tasks | Package tasks is an easy to use in-process scheduler | Job Scheduling library
kandi X-RAY | tasks Summary
kandi X-RAY | tasks Summary
Package tasks is an easy to use in-process scheduler for recurring tasks in Go. Tasks is focused on high frequency tasks that run quick, and often. The goal of Tasks is to support concurrent running tasks at scale without scheduler induced jitter. Tasks is focused on accuracy of task execution. To do this each task is called within it's own goroutine. This ensures that long execution of a single invocation does not throw the schedule as a whole off track. As usage of this scheduler scales, it is expected to have a larger number of sleeping goroutines. As it is designed to leverage Go's ability to optimize goroutine CPU scheduling. For simplicity this task scheduler uses the time.Duration type to specify intervals. This allows for a simple interface and flexible control over when tasks are executed.
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Top functions reviewed by kandi - BETA
- Add a new task
- New returns a new Scheduler
tasks Key Features
tasks Examples and Code Snippets
Lines of Code : 49License : Permissive (MIT)
Copy// Start the Scheduler
scheduler := tasks.New()
defer scheduler.Stop()
// Add a task
id, err := scheduler.Add(&tasks.Task{
Interval: time.Duration(30 * time.Second),
TaskFunc: func() error {
// Put your logic here
}(),
})
if err != nil Community Discussions
Trending Discussions on tasks
QUESTION
I'm trying to understand how parallelization works in Durable Function. I have a durable function with the following code:
...ANSWER
Answered 2021-Jun-10 at 08:44There are two approaches that are possible. The first is to use a suborchestrator for each job so that each suborchestrator handles just a specific job. Here is the docs for this approach https://docs.microsoft.com/en-us/azure/azure-functions/durable/durable-functions-sub-orchestrations?tabs=csharp Example from docs seem to be alike to yours.
The other is to use ContinueWith so that each job has its own "chain"
QUESTION
I am trying to inject code for a platform I use with my clients on Cloudflare. I would like to be able to add the following CSS only IF the class: badge-icon.icon-template is NOT present. I would like to use javascript for this (I think this is the best solution). Can someone help?
...ANSWER
Answered 2021-Jun-15 at 20:44
if (!document.getElementsByClassName("badge-icon")[0] && !document.getElementsByClassName("icon-template")[0]) {
// inject code
}
QUESTION
I am writing a program in python to have a user input multiple websites then request and scrape those websites for their titles and output it. However, when the program surpasses 8 websites the program crashes every time. I am not sure if it is a memory problem, but I have been looking all over and can't find any one who has had the same problem. The code is below (I added 9 lists so all you have to do is copy and paste the code to see the issue).
...ANSWER
Answered 2021-Jun-15 at 19:45To avoid the page from crashing, add the user-agent header to the headers= parameter in requests.get(), otherwise, the page thinks that your a bot and will block you.
QUESTION
I'm trying to parallelize a merge-sort algorithm. What I'm doing is dividing the input array for each thread, then merging the threads results. The way I'm trying to merge the results is something like this:
...ANSWER
Answered 2021-Jun-15 at 01:58I'm trying to parallelize a merge-sort algorithm. What I'm doing is dividing the input array for each thread, then merging the threads results.
Ok, but yours is an unnecessarily difficult approach. At each step of the merge process, you want half of your threads to wait for the other half to finish, and the most natural way for one thread to wait for another to finish is to use pthread_join(). If you wanted all of your threads to continue with more work after synchronizing then that would be different, but in this case, those that are not responsible for any more merges have nothing at all left to do.
This is what I've tried:
QUESTION
I use the following code to update my widget's timeline, but the "result" which I fetched from the core data is not up-to-date.
My logic is when detecting the host app goes to background I call "WidgetCenter.shared.reloadAllTimelines()" and fetch the core data in the "getTimeline" function. After printing out the result, it is old data. Also I fetch the data with the same predicate under the .background, the data is up-to-date.
Also I show the date in the widget view body, when I close the host app, the date is refreshing. Means that the upper refreshing logic works fine. But just always get the old data.
Could someone help me out?
...ANSWER
Answered 2021-Jun-15 at 17:05Update:
I added the following code to refresh the core data before I fetch. Everything work as expect.
QUESTION
I have a generator object, that loads quite big amount of data and hogs the I/O of the system. The data is too big to fit into memory all at once, hence the use of generator. And I have a consumer that all of the CPU to process the data yielded by generator. It does not consume much of other resources. Is it possible to interleave these tasks using threads?
For example I'd guess it is possible to run the simplified code below in 11 seconds.
...ANSWER
Answered 2021-Jun-15 at 16:02Send your data to separate processes. I used concurrent.futures because I like the simple interface.
This runs in about 11 seconds on my computer.
QUESTION
A few days ago my code for sending Push notifications stopped working :(
The program began to hang on the last line apnsBroker.Stop();
I use NuGet package PushSharp.Core https://github.com/mitch-tofi/PushSharp.Core
...ANSWER
Answered 2021-Apr-27 at 13:30We're looking in to the same issue currently and it seems apple are disabling the old binary interface which push sharp uses.
https://developer.apple.com/news/?id=c88acm2b
pushsharp has it on the roadmap to support the new interface but not completed yet.
Found this library which seems easy enough to use as a solution. hope this helps.
QUESTION
I've been experimenting with the Kotlin coroutines in android. I used the following code trying to understand the behavior of it:
...ANSWER
Answered 2021-Jun-15 at 14:51This is exactly the reason why coroutines were invented and how they differ from threaded concurrency. Coroutines don't block, but suspend (well, they can do both). And "suspend" isn't just another name for "block". When they suspend (e.g. by invoking join()), they effectively free the thread that runs them, so it can do something else somewhere else. And yes, it sounds like something that is technically impossible, because we are in the middle of executing the code of some function and we have to wait there, but well... welcome to coroutines :-)
You can think of it as the function is being cut into two parts: before join() and after it. First part schedules the background operation and immediately returns. When background operation finishes, it schedules the second part on the main thread. This is not how coroutines works internally (functions aren't really cut, they create continuations), but this is how you can easily imagine them working if you are familiar with executors or event loops.
delay() is also a suspending function, so it frees the thread running it and schedules execution of the code below it after a specified duration.
QUESTION
In below shown DAG I want to execute task d no matter whether tasks b & c are success or failed, But for task e If tasks b, c & d are success then only it should be triggered. DAG Image
Written below code but it is not working:
...ANSWER
Answered 2021-Jun-15 at 13:49You need to code the branch of your workflow tree in separate statements.
QUESTION
I am trying to run a simple parallel program on a SLURM cluster (4x raspberry Pi 3) but I have no success. I have been reading about it, but I just cannot get it to work. The problem is as follows:
I have a Python program named remove_duplicates_in_scraped_data.py. This program is executed on a single node (node=1xraspberry pi) and inside the program there is a multiprocessing loop section that looks something like:
...ANSWER
Answered 2021-Jun-15 at 06:17Pythons multiprocessing package is limited to shared memory parallelization. It spawns new processes that all have access to the main memory of a single machine.
You cannot simply scale out such a software onto multiple nodes. As the different machines do not have a shared memory that they can access.
To run your program on multiple nodes at once, you should have a look into MPI (Message Passing Interface). There is also a python package for that.
Depending on your task, it may also be suitable to run the program 4 times (so one job per node) and have it work on a subset of the data. It is often the simpler approach, but not always possible.
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