mutable state in collection - f#

I'm pretty new to functional programming so this might be a question due to misconception, but I can't get my head around this - from an OOP point of view it seems so obvious...
scenario:
Assume you have an actor or micro-service like architecture approach where messages/requests are sent to some components that handle them and reply. Assume now, one of the components stores some of the data from the requests for future requests (e.g. it calculates a value and stores it in a cache so that the next time the same request occurs, no calculation is needed).
The data can be hold in memory.
question:
How do you in functional programming in general, and especially in f#, handle such a scenario? I guess a static dictionary is not a functional approach and I don't want to include any external things like data stores if possible.
Or more precise:
If an application creates data that will be used later in the processing again, where do we store the data?
example: You have an application that executes some sort of tasks on some initial data. First, you store the inital data (e.g. add it to a dictionary), then you execute the first task that does some processing based on a subset of the data, then you execute the second task that adds additional data and so on until all tasks are done...
Now the basic approach (from my understanding) would be to define the data and use the tasks as some sort of processing-chain that forward the processed data, like initial-data -> task-1 -> task-2 -> ... -> done
but that does not fit an architecture where getting/adding data is done message-based and asynchronous.
approach:
My initial approach was this
type Record = { }
let private dummyStore = new System.Collections.Concurrent.ConcurrentBag<Record>()
let search comparison =
let matchingRecords = dummyStore |> Seq.where (comparison)
if matchingRecords |> Seq.isEmpty
then EmptyFailedRequest
else Record (matchingRecords |> Seq.head)
let initialize initialData =
initialData |> Seq.iter (dummyStore.Add)
let add newRecord =
dummyStore.Add(newRecord)
encapsulated in a module that looks to me like an OOP approach.
After #Gustavo asked me to provide an example and considering his suggestion I've realized that I could do it like this (go one level higher to the place where the functions are actually called):
let handleMessage message store =
// all the operations from above but now with Seq<Record> -> ... -> Seq<Record>
store
let agent = MailboxProcessor.Start(fun inbox->
let rec messageLoop store = async{
let! msg = inbox.Receive()
let modifiedStore = handleMessage msg store
return! messageLoop modifiedStore
}
messageLoop Seq.empty
)
This answers the question for me well since it removed mutability and shared state at all. But when just looking at the first approach, I cannot think of any solution w/o the collection outside the functions
Please note that this question is in f# to explain the environment, the syntax etc. I don't want a solution that works because f# is multi-paradigm, I would like to get a functional approach for that.
I've read all questions that I could find on SO so far but they either prove the theoretical possibility or they use collections for this scenario - if duplicated please point me the right direction.

You can use a technique called memoization which is very common in FP.
And it consists precisely on keeping a dictionary with the calculated values.
Here's a sample implementation:
open System
open System.Collections.Concurrent
let getOrAdd (a:ConcurrentDictionary<'A,'B>) (b:_->_) k = a.GetOrAdd(k, b)
let memoize f =
let dic = new ConcurrentDictionary<_,_>()
getOrAdd dic f
Note that with memoize you can decorate any function and get a memoized version of it. Here's a sample:
let f x =
printfn "calculating f (%i)" x
2 * x
let g = memoize f // g is the memoized version of f
// test
> g 5 ;;
calculating f (5)
val it : int = 10
> g 5 ;;
val it : int = 10
You can see that in the second execution the value was not calculated.

Related

F# Observable - Converting an event stream to a list

I was writing an unit test that verified the events fired from a class. I followed the standard "IEvent<_>, Publish, Trigger inside an FSharp type" pattern.
Can you recommend the "functional" way to achieve that?
Here are the options I can think of:
Convert the event stream into a list of strings and compare that list with an expected list
(not sure if there is a way) Convert the expected list into an event stream and compare the two streams.
Pointer to a code snipped will greatly help.
Thanks!
Edit 1: Answering Mark's question:
This is what I have as of now:
let expectedFiles = [ "c:\a\1"
"c:\a\2" ]
[<Fact>]
let ``Can find files from a folder`` () =
let ad = new FileSearchAdapter()
let foundFiles = ref []
ad.FileFound
|> Observable.scan (fun acc e -> e::acc) []
|> Observable.add (fun acc -> foundFiles := acc)
ad.FindFiles #"c:\a"
Assert.Equal<string list>(expectedFiles, !foundFiles)
The issues here I feel are the [a] use of reference cell [b] the observable.add is essentially overwriting the reference for each event.
Is there a functional way to achieve the same?
Events are all about side-effects, so it's limited how much sense it makes to try to be all Functional about it.
(Yes: you can build Reactive systems where immutable event data flows through a system, being filtered and aggregated along the way, but at the source, that an event is raised, is a side-effect.)
Given that a unit test tests a unit in isolation from its dependencies, testing that events are correctly raised, exercises the isolated, 'un-functional' part of a system, so I don't think you have to do it in a Functional way.
Here's a simpler alternative:
open System.Collections.Generic
let ``Can find files from a folder`` () =
let ad = new FileSearchAdapter()
let foundFiles = List<string>()
ad.FileFound.Add(fun (sender, args) -> foundFiles.Add args)
ad.FindFiles "c:\a"
let expectedFiles = [ "c:\a\1"; "c:\a\2" ]
expectedFiles = (foundFiles |> Seq.toList)
(This test function is just a normal function that returns bool, but I'm sure you know how to convert it to a unit test.)

How does one generate a "complex" object in FsCheck?

I'd like to create an FsCheck Generator to generate instances of a "complex" object. By complex, I mean an existing class in C# that has a number of child properties and collections. These properties and collections in turn need to have data generated for them.
Imagine this class was named Menu with child collections Dishes and Drinks (I'm making this up so ignore the crappy design). I want to do the following:
Generate a variable number of Dishes and a variable number of Drinks.
Generate the Dish and Drink instances using the FsCheck API to populate their properties.
Set some other primitive properties on the Menu instance using the FsCheck API.
How does one go about writing a generator for this type of instance? Is this a bad idea? (I'm new to property based testing). I have read the docs, but have clearly failed to internalise it all so far.
There is a nice example for generating a record, but this is really only generating 3 values of the same type float.
This is not a bad idea - in fact it's the whole point that you are able to do this. FsCheck's generators are fully compositional.
Note first that if you have immutable objects whose constructors take primitive types, like your Drink and Dish looks like, FsCheck can generate these out of the box (using reflection)
let drinkArb = Arb.from<Drink>
let dishArb = Arb.from<Dish>
should give you an Arbitrary instance, which is a generator (generates a random Drink instance) and a shrinker (takes a Drink instance and makes it 'smaller' - this helps with debugging, esp. for composite structures, where you get a small counter-example if your test fails).
This breaks down fairly quickly though - in your example you probably don't want negative integers for the number of drinks or the number of dishes. The above code will generate negative numbers though. Sometimes this is easy to fix if your type is really just a wrapper of some sort around another type, using Arb.convert, e.g.
let drinksArb = Arb.Default.PositiveInt() |> Arb.convert (fun positive -> new Drinks(positive) (fun drinks -> drinks.Amount)
You need to provide to and from conversions to Arb.convert and presto, new arbitrary instance for Drinks that maintains your invariant. Other invariants may not be so easy to maintain of course.
After that it becomes a bit harder to generate a generator and a shrinker at the same time from those two pieces. Always start with the generator, then shrinker comes later if (when) you need it. #simonhdickson's example looks reasonable. If you have the arbitrary instances above, you can get at their generator by calling .Generator.
let drinksGen = drinksArb.Generator
Once you have the parts generators (Drink and Dish), you can indeed compose them together as #simonhdickson proposes:
let menuGenerator =
Gen.map3 (fun a b c -> Menu(a,b,c)) (Gen.listOf dishGenerator) (Gen.listOf drinkGenerator) (Arb.generate<int>)
Divide and conquer! Overall have a look at what intellisense on Gen gives you to get some ideas of how to compose generators.
There might be a better way of describing this, but I think this might do what you're thinking of. Each of the Drink/Dish types could take further parameters using the same kind of style as the menuGenerator does
type Drink() =
member m.X = 1
type Dish() =
member m.Y = 2
type Menu(dishes:Dish list, drinks:Drink list, total:int) =
member m.Dishes = dishes
member m.Drinks = drinks
member m.Total = total
let drinkGenerator = Arb.generate<unit> |> Gen.map (fun () -> Drink())
let dishGenerator = Arb.generate<unit> |> Gen.map (fun () -> Dish())
let menuGenerator =
Gen.map3 (fun a b c -> Menu(a,b,c)) <| Gen.listOf dishGenerator <| Gen.listOf drinkGenerator <| Arb.generate<int>

Function composition - when?

Given the following two approaches, what would the cons and pros of both, when it comes to function composition?
Approach 1
let isNameTaken source name =
source |> Query.Exists(fun z -> z.Name = name)
let usage : Customer = isNameTaken source "Test"
Approach 2
let isNameTaken f name =
f(fun z -> z.Name = name)
let usage : Customer = isNameTaken (source |> Query.Exists) "Test"
Is it just silly to pass (source |> Query.Exists) in Approach 2 - is it too extreme?
It depends on the wider context. I would generally prefer the first approach, unless you have some really good reason for using the second style (e.g. there is a number of functions similar to Query.Exists that you need to apply in a similar style).
Aside - I think your second example has a couple of issues (e.g. the piping in source |> Query.Exists would have to be replaced with (fun pred -> source |> Query.Exists pred) which makes it uglier.
Even then, the second approach does not really give you much benefit - your isNameTaken is simply a function that tests whether a customer name equals a given name and then it passes that as an argument to some f - you could just define a function that tests name equality and write something like this:
let nameEquals name (customer:Customer) =
customer.Name = name
let usage = source |> Query.Exists (nameEquals "Test")
More generally, I think it is always preferable to write code so that the caller can compose the pieces that are available to them (like Query.Exists, nameEquals etc.) rather than In a way that requires the caller to fill some holes of a particular required shape (e.g. implement a function with specified signature).
I think the answer to your question has to do with two main criteria. Which is more important, the readability of the code or the decoupling of the query from isNameTaken. In this particular case, I'm not sure that you get much at all from decoupling the query and it also seems like your decoupling is partial.
The thing I don't like about this is that in both cases, you've got z.Name tightly coupled into isNameTaken, which means that isNameTaken needs to know about the type of z. If that's OK with you then fine.

How does F#'s async really work?

I am trying to learn how async and let! work in F#.
All the docs i've read seem confusing.
What's the point of running an async block with Async.RunSynchronously? Is this async or sync? Looks like a contradiction.
The documentation says that Async.StartImmediate runs in the current thread. If it runs in the same thread, it doesn't look very asynchronous to me... Or maybe asyncs are more like coroutines rather than threads. If so, when do they yield back an forth?
Quoting MS docs:
The line of code that uses let! starts the computation, and then the thread is suspended
until the result is available, at which point execution continues.
If the thread waits for the result, why should i use it? Looks like plain old function call.
And what does Async.Parallel do? It receives a sequence of Async<'T>. Why not a sequence of plain functions to be executed in parallel?
I think i'm missing something very basic here. I guess after i understand that, all the documentation and samples will start making sense.
A few things.
First, the difference between
let resp = req.GetResponse()
and
let! resp = req.AsyncGetReponse()
is that for the probably hundreds of milliseconds (an eternity to the CPU) where the web request is 'at sea', the former is using one thread (blocked on I/O), whereas the latter is using zero threads. This is the most common 'win' for async: you can write non-blocking I/O that doesn't waste any threads waiting for hard disks to spin around or network requests to return. (Unlike most other languages, you aren't forced to do inversion of control and factor things into callbacks.)
Second, Async.StartImmediate will start an async on the current thread. A typical use is with a GUI, you have some GUI app that wants to e.g. update the UI (e.g. to say "loading..." somewhere), and then do some background work (load something off disk or whatever), and then return to the foreground UI thread to update the UI when completed ("done!"). StartImmediate enables an async to update the UI at the start of the operation and to capture the SynchronizationContext so that at the end of the operation is can return to the GUI to do a final update of the UI.
Next, Async.RunSynchronously is rarely used (one thesis is that you call it at most once in any app). In the limit, if you wrote your entire program async, then in the "main" method you would call RunSynchronously to run the program and wait for the result (e.g. to print out the result in a console app). This does block a thread, so it is typically only useful at the very 'top' of the async portion of your program, on the boundary back with synch stuff. (The more advanced user may prefer StartWithContinuations - RunSynchronously is kinda the "easy hack" to get from async back to sync.)
Finally, Async.Parallel does fork-join parallelism. You could write a similar function that just takes functions rather than asyncs (like stuff in the TPL), but the typical sweet spot in F# is parallel I/O-bound computations, which are already async objects, so this is the most commonly useful signature. (For CPU-bound parallelism, you could use asyncs, but you could also use TPL just as well.)
The usage of async is to save the number of threads in usage.
See the following example:
let fetchUrlSync url =
let req = WebRequest.Create(Uri url)
use resp = req.GetResponse()
use stream = resp.GetResponseStream()
use reader = new StreamReader(stream)
let contents = reader.ReadToEnd()
contents
let sites = ["http://www.bing.com";
"http://www.google.com";
"http://www.yahoo.com";
"http://www.search.com"]
// execute the fetchUrlSync function in parallel
let pagesSync = sites |> PSeq.map fetchUrlSync |> PSeq.toList
The above code is what you want to do: define a function and execute in parallel. So why do we need async here?
Let's consider something big. E.g. if the number of sites is not 4, but say, 10,000! Then There needs 10,000 threads to run them in parallel, which is a huge resource cost.
While in async:
let fetchUrlAsync url =
async { let req = WebRequest.Create(Uri url)
use! resp = req.AsyncGetResponse()
use stream = resp.GetResponseStream()
use reader = new StreamReader(stream)
let contents = reader.ReadToEnd()
return contents }
let pagesAsync = sites |> Seq.map fetchUrlAsync |> Async.Parallel |> Async.RunSynchronously
When the code is in use! resp = req.AsyncGetResponse(), the current thread is given up and its resource could be used for other purposes. If the response comes back in 1 second, then your thread could use this 1 second to process other stuff. Otherwise the thread is blocked, wasting thread resource for 1 second.
So even your are downloading 10000 web pages in parallel in an asynchronous way, the number of threads are limited to a small number.
I think you are not a .Net/C# programmer. The async tutorial usually assumes that one knows .Net and how to program asynchronous IO in C#(a lot of code). The magic of Async construct in F# is not for parallel. Because simple parallel could be realized by other constructs, e.g. ParallelFor in the .Net parallel extension. However, the asynchronous IO is more complex, as you see the thread gives up its execution, when the IO finishes, the IO needs to wake up its parent thread. This is where async magic is used for: in several lines of concise code, you can do very complex control.
Many good answers here but I thought I take a different angle to the question: How does F#'s async really work?
Unlike async/await in C# F# developers can actually implement their own version of Async. This can be a great way to learn how Async works.
(For the interested the source code to Async can be found here: https://github.com/Microsoft/visualfsharp/blob/fsharp4/src/fsharp/FSharp.Core/control.fs)
As our fundamental building block for our DIY workflows we define:
type DIY<'T> = ('T->unit)->unit
This is a function that accepts another function (called the continuation) that is called when the result of type 'T is ready. This allows DIY<'T> to start a background task without blocking the calling thread. When the result is ready the continuation is called allowing the computation to continue.
The F# Async building block is a bit more complicated as it also includes cancellation and exception continuations but essentially this is it.
In order to support the F# workflow syntax we need to define a computation expression (https://msdn.microsoft.com/en-us/library/dd233182.aspx). While this is a rather advanced F# feature it's also one of the most amazing features of F#. The two most important operations to define are return & bind which are used by F# to combine our DIY<_> building blocks into aggregated DIY<_> building blocks.
adaptTask is used to adapt a Task<'T> into a DIY<'T>.
startChild allows starting several simulatenous DIY<'T>, note that it doesn't start new threads in order to do so but reuses the calling thread.
Without any further ado here's the sample program:
open System
open System.Diagnostics
open System.Threading
open System.Threading.Tasks
// Our Do It Yourself Async workflow is a function accepting a continuation ('T->unit).
// The continuation is called when the result of the workflow is ready.
// This may happen immediately or after awhile, the important thing is that
// we don't block the calling thread which may then continue executing useful code.
type DIY<'T> = ('T->unit)->unit
// In order to support let!, do! and so on we implement a computation expression.
// The two most important operations are returnValue/bind but delay is also generally
// good to implement.
module DIY =
// returnValue is called when devs uses return x in a workflow.
// returnValue passed v immediately to the continuation.
let returnValue (v : 'T) : DIY<'T> =
fun a ->
a v
// bind is called when devs uses let!/do! x in a workflow
// bind binds two DIY workflows together
let bind (t : DIY<'T>) (fu : 'T->DIY<'U>) : DIY<'U> =
fun a ->
let aa tv =
let u = fu tv
u a
t aa
let delay (ft : unit->DIY<'T>) : DIY<'T> =
fun a ->
let t = ft ()
t a
// starts a DIY workflow as a subflow
// The way it works is that the workflow is executed
// which may be a delayed operation. But startChild
// should always complete immediately so in order to
// have something to return it returns a DIY workflow
// postProcess checks if the child has computed a value
// ie rv has some value and if we have computation ready
// to receive the value (rca has some value).
// If this is true invoke ca with v
let startChild (t : DIY<'T>) : DIY<DIY<'T>> =
fun a ->
let l = obj()
let rv = ref None
let rca = ref None
let postProcess () =
match !rv, !rca with
| Some v, Some ca ->
ca v
rv := None
rca := None
| _ , _ -> ()
let receiver v =
lock l <| fun () ->
rv := Some v
postProcess ()
t receiver
let child : DIY<'T> =
fun ca ->
lock l <| fun () ->
rca := Some ca
postProcess ()
a child
let runWithContinuation (t : DIY<'T>) (f : 'T -> unit) : unit =
t f
// Adapts a task as a DIY workflow
let adaptTask (t : Task<'T>) : DIY<'T> =
fun a ->
let action = Action<Task<'T>> (fun t -> a t.Result)
ignore <| t.ContinueWith action
// Because C# generics doesn't allow Task<void> we need to have
// a special overload of for the unit Task.
let adaptUnitTask (t : Task) : DIY<unit> =
fun a ->
let action = Action<Task> (fun t -> a ())
ignore <| t.ContinueWith action
type DIYBuilder() =
member x.Return(v) = returnValue v
member x.Bind(t,fu) = bind t fu
member x.Delay(ft) = delay ft
let diy = DIY.DIYBuilder()
open DIY
[<EntryPoint>]
let main argv =
let delay (ms : int) = adaptUnitTask <| Task.Delay ms
let delayedValue ms v =
diy {
do! delay ms
return v
}
let complete =
diy {
let sw = Stopwatch ()
sw.Start ()
// Since we are executing these tasks concurrently
// the time this takes should be roughly 700ms
let! cd1 = startChild <| delayedValue 100 1
let! cd2 = startChild <| delayedValue 300 2
let! cd3 = startChild <| delayedValue 700 3
let! d1 = cd1
let! d2 = cd2
let! d3 = cd3
sw.Stop ()
return sw.ElapsedMilliseconds,d1,d2,d3
}
printfn "Starting workflow"
runWithContinuation complete (printfn "Result is: %A")
printfn "Waiting for key"
ignore <| Console.ReadKey ()
0
The output of the program should be something like this:
Starting workflow
Waiting for key
Result is: (706L, 1, 2, 3)
When running the program note that Waiting for key is printed immidiately as the Console thread is not blocked from starting workflow. After about 700ms the result is printed.
I hope this was interesting to some F# devs
Lots of great detail in the other answers, but as I beginner I got tripped up by the differences between C# and F#.
F# async blocks are a recipe for how the code should run, not actually an instruction to run it yet.
You build up your recipe, maybe combining with other recipes (e.g. Async.Parallel). Only then do you ask the system to run it, and you can do that on the current thread (e.g. Async.StartImmediate) or on a new task, or various other ways.
So it's a decoupling of what you want to do from who should do it.
The C# model is often called 'Hot Tasks' because the tasks are started for you as part of their definition, vs. the F# 'Cold Task' models.
The idea behind let! and Async.RunSynchronously is that sometimes you have an asynchronous activity that you need the results of before you can continue. For example, the "download a web page" function may not have a synchronous equivalent, so you need some way to run it synchronously. Or if you have an Async.Parallel, you may have hundreds of tasks all happening concurrently, but you want them all to complete before continuing.
As far as I can tell, the reason you would use Async.StartImmediate is that you have some computation that you need to run on the current thread (perhaps a UI thread) without blocking it. Does it use coroutines? I guess you could call it that, although there isn't a general coroutine mechanism in .Net.
So why does Async.Parallel require a sequence of Async<'T>? Probably because it's a way of composing Async<'T> objects. You could easily create your own abstraction that works with just plain functions (or a combination of plain functions and Asyncs, but it would just be a convenience function.
In an async block you can have some synchronous and some async operations, so, for example, you may have a web site that will show the status of the user in several ways, so you may show if they have bills that are due shortly, birthdays coming up and homework due. None of these are in the same database, so your application will make three separate calls. You may want to make the calls in parallel, so that when the slowest one is done, you can put the results together and display it, so, the end result will be that the display is based on the slowest. You don't care about the order that these come back, you just want to know when all three are received.
To finish my example, you may then want to synchronously do the work to create the UI to show this information. So, at the end, you wanted this data fetched and the UI displayed, the parts where order doesn't matter is done in parallel, and where order matters can be done in a synchronous fashion.
You can do these as three threads, but then you have to keep track and unpause the original thread when the third one is finished, but it is more work, it is easier to have the .NET framework take care of this.

How do you declare the values of a dictionary entry as mutable?

The Google yields plenty of example of adding and deleting entries in an F# dictionary (or other collection). But I don't see examples to the equivalent of
myDict["Key"] = MyValue;
I've tried
myDict.["Key"] <- MyValue
I have also attempted to declare the Dictionary as
Dictionary<string, mutable string>
as well several variants on this. However, I haven't hit on the correct combination yet... if it is actually possible in F#.
Edit: The offending code is:
type Config(?fileName : string) =
let fileName = defaultArg fileName #"C:\path\myConfigs.ini"
static let settings =
dict[ "Setting1", "1";
"Setting2", "2";
"Debug", "0";
"State", "Disarray";]
let settingRegex = new Regex(#"\s*(?<key>([^;#=]*[^;#= ]))\s*=\s*(?<value>([^;#]*[^;# ]))")
do File.ReadAllLines(fileName)
|> Seq.map(fun line -> settingRegex.Match(line))
|> Seq.filter(fun mtch -> mtch.Success)
|> Seq.iter(fun mtch -> settings.[mtch.Groups.Item("key").Value] <- mtch.Groups.Item("value").Value)
The error I'm getting is:
System.NotSupportedException: This value may not be mutated
at Microsoft.FSharp.Core.ExtraTopLevelOperators.dict#37-2.set_Item(K key, V value)
at <StartupCode$FSI_0036>.$FSI_0036_Config.$ctor#25-6.Invoke(Match mtch)
at Microsoft.FSharp.Collections.SeqModule.iter[T](FastFunc`2 action, IEnumerable`1 sequence)
at FSI_0036.Utilities.Config..ctor(Option`1 fileName)
at <StartupCode$FSI_0041>.$FSI_0041.main#()
stopped due to error
f# has two common associative data structures:
The one you are most used to, the mutable Dictionary which it inherits that's to it's presence in the BCL and uses a hashtable under the hood.
let dict = new System.Collections.Generic.Dictionary<string,int>()
dict.["everything"] <- 42
The other is known as Map and is, in common functional style, immutable and implemented with binary trees.
Instead of operations that would change a Dictionary, maps provide operations which return a new map which is the result of whatever change was requested. In many cases, under the hood there is no need to make an entirely new copy of the entire map, so those parts that can be shared normally are. For example:
let withDouglasAdams = Map.add "everything" 42 Map.empty
The value withDouglasAdams will remain forever as an association of "everything" to 42. so if you later do:
let soLong = Map.remove "everything" withDouglasAdams
Then the effect of this 'removal' is only visible via the soLong value.
F#'s Map is, as mentioned, implemented as a binary tree. Lookup is therefore O(log n) whereas a (well behaved) dictionary should be O(1). In practice a hash based dictionary will tend to outperform the tree based one in almost all simple (low number of elements, low probability of collision) as such is commonly used. That said the immutable aspect of the Map may allow you to use it in situations where the dictionary would instead require more complex locking or to write more 'elegant' code with fewer side effects and thus it remains a useful alternative.
This is not however the source of your problem. The dict 'operator' returns an explicity immutable IDictionary<K,T> implementation (despite not indicating this in it's documentation).
From fslib-extra-pervasives.fs (note also the use of options on the keys):
let dict l =
// Use a dictionary (this requires hashing and equality on the key type)
// Wrap keys in an Some(_) option in case they are null
// (when System.Collections.Generic.Dictionary fails). Sad but true.
let t = new Dictionary<Option<_>,_>(HashIdentity.Structural)
for (k,v) in l do
t.[Some(k)] <- v
let d = (t :> IDictionary<_,_>)
let c = (t :> ICollection<_>)
let ieg = (t :> IEnumerable<_>)
let ie = (t :> System.Collections.IEnumerable)
// Give a read-only view of the dictionary
{ new IDictionary<'key, 'a> with
member s.Item
with get x = d.[Some(x)]
and set (x,v) = raise (NotSupportedException(
"This value may not be mutated"))
...
What error do you get? I tried the following and it compiles just fine
let map = new System.Collections.Generic.Dictionary<string,int>()
map.["foo"] <- 42
EDIT Verify that this code ran just fine as well .

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