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I want to see the intrinsic difference between a thread and a long-running go block in Clojure. In particular, I want to figure out which one I should use in my context.
I understand if one creates a go-block, then it is managed to run in a so-called thread-pool, the default size is 8. But thread will create a new thread.
In my case, there is an input stream that takes values from somewhere and the value is taken as an input. Some calculations are performed and the result is inserted into a result channel. In short, we have input and out put channel, and the calculation is done in the loop. So as to achieve concurrency, I have two choices, either use a go-block or use thread.
I wonder what is the intrinsic difference between these two. (We may assume there is no I/O during the calculations.) The sample code looks like the following:
(go-loop []
(when-let [input (<! input-stream)]
... ; calculations here
(>! result-chan result))
(recur))
(thread
(loop []
(when-let [input (<!! input-stream)]
... ; calculations here
(put! result-chan result))
(recur)))
I realize the number of threads that can be run simultaneously is exactly the number of CPU cores. Then in this case, is go-block and thread showing no differences if I am creating more than 8 thread or go-blocks?
I might want to simulate the differences in performance in my own laptop, but the production environment is quite different from the simulated one. I could draw no conclusions.
By the way, the calculation is not so heavy. If the inputs are not so large, 8,000 loops can be run in 1 second.
Another consideration is whether go-block vs thread will have an impact on GC performance.
There's a few things to note here.
Firstly, the thread pool that threads are created on via clojure.core.async/thread is what is known as a cached thread pool, meaning although it will re-use recently used threads inside that pool, it's essentially unbounded. Which of course means it could potentially hog a lot of system resources if left unchecked.
But given that what you're doing inside each asynchronous process is very lightweight, threads to me seem a little overkill. Of course, it's also important to take into account the quantity of items you expect to hit the input stream, if this number is large you could potentially overwhelm core.async's thread pool for go macros, potentially to the point where we're waiting for a thread to become available.
You also didn't mention preciously where you're getting the input values from, are the inputs some fixed data-set that remains constant at the start of the program, or are inputs continuously feed into the input stream from some source over time?
If it's the former then I would suggest you lean more towards transducers and I would argue that a CSP model isn't a good fit for your problem since you aren't modelling communication between separate components in your program, rather you're just processing data in parallel.
If it's the latter then I presume you have some other process that's listening to the result channel and doing something important with those results, in which case I would say your usage of go-blocks is perfectly acceptable.
This is a followup to a previous thread with a similar name.
It has an accepted answer, but that answer does not really answer the question. From that thread, here is the use-case:
if len(myChannel) > 0 {
// Possible issue here: length could have changed to 0 making this blocking
elm := <- myChannel
return elm
}
The OP calls it a "Possible issue", but it's a Definite Issue: a race condition in which another consumer may have pulled a value from the channel between the evaluation of the if condition and execution of the two statements.
Now, we are told the Go Way is to favor channels over mutex, but here it seems we can not acheive even basic non-blocking read (by polling length and reading atomically) without pairing a mutex and a channel together, and using our new concurrency data type instead of a channel.
Can that be right? Is there really no way to reliably ensure a recv does not block by checking ahead for space? (Compare with BlockingQueue.poll() in Java, or similar facilities in other queue-based messaging IPC facilities...)
This is exactly what default cases in select are for:
var elm myType
select {
case elm = <-myChannel:
default:
}
return elm
This assigns elm if it can, and otherwise returns a zero value. See "A leaky buffer" from Effective Go for a somewhat more extensive example.
Rob Napier's answer is correct.
However, you are possibly trying too hard to achieve non-blocking behaviour, assuming that it is an anti-pattern.
With Go, you don't have to worry about blocking. Go ahead, block without guilt. It can make code much easier to write, especially when dealing with i/o.
CSP allows you to design data-driven concurrent programs that can scale very well (because of not using mutexes too much). Small groups of goroutines communicating via channels can behave like a component of a larger system; these components (also communicating via channels) can be grouped into larger components; this pattern repeats at increasing scales.
Conventionally, people start with sequential code and then try to add concurrency by adding goroutines, channels, mutexes etc. As an exercise, try something different: try designing a system to be maximally concurrent - use goroutines and channels as deeply as you possibly can. You might be unimpressed with the performance you achieve ... so then perhaps try to consider how to improve it by combining (rather than dividing) blocks, reducing the total number of goroutines and so achieving a more optimal concurrency.
Wanting to be sure we're using the correct synchronization (and no more than necessary) when writing threadsafe code in JRuby; specifically, in a Puma instantiated Rails app.
UPDATE: Extensively re-edited this question, to be very clear and use latest code we are implementing. This code uses the atomic gem written by #headius (Charles Nutter) for JRuby, but not sure it is totally necessary, or in which ways it's necessary, for what we're trying to do here.
Here's what we've got, is this overkill (meaning, are we over/uber-engineering this), or perhaps incorrect?
ourgem.rb:
require 'atomic' # gem from #headius
SUPPORTED_SERVICES = %w(serviceABC anotherSvc andSoOnSvc).freeze
module Foo
def self.included(cls)
cls.extend(ClassMethods)
cls.send :__setup
end
module ClassMethods
def get(service_name, method_name, *args)
__cached_client(service_name).send(method_name.to_sym, *args)
# we also capture exceptions here, but leaving those out for brevity
end
private
def __client(service_name)
# obtain and return a client handle for the given service_name
# we definitely want to cache the value returned from this method
# **AND**
# it is a requirement that this method ONLY be called *once PER service_name*.
end
def __cached_client(service_name)
##_clients.value[service_name]
end
def __setup
##_clients = Atomic.new({})
##_clients.update do |current_service|
SUPPORTED_SERVICES.inject(Atomic.new({}).value) do |memo, service_name|
if current_services[service_name]
current_services[service_name]
else
memo.merge({service_name => __client(service_name)})
end
end
end
end
end
end
client.rb:
require 'ourgem'
class GetStuffFromServiceABC
include Foo
def self.get_some_stuff
result = get('serviceABC', 'method_bar', 'arg1', 'arg2', 'arg3')
puts result
end
end
Summary of the above: we have ##_clients (a mutable class variable holding a Hash of clients) which we only want to populate ONCE for all available services, which are keyed on service_name.
Since the hash is in a class variable (and hence threadsafe?), are we guaranteed that the call to __client will not get run more than once per service name (even if Puma is instantiating multiple threads with this class to service all the requests from different users)? If the class variable is threadsafe (in that way), then perhaps the Atomic.new({}) is unnecessary?
Also, should we be using an Atomic.new(ThreadSafe::Hash) instead? Or again, is that not necessary?
If not (meaning: you think we do need the Atomic.news at least, and perhaps also the ThreadSafe::Hash), then why couldn't a second (or third, etc.) thread interrupt between the Atomic.new(nil) and the ##_clients.update do ... meaning the Atomic.news from EACH thread will EACH create two (separate) objects?
Thanks for any thread-safety advice, we don't see any questions on SO that directly address this issue.
Just a friendly piece of advice, before I attempt to tackle the issues you raise here:
This question, and the accompanying code, strongly suggests that you don't (yet) have a solid grasp of the issues involved in writing multi-threaded code. I encourage you to think twice before deciding to write a multi-threaded app for production use. Why do you actually want to use Puma? Is it for performance? Will your app handle many long-running, I/O-bound requests (like uploading/downloading large files) at the same time? Or (like many apps) will it primarily handle short, CPU-bound requests?
If the answer is "short/CPU-bound", then you have little to gain from using Puma. Multiple single-threaded server processes would be better. Memory consumption will be higher, but you will keep your sanity. Writing correct multi-threaded code is devilishly hard, and even experts make mistakes. If your business success, job security, etc. depends on that multi-threaded code working and working right, you are going to cause yourself a lot of unnecessary pain and mental anguish.
That aside, let me try to unravel some of the issues raised in your question. There is so much to say that it's hard to know where to start. You may want to pour yourself a cold or hot beverage of your choice before sitting down to read this treatise:
When you talk about writing "thread-safe" code, you need to be clear about what you mean. In most cases, "thread-safe" code means code which doesn't concurrently modify mutable data in a way which could cause data corruption. (What a mouthful!) That could mean that the code doesn't allow concurrent modification of mutable data at all (using locks), or that it does allow concurrent modification, but makes sure that it doesn't corrupt data (probably using atomic operations and a touch of black magic).
Note that when your threads are only reading data, not modifying it, or when working with shared stateless objects, there is no question of "thread safety".
Another definition of "thread-safe", which probably applies better to your situation, has to do with operations which affect the outside world (basically I/O). You may want some operations to only happen once, or to happen in a specific order. If the code which performs those operations runs on multiple threads, they could happen more times than desired, or in a different order than desired, unless you do something to prevent that.
It appears that your __setup method is only called when ourgem.rb is first loaded. As far as I know, even if multiple threads require the same file at the same time, MRI will only ever let a single thread load the file. I don't know whether JRuby is the same. But in any case, if your source files are being loaded more than once, that is symptomatic of a deeper problem. They should only be loaded once, on a single thread. If your app handles requests on multiple threads, those threads should be started up after the application has loaded, not before. This is the only sane way to do things.
Assuming that everything is sane, ourgem.rb will be loaded using a single thread. That means __setup will only ever be called by a single thread. In that case, there is no question of thread safety at all to worry about (as far as initialization of your "client cache" goes).
Even if __setup was to be called concurrently by multiple threads, your atomic code won't do what you think it does. First of all, you use Atomic.new({}).value. This wraps a Hash in an atomic reference, then unwraps it so you just get back the Hash. It's a no-op. You could just write {} instead.
Second, your Atomic#update call will not prevent the initialization code from running more than once. To understand this, you need to know what Atomic actually does.
Let me pull out the old, tired "increment a shared counter" example. Imagine the following code is running on 2 threads:
i += 1
We all know what can go wrong here. You may end up with the following sequence of events:
Thread A reads i and increments it.
Thread B reads i and increments it.
Thread A writes its incremented value back to i.
Thread B writes its incremented value back to i.
So we lose an update, right? But what if we store the counter value in an atomic reference, and use Atomic#update? Then it would be like this:
Thread A reads i and increments it.
Thread B reads i and increments it.
Thread A tries to write its incremented value back to i, and succeeds.
Thread B tries to write its incremented value back to i, and fails, because the value has already changed.
Thread B reads i again and increments it.
Thread B tries to write its incremented value back to i again, and succeeds this time.
Do you get the idea? Atomic never stops 2 threads from running the same code at the same time. What it does do, is force some threads to retry the #update block when necessary, to avoid lost updates.
If your goal is to ensure that your initialization code will only ever run once, using Atomic is a very inappropriate choice. If anything, it could make it run more times, rather than less (due to retries).
So, that is that. But if you're still with me here, I am actually more concerned about whether your "client" objects are themselves thread-safe. Do they have any mutable state? Since you are caching them, it seems that initializing them must be slow. Be that as it may, if you use locks to make them thread-safe, you may not be gaining anything from caching and sharing them between threads. Your "multi-threaded" server may be reduced to what is effectively an unnecessarily complicated, single-threaded server.
If the client objects have no mutable state, good for you. You can be "free and easy" and share them between threads with no problems. If they do have mutable state, but initializing them is slow, then I would recommend caching one object per thread, so they are never shared. Thread[] is your friend there.
Actually I am using visual C++ to try to bind lua functions as callbacks for socket events(in another thread). I initialize the lua stuff in one thread and the socket is in another thread, so every time the socket sends/receives a message, it will call the lua function and the lua function determines what it should do according to the 'tag' within the message.
So my questions are:
Since I pass the same Lua state to lua functions, is that safe? Doesn't it need some kinda protection? The lua functions are called from another thead so I guess they might be called simultaneously.
If it is not safe, what's the solution for this case?
It is not safe to call back asynchronously into a Lua state.
There are many approaches to dealing with this. The most popular involve some kind of polling.
A recent generic synchronization library is DarkSideSync
A popular Lua binding to libev is lua-ev
This SO answer recommends Lua Lanes with LuaSocket.
It is not safe to call function within one Lua state simultaneously in multiple threads.
I was dealing with the same problem, since in my application all basics such as communication are handled by C++ and all the business logic is implemented in Lua. What I do is create a pool of Lua states that are all created and initialised on an incremental basis (once there's not enough states, create one and initialise with common functions / objects). It works like this:
Once a connection thread needs to call a Lua function, it checks out an instance of Lua state, initialises specific globals (I call it a thread / connection context) in a separate (proxy) global table that prevents polluting the original global, but is indexed by the original global
Call a Lua function
Check the Lua state back in to the pool, where it is restored to the "ready" state (dispose of the proxy global table)
I think this approach would be well suited for your case as well. The pool checks each state (on an interval basis) when it was last checked out. When the time difference is big enough, it destroys the state to preserve resources and adjust the number of active states to current server load. The state that is checked out is the most recently used among the available states.
There are some things you need to consider when implementing such a pool:
Each state needs to be populated with the same variables and global functions, which increases memory consumption.
Implementing an upper limit for state count in the pool
Ensuring all the globals in each state are in a consistent state, if they happen to change (here I would recommend prepopulating only static globals, while populating dynamic ones when checking out a state)
Dynamic loading of functions. In my case there are many thousands of functions / procedures that can be called in Lua. Having them constantly loaded in all states would be a huge waste. So instead I keep them byte code compiled on the C++ side and have them loaded when needed. It turns out not to impact performance that much in my case, but your mileage may vary. One thing to keep in mind is to load them only once. Say you invoke a script that needs to call another dynamically loaded function in a loop. Then you should load the function as a local once before the loop. Doing it otherwise would be a huge performance hit.
Of course this is just one idea, but one that turned out to be best suited for me.
It's not safe, as the others mentioned
Depends on your usecase
Simplest solution is using a global lock using the lua_lock and lua_unlock macros. That would use a single Lua state, locked by a single mutex. For a low number of callbacks it might suffice, but for higher traffic it probably won't due to the overhead incurred.
Once you need better performance, the Lua state pool as mentioned by W.B. is a nice way to handle this. Trickiest part here I find synchronizing the global data across the multiple states.
DarkSideSync, mentioned by Doug, is useful in cases where the main application loop resides on the Lua side. I specifically wrote it for that purpose. In your case this doesn't seem a fit. Having said that; depending on your needs, you might consider changing your application so the main loop does reside on the Lua side. If you only handle sockets, then you can use LuaSocket and no synchronization is required at all. But obviously that depends on what else the application does.
I've read a lot recently about how writing multi-threaded apps is a huge pain in the neck, and have learned enough about the topic to understand, at least at some level, why it is so.
I've read that using functional programming techniques can help alleviate some of this pain, but I've never seen a simple example of functional code that is concurrent. So, what are some alternatives to using threads? At least, what are some ways to abstract them away so you needn't think about things like locking and whether a particular library's objects are thread-safe.
I know Google's MapReduce is supposed to help with the problem, but I haven't seen a succinct explanation of it.
Although I'm giving a specific example below, I'm more curious of general techniques than solving this specific problem (using the example to help illustrate other techniques would be helpful though).
I came to the question when I wrote a simple web crawler as a learning exercise. It works pretty well, but it is slow. Most of the bottleneck comes from downloading pages. It is currently single threaded, and thus only downloads a single page at a time. Thus, if the pages can be downloaded concurrently, it would speed things up dramatically, even if the crawler ran on a single processor machine. I looked into using threads to solve the issue, but they scare me. Any suggestions on how to add concurrency to this type of problem without unleashing a terrible threading nightmare?
The reason functional programming helps with concurrency is not because it avoids using threads.
Instead, functional programming preaches immutability, and the absence of side effects.
This means that an operation could be scaled out to N amount of threads or processes, without having to worry about messing with shared state.
Actually, threads are pretty easy to handle until you need to synchronize them. Usually, you use threadpool to add task and wait till they are finished.
It is when threads need to communicate and access shared data structures that multi threading becomes really complicated. As soon as you have two locks, you can get deadlocks, and this is where multithreading gets really hard. Sometimes, your locking code could be wrong by just a few instructions. In that case, you could only see bugs in production, on multi-core machines (if you developed on single core, happened to me) or they could be triggered by some other hardware or software. Unit testing doesn't help much here, testing finds bugs, but you can never be as sure as in "normal" apps.
I'll add an example of how functional code can be used to safely make code concurrent.
Here is some code you might want to do in parallel, so you don't have wait for one file to finish to start downloading the next:
void DownloadHTMLFiles(List<string> urls)
{
foreach(string url in urls)
{
DownlaodOneFile(url); //download html and save it to a file with a name based on the url - perhaps used for caching.
}
}
If you have a number of files the user might spend a minute or more waiting for them all. We can re-write this code functionally like this, and it basically does the exact same thing:
urls.ForEach(DownloadOneFile);
Note that this still runs sequentially. However, not only is it shorter, we've gained an important advantage here. Since each call to the DownloadOneFile function is completely isolated from the others (for our purposes, available bandwidth isn't an issue) you could very easily swap out the ForEach function for another very similar function: one that kicks off each call to DownlaodOneFile on a separate thread from a threadpool.
It turns out .Net has just such a function availabe using Parallel Extensions. So, by using functional programming you can change one line of code and suddenly have something run in parallel that used to run sequentially. That's pretty powerful.
There are a couple of brief mentions of asynchronous models but no one has really explained it so I thought I'd chime in. The most common method I've seen used as an alternative for multi-threading is asynchronous architectures. All that really means is that instead of executing code sequentially in a single thread, you use a polling method to initiate some functions and then come back and check periodically until there's data available.
This really only works in models like your aforementioned crawler, where the real bottleneck is I/O rather than CPU. In broad strokes, the asynchronous approach would initiate the downloads on several sockets, and a polling loop periodically checks to see if they're finished downloading and when that's done, we can move on to the next step. This allows you to run several downloads that are waiting on the network, by context switching within the same thread, as it were.
The multi-threaded model would work much the same, except using a separate thread rather than a polling loop checking multiple sockets in the same thread. In an I/O bound application, asynchronous polling works almost as well as threading for many use cases, since the real problem is simply waiting for the I/O to complete and not so much the waiting for the CPU to process the data.
Another real world example is for a system that needed to execute a number of other executables and wait for results. This can be done in threads, but it's also considerably simpler and almost as effective to simply fire off several external applications as Process objects, then check back periodically until they're all finished executing. This puts the CPU-intensive parts (the running code in the external executables) in their own processes, but the data processing is all handled asynchronously.
The Python ftp server lib I work on, pyftpdlib uses the Python asyncore library to handle serving FTP clients with only a single thread, and asynchronous socket communication for file transfers and command/response.
See for further reading the Python Twisted library's page on Asynchronous Programming - while somewhat specific to using Twisted, it also introduces async programming from a beginner perspective.
Concurrency is quite a complicated subject in computer science, which demands good understanding of hardware architecture as well as operating system behavior.
Multi-threading has many implementations based on your hardware and your hosting OS, and as tough as it is already, the pitfalls are numerous. It should be noted that in order to achieve "true" concurrency, threads are the only way to go. Basically, threads are the only way for you as a programmer to share resources between different parts of your software while allowing them to run in parallel. By parallel you should consider that a standard CPU (dual/multi-cores aside) can only do one thing at a time. Concepts like context switching now come into play, and they have their own set of rules and limitations.
I think you should seek more generic background on the subject, like you are saying, before you go about implementing concurrency in your program.
I guess the best place to start is the wikipedia article on concurrency, and go on from there.
What typically makes multi-threaded programming such a nightmare is when threads share resources and/or need to communicate with each other. In the case of downloading web pages, your threads would be working independently, so you may not have much trouble.
One thing you may want to consider is spawning multiple processes rather than multiple threads. In the case you mention--downloading web pages concurrently--you could split the workload up into multiple chunks and hand each chunk off to a separate instance of a tool (like cURL) to do the work.
If your goal is to achieve concurrency it will be hard to get away from using multiple threads or processes. The trick is not to avoid it but rather to manage it in a way that is reliable and non-error prone. Deadlocks and race conditions in particular are two aspects of concurrent programming that are easy to get wrong. One general approach to manage this is to use a producer/consumer queue... threads write work items to the queue and workers pull items from it. You must make sure you properly synchronize access to the queue and you're set.
Also, depending on your problem, you may also be able to create a domain specific language which does away with concurrency issues, at least from the perspective of the person using your language... of course the engine which processes the language still needs to handle concurrency, but if this will be leveraged across many users it could be of value.
There are some good libraries out there.
java.util.concurrent.ExecutorCompletionService will take a collection of Futures (i.e. tasks which return values), process them in background threads, then bung them in a Queue for you to process further as they complete. Of course, this is Java 5 and later, so isn't available everywhere.
In other words, all your code is single threaded - but where you can identify stuff safe to run in parallel, you can farm it off to a suitable library.
Point is, if you can make the tasks independent, then thread safety isn't impossible to achieve with a little thought - though it is strongly recommended you leave the complicated bit (like implementing the ExecutorCompletionService) to an expert...
One simple way to avoid threading in your simple scenario, Is to download from different processes. The main process will invoke other processes with parameters that will download the files to local directory, And then the main process can do the real job.
I don't think that there are any simple solution to those problems. Its not a threading problem. Its the concurrency that brake the human mind.
You might watch the MSDN video on the F# language: PDC 2008: An introduction to F#
This includes the two things you are looking for. (Functional + Asynchronous)
For python, this looks like an interesting approach: http://members.verizon.net/olsongt/stackless/why_stackless.html#introduction
Use Twisted. "Twisted is an event-driven networking engine written in Python" http://twistedmatrix.com/trac/. With it, I could make 100 asynchronous http requests at a time without using threads.
Your specific example is seldom solved with multi-threading. As many have said, this class of problems is IO-bound, meaning the processor has very little work to do, and spends most of it's time waiting for some data to arrive over the wire and to process that, and similarly it has to wait for disk buffers to flush so that it can put more of the recently downloaded data on disk.
The method to performance is through the select() facility, or an equivalent system call. The basic process is to open a number of sockets (for the web crawler downloads) and file handles (for storing them to disk). Next you set all of the different sockets and fh to non-blocking mode, meaning that instead of making your program wait until data is available to read after issuing a request, it returns right away with a special code (usually EAGAIN) to indicate that no data is ready. If you looped through all of the sockets in this way you would be polling, which works well, but is still a waste of cpu resources because your reads and writes will almost always return with EAGAIN.
To get around this, all of the sockets and fp's will be collected into a 'fd_set', which is passed to the select system call, then your program will block, waiting on ANY of the sockets, and will awaken your program when there's some data on any of the streams to process.
The other common case, compute bound work, is without a doubt best addressed with some sort of true parallelism (as apposed to the asynchronous concurrency presented above) to access the resources of multiple cpu's. In the case that your cpu bound task is running on a single threaded archetecture, definately avoid any concurrency, as the overhead will actually slow your task down.
Threads are not to be avoided nor are they "difficult". Functional programming is not necessarily the answer either. The .NET framework makes threading fairly simple. With a little thought you can make reasonable multithreaded programs.
Here's a sample of your webcrawler (in VB.NET)
Imports System.Threading
Imports System.Net
Module modCrawler
Class URLtoDest
Public strURL As String
Public strDest As String
Public Sub New(ByVal _strURL As String, ByVal _strDest As String)
strURL = _strURL
strDest = _strDest
End Sub
End Class
Class URLDownloader
Public id As Integer
Public url As URLtoDest
Public Sub New(ByVal _url As URLtoDest)
url = _url
End Sub
Public Sub Download()
Using wc As New WebClient()
wc.DownloadFile(url.strURL, url.strDest)
Console.WriteLine("Thread Finished - " & id)
End Using
End Sub
End Class
Public Sub Download(ByVal ud As URLtoDest)
Dim dldr As New URLDownloader(ud)
Dim thrd As New Thread(AddressOf dldr.Download)
dldr.id = thrd.ManagedThreadId
thrd.SetApartmentState(ApartmentState.STA)
thrd.IsBackground = False
Console.WriteLine("Starting Thread - " & thrd.ManagedThreadId)
thrd.Start()
End Sub
Sub Main()
Dim lstUD As New List(Of URLtoDest)
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file0.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file1.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file2.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file3.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file4.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file5.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file6.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file7.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file8.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file9.txt"))
For Each ud As URLtoDest In lstUD
Download(ud)
Next
' you will see this message in the middle of the text
' pressing a key before all files are done downloading aborts the threads that aren't finished
Console.WriteLine("Press any key to exit...")
Console.ReadKey()
End Sub
End Module