I got a question for you guys and its not as specific as usual, which could make it a little annoying to answer.
The tool i'm working with is Camunda in combination with Groovy scripts and the goal is to reduce the maximum cpu load (or peak load). I'm doing this by "stretching" the work load over a certain time frame since the platform seems to be unhappy with huge work load inputs in a short amount of time. The resulting problem is that Camunda wont react smoothly when someone tries to operate it at the UI - Level.
So i wrote a small script which basically just lets each individual process determine his own "time to sleep" before running, if a certain threshold is exceeded. This is based on how many processes are trying to run at the same time as the individual process.
It looks like:
Process wants to start -> Process asks how many other processes are running ->
waitingTime = numberOfProcesses * timeToSleep * iterationOfMeasures
CPU-Usage Curve 1,3 without the Script. Curve 2,4 With the script
Testing it i saw that i could stretch the work load and smoothe out the UI - Levels. But now i need to describe why this is working exactly.
The Questions are:
What does a sleep method do exactly ?
What does the sleep method do on CPU - Level?
How does an OS-Scheduler react to a Sleep Method?
Namely: Does the scheduler reschedule or just simply "wait" for the time given?
How can i recreate and test the question given above?
The main goal is not for you to answer this, but could you give me a hint for finding the right Literature to answer these questions? Maybe you remember a book which helped you understand this kind of things or a Professor recommended something to you. (Mine wont answer, and i cant blame him)
I'm grateful for hints and or recommendations !
i'm sure you could use timer event
https://docs.camunda.org/manual/7.15/reference/bpmn20/events/timer-events/
it allows to postpone next task trigger for some time defined by expression.
about sleep in java/groovy: https://www.javamex.com/tutorials/threads/sleep.shtml
using sleep is blocking current thread in groovy/java/camunda.
so instead of doing something effective it's just blocked.
Related
I am trying to implement a Django web application (on Python 3.8.5) which allows a user to create “activities” where they define an activity duration and then set the activity status to “In progress”.
The POST action to the View writes the new status, the duration and the start time (end time, based on start time and duration is also possible to add here of course).
The back-end should then keep track of the duration and automatically change the status to “Finished”.
User actions can also change the status to “Finished” before the calculated end time (i.e. the timer no longer needs to be tracked).
I am fairly new to Python so I need some advice on the smartest way to implement such a concept?
It needs to be efficient and scalable – I’m currently using a Heroku Free account so have limited system resources, but efficiency would also be important for future production implementations of course.
I have looked at the Python threading Timer, and this seems to work on a basic level, but I’ve not been able to determine what kind of constraints this places on the system – e.g. whether the spawned Timer thread might prevent the main thread from finishing and releasing resources (i.e. Heroku Dyno threads), etc.
I have read that persistence might be a problem (if the server goes down), and I haven’t found a way to cancel the timer from another process (the .cancel() method seems to rely on having the original object to cancel, and I’m not sure if this is achievable from another process).
I was also wondering about a more “background” approach, i.e. a single process which is constantly checking the database looking for activity records which have reached their end time and swapping the status.
But what would be the best way of implementing such a server?
Is it practical to read the database every second to find records with an end time of “now”? I need the status to change in real-time when the end time is reached.
Is something like Celery a good option, or is it overkill for a single process like this?
As I said I’m fairly new to these technologies, so I may be missing other obvious solutions – please feel free to enlighten me!
Thanks in advance.
To achieve this you need some kind of scheduling tasks functionality. For a fast simpler implementation is a good solution to use the Timer object from the
Threading module.
A more complete solution is tu use Celery. If you are new, deeping in it will give you a good value start using celery as a queue manager distributing your work easily across several threads or process.
You mentioned that you want it to be efficient and scalable, so I guess you will want to implement similar functionalities that will require multiprocessing and schedule so for that reason my recommendation is to use celery.
You can integrate it into your Django application easily following the documentation Integrate Django with Celery.
I need to run different Python processes, in a certain order of priority.
Specifically, I have 3 processes, and I need them to work this way:
An object detection script, used to locate a person and their position. I need this one to run continuously at a high FPS;
another process that, once some conditions are met (when the person is present in the picture in the required position) starts taking screenshots of the image for a certain amount of time;
another script that analyzes the screenshots taken by the second one.
I wrote the 3 scripts already and they work fine, but the problem is that process 3 is particularly computationally demanding, and I don't want it to prevent processes 1 and 2 from running smoothly.
My idea is that I could give highest priority to process 1, and send screenshots taken by process 2...to a queue, or something like this.
When the person is not detected in the picture, I could run process 3, and empty the queue as the screenshots are analyzed. However, script 3 should still run with limited resources, so that FPS of script 1 isn't affected too much, and it can still detect if the person enters the picture again.
I'm afraid this might all be a little vague, but could you please suggest me a way or tool I could use to manage the processes this way?
So far, I tried simply saving the screenshots to a folder, but I don't know how to limit the resources usage by process 3.
I'm familiar with the basic usage of Docker, so I was thinking that maybe I could:
run the processes in different containers, limiting resources allocated to the 3rd one (?);
use a message broker (Kafka, RabbitMQ?) to store screenshots;
but again, I'm a newbie when it comes to this stuff (speaking of which, I hope I tagged this question correctly), so I don't know if it's an efficient way to to do this (or if it can be done this way, for that matter).
I know there are some questions about this topic but none of these helped me to find a solution.
I've got two Timeline Animations, I want to execute them after a delay of a few seconds. I'm gonna show you an example:
Every time I click my mouse, the Animation shall reset to its default delay time, let's say 5 seconds. If I'll do nothing the time's running away until it's zero. And when I reach the 0 seconds, the Animation has to start(). And so on.
Of course Thread.sleep() would make my UI freeze until the mission is done.
And I don't know whether I should use Thread, Task or other classes because the work is not that complex.
There are a bunch of ways to do it, but I'm not experienced in multithreading and I wanna learn to make it efficiently. Thank you guys a lot.
You can probably achieve what you want using
timeline.setDelay(...);
to specify a delay before the timeline starts,
timeline.setCycleCount(Animation.INDEFINITE);
to make it repeat indefinitely, and
timeline.playFromStart();
to make it start again from the beginning (after its specified delay).
I wrote a test program 'cputest.py' in python like this:
import time
while True:
for _ in range(10120*40):
pass
time.sleep(0.008)
, which costs 80% cpu when running in a container (without interference of other runnng containers).
Then I ran this program in two containers by the following two commands:
docker run -d -c 256 --cpuset=1 IMAGENAME python /cputest.py
docker run -d -c 1024 --cpuset=1 IMAGENAME python /cputest.py
and used 'top' to view their cpu costs. It turned out that they relatively cost 30% and 67% cpu. I'm pretty puzzled by this result. Would anyone kindly explain it for me? Many thanks!
I sat down last night and tried to figure this out on my own, but ended up not being able to explain the 70 / 30 split either. So, I sent an email to some other devs and got this response, which I think makes sense:
I think you are slightly misunderstanding how task scheduling works - which is why the maths doesn't work. I'll try and dig out a good article but at a basic level the kernel assigns slices of time to each task that needs to execute and allocates slices to tasks with the given priorities.
So with those priorities and the tight looped code (no sleep) the kernel assigns 4/5 of the slots to a and 1/5 to b. Hence the 80/20 split.
However when you add in sleep it gets more complex. Sleep basically tells the kernel to yield the current task and then execution will return to that task after the sleep time has elapsed. It could be longer than the time given - especially if there are higher priority tasks running. When nothing else is running the kernel then just sits idle for the sleep time.
But when you have two tasks the sleeps allow the two tasks to interweave. So when one sleeps the other can execute. This likely leads to a complex execution which you can't model with simple maths. Feel free to prove me wrong there!
I think another reason for the 70/30 split is the way you are doing "80% load". The numbers you have chosen for the loop and the sleep just happen to work on your PC with a single task executing. You could try moving the loop to be based on elapsed time - so loop for 0.8 then sleep for 0.2. That might give you something closer to 80/20 but I don't know.
So in essence, your time.sleep() call is skewing your expected numbers, removing the time.sleep() causes the CPU load to be far closer to what you'd expect.
Concise-ish problem explanation:
I'd like to be able to run multiple (we'll say a few hundred) shell commands, each of which starts a long running process and blocks for hours or days with at most a line or two of output (this command is simply a job submission to a cluster). This blocking is helpful so I can know exactly when each finishes, because I'd like to investigate each result and possibly re-run each multiple times in case they fail. My program will act as a sort of controller for these programs.
for all commands in parallel {
submit_job_and_wait()
tries = 1
while ! job_was_successful and tries < 3{
resubmit_with_extra_memory_and_wait()
tries++
}
}
What I've tried/investigated:
I was so far thinking it would be best to create a thread for each submission which just blocks waiting for input. There is enough memory for quite a few waiting threads. But from what I've read, perl threads are closer to duplicate processes than in other languages, so creating hundreds of them is not feasible (nor does it feel right).
There also seem to be a variety of event-loop-ish cooperative systems like AnyEvent and Coro, but these seem to require you to rely on asynchronous libraries, otherwise you can't really do anything concurrently. I can't figure out how to make multiple shell commands with it. I've tried using AnyEvent::Util::run_cmd, but after I submit multiple commands, I have to specify the order in which I want to wait for them. I don't know in advance how long each submission will take, so I can't recv without sometimes getting very unlucky. This isn't really parallel.
my $cv1 = run_cmd("qsub -sync y 'sleep $RANDOM'");
my $cv2 = run_cmd("qsub -sync y 'sleep $RANDOM'");
# Now should I $cv1->recv first or $cv2->recv? Who knows!
# Out of 100 submissions, I may have to wait on the longest one before processing any.
My understanding of AnyEvent and friends may be wrong, so please correct me if so. :)
The other option is to run the job submission in its non-blocking form and have it communicate its completion back to my process, but the inter-process communication required to accomplish and coordinate this across different machines daunts me a little. I'm hoping to find a local solution before resorting to that.
Is there a solution I've overlooked?
You could rather use Scientific Workflow software such as fireworks or pegasus which are designed to help scientists submit large numbers of computing jobs to shared or dedicated resources. But they can also do much more so it might be overkill for your problem, but they are still worth having a look at.
If your goal is to try and find the tightest memory requirements for you job, you could also simply submit your job with a large amount or requested memory, and then extract actual memory usage from accounting (qacct), or , cluster policy permitting, logging on the compute node(s) where your job is running and view the memory usage with top or ps.