When crawling on webpages I need to be careful as to not make too many requests to the same domain, for example I want to put 1 s between requests. From what I understand it is the time between requests that is important. So to speed things up I want to use async workflows in F#, the idea being make your requests with 1 sec interval but avoid blocking things while waiting for request response.
let getHtmlPrimitiveAsyncTimer (uri : System.Uri) (timer:int) =
async{
let req = (WebRequest.Create(uri)) :?> HttpWebRequest
req.UserAgent<-"Mozilla"
try
Thread.Sleep(timer)
let! resp = (req.AsyncGetResponse())
Console.WriteLine(uri.AbsoluteUri+" got response")
use stream = resp.GetResponseStream()
use reader = new StreamReader(stream)
let html = reader.ReadToEnd()
return html
with
| _ as ex -> return "Bad Link"
}
Then I do something like:
let uri1 = System.Uri "http://rue89.com"
let timer = 1000
let jobs = [|for i in 1..10 -> getHtmlPrimitiveAsyncTimer uri1 timer|]
jobs
|> Array.mapi(fun i job -> Console.WriteLine("Starting job "+string i)
Async.StartAsTask(job).Result)
Is this alright ? I am very unsure about 2 things:
-Does the Thread.Sleep thing work for delaying the request ?
-Is using StartTask a problem ?
I am a beginner (as you may have noticed) in F# (coding in general actually ), and everything envolving Threads scares me :)
Thanks !!
I think what you want to do is
- create 10 jobs, numbered 'n', each starting 'n' seconds from now
- run those all in parallel
Approximately like
let makeAsync uri n = async {
// create the request
do! Async.Sleep(n * 1000)
// AsyncGetResponse etc
}
let a = [| for i in 1..10 -> makeAsync uri i |]
let results = a |> Async.Parallel |> Async.RunSynchronously
Note that of course they all won't start exactly now, if e.g. you have a 4-core machine, 4 will start running very soon, but then quickly execute up to the Async.Sleep, at which point the next 4 will run up until their sleeps, and so forth. And then in one second the first async wakes up and posts a request, and another second later the 2nd async wakes up, ... so this should work. The 1s is only approximate, since they're starting their timers each a very tiny bit staggered from one another... you may want to buffer it a little, e.g. 1100 ms or something if the cut-off you need is really exactly a second (network latencies and whatnot still leave a bit of this outside the possible control of your program probably).
Thread.Sleep is suboptimal, it will work ok for a small number of requests, but you're burning a thread, and threads are expensive and it won't scale to a large number.
You don't need StartAsTask unless you want to interoperate with .NET Tasks or later do a blocking rendezvous with the result via .Result. If you just want these to all run and then block to collect all the results in an array, Async.Parallel will do that fork-join parallelism for you just fine. If they're just going to print results, you can fire-and-forget via Async.Start which will drop the results on the floor.
(An alternative strategy is to use an agent as a throttle. Post all the http requests to a single agent, where the agent is logically single-threaded and sits in a loop, doing Async.Sleep for 1s, and then handling the next request. That's a nice way to make a general-purpose throttle... may be blog-worthy for me, come to think of it.)
Related
I have a tool that is using a Telegram chatbot to interact with its users.
Telegram limits the call rate, so I use a queue system that gets flushed at regular intervals.
So, the current code is very basic:
// flush the message queue
let flushMessageQueue() =
if not messageQueue.IsEmpty then <- messageQueue is a ConcurrentQueue
// get all the messages
let messages =
messageQueue
|> Seq.unfold(fun q ->
match q.TryDequeue () with
| true, m -> Some (m, q)
| _ -> None)
// put all the messages in a single string
let messagesString = String.Join("\n", messages)
// send the data
client.SendTextMessageAsync(chatId, messagesString, ParseMode.Markdown)
|> Async.AwaitTask
|> Async.RunSynchronously
|> ignore
this is called at regular interval, while the write is:
// broadcast message
let broadcastMessage message =
messageQueue.Enqueue(message)
printfn "%s" (message.Replace ("```", String.Empty))
But as messages became more complex, two problems came at once:
Part of the output is formatted text with simple markdown:
Some blocks of lines are wrapped between ``` sections
There are some ``` sections as well inside some lines
The text is UTF-8 and uses a bunch of symbols
Some example of text may be:
```
this is a group of lines
with one, or many many lines
```
and sometimes there are things ```like this``` as well
And... I found out that Telegram limits message size to 4kb as well
So, I thought of two things:
I can maintain a state with the open / close ``` and pull from a queue, wrap each line in triple back ticks based on the state and push into another queue that will be used to make the 4kb block.
I can keep taking messages from the re-formatted queue and aggregate them until I reach 4kb, or the end of the queue and loop around.
Is there an elegant way to do this in F#?
I remember seeing a snippet where a collection function was used to aggregate data until a certain size but it looked very inefficient as it was making a collection of line1, line1+line2, line1+line2+line3... and then picking the one with the right size.
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.
I wanna measure the performance to my database by measuring the time taken to do something as the number of processes increase. The intention is to plot a graph of performance vs number of processes after, anyone has an idea how? i am a beginner in elrlang please helo
Assuming your database is mnesia, this should not be hard. one way would be to have a write function and a read function. However, note that there are several Activity access contexts with mnesia. To test write times, you should NOT use the context of transaction because it returns immediately to the calling process, even before a disc write has occured. However, for disc writes, its important that you look at the context called: sync_transaction. Here is an example:
write(Record)->
Fun = fun(R)-> mnesia:write(R) end,
mnesia:activity(sync_transaction,Fun,[Record],mnesia_frag).
The function above will return only when all active replicas of the mnesia table have committed the record onto the data disc file. Hence to test the speed as processes increase, you need to have a record generator,a a process spawner , the write function and finally a timing mechanism. For timing, we have a built in function called: timer:tc/1, timer:tc/2 and timer:tc/3 which returns the exact time it took to execute (completely) a given function. To cut the story short, this is how i would do this:
-module(stress_test).
-compile(export_all).
-define(LIMIT,10000).
-record(book,{
isbn,
title,
price,
version}).
%% ensure this table is {type,bag}
-record(write_time,{
isbn,
num_of_processes,
write_time
}).
%% Assuming table (book) already exists
%% Assuming mnesia running already
start()->
ensure_gproc(),
tv:start(),
spawn_many(?LIMIT).
spawn_many(0)-> ok;
spawn_many(N)->
spawn(?MODULE,process,[]),
spawn_many(N - 1).
process()->
gproc:reg({n, l,guid()},ignored),
timer:apply_interval(timer:seconds(2),?MODULE,write,[]),
receive
<<"stop">> -> exit(normal)
end.
total_processes()->
proplists:get_value(size,ets:info(gproc)) div 3.
ensure_gproc()->
case lists:keymember(gproc,1,application:which_applications()) of
true -> ok;
false -> application:start(gproc)
end.
guid()->
random:seed(now()),
MD5 = erlang:md5(term_to_binary([random:uniform(152629977),{node(), now(), make_ref()}])),
MD5List = lists:nthtail(3, binary_to_list(MD5)),
F = fun(N) -> f("~2.16.0B", [N]) end,
L = [F(N) || N <- MD5List],
lists:flatten(L).
generate_record()->
#book{isbn = guid(),title = guid(),price = guid()}.
write()->
Record = generate_record(),
Fun = fun(R)-> ok = mnesia:write(R),ok end,
%% Here is now the actual write we measure
{Time,ok} = timer:tc(mnesia,activity,[sync_transaction,Fun,[Record],mnesia_frag]),
%% The we save that time, the number of processes
%% at that instant
NoteTime = #write_time{
isbn = Record#book.isbn,
num_of_processes = total_processes(),
write_time = Time
},
mnesia:activity(transaction,Fun,[NoteTime],mnesia_frag).
Now there are dependencies here, especially: gproc download and build it into your erlang lib path from here Download Gproc.To run this, just call: stress_test:start(). The table write_time will help you draw a graph of number of processes against time taken to write. As the number of processes increase from 0 to the upper limit (?LIMIT), we note the time taken to write a given record at the given instant and we also note the number of processes at that time.UPDATE
f(S)-> f(S,[]).
f(S,Args) -> lists:flatten(io_lib:format(S, Args)).
That is the missing function. Apologies.... Remember to study the table write_time, using the application tv, a window is opened in which you can examine the mnesia tables. Use this table to see increasing write times/ or decreasing performance as number of processes increase from time to time. An element i have left out is to note the actual time of the write action using time() which may be important parameter. You may add it in the table definition of the write_time table.
Also look at http://wiki.basho.com/Benchmarking.html
you might look at tsung http://tsung.erlang-projects.org/
I was under the impression that let! in f# was smart enough to excute sequences of assignments in parallell.
However, the following sample displays a different behavior, assignment of a,b,c seems to execute synchronously.
let sleep sec =
async
{
System.Threading.Thread.Sleep(sec * 1000)
return sec
}
let bar = async
{
let! a = sleep 1
let! b = sleep 3
let! c = sleep 3
return a+b+c
}
let foo = Async.RunSynchronously(bar)
printfn "%d" foo
Is that how it is/should be?
And if I want to execute a,b,c in parallell, am I supposed to use Async.Parallell ... |> Async.RunSynchronously ... then?
The above sample is ofcourse useless , the real usecase would be something like query a DB and call some webservices at the same time.
As Richard points out, asynchronous workflows are still fully sequential. I don't think that any projects attempting to do fully automatic parallelization have been fully successful, because doing that is just too difficult.
However, asynchronous workflows still make parallelization easier. The key thing is that they make it possible to do waiting without blocking threads (which is essential for scalability) and they also support automatic cancellation and easy exception handling to make your life easier. There are various patterns that allow you to parallelize code in asynchronous workflows.
Task-based you can start your three tasks in background and then wait until all of them complete (this is probably what you were expecting, so here is how to write that explicitly):
let bar = async {
let! atask = sleep 1 |> Async.StartChild
let! btask = sleep 3 |> Async.StartChild
let! ctask = sleep 3 |> Async.StartChild
let! a = atask
let! b = btask
let! c = ctask
return a + b + c }
Data-parallel - if you have multiple workflows of the same type then you can create a workflow that runs all of them in parallel using Async.Parallel. When you then use let! it runs all three tasks and waits until they complete:
let bar = async {
let! all = Async.Parallel [ sleep 1; sleep 3; sleep 3 ]
return all.[0] + all.[1] + all.[2] }
Don Syme has an article discussing various patterns based on asynchronous workflows and you can find a comprehensive example in financial dashboard sample
let!, in an async block (or more correctly "computation expression") executes the expression asynchronously but the block as a whole is still executed linearly. This is the benefit of async computation expressions: making a sequence of dependent asynchronous operations much easier to write by performing continuation passing for you.
(Other types of computation expression provide their own semantics for let!, yield!, etc.)
To perform parallel/concurrent execution you need multiple async expressions executed separately.
I was under the impression
You've misunderstood (quite understandably).
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.