOSim(in both flavours: IOSim and IOSimPOR) is executed in lazy ST monad. This means that the trace is created lazily. This allows us to provide a simple MonadFix instance, but also allows one to simulate applications that never exit and then only analyse a finite portion of a trace.


io-sim was developed by Well-Typed and IOG. We would like to specially thank Duncan Coutts for his ideas, help, guidance and contributions. IOSimPOR was developed by John Hughes from QuviQ.