- Concurrent tasks progress at the same time in the worker system but they don’t progress simultaneously. Parallel tasks are executed by different workers at the same time.
- Concurrency refers to how a worker system handles multiple tasks while parallelism refers to how a worker system handles a single task.
- Concurrent tasks can either be executed sequentially whereby one task is completed before another one starts or concurrently where there is switching between tasks until all tasks are completed.
- Parallel tasks are split into subtasks that are assigned to multiple workers and then completed simultaneously.
- A worker system can carry out both parallel and concurrent tasks by working on multiple tasks at the same time while also breaking down each task into sub-tasks that are executed simultaneously.
Concurrent vs parallel tasks can be a bit confusing because the dictionary definitions of these terms are different from how they are applied in a worker system. However, when they are placed in the context of a worker system that improves the flexibility and scalability of application production, their use cases become very clear.
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Overview of Concurrent vs Parallel Tasks
When you are running a production-scale cloud application, there are many tasks that you need to process. You might need to process images, extract data, or send push notifications all the same time. The question then becomes, how do you run all these tasks within the shortest time and at scale? The answer is by using a worker system like IronWorker that can run both concurrent and parallel tasks in the background efficiently and on a massive scale.
The choice between running concurrent or parallel tasks in a worker system depends on whether you want to complete one task faster by increasing the resources (workers) used to execute the task or if you want to finish a set of tasks faster by maximizing the resources available. When should you use parallel tasks and when should you use concurrent tasks in your worker system? The answer lies in understanding parallel tasks and concurrent tasks separately plus their uses cases.
Concurrent Tasks in Detail
Concurrent tasks are multiple tasks that progress at the same time in the worker system but are not executed simultaneously. The worker system switches between tasks until all the tasks are completed or it can work sequentially by completing one task before moving to the next task.
Let’s use the example of an application that has to process several background jobs including processing images, processing webhooks, sending push notifications, and processing big data. These tasks progress concurrently but are not executed at the same time. The worker system might alternate between processing images and sending push notifications until the tasks are complete or prioritize each task sequentially.
Benefits of concurrent tasks
Concurrent tasks make it easy to solve large tasks faster and at scale. With a worker system like IronWorker, you can schedule your concurrent tasks which makes it easy for you to optimize your application.
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Parallel Tasks in Detail
Parallel tasks are broken down into subtasks that are then executed at the same time. Parallel tasks are useful when you want to complete one task faster by committing more workers to it.
For instance, if you have an application that needs to send out 100,000 push notifications every day you can schedule 1 worker to send out the notifications serially, which would take a long time. Alternatively, you can schedule 100 workers to send 1,000 notifications each and significantly reduce the amount of time it takes to send the notifications.
Benefits of parallel tasks
Parallel tasks are useful for breaking down one large task into smaller subtasks that are assigned to different workers for faster execution. A great example of how parallel tasks can speed up execution is how an IronWorker user reduced a 9-hour job to a 9-minute job.
Concurrent vs Parallel Tasks Comparison
How IronWorker Can Speed Up Your Background Job Processing
If you are looking to process background jobs at a massive scale using a flexible platform, check out IronWorker.
IronWorker is capable of processing your large tasks such as image processing, web crawling, and sending push notifications concurrently and at an elastic scale.
When your tasks, traffic, or client demands increase, IronWorker has an auto-scaling algorithm that increases the number of workers assigned to a task. Moreover, big tasks like data analytics can be broken down into subtasks and then assigned more workers to solve them faster.
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