When the Go Scheduler Creates a New M and P
In the Go programming language, goroutines are executed by a combination of operating system (OS) threads, goroutine processors (P), and machine resources (M). While it's essential to understand how these components interact, it can be challenging to determine when new M and P are created.
Understanding the Creation of M and P
M is short for machine, and it represents a lightweight OS thread that executes goroutines. P, on the other hand, stands for processor and manages the execution of goroutines on a specific M. The Go scheduler creates new M as needed to handle the goroutines.
In Case of Blocking Tasks
In your example, you have defined a test function that spawns multiple goroutines to perform database operations. Each goroutine is designed to block for a certain duration, simulating a scenario where goroutines are not actively executing. In this case, the Go scheduler will not create additional M since the existing M can handle the blocking goroutines without interruption.
However, if a goroutine performs a system call or encounters a blocking operation, the M executing that goroutine will temporarily block. To maintain responsiveness, the scheduler will create a new M and assign it to a new P to execute any pending goroutines.
M and P Relationship
It's important to note that there is a 1:1 relationship between M and P. A single M can only run on one P at a time, and vice versa. When an M blocks, the scheduler will search for an idle P. If no idle P is available, it will create a new P and assign it to the waiting M.
Additional Resources
For further exploration of this topic, refer to the following materials:
- https://www.programmersought.com/article/79557885527/
- go-goroutine-os-thread-and-cpu-management
Understanding when the Go scheduler creates new M and P is crucial for optimizing your Go applications. By considering the potential for blocking operations and the 1:1 relationship between M and P, you can design your code for efficient goroutine management and maximize concurrency within the limits of available resources.
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