I have a service that authenticates a user and I put delayElement for 2 seconds when it is about to return a 401.
When I run through artillery, it seems to start timing out some of the requests and I was wondering what kind of resources are used for delayElement as I was sort of hoping it just switches context away from the current request and goes to the next one that needs processing and just alarm itself back later to return the response.
private Mono<R> addDelaySpecifiedInServiceResponse(AuthServiceResponse<R> serviceResponse) {
return fromCallable(serviceResponse::getOperationResponse)
.delayElement(serviceResponse.getDelay(), Schedulers.newParallel("penalty"));
}
I tried different schedulers and seem to get a similar result.
delayElement in essence schedules a task on the provided Scheduler (or the common parallel scheduler by default), so yes it "alarms itself back later" if I understand your comment correctly.
I have a simple series of chained operations that retrieve and persist some data using a Panache repository, running in a Quarkus service. Where these operations are parallelised a ContextNotActiveException is thrown. Where the parallelisation is removed, the code works as intended.
This code works:
dataRepository.get()
.map { convert(it) }
.forEach { perist(it) }
This code does not:
dataRepository.get()
.parallelStream()
.map { convert(it) }
.forEach { perist(it) }
The Quarkus documentation is pretty limited, only addressing use of mutiny or RX.
How can I propagate the context such that parallelStream() will work?
Unfortunately Context Propagation does not play well with parallel Java streams, because making a stream parallel automatically moves the execution to the ForkJoinPool, which means you lose the context. You'll need to handle the parallelism differently, without having the Java streams do it for you - you will probably want to use the org.eclipse.microprofile.context.ManagedExecutor.
Assuming that it's the convert method which, for whatever reason, requires an active request context, you will need to dispatch its invocation into the managed executor. This will make sure that the context is propagated.
In Java code, one close equivalent to your code that I can think of is this:
#Inject
org.eclipse.microprofile.context.ManagedExecutor executor;
(...)
dataRepository.streamAll()
.forEach(i -> {
executor.supplyAsync(() -> {
return convert(i);
}).thenAccept(persist(i));
});
I have a very simple test cases(scalatest, but doesn't matter) and I provide two implementation of accessing some resources, this method returns either Try or some case class instance.
Test cases:
"ResourceLoader" must
"successfully initialize resource" in {
/async code test
noException should be thrownBy Await.result(ResourceLoader.initializeRemoteResourceAsync(credentials, networkConfig), Duration.Inf)
}
"ResourceLoader" must
"successfully sync initialize remote resources" in {
noException should be thrownBy ResourceLoader.initializeRemoteResource(credentials, networkConfig)
}
This tests testing different code which access some remote resources
Sync version
def initializeRemoteResource(credentials: Credentials, absolutePathToNetworkConfig: String): Resource = {
//some code accessing remote server
}
Async version
def initializeRemoteResourceAsync(credentials: Credentials, absolutePathToNetworkConfig: String): Future[Try[Resource]] = {
Future {
//the same code as in sync version
}
}
In IDEA test tab I see that future based version is twice slower then sync version, my question is there overhead for calling Await.result explicitly? If not, why it slows down the execution? Appreciate any help, Thanks.
Note: I know it is not the best way to measure performance of production system. But it at list says how much time was spend on each test case.
Yes, there will be a small overhead for Await.result, but in practice it probably doesn't amount to much. Future {} requires an ExecutionContext (thread pool or thread creator) in implicit scope so you won't be able to successfully use it without importing the default execution context (which will simply spawn a thread) or some other context. If you're using the default execution context, for example, you will have two threads instead of one which will involve some overhead for context switching. It shouldn't be much though. If 'twice as slow' means 40ms instead of 20 then perhaps it's not worth worrying about.
could someone help me to read NewRelic Summary and Trace details. Following screenshots have trace for a single transaction, which do not create any query to the database. It is just a simple query with few lines of Scala template code, which renders HTML page and returns it to the client. This is just a single transaction that is currently running in production. Production has plenty of more complex transaction running which do lots of external calls to Mongo, Maria, Queue, etc.
Does the trace reveal anything about where bottleneck could be? Are we for example running out of Threads or Workers. As I told most of the transactions do lots of web external calls, which might reserve single Thread for quite long time. How one can actually study if Threads or Workers are running out in Play application? We are using 2.1.4.
What actually happens in following calls?
Promise.apply 21.406ms
Async Wait 21.406ms
Actor.tell 48.366ms
PlayDefaultUpstreamHandler 6.292ms
Edit:
What is the purpose of following calls? Those have super high average call times.
scala.concurrent.impl.CallbackRunnable.run()
scala.concurrent.impl.Future$PromiseCompletingRunnable.run()
org.jboss.netty.handler.codec.http.HttpRequestDecoder.unfoldAndFireMessageReceived()
Edit:
play {
akka {
event-handlers = ["akka.event.slf4j.Slf4jEventHandler"]
loglevel = WARNING
actor {
default-dispatcher = {
fork-join-executor {
parallelism-min = 350
parallelism-max = 350
}
}
exports = {
fork-join-executor {
parallelism-min = 10
parallelism-max = 10
}
}
}
}
}
I'm not sure if this will help you 1 year later but I think the performance problems you were hitting are not related to Play, Akka or Netty.
The problem will be in your code business logic or in the database access. The big times that you see for PromiseCompletingRunnable and unfoldAndFireMessageReceived are misleading. This times are reported by newrelic in a wrong and misleading way. Please read this post:
Extremely slow play framework 2.3 request handling code
I faced a similar problem, and mine was in the database but newrelic reported big times in netty.
I hope this helps you even now.
We recently developed a site based on SOA but this site ended up having terrible load and performance issues when it went under load. I posted a question related this issue here:
ASP.NET website becomes unresponsive under load
The site is made of an API (WEB API) site which is hosted on a 4-node cluster and a web site which is hosted on another 4-node cluster and makes calls to the API. Both are developed using ASP.NET MVC 5 and all actions/methods are based on async-await method.
After running the site under some monitoring tools such as NewRelic, investigating several dump files and profiling the worker process, it turned out that under a very light load (e.g. 16 concurrent users) we ended up having around 900 threads which utilized 100% of CPU and filled up the IIS thread queue!
Even though we managed to deploy the site to the production environment by introducing heaps of caching and performance amendments many developers in our team believe that we have to remove all async methods and covert both API and the web site to normal Web API and Action methods which simply return an Action result.
I personally am not happy with approach because my gut feeling is that we have not used the async methods properly otherwise it means that Microsoft has introduced a feature that basically is rather destructive and unusable!
Do you know any reference that clears it out that where and how async methods should/can be used? How we should use them to avoid such dramas? e.g. Based on what I read on MSDN I believe the API layer should be async but the web site could be a normal no-async ASP.NET MVC site.
Update:
Here is the async method that makes all the communications with the API.
public static async Task<T> GetApiResponse<T>(object parameters, string action, CancellationToken ctk)
{
using (var httpClient = new HttpClient())
{
httpClient.BaseAddress = new Uri(BaseApiAddress);
var formatter = new JsonMediaTypeFormatter();
return
await
httpClient.PostAsJsonAsync(action, parameters, ctk)
.ContinueWith(x => x.Result.Content.ReadAsAsync<T>(new[] { formatter }).Result, ctk);
}
}
Is there anything silly with this method? Note that when we converted all method to non-async methods we got a heaps better performance.
Here is a sample usage (I've cut the other bits of the code which was related to validation, logging etc. This code is the body of a MVC action method).
In our service wrapper:
public async static Task<IList<DownloadType>> GetSupportedContentTypes()
{
string userAgent = Request.UserAgent;
var parameters = new { Util.AppKey, Util.StoreId, QueryParameters = new { UserAgent = userAgent } };
var taskResponse = await Util.GetApiResponse<ApiResponse<SearchResponse<ProductItem>>>(
parameters,
"api/Content/ContentTypeSummary",
default(CancellationToken));
return task.Data.Groups.Select(x => x.DownloadType()).ToList();
}
And in the Action:
public async Task<ActionResult> DownloadTypes()
{
IList<DownloadType> supportedTypes = await ContentService.GetSupportedContentTypes();
Is there anything silly with this method? Note that when we converted
all method to non-async methods we got a heaps better performance.
I can see at least two things going wrong here:
public static async Task<T> GetApiResponse<T>(object parameters, string action, CancellationToken ctk)
{
using (var httpClient = new HttpClient())
{
httpClient.BaseAddress = new Uri(BaseApiAddress);
var formatter = new JsonMediaTypeFormatter();
return
await
httpClient.PostAsJsonAsync(action, parameters, ctk)
.ContinueWith(x => x.Result.Content
.ReadAsAsync<T>(new[] { formatter }).Result, ctk);
}
}
Firstly, the lambda you're passing to ContinueWith is blocking:
x => x.Result.Content.ReadAsAsync<T>(new[] { formatter }).Result
This is equivalent to:
x => {
var task = x.Result.Content.ReadAsAsync<T>(new[] { formatter });
task.Wait();
return task.Result;
};
Thus, you're blocking a pool thread on which the lambda is happened to be executed. This effectively kills the advantage of the naturally asynchronous ReadAsAsync API and reduces the scalability of your web app. Watch out for other places like this in your code.
Secondly, an ASP.NET request is handled by a server thread with a special synchronization context installed on it, AspNetSynchronizationContext. When you use await for continuation, the continuation callback will be posted to the same synchronization context, the compiler-generated code will take care of this. OTOH, when you use ContinueWith, this doesn't happen automatically.
Thus, you need to explicitly provide the correct task scheduler, remove the blocking .Result (this will return a task) and Unwrap the nested task:
return
await
httpClient.PostAsJsonAsync(action, parameters, ctk).ContinueWith(
x => x.Result.Content.ReadAsAsync<T>(new[] { formatter }),
ctk,
TaskContinuationOptions.None,
TaskScheduler.FromCurrentSynchronizationContext()).Unwrap();
That said, you really don't need such added complexity of ContinueWith here:
var x = await httpClient.PostAsJsonAsync(action, parameters, ctk);
return await x.Content.ReadAsAsync<T>(new[] { formatter });
The following article by Stephen Toub is highly relevant:
"Async Performance: Understanding the Costs of Async and Await".
If I have to call an async method in a sync context, where using await
is not possible, what is the best way of doing it?
You almost never should need to mix await and ContinueWith, you should stick with await. Basically, if you use async, it's got to be async "all the way".
For the server-side ASP.NET MVC / Web API execution environment, it simply means the controller method should be async and return a Task or Task<>, check this. ASP.NET keeps track of pending tasks for a given HTTP request. The request is not getting completed until all tasks have been completed.
If you really need to call an async method from a synchronous method in ASP.NET, you can use AsyncManager like this to register a pending task. For classic ASP.NET, you can use PageAsyncTask.
At worst case, you'd call task.Wait() and block, because otherwise your task might continue outside the boundaries of that particular HTTP request.
For client side UI apps, some different scenarios are possible for calling an async method from synchronous method. For example, you can use ContinueWith(action, TaskScheduler.FromCurrentSynchronizationContext()) and fire an completion event from action (like this).
async and await should not create a large number of threads, particularly not with just 16 users. In fact, it should help you make better use of threads. The purpose of async and await in MVC is to actually give up the thread pool thread when it's busy processing IO bound tasks. This suggests to me that you are doing something silly somewhere, such as spawning threads and then waiting indefinitely.
Still, 900 threads is not really a lot, and if they're using 100% cpu, then they're not waiting.. they're chewing on something. It's this something that you should be looking into. You said you have used tools like NewRelic, well what did they point to as the source of this CPU usage? What methods?
If I were you, I would first prove that merely using async and await are not the cause of your problems. Simply create a simple site that mimics the behavior and then run the same tests on it.
Second, take a copy of your app, and start stripping stuff out and then running tests against it. See if you can track down where the problem is exactly.
There is a lot of stuff to discuss.
First of all, async/await can help you naturally when your application has almost no business logic. I mean the point of async/await is to do not have many threads in sleep mode waiting for something, mostly some IO, e.g. database queries (and fetching). If your application does huge business logic using cpu for 100%, async/await does not help you.
The problem of 900 threads is that they are inefficient - if they run concurrently. The point is that it's better to have such number of "business" threads as you server has cores/processors. The reason is thread context switching, lock contention and so on. There is a lot of systems like LMAX distruptor pattern or Redis which process data in one thread (or one thread per core). It's just better as you do not have to handle locking.
How to reach described approach? Look at disruptor, queue incoming requests and processed them one by one instead of parallel.
Opposite approach, when there is almost no business logic, and many threads just waits for IO is good place where to put async/await into work.
How it mostly works: there is a thread which reads bytes from network - mostly only one. Once some some request arrive, this thread reads the data. There is also limited thread pool of workers which processes requests. The point of async is that once one processing thread is waiting for some thing, mostly io, db, the thread is returned in poll and can be used for another request. Once IO response is ready, some thread from pool is used to finish the processing. This is the way how you can use few threads to server thousand request in a second.
I would suggest that you should draw some picture how your site is working, what each thread does and how concurrently it works. Note that it's necessary to decide whether throughput or latency is important for you.