I have an API Service, that sometimes takes a while to spit out the answer, like maybe 120 to 200 seconds. Most of the time the answer will be produced in 1 to 5 seconds.
When there are let's say 20 requests to IIS that have long-running answers between 120 - 200 seconds. All the other incoming requests seem to take much longer to process and everything gets real slow until those 20 requests produce results.
I have the application pool set 50,000 concurrent requests, but I see this behaviour with as little as 100 x 200 requests and then the requests keep growing until the long running ones complete.
I was under the impression that all requests will run normally and independently of one another. And a few shouldn't slow down the rest. The CPU on the machine never churns over 1%.
Is there anything I might be overlooking? Configuration wise?
After reading lex li answer, and setting the max worker threads might help, how many CPU's is this considered?
And how many worker threads will this enable?
<processModel autoConfig="false" requestQueueLimit="100000" maxWorkerThreads="200" maxIoThreads="200" minWorkerThreads="50" minIoThreads="50" />
And how many under this scenario?
Related
I wrote a simple node.js program that sends out 1000 http requests and it records when these requests comes back and just increases counter by 1 upon response. Endpoint is very light weight and it just has simple http resonse without any heavy html. I recorded that it returns me around 200-300 requests per second for 3 seconds. On other hand, when i start this same process 3 times (4 total processes = amount of my available cores) i notice that it performs x4 faster. So i receive aproximately 300 * 4 requests per second back. I want to understand what happens when epoll gets triggered upon Kernel notifying the poll about new file descriptor being ready to compute (new tcp payload arrived). Does v8 take out this filedescriptor and read the data / manipulate with it and where is the actuall bottleneck? Is it in computing and unpacking the payload? It seems that only 1 core is working on sending/receiving these requests for this 1 process and when i start multiple (on amount of my cores), it performs faster.
where is the actuall bottleneck?
Sounds like you're interested in profiling your application. See https://nodejs.org/en/docs/guides/simple-profiling/ for the official documentation on that.
What I can say up front is that V8 does not deal with file descriptors or epoll, that's all Node territory. I don't know Node's internals myself (only V8), but I do know that the code is on https://github.com/nodejs/node, so you can look up how any given feature is implemented.
I have a Python project with a server that distributes work to one or more clients. Each client is given a number of assignments which contain parameters for querying a target API. This includes a maximum number of requests per second they can make with a given API key. The clients process the response and send the results back to the server to store into a database.
Both the server and clients use Tornado for asynchronous networking. My initial implementation for the clients relied on the PeriodicCallback to ensure that n-number of calls to the API would occur. I thought that this was working properly as my tests would last 1-2 minutes.
I added some telemetry to collect statistics on performance and noticed that the clients were actually having issues after almost exactly 2 minutes of runtime. I had set the API requests to 20 per second (the maximum allowed by the API itself) which the clients could reliably hit. However, after 2 minutes performance would fluctuate between 12 and 18 requests per second. The number of active tasks steadily increased until it hit the maximum amount of active assignments (100) given from the server and the HTTP request time to the API was reported by Tornado to go from 0.2-0.5 seconds to 6-10 seconds. Performance is steady if I only do 14 requests per second. Anything higher than 15 requests will experience issues 2-3 minutes after starting. Logs can be seen here. Notice how the column of "Active Queries" is steady until 01:19:26. I've truncated the log to demonstrate
I believed the issue was the use of a single process on the client to handle both communication to the server and the API. I proceeded to split the primary process into several different processes. One handles all communication to the server, one (or more) handles queries to the API, another processes API responses into a flattened class, and finally a multiprocessing Manager for Queues. The performance issues were still present.
I thought that, perhaps, Tornado was the bottleneck and decided to refactor. I chose aiohttp and uvloop. I split the primary process in a similar manner to that in the previous attempt. Unfortunately, performance issues are unchanged.
I took both refactors and enabled them to split work into several querying processes. However, no matter how much you split the work, you still encounter problems after 2-3 minutes.
I am using both Python 3.7 and 3.8 on MacOS and Linux.
At this point, it does not appear to be a limitation of a single package. I've thought about the following:
Python's asyncio library cannot handle more than 15 coroutines/tasks being generated per second
I doubt that this is true given that different libraries claim to be able to handle several thousand messages per second simultaneously. Also, we can hit 20 requests per second just fine at the start with very consistent results.
The API is unable to handle more than 15 requests from a single client IP
This is unlikely as I am not the only user of the API and I can request 20 times per second fairly consistently over an extended period of time if I over-subscribe processes to query from the API.
There is a system configuration causing the limitation
I've tried both MacOS and Debian which yield the same results. It's possible that's it a *nix problem.
Variations in responses cause a backlog which grows linearly until it cannot be tackled fast enough
Sometimes responses from the API grow and shrink between 0.2 and 1.2 seconds. The number of active tasks returned by asyncio.all_tasks remains consistent in the telemetry data. If this were true, we wouldn't be consistently encountering the issue at the same time every time.
We're overtaxing the hardware with the number of tasks generated per second and causing thermal throttling
Although CPU temperatures spike, neither MacOS nor Linux report any thermal throttling in the logs. We are not hitting more than 80% CPU utilization on a single core.
At this point, I'm not sure what's causing it and have considered refactoring the clients into a different language (perhaps C++ with Boost libraries). Before I dive into something so foolish, I wanted to ask if I'm missing something simple.
Conclusion
Performance appears to vary wildly depending on time of day. It's likely to be the API.
How this conclusion was made
I created a new project to demonstrate the capabilities of asyncio and determine if it's the bottleneck. This project takes two websites, one to act as the baseline and the other is the target API, and runs through different methods of testing:
Spawn one process per core, pass a semaphore, and query up to n-times per second
Create a single event loop and create n-number of tasks per second
Create multiple processes with an event loop each to distribute the work, with each loop performing (n-number / processes) tasks per second
(Note that spawning processes is incredibly slow and often commented out unless using high-end desktop processors with 12 or more cores)
The baseline website would be queried up to 50 times per second. asyncio could complete 30 tasks per second reliably for an extended period, with each task completing their run in 0.01 to 0.02 seconds. Responses were very consistent.
The target website would be queried up to 20 times per second. Sometimes asyncio would struggle despite circumstances being identical (JSON handling, dumping response data to queue, returning immediately, no CPU-bound processing). However, results varied between tests and could not always be reproduced. Responses would be under 0.4 seconds initially but quickly increase to 4-10 seconds per request. 10-20 requests would return as complete per second.
As an alternative method, I chose a parent URI for the target website. This URI wouldn't have a large query to their database but instead be served back with a static JSON response. Responses bounced between 0.06 seconds to 2.5-4.5 seconds. However, 30-40 responses would be completed per second.
Splitting requests across processes with their own event loop would decrease response time in the upper-bound range by almost half, but still took more than one second each to complete.
The inability to reproduce consistent results every time from the target website would indicate that it's a performance issue on their end.
I'm running load tests on AWS Lambda with Charlesproxy, but am confused by the timelines chart produced. I've setup a test with 100 concurrent connections and expect varying degrees of latency, but expect all 100 requests to be kicked off at the same time (hence concurrent setting in charlesproxy repeat advanced feature), but I'm seeing some requests get started a bit late ... that is if I understand the chart correctly.
With only 100 invocations, I should be well within the concurrency max set by AWS Lambda, so why then are these request being kicked off late (see requests 55 - 62 on attached image)?
Lambda can take from a few hundred milliseconds to 1-2 seconds to start up when it's in "cold state". Cold means it needs to download your package, unpack it, load in memory, then start executing your code. After execution, this container is kept "alive" for about 5 to 30 minutes ("warm state"). If you request again while it's warm, container startup is much faster.
You probably had a few containers already warm when you started your test. Those started up faster. Since the other requests came concurrently, Lambda needed to start more containers and those came from a "cold state", thus the time difference you see in the chart.
I'm constantly querying a server for a list of items. Usually the data returned is "offers:" with a blank list, 99.999% of the time this is the data I get back from my request. So, pretty small payload.
I have a ping of 35ms pretty rock solid, jitter is about 0.2ms
But when running a single loop of requests, I get updates about every 300ms.
My goal is to make this as fast as I possibly can, so I parallelized it, running 8 of the loops. But now, I see that frequently 4 or more of the threads will seem to run simultaneously. Giving me 4 requests within 10 or 20ms of each other, and leaving periods of up to 200ms with no requests being processed.
The server I'm interfacing is of unknown specs, but it's a large company I'm interfacing with and I'd assume that the server I am communicating with is more than capable of handling anything I could possibly throw at it.
I'm working on a crawler and I've noticed that by setting the length of time for waiting 1 minute per request has made the application more reliable and I now get fewer connection resets. Can you recommend a reasonable amount of time to wait? I think 1 minute is quite the belts and braces approach and I would like to reduce this ideally.