My project consists of consuming an api that is built on top of the aws lambda service. Technically, the leader who built the api tells me that there is no fixed request limit since the service is elastic, but it is important to take into account the number of requests per second that the api can support.
To control the limit of requests per second (concurrently), the python script that I am developing uses asyncio and httpx to consume the api concurrently, and taking advantage of the max_connections parameter of httpx.Limits I am trying to find the optimal value so that the api does not freeze.
My problem is that I don't know if I am misinterpreting the use of the max_connections parameter, since when testing with a value of 1000, my understanding tells me that per second I am making 1000 requests concurrently to the api, but even so, the api after a certain time freezes.
I would like to be able to control the limit of requests per second without the need to use third-party libraries.
How could I do it?
Here is my MWE
async def consume(client, endpoint: str = '/create', reg):
data = {"param1": reg[1]}
response = await client.post(url=endpoint, data=json.dumps(data))
return response.json()
async def run(self, regs):
# Empty list to consolidate all responses
results = []
# httpx limits configuration
limits = httpx.Limits(max_keepalive_connections=None, max_connections=1000)
timeout = httpx.Timeout(connect=60.0, read=30.0, write=30.0, pool=60.0)
# httpx client context
async with httpx.AsyncClient(base_url='https://apiexample', headers={'Content-Type': 'application/json'},
limits=limits, timeout=timeout) as client:
# regs is a list of more than 1000000 tuples
tasks = [asyncio.ensure_future(consume(client=client, reg=reg))
for reg in regs]
result = await asyncio.gather(*tasks)
results += result
return results
Thanks in advance.
Your leader is wrong - there is a request limit for AWS lambda (it's 1000 concurrent executions by default).
AWS API is highly unlikely to "freeze" (there are many layers of protection), so I would look for a problem on your side.
Start debugging, by lowering the concurent connections setting (e.g. 100), and explore other settings if this doesn't fix the issue..
More info: https://www.bluematador.com/blog/why-aws-lambda-throttles-functions
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 am using Azure Functions, and have stumbled upon an issue.
When requesting large amount of data from an external source, it seems the stream is shut. I have simplified the example as simple as I can below it will silently fail, and return 500. By silent I mean there are no errors in Application Insights that I can see, and no way to know the issue.
This works locally as an azure function.
Is there some sort of limit on data that can be read? It doesn't take much memory (70mb or so locally).
So really banging my head against a wall the last two days on this one. Any help appreciated!
[FunctionName("FeedDownloads")]
public HttpResponseMessage Run([HttpTrigger(AuthorizationLevel.Anonymous, "get" )]HttpRequestMessage req,
ILogger log)//, [FromQuery]string format = "", [FromQuery]bool debug = false)
{
System.Net.HttpWebRequest webRequest = (System.Net.HttpWebRequest)System.Net.WebRequest.Create("{large 1.5GB gzipped file}");
webRequest.AutomaticDecompression = System.Net.DecompressionMethods.GZip;
var webRequestResponse = webRequest.GetResponse();
var res = req.CreateResponse(HttpStatusCode.OK);
res.Content = new StreamContent(webRequestResponse.GetResponseStream());
res.Content.Headers.ContentType = new MediaTypeHeaderValue(webRequestResponse.ContentType);
return res;
}
Azure Functions are not meant for long term communication with the client devices. Large, long-running functions can cause unexpected timeout issues.
There are several other problems that you must take a note of before anything. And this is an excerpt from official documentation, functions-best-practices
. Functions should be stateless and idempotent if possible. Associate any required state information with your data. For example, an order being processed would likely have an associated state member. A function could process an order based on that state while the function itself remains stateless.
I am sending data (4MB) as gipped request to azure functions with javascript runtime and HttpTrigger. In the function, I decompress the data and process it. It takes 6-7 Seconds to run the code in function but the round trip of request takes almost 60 Seconds. I understand that it takes some time to upload the request but I didn't expect such huge delay. How can I debug where the time is going?
It is not cold start issue as request takes 60 Seconds consistently.
As far as I'm aware Azure Functions doesn't yet support attaching the App Insights Profiler, however you could add your own telemetry.
This won't necessarily help if the time is being spent inside the Azure functions runtime, but it could help alleviate if the bottleneck is during de-compression / processing:
https://learn.microsoft.com/en-us/azure/application-insights/application-insights-custom-operations-tracking?toc=/azure/azure-monitor/toc.json#outgoing-dependencies-tracking
The general approach for custom dependency tracking is to:
Call the TelemetryClient.StartOperation (extension) method that fills the DependencyTelemetry properties that are needed for correlation and some other properties (start time stamp, duration).
Set other custom properties on the DependencyTelemetry, such as the name and any other context you need.
Make a dependency call and wait for it.
Stop the operation with StopOperation when it's finished.
Handle exceptions.
Example from the docs:
public async Task RunMyTaskAsync()
{
using (var operation = telemetryClient.StartOperation<DependencyTelemetry>("task 1"))
{
try
{
var myTask = await StartMyTaskAsync();
// Update status code and success as appropriate.
}
catch(...)
{
// Update status code and success as appropriate.
}
}
}
Environment: Webphere 6, Solaris box, Thick client, Java web app.
Number of request can be between 400 - 600. On each request to server, I am creating 15 threads( using Java ExecutorService) for requesting 15 different webservies simultaneously and group all the responses data together and send it back to user.
Load test fails at nearly 150 - 170 users. CPU and memory spikes are seen in DB servicing these webservices and eventually after a very short period of time app server too crashes.
Response time of webservice is 10-12 sec max and 4-6 secs min. Connection pooling size of the DB is 40.
I am assuming that 150 request are creating 150*15=2250 threads and app server resources are being spiked and hence crashing. So I want to use App server threadpool and have threadCount say 100 (may not be good number..). One thing thats troubling me is, with 100 threads I can process first 6 (6*15 = 90) requests and 10 calls of 7th request. The next requests have to wait for 10-15 secs for getting the threads back and then another 10-15 secs for its own webservice call. Is this approach even good?
Another idea was asynchronous beans provided in Websphere. Which one suits my requirement.
Please suggest!!. Calling one webservice after another takes a total of 15*(lets say 4sec for each request) = 60 secs which is really bad. So calling webserices together is what I want to do.
Managing your threads in application servers is not recommended. If you are using EJBs, the spec disallows that.
Why don't you use use a caching solution to improve the performance? The first few requests will be slower, but once the cache is hot everything will be very fast.
If caching the data is not feasible, what about changing the client to make multiple requests to the server, instead of splitting one request in multiple threads? You would need to change your web application, so that each method would call one web service. The client would call (in parallel) each method needed for the current page and assemble the final result (it may be possible to display partial results if you wish). By doing this you will do work in parallel and won't violate the spec.
I assume you have something like this, in your server:
public Result retriveData(Long id) {
Result myResult = new Result();
//...
//do some stuff
myResult.setSomeData(slowWebService1.retriveSomeData(id));
myResult.setSomeOtherData(slowWebService2.retriveSomeOtherData(id));
myResult.setData(slowWebService3.retriveData(id));
return myResult;
}
In your client:
Result result = webApplication.retriveData(10);
//use the result
My proposal, is to split the calls in multiple methods:
public SomeData retriveSomeData(Long id) {
//do some stuff
SomeData data = slowWebService1.retriveSomeData(id);
//do more stuff
return data;
}
public SomeOtherData retriveSomeOtherData(Long id) {
//do some stuff
SomeOtherData data = slowWebService2.retriveSomeOtherData(id);
//do more stuff
return data;
}
public Data retriveData(Long id) {
//do some stuff
Data data = slowWebService3.retriveData(id);
//do more stuff
return data;
}
In your client:
//Call these methods in parallel, if you were using Swing, this could be done with
//SwingWorker (I have no idea how to it with Flash :)).
//You can either wait for all methods to return or show partial results.
callInBackground(webApplication.retriveSomeData(10), useDataWhenDone);
callInBackground(webApplication.retriveSomeOtherData(10), useDataWhenDone);
callInBackground(webApplication.retriveData(10), useDataWhenDone);
By doing this you are calling only your web application, just like before, so there shouldn't be any security issues.
I am not familiar with Websphere, so I can't tell if using its asynchronous beans are better than this, but IMHO you should avoid starting threads manually.