Concurrency in Rust - PART 1
As per Rust documentation, concurrency in Rust is Fearless concurrency. Handling concurrency in a safe and efficient way is one of the Rust goal. The Ownership is the back bone of rust concurrency.
Concurrency means multiple computations at the same time.
- In fact, concurrency is essential part of modern programming
eg > websites should handle multiple users at the same time
Graphical user interface need to do some background work when users are not interrupting the screens.
Multiple applications needs to run on the cloud (server)
Travel reservation system
Many algorithms can be broken down to concurrent parts -
merge sort
quick sort
summing a list by summing fragments in parallel
Concurrency can be achieved in Hardware level and software level. Here we will be talking about software concurrency and what makes Rust an efficient programming language to handle this.
Hardware examples:
- A single processor can have multiple execution pipelines (but only one set of registers)
- A processor can have multiple cores (multicore)
- A computer can have multiple processors
- A network can have multiple computers (Grid computing happens on these)
- An internet has multiple networks
- All combinations of the above
Software examples:
- Multiprogramming, or running a lot of programs concurrently (the O.S. has to multiplex their execution on available processors). For example:
- downloading a file
- listening to streaming audio
- having a clock running
- chatting
- analyzing data from some weird experiment
- streaming a movie
Two Models for Concurrent Programming
- Shared Memory - In this case, the same memory will be shared amount the processors /threads or programs.
A and B programs can be trying to use the same object .
A and B threads can be using the same file system that they can read and write. etc… - Message passing -
There will be a transmitter and a receiver in this model. They communicate each other through a communication channel. Modules sends the messages and the incoming messages will be queued up for modules.
A web browser and web server communication is an example. Web browser client request for a connection and web page, the server sends the data back to browser.
Jumping directly into Rust concurrency
Process. A process is an instance of a running program that is isolated from other processes on the same machine. In particular, it has its own private section of the machine’s memory.
Thread. A thread is a locus of control inside a running program. Think of it as a place in the program that is being run, plus the stack of method calls that led to that place to which it will be necessary to return through.
Threads are automatically ready for shared memory, because threads share all the memory in the process. It needs special effort to get “thread-local” memory that’s private to a single thread. It’s also necessary to set up message-passing explicitly, by creating and using queue data structures.
use::std::thread;
fn main() {
// spawning a new thread
let handle = thread::spawn(||{
loop {
println!("thread1");
}
});
//looping the main thread
loop{
println!("thread2");
}
}
thread::spawn function creates the new thread and returns a handle to it. The parameter would be a closure . Here it is an infinite loop that prints thread1.
You will see thread1 and thread2 printing in the screen. Unfortunately I could not get thread2 in the screenshot!
For listing the threads running under the process, you can use below command in linux
get the process id using command
ps -e
then use top -H -p processid
The memory and CPU usage for each threads can be listed using above command.
Run the below code and see the output …
The program just print “hello world”. It will not wait for the inner thread to complete and print the vector.
use::std::thread;
fn main() {
let v = vec![1,2,3];
let handle = thread::spawn(||{
println!("{:?}",v);
});
// handle.join().unwrap();
println!("Hello, world!");
}
Now remove comments (//) from handle.join().unwrap(); and run the program. You would assume the main thread wait for the inner thread to print the vector values as handle.join() makes main thread to wait/join with inner thread.
But,
We do get a compile time error . The error says the variable vector “v” does not live long enough. If the main thread complete without waiting for inner thread to complete (as we are seen above). The borrowed vector points to wrong memory when its tries to execute. Here Rust help us.
Rust compiler caught that scenario and threw an error .
Compiling concurrent-proj2 v0.1.0 (/home/naveen/rustprojects/concurrent-proj2)
error[E0597]: `v` does not live long enough
--> src/main.rs:11:19
|
10 | let handle = thread::spawn(||{
| -- value captured here
11 | println!("{:?}",v);
| ^ borrowed value does not live long enough
...
16 | }
| - `v` dropped here while still borrowed
We can use the move keyword to transfer the ownership of the variables that we are going to use in the inner thread from the environment.
Below program prints both vector values and helloworld.
use::std::thread;
fn main() {
let v = vec![1,2,3];
let handle = thread::spawn(move ||{
println!("{:?}",v);
});
handle.join().unwrap();
println!("Hello, world!");
}
