Explain these terms in simple terms. With some real life example, how parallelsm factor involves the pool size growth parallelism-min = 2 parallelism-factor = 2.0 parallelism-max = 10.
This topic is based on a conversation between an anonymous user of Akka and our “Ask AI about Akka” bot in the documentation.
In Akka, the terms parallelism-min, parallelism-factor, and parallelism-max are used to configure the size of the thread pool for a dispatcher. These parameters are used when the dispatcher’s executor is set to “fork-join-executor” or “thread-pool-executor”.
parallelism-min: This is the minimum number of threads that the thread pool should have. No matter how the parallelism-factor is calculated, the thread pool will not have fewer than this number of threads. For example, if parallelism-min is set to 2, there will always be at least 2 threads in the pool.
parallelism-factor: This is a multiplier that is used to calculate the ideal number of threads for the thread pool. It is multiplied by the number of available processors (cores) on the machine. For example, if parallelism-factor is set to 2.0 and your machine has 4 cores, the ideal thread pool size would be 8 (4 cores * 2.0).
parallelism-max: This is the maximum number of threads that the thread pool can have. No matter how the parallelism-factor is calculated, the thread pool will not have more than this number of threads. For example, if parallelism-max is set to 10, there will never be more than 10 threads in the pool, even if the parallelism-factor calculation would result in a higher number.
Let’s consider a real-life example. Suppose you are managing a team of workers in a factory. The parallelism-min is the minimum number of workers you need to keep the factory running. The parallelism-factor is a factor you use to calculate the ideal number of workers based on the number of available workstations. And the parallelism-max is the maximum number of workers the factory can accommodate.
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Also explain throughout in simple term.
In the context of Akka and computing, “throughput” generally refers to the amount of work that can be performed by a system or component in a given period of time. It’s a measure of the capacity and speed of data flow in the system.
For example, in an Akka-based application, the throughput might refer to the number of messages that an actor can process per second. If an actor has a high throughput, it means it can process a large number of messages quickly.
In a real-life scenario, consider a highway. The throughput of the highway could be considered the number of cars that can pass through it per hour. A highway with a high throughput can handle a large number of cars moving quickly, while a highway with low throughput might become congested and slow.
In Akka’s configuration, you might see a setting like throughput = 100, which means the system will process 100 messages per actor before moving on to the next actor.
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If my dispatcher having max of 8 threads and througput set 100 the 9 actors will use, the 9th actor will wait for other 8 actor should process 100 messages.
Yes, you’re correct. In Akka, the dispatcher uses a thread pool to process messages for actors. If you have more actors than threads in the pool, some actors will have to wait for a thread to become available before they can process their messages.
The throughput setting determines how many messages an actor will process before giving up its thread. If throughput is set to 100, then each actor will process 100 messages (if available) before it gives up its thread to the next actor.
So, in your example, if you have 9 actors and a thread pool of size 8 (i.e., 8 threads), then yes, the 9th actor will have to wait until one of the other actors has finished processing its batch of 100 messages and releases its thread. Once a thread becomes available, the 9th actor can start processing its messages.
This is a simplification and actual behavior can depend on other factors like the dispatcher’s configuration and the nature of the actors’ work (CPU-bound, blocking IO, etc.).
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Explain the difference between thread pool vs forkjoin pool executor.
In Akka, both thread pool and fork-join pool are types of executors that can be used for dispatchers. They are used to manage threads that execute tasks (like processing actor messages). The choice between them depends on the nature of the tasks.
Thread Pool Executor: This executor uses a fixed number of threads. It’s suitable for tasks that perform blocking operations, like IO operations, because blocking operations can tie up threads for a long time. If a task blocks on a thread from a thread pool executor, other tasks can still proceed on the other threads. You can configure the size of the thread pool using the fixed-pool-size setting.
Fork-Join Executor: This executor is designed for tasks that can be split into smaller tasks and the results of those tasks can then be combined to produce a result. It’s suitable for tasks that perform compute-intensive operations and can take advantage of multiple processors. The fork-join executor uses a work-stealing algorithm, where idle threads can steal tasks from busier threads. You can configure the parallelism level of the fork-join executor using the parallelism-min, parallelism-factor, and parallelism-max settings.
Here’s an example of how to configure a dispatcher to use a thread pool executor:
my-dispatcher {
type = Dispatcher
executor = "thread-pool-executor"
thread-pool-executor {
fixed-pool-size = 32
}
throughput = 1
}
And here’s an example of how to configure a dispatcher to use a fork-join executor:
my-dispatcher {
type = Dispatcher
executor = "fork-join-executor"
fork-join-executor {
parallelism-min = 2
parallelism-factor = 2.0
parallelism-max = 10
}
throughput = 100
}
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How many threads that core can create because if I am set factor 100 and max is 800, 800 threads will create ( 100 thread per core) or not?
The number of threads that can be created is determined by the parallelism-factor and parallelism-max settings, but it’s also influenced by the number of available processors (cores) on your machine.
The parallelism-factor is multiplied by the number of available processors to calculate the ideal number of threads. For example, if parallelism-factor is set to 100 and your machine has 4 cores, the ideal thread pool size would be 400 (4 cores * 100).
However, the parallelism-max setting puts a cap on the maximum number of threads. Even if the parallelism-factor calculation would result in a higher number, the thread pool will not have more than parallelism-max threads.
So, in your example, if you set parallelism-factor to 100 and parallelism-max to 800, the number of threads will be the lesser of the two calculations: available processors * parallelism-factor and parallelism-max. If your machine has 8 or fewer cores, the number of threads will be available processors * 100. If your machine has more than 8 cores, the number of threads will be capped at 800.
Please note that creating a large number of threads can lead to significant overhead and might not improve performance. It’s important to tune these settings based on the nature of your tasks and the characteristics of your hardware.
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