Laravel Jobs and Events processing in same queue worker - laravel

I'm trying to run a time-intensive process in my jobs queue. I have an event being triggered which sends out a socket command to connected users and I have a job that gets queued after that which can run in the background. Before now, I've never run php artisan queue:work and my events system was working flawlessly. Now I'm trying to process my jobs and my events AND jobs are trying to be processed by the same queue worker.
Here is the code that I use to trigger these:
event(new ActivateItemAndUpdateRotation($id));
ChangeStatus::dispatch($id, $status);
This is really bad for my performance because the users can change statuses really quickly which needs to be updated rapidly and the jobs can just do their thing as they're able to in the background. I've tried adding the jobs to a specific queue and only running that queue worker, but then the events don't get processed at all. So really I have two questions:
how have events been getting processed without running a worker up until now?
is it necessary to have two workers running now to process the events and the jobs asynchronously?

Related

Pause Queue in a Job

I have following problem to solve:
Multiple Users can submit Jobs to a Queue via a Web Interface.
This Jobs are then stored in the Database via the database Queue Driver.
Now my problem is: I want the Queue to run all Jobs until inside a job I say something like $queue->pause() because for the next Job to run I need some confirmation from the User.
How would I do something like this?
run jobs
inside one job the job determines that it needs some confirmation from the user
halt the queue and keep that job which needs the confirmation in the queue
any user on the website can press a button which then would delete this confirmation job and start the queue again.
My current "solution" which didn't work was this:
create 2 different job types:
ImageProcessingJob
UserNotificaitonJob
The queue worked all ImageProcessingJobs until it hit a UserNotificationJob.
Inside the UserNotificationJob->handle() I called Artisan::call("queue:restart"); which stopped the Queue.
The problem with this solution is: The UserNotificationJob also got deleted. So if I would start the Queue again the Queue would immediately start with the remainig ImageProcessingJobs without waiting for the actual confirmation.
I'm also open to other architectural solutions without a Queue system.
One approach which avoids pausing the queue, is to have the UserNotificationJob wait on a SyncEvent (the SyncEvent is set when then confirmation comes back from the user). You can have this wait timing-out if you like, but then you need to repost the job to the queue. If you decide to timeout and repost, you can use job chaining to setup dependencies between jobs so that nothing can be run until the UserNotificationJob competes.
Another approach might be to simply avoid posting the remaining jobs until the confirmation is sent from the user.

Laravel Queue Timeout Error

I am creating Laravel Jobs for sending emails and add them in Laravel Queue. Everything works fine, but the timeout of laravel queue is 300 seconds. How can I extend this time? Or I want to run this queue listen forever because anytime mails can be send due to user interaction. Any one can help?
To run a queue listener in the background, you need to configure it via Supervisor which is a process monitor for Linux. You can even assign the number of workers using this.
To configure the timeout, you can use the option timeout in the queue:listen command. The command will be:
php artisan queue:listen --timeout=500
The best way, You need separate data by page push to queue, instead of 1 queue large data, we have many queues waiting run backgrounds, if in you increase speed, you can make multiple jobs cath queue

How can I figure out why a Laravel Queue Worker is running?

A Laravel queue worker was producing a lot of error log entries due to the DB server crashing, in turn Laravel's log grew to 150gb within just two hours, filling up the entire hard drive so that several web apps stopped working.
But actually there is only a queue worker for sending emails in our system and no emails have been sent during the past days. So why is there still a queue worker running?
Are there other reasons why a queue worker might be accessing the DB in a Laravel system besides starting it "manually" (i.e. in our case - by the command that sends mails)?
We're currently using Laravel 5.1.
First, the Laravel worker is using DB to store its job details.
And, you should indicate the maximum number of times a job should be attempted using:
php artisan queue:listen connection-name --tries=3
Then setup a provider to handle if any queued job fails.
For your main question, you should install supervisor, then you can have a UI to manage your workers.

Beanstalkd running all jobs in the queue simultaneously on forge queue workers

I'm running an API build on Laravel Lumen 5.1, but I can't seem to get the Forge Queue Worker to work properly when using beanstalkd as a driver. It seems to run all the jobs in the queue simultaneously
I'm using the Forge UI to set up the driver
Queue Worker setup
And the .env drivers
The .env drivers
The queue system works fine when running it manually without any worker processing it.
If you need any more informations to help me, please just ask!
The purpose of the message queue is to allow parallel processing. If you have more workers eg: more threads than it will run simultaneously as many jobs.
In order to achieve non simultaneously that is counter intuitive and against the message queue principle. You can achieve that with 1 single worker, but it's not recommended as you don't leverage the power and scalability.

How does Celery work?

I have recently started working on distributed computing for increasing the computation speed. I opted for Celery. However, I am not very familiar with some terms. So, I have several related questions.
From the Celery docs:
What's a Task Queue?
...
Celery communicates via messages, usually using a broker to mediate between clients and workers. To initiate a task the client adds a message to the queue, the broker then delivers that message to a worker.
What are clients (here)? What is a broker? Why are messages delivered through a broker? Why would Celery use a backend and queues for interprocess communication?
When I execute the Celery console by issuing the command
celery worker -A tasks --loglevel=info --concurrency 5
Does this mean that the Celery console is a worker process which is in charge of 5 different processes and keeps track of the task queue? When a new task is pushed into the task queue, does this worker assign the task/job to any of the 5 processes?
Last question first:
celery worker -A tasks --loglevel=info --concurrency 5
You are correct - the worker controls 5 processes. The worker distributes tasks among the 5 processes.
A "client" is any code that runs celery tasks asynchronously.
There are 2 different types of communication - when you run apply_async you send a task request to a broker (most commonly rabbitmq) - this is basically a set of message queues.
When the workers finish they put their results into the result backend.
The broker and results backend are quite separate and require different kinds of software to function optimally.
You can use RabbitMQ for both, but once you reach a certain rate of messages it will not work properly. The most common combination is RabbitMQ for broker and Redis for results.
We can take analogy of assembly line packaging in a factory to understand the working of celery.
Each product is placed on a conveyor belt.
The products are processed by machines.
At the end all the processed product is stored in one place one by one.
Celery working:
Note: Instead of taking each product for processing as they are placed on convey belt, In celery the queue is maintained whose output will be fed to a worker for execution one per task (sometimes more than one queue is maintained).
Each request (which is a task) is send to a queue (Redis/Rabbit MQ) and an acknowledgment is send back.
Each task is assigned to a specific worker which executes the task.
Once the worker has finished the task its output is stored in the result backend (Redis).

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