On a java webserver, I would like to know if there are any active web requests.
My first idea was something like:
public static AtomicInteger count = new AtomicInteger();
public void processRequest() {
count.incrementAndGet();
// process my request
count.decrementAndGet();
}
public boolean areThereActiveRequests() {
return 0 == count.get();
}
This obviously makes web requests block (albeit quickly) against each other waiting for a lock on count() when incrementing or decrementing - which we don't want for a highly scalable server.
Note that there may be synchronization issues with whatever code calls areThereActiveRequests - the value it returns may be stale by the time it's returned - but that's ok for my purposes.
Any ideas?
Your web requests will not block against each other waiting for a lock, as AutomicInteger doesn't require any locks and utilizes Hardware synchronization primitive.
Check this for details
Therefore your current code looks good for the problem you are trying to solve.
Related
What is an appropriate solution for long running computations in Azure App Service and .NET Core 3.1 in an application that has no need for a database and no IO to anything outside of this application ? It is a computation task.
Specifically, the following is unreliable and needs a solution.
[Route("service")]
[HttpPost]
public Outbound Post(Inbound inbound)
{
Debug.Assert(inbound.Message.Equals("Hello server."));
Outbound outbound = new Outbound();
long Billion = 1000000000;
for (long i = 0; i < 33 * Billion; i++) // 230 seconds
;
outbound.Message = String.Format("The server processed inbound object.");
return outbound;
}
This sometimes returns a null object to the HttpClient (not shown). A smaller workload will always succeed. For example 3 billion iterations always succeeds. A bigger number would be nice specifically 240 billion is a requirement.
I think in the year 2020 a reasonable goal in Azure App Service with .NET Core might be to have a parent thread count to 240 billion with the help of 8 child threads so each child counts to 30 billion and the parent divides an 8 M byte inbound object into smaller objects inbound to each child. Each child receives a 1 M byte inbound and returns to the parent a 1 M byte outbound. The parent re-assembles the result into a 8 M byte outbound.
Obviously the elapsed time will be 12.5%, or 1/8, or one-eighth, of the time a single thread implementation would need. The time to cut-up and re-assemble objects is small compared to the computation time. I am assuming the time to transmit the objects is very small compared to the computation time so the 12.5% expectation is roughly accurate.
If I can get 4 or 8 cores that would be good. If I can get threads that give me say 50% of the cycles of a core, then I would need may be 8 or 16 threads. If each thread gives me 33% of the cycles of a core then I would need 12 or 24 threads.
I am considering the BackgroundService class but I am looking for confirmation that this is the correct approach. Microsoft says...
BackgroundService is a base class for implementing a long running IHostedService.
Obviously if something is long running it would be better to make it finish sooner by using multiple cores via System.Threading but this documentation seems to mention System.Threading only in the context of starting tasks via System.Threading.Timer. My example code shows there is no timer needed in my application. An HTTP POST will serve as the occasion to do work. Typically I would use System.Threading.Thread to instantiate multiple objects to use multiple cores. I find the absence of any mention of multiple cores to be a glaring omission in the context of a solution for work that takes a long time but may be there is some reason Azure App Service doesn't deal with this matter. Perhaps I am just not able to find it in tutorials and documentation.
The initiation of the task is the illustrated HTTP POST controller. Suppose the longest job takes 10 minutes. The HTTP client (not shown) sets the timeout limit to 1000 seconds which is much more than 10 minutes (600 seconds) in order for there to be a margin of safety. HttpClient.Timeout is the relevant property. For the moment I am presuming the HTTP timeout is a real limit; rather than some sort of non-binding (fake limit) such that some other constraint results in the user waiting 9 minutes and receiving an error message. A real binding limit is a limit for which I can say "but for this timeout it would have succeeded". If the HTTP timeout is not the real binding limit and there is something else constraining the system, I can adjust my HTTP controller to instead have three (3) POST methods. Thus POST1 would mean start a task with the inbound object. POST2 means tell me if it is finished. POST3 means give me the outbound object.
What is an appropriate solution for long running computations in Azure App Service and .NET Core 3.1 in an application that has no need for a database and no IO to anything outside of this application ? It is a computation task.
Prologue
A few years ago a ran in to a pretty similar problem. We needed a service that could process large amounts of data. Sometimes the processing would take 10 seconds, other times it could take an hour.
At first we did it how your question illustrates: Send a request to the service, the service processes the data from the request and returns the response when finished.
Issues At Hand
This was fine when the job only took around a minute or less, but anything above this, the server would shut down the session and the caller would report an error.
Servers have a default of around 2 minutes to produce a response before it gives up on the request. It doesn't quit the processing of the request... but it does quit the HTTP session. It doesn't matter what parameters you set on your HttpClient, the server is the one that delegates how long is too long.
Reasons For Issues
All this is for good reasons. Server sockets are extremely expensive. You have a finite amount to go around. The server is trying to protect your service by severing requests that are taking longer than a specified time in order to avoid socket starvation issues.
Typically you want your HTTP requests to take only a few milliseconds. If they are taking longer than this, you will eventually run in to socket issues if your service has to fulfil other requests at a high rate.
Solution
We decided to go the route of IHostedService, specifically the BackgroundService. We use this service in conjunction with a Queue. This way you can set up a queue of jobs and the BackgroundService will process them one at a time (in some instances we have service processing multiple queue items at once, in others we scaled horizontally producing two or more queues).
Why an ASP.NET Core service running a BackgroundService? I wanted to handle this without tightly-coupling to any Azure-specific constructs in case we needed to move out of Azure to some other cloud service (back in the day we were contemplating this for other reasons we had at the time.)
This has worked out quite well for us and we haven't seen any issues since.
The work flow goes like this:
Caller sends a request to the service with some parameters
Service generates a "job" object and returns an ID immediately via 202 (accepted) response
Service places this job in to a queue that is being maintained by a BackgroundService
Caller can query the job status and get information about how much has been done and how much is left to go using this job ID
Service finishes the job, puts the job in to a "completed" state and goes back to waiting on the queue to produce more jobs
Keep in mind your service has the capability to scale horizontally where there would be more than one instance running. In this case I am using Redis Cache to store the state of the jobs so that all instances share the same state.
I also added in a "Memory Cache" option to test things locally if you don't have a Redis Cache available. You could run the "Memory Cache" service on a server, just know that if it scales then your data will be inconsistent.
Example
Since I'm married with kids, I really don't do much on Friday nights after everyone goes to bed, so I spent some time putting together an example that you can try out. The full solution is also available for you to try out.
QueuedBackgroundService.cs
This class implementation serves two specific purposes: One is to read from the queue (the BackgroundService implementation), the other is to write to the queue (the IQueuedBackgroundService implementation).
public interface IQueuedBackgroundService
{
Task<JobCreatedModel> PostWorkItemAsync(JobParametersModel jobParameters);
}
public sealed class QueuedBackgroundService : BackgroundService, IQueuedBackgroundService
{
private sealed class JobQueueItem
{
public string JobId { get; set; }
public JobParametersModel JobParameters { get; set; }
}
private readonly IComputationWorkService _workService;
private readonly IComputationJobStatusService _jobStatusService;
// Shared between BackgroundService and IQueuedBackgroundService.
// The queueing mechanism could be moved out to a singleton service. I am doing
// it this way for simplicity's sake.
private static readonly ConcurrentQueue<JobQueueItem> _queue =
new ConcurrentQueue<JobQueueItem>();
private static readonly SemaphoreSlim _signal = new SemaphoreSlim(0);
public QueuedBackgroundService(IComputationWorkService workService,
IComputationJobStatusService jobStatusService)
{
_workService = workService;
_jobStatusService = jobStatusService;
}
/// <summary>
/// Transient method via IQueuedBackgroundService
/// </summary>
public async Task<JobCreatedModel> PostWorkItemAsync(JobParametersModel jobParameters)
{
var jobId = await _jobStatusService.CreateJobAsync(jobParameters).ConfigureAwait(false);
_queue.Enqueue(new JobQueueItem { JobId = jobId, JobParameters = jobParameters });
_signal.Release(); // signal for background service to start working on the job
return new JobCreatedModel { JobId = jobId, QueuePosition = _queue.Count };
}
/// <summary>
/// Long running task via BackgroundService
/// </summary>
protected override async Task ExecuteAsync(CancellationToken stoppingToken)
{
while(!stoppingToken.IsCancellationRequested)
{
JobQueueItem jobQueueItem = null;
try
{
// wait for the queue to signal there is something that needs to be done
await _signal.WaitAsync(stoppingToken).ConfigureAwait(false);
// dequeue the item
jobQueueItem = _queue.TryDequeue(out var workItem) ? workItem : null;
if(jobQueueItem != null)
{
// put the job in to a "processing" state
await _jobStatusService.UpdateJobStatusAsync(
jobQueueItem.JobId, JobStatus.Processing).ConfigureAwait(false);
// the heavy lifting is done here...
var result = await _workService.DoWorkAsync(
jobQueueItem.JobId, jobQueueItem.JobParameters,
stoppingToken).ConfigureAwait(false);
// store the result of the work and set the status to "finished"
await _jobStatusService.StoreJobResultAsync(
jobQueueItem.JobId, result, JobStatus.Success).ConfigureAwait(false);
}
}
catch(TaskCanceledException)
{
break;
}
catch(Exception ex)
{
try
{
// something went wrong. Put the job in to an errored state and continue on
await _jobStatusService.StoreJobResultAsync(jobQueueItem.JobId, new JobResultModel
{
Exception = new JobExceptionModel(ex)
}, JobStatus.Errored).ConfigureAwait(false);
}
catch(Exception)
{
// TODO: log this
}
}
}
}
}
It is injected as so:
services.AddHostedService<QueuedBackgroundService>();
services.AddTransient<IQueuedBackgroundService, QueuedBackgroundService>();
ComputationController.cs
The controller used to read/write jobs looks like this:
[ApiController, Route("api/[controller]")]
public class ComputationController : ControllerBase
{
private readonly IQueuedBackgroundService _queuedBackgroundService;
private readonly IComputationJobStatusService _computationJobStatusService;
public ComputationController(
IQueuedBackgroundService queuedBackgroundService,
IComputationJobStatusService computationJobStatusService)
{
_queuedBackgroundService = queuedBackgroundService;
_computationJobStatusService = computationJobStatusService;
}
[HttpPost, Route("beginComputation")]
[ProducesResponseType(StatusCodes.Status202Accepted, Type = typeof(JobCreatedModel))]
public async Task<IActionResult> BeginComputation([FromBody] JobParametersModel obj)
{
return Accepted(
await _queuedBackgroundService.PostWorkItemAsync(obj).ConfigureAwait(false));
}
[HttpGet, Route("computationStatus/{jobId}")]
[ProducesResponseType(StatusCodes.Status200OK, Type = typeof(JobModel))]
[ProducesResponseType(StatusCodes.Status404NotFound, Type = typeof(string))]
public async Task<IActionResult> GetComputationResultAsync(string jobId)
{
var job = await _computationJobStatusService.GetJobAsync(jobId).ConfigureAwait(false);
if(job != null)
{
return Ok(job);
}
return NotFound($"Job with ID `{jobId}` not found");
}
[HttpGet, Route("getAllJobs")]
[ProducesResponseType(StatusCodes.Status200OK,
Type = typeof(IReadOnlyDictionary<string, JobModel>))]
public async Task<IActionResult> GetAllJobsAsync()
{
return Ok(await _computationJobStatusService.GetAllJobsAsync().ConfigureAwait(false));
}
[HttpDelete, Route("clearAllJobs")]
[ProducesResponseType(StatusCodes.Status200OK)]
[ProducesResponseType(StatusCodes.Status401Unauthorized)]
public async Task<IActionResult> ClearAllJobsAsync([FromQuery] string permission)
{
if(permission == "this is flakey security so this can be run as a public demo")
{
await _computationJobStatusService.ClearAllJobsAsync().ConfigureAwait(false);
return Ok();
}
return Unauthorized();
}
}
Working Example
For as long as this question is active, I will maintain a working example you can try out. For this specific example, you can specify how many iterations you would like to run. To simulate long-running work, each iteration is 1 second. So, if you set the iteration value to 60, it will run that job for 60 seconds.
While it's running, run the computationStatus/{jobId} or getAllJobs endpoint. You can watch all the jobs update in real time.
This example is far from a fully-functioning-covering-all-edge-cases-full-blown-ready-for-production example, but it's a good start.
Conclusion
After a few years of working in the back-end, I have seen a lot of issues arise by not knowing all the "rules" of the back-end. Hopefully this answer will shed some light on issues I had in the past and hopefully this saves you from having to deal with said problems.
One option could be to try out Azure Durable Functions, which are more oriented to long-running jobs that warrant checkpoints and state as against attempting to finish within the context of the triggering request. It also has the concept of fan-out/fan-in, in case what you're describing could be divided into smaller jobs with an aggregated result.
If just raw compute is the goal, Azure Batch might be a better option since it facilitates that scaling.
I assume the actual work that needs be done is something other than iterating over a loop doing nothing, so in terms of possible parallelization I can't offer much help right now. Is the work CPU intensive or IO related?
When it comes to long running work in an Azure App Service, one of the option is to use a Web Job. A possible solution would be to post the request for computation to a queue (Storage Queue or Azure Message Bus Queues). The webjob then processes those messages and possibly puts a new message on another queue that the requester can use to handle the results.
If the time needed for processing is guaranteed to be less than 10 minutes you could replace the Web Job with an Queue Triggered Azure Function. It is a serverless offering on Azure with great scaling possibilities.
Another option is indeed using a Service Worker or an instance of an IHostingService and do some queue processing there.
Since you're saying that your computation succeeds at fewer iterations, a simple solution is to simply save your results periodically and resume the computation.
For example, say you need to perform 240 Billion iterations, and you know that the highest number of iterations to perform reliably is 3 Billion iterations, I would set up the following:
A slave, that actually performs the task (240Billion iterations)
A master that periodically received input from the slave about progress.
The slave can periodically send a message to the master (say once every 2billion iterations ?). This message could contain whatever is relevant to resume the computation should the computation be interrupted.
The master should keep track of the slave. If the master determines that the slave has died / crashed / whatever, the master should simply create a new slave which should resume computation from the last reported position.
How exactly you implement the master and slave is a matter of your personal preference.
Rather than have a single loop perform 240 billion iterations, if you can split your computation across nodes, I would try to simultaneously compute the solution in parallel across as many nodes as possible.
I personally use node.js for multicore projects. Although you are using asp.net, I include this example of node.js to illustrate the architecture that works for me.
Node.js on multi-core machines
https://dzone.com/articles/multicore-programming-in-nodejs
As Noah Stahl has mentioned in his answer, Azure Durable Functions and Azure Batch seem like options to help you achieve your goal on your platform. Please see his answer for more details.
The standard answer is to use asynchronous messaging. I have a blog series on the topic. This is particularly the case since you're already in Azure.
You already have an Azure web app service, but now you want to run code outside of a request - "request-extrinsic code". The proper way to run that code is in a separate process - Azure Functions or Azure WebJobs are a good match for Azure webapps.
First, you want a durable queue. Azure Storage Queues are a good fit since you're in Azure anyway. Then your webapi can just write a message into the queue and return. The important part here is that this is a durable queue, not an in-memory queue.
Meanwhile, the Azure Function / WebJob is processing that queue. It will pick up the work from the queue and execute it.
The final piece of the puzzle is the completion notification. This is a pretty common approach:
I can adjust my HTTP controller to instead have three (3) POST methods. Thus POST1 would mean start a task with the inbound object. POST2 means tell me if it is finished. POST3 means give me the outbound object.
To do this, your background processor should save the "in-progress" / "complete/result" state somewhere where the webapi process can access it. If you already have a shared database (and it makes sense to keep results), then this may be the easiest choice. I would also consider using Azure Cosmos DB, which has a nice time-to-live setting so the background service can inject the results that are "good for 24 hours" or whatever, after which they're automatically cleaned up.
I have a system which runs multiple service (long lived) and worker (short lived) threads. They all share a state which contains objects. Any thread can request an object an any time, through a singleton-of-sorts class called ObjectManager. If the object is not available it needs to be loaded.
Here's some pseudo-code of how object loading looks now:
class ObjectManager {
getLoadinData(path) {
if (hasLoadingDataFor(path))
return whatWeHave()
else {
loadingData = createNewLoadingData();
loadingData.path = path;
pushLoadingTaskToLoadingThread(loadingData);
return loadingData;
}
}
// loads object and blocks until it's loaded
loadObjectSync(path) {
loadingData = getLoadinData(path);
waitFor(loadingData.conditionVar);
return loadingData.loadedObject;
}
// initiates a load and calls a callback when done
loadObjectAsync(path, callback) {
loadingData = getLoadinData(path);
loadingData.callbacks.add(callback);
}
// dedicated loading thread
loadingThread() {
while (running) {
loadingData = waitForLoadingData();
object = readObjectFromDisk(loadingData.path);
object.onLoaded(); // !!!!
loadingData.object = object;
// unblock cv waiters
loadingData.conditionVar.notifyAll();
// call callbacks
loadingData.callbacks.callAll(object);
}
}
}
The problem is the line object.onLoaded. I have no control over this function. Some objects might decide that they need other objects to be valid. So in their onLoaded method they might call loadObjectSync. Uh-oh! This (naturally) dead locks. It blocks the loading loop until the loading loop makes more iterations.
What I could do to solve this is leave the onLoaded call to the initiating threads. This will change loadObjectSync to something like:
loadObjectSync(path) {
loadingData = getLoadinData(path);
waitFor(loadingData.conditionVar);
if (loadingData.wasCreatedInThisThread()) {
object.onLoaded();
loadingData.onLoadedConditionVar.notifyAll();
loadingData.callbacks.callAll(object);
}
else {
// wait more
waitFor(loadingData.onLoadedConditionVar);
}
return loadingData.loadedObject;
}
... but then the problem is that if I have no calls for loadSync and only for loadAsync or simply the loadAsync call was the first to create the loading data, there will be no one to finalize the object. So to make this work, I have to introduce another thread finalizes objects whose loadingData was created by loadObjectAsync.
It seems that it would work. But I have a simpler idea! What if I change getLoadingData instead. What if it does this:
getLoadinData(path) {
if (hasLoadingDataFor(path))
return whatWeHave()
else {
loadingData = createNewLoadingData();
loadingData.path = path;
///
thread = spawnLoadingThread(loadingData);
thread.detach();
///
return loadingData;
}
}
Spawn a new thread for every object load. Thus there is no dead lock. Every loading thread can safely block until it's done. The rest of the code remains exactly as it is.
This means potentially tens (or why not thousands in certain edge cases) active threads, waiting on condition variables. I know that spawning threads has its overhead but I think it would be negligible compared to the cost of I/O from readObjectFromDisk
So my question is: Is this terrible? Can this somehow backfire?
The target platform is conventional desktop machines. But this software is supposed to run for a long time without stopping: weeks, maybe months.
Alternatively... even though I have an idea how to solve this if the thread-per-load turns out to be terrible, can this be solved in another way?
Very interesting! This is a problem I have bumped into a couple of times, trying to add a synchronous interface to a fundamentally asynchronous operation (i.e. file load, or in my case, network write) that is performed by a service thread.
My own preference would be to not provide the synchronous interface. Why? Because it keeps the code simpler in design & implementation and easier to reason about -- always important for multi-threading.
Benefits of sticking to single thread & async only is that you only have 1 service thread, so resource growth is not a concern, plus the user callbacks are always invoked on this same thread, which simplifies thread-safety concerns for users of ObjectManager (if you have multiple callback threads, every user callback must be thread safe, so it's an important choice to make). However sticking to only an async interface does mean the user of ObjectManager has more work to do.
But if you do want to keep the synchronous interface, then another approach that I have taken could work for you. You stick to a single service thread but inside the implementation of loadObjectSync you check the thread-ID to determine if the invoker is the service thread or any-other thread. If it is any-other thread you queue the request and safely block. But if it is the service thread, you can immediately load the object, say by calling a new function loadObjectImpl. You will need to grab the thread-ID of the service thread during initialization and store it within the ObjectManager instance, and use that for thread identification. And you will need a new function which is basically just the internal scope of the loadingThread function -- i.e. a new function called something like loadObjectImpl.
consider a multiplayer game that every client can request for some action in server.so that client can send a request continuously.
should i have to implement lock statement on each method that client can call to avoid multiple accessing thread(client)?
something like this one?
https://learn.microsoft.com/en-us/dotnet/csharp/language-reference/keywords/lock-statement
is there any better solution?
my game server is photon engine.
if i place this code in a loop with 200 iterate without lock statement,it will show me some ("its not 11") result from multiple threads.
public static number n1 = new number();
public static void PlusAndMinusInt()
{
lock (n1)
{
n1.x++;
Console.WriteLine($"{n1.x}");
if (n1.x != 11)
Console.WriteLine($"its not 11");
n1.x--;
Console.WriteLine($"{n1.x}");
}
}
well i think i got that.
There is no 'at the same time'
When they are called from the same unity client they will have an order and will be executed in that order, if they happen from different clients they will be processed in parallel potentially as they are on different fibers etc
I don't use Photon, but I think the multithreading synchronizing problem I encountered may be similar.
I once used a Socket library, where each socket can set event triggers upon receiving messages, and it uses multithreads to handle the them;
The solution working for me is to use the ConcurrentQueue ; we do not really handle the message immediately.
Rather, the messages are pushed in this queue, and are later de-queued/handled in the Main Thread.
This saved me the hassle of using lock everywhere; hope that is what you want.
I have a situation where I need a set of operations be enclosed into a single transaction and be thread safe from a MDB.
If thread A executes the instruction 1, do not want other threads can read, at least not the same, data that thread A is processing.
In the code below since IMAGE table contains duplicated data, coming from different sources, this will lead in a duplicated INFRANCTION. Situation that needs to be avoided.
The actual solution that I found is declaring a new transaction for each new message and synchronize the entire transaction.
Simplifying the code:
#Stateless
InfranctionBean{
#TransactionAttribute(TransactionAttributeType.REQUIRES_NEW)
checkInfranction(String plate){
1. imageBean.getImage(plate); // read from table IMAGE
2. infranctionBean.insertInfranction(String plate); // insert into table INFRANCTION
3. imageBean.deleteImage(String plate); //delete from table IMAGE
}
}
#MessageDriven
public class ImageReceiver {
private static Object lock = new Object();
public void onMessage(Message msg){
String plate = msg.plate;
synchronized (lock) {
infanctionBean.checkInfranction(plate);
}
}
}
I am aware that using synchronized blocks inside the EJB is not recommanded by EJB specification. This can lead even in problems if the applicaton server runs in two node cluster.
Seems like EE6 has introduced a solution for this scenario, which is the EJB Singleton.
In this case, my solution would be something like this:
#ConcurrencyManagement(ConcurrencyManagementType.CONTAINER)
#Singleton
InfranctionBean{
#Lock(LockType.WRITE)
checkInfranction(String plate){
1...
2...
3...
}
}
And from MDB would not be neccessary the usage of synchronized block since the container will handle the concurrency.
With #Lock(WRITE) the container guarantees the access of single thread inside checkInfranction().
My queston is: How can I handle this situation in EE5? There is a cleaner solution without using synchronized block?
Environment: Java5,jboss-4.2.3.GA,Oracle10.
ACTUAL SOLUTION
#Stateless
InfranctionBean{
#TransactionAttribute(TransactionAttributeType.REQUIRES_NEW)
checkInfranction(String plate){
1. imageBean.lockImageTable(); // lock table IMAGE in exclusive mode
2. imageBean.getImage(plate); // read from table IMAGE
3. infranctionBean.insertInfranction(String plate); // insert into table INFRANCTION
4. imageBean.deleteImage(String plate); //delete from table IMAGE
}
}
#MessageDriven
public class ImageReceiver {
public void onMessage(Message msg){
infanctionBean.checkInfranction(msg.plate);
}
}
On 20.000 incoming messages (half of them simultaneously) seems the application works ok.
#Lock(WRITE) is only a lock within a single application/JVM, so unless you can guarantee that only one application/JVM is accessing the data, you're not getting much protection anyway. If you're only looking for single application/JVM protection, the best solution in EE 5 would be a ReadWriteLock or perhaps a synchronized block. (The EJB specification has language to dissuade applications from doing this to avoid compromising the thread management of the server, so take care that you don't block indefinitely, that you don't ignore interrupts, etc.)
If you're looking for a more robust cross-application/JVM solution, I would use database locks or isolation levels rather than trying to rely on JVM synchronized primitives. That is probably the best solution regardless of the EJB version being used.
I’m trying to issue web requests asynchronously. I have my code working fine except for one thing: There doesn’t seem to be a built-in way to specify a timeout on BeginGetResponse. The MSDN example clearly show a working example but the downside to it is they all end up with a
SomeObject.WaitOne()
Which again clearly states it blocks the thread. I will be in a high load environment and can’t have blocking but I also need to timeout a request if it takes more than 2 seconds. Short of creating and managing a separate thread pool, is there something already present in the framework that can help me?
Starting examples:
http://msdn.microsoft.com/en-us/library/ms227433(VS.100).aspx
http://msdn.microsoft.com/en-us/library/system.net.httpwebrequest.begingetresponse.aspx
What I would like is a way for the async callback on BeginGetResponse() to be invoked after my timeout parameter expires, with some indication that a timeout occurred.
The seemingly obvious TimeOut parameter is not honored on async calls.
The ReadWriteTimeout parameter doesn't come into play until the response returns.
A non-proprietary solution would be preferable.
EDIT:
Here's what I came up with: after calling BeginGetResponse, I create a Timer with my duration and that's the end of the "begin" phase of processing. Now either the request will complete and my "end" phase will be called OR the timeout period will expire.
To detect the race and have a single winner I call increment a "completed" counter in a thread-safe manner. If "timeout" is the 1st event to come back, I abort the request and stop the timer. In this situation, when "end" is called the EndGetResponse throws an error. If the "end" phase happens first, it increments the counter and the "timeout" foregoes aborting the request.
This seems to work like I want while also providing a configurable timeout. The downside is the extra timer object and the callbacks which I make no effort to avoid. I see 1-3 threads processing various portions (begin, timed out, end) so it seems like this working. And I don't have any "wait" calls.
Have I missed too much sleep or have I found a way to service my requests without blocking?
int completed = 0;
this.Request.BeginGetResponse(GotResponse, this.Request);
this.timer = new Timer(Timedout, this, TimeOutDuration, Timeout.Infinite);
private void Timedout(object state)
{
if (Interlocked.Increment(ref completed) == 1)
{
this.Request.Abort();
}
this.timer.Change(Timeout.Infinite, Timeout.Infinite);
this.timer.Dispose();
}
private void GotRecentSearches(IAsyncResult result)
{
Interlocked.Increment(ref completed);
}
You can to use a BackgroundWorker to run your HttpWebRequest into a separated thread, so your main thread still alive. So, this background thread will be blocked, but first one don't.
In this context, you can to use a ManualResetEvent.WaitOne() just like in that sample: HttpWebRequest.BeginGetResponse() method.
What kind of an application is this? Is this a service proces/ web application/console app?
How are you creating your work load (i.e requests)? If you have a queue of work that needs to be done, you can start off 'N' number of async requests (with the framework for timeouts that you have built) and then, once each request completes (either with timeout or success) you can grab the next request from the queue.
This will thus become a Producer/consumer pattern.
So, if you configure your application to have a maximum of "N' requests outstanding, you can maintain a pool of 'N' timers that you reuse (without disposing) between the requests.
Or, alternately, you can use ThreadPool.SetTimerQueueTimer() to manage your timers. The threadpool will manage the timers for you and reuse the timer between requests.
Hope this helps.
Seems like my original approach is the best thing available.
If you can user async/await then
private async Task<WebResponse> getResponseAsync(HttpWebRequest request)
{
var responseTask = Task.Factory.FromAsync(request.BeginGetResponse, ar => (HttpWebResponse)request.EndGetResponse(ar), null);
var winner = await (Task.WhenAny(responseTask, Task.Delay(new TimeSpan(0, 0, 20))));
if (winner != responseTask)
{
throw new TimeoutException();
}
return await responseTask;
}