When Should You Use Threads?

Understanding Threads, Processes, and When to Reach for Concurrency

Introduction

Threads are a fundamental concept in modern software engineering, but knowing when to use them is crucial. Let’s break down what threads are, why they matter, and when they’re the right tool for the job—using clear analogies, technical explanations, and Rust code examples to illustrate the concepts.

What Are Threads and Processes?

Think of your computer as a busy restaurant:

A program is like a recipe book sitting on a shelf—static, waiting to be used.
A process is a chef who picks up a recipe and starts cooking. Each chef (process) works independently, with their own set of ingredients (memory).
A thread is like a sous-chef working alongside the main chef, sharing the same kitchen and ingredients, but able to prepare different parts of the meal at the same time.

Threads are “lightweight” because they share the same memory space (kitchen) as their parent process, making communication fast but also introducing challenges—like not bumping into each other or mixing up ingredients.

Diagram: Program, Process, and Thread Relationship

graph TD
    Program["Program (Recipe Book)"] -->|Start| Process["Process (Chef)"]
    Process -->|Spawns| Thread1["Thread (Sous-chef 1)"]
    Process -->|Spawns| Thread2["Thread (Sous-chef 2)"]
    Process -->|Spawns| Thread3["Thread (Sous-chef 3)"]
    Process -.->|Owns| Memory["Memory (Ingredients)"]
    Thread1 -.-> Memory
    Thread2 -.-> Memory
    Thread3 -.-> Memory

Why Not Always Use Threads?

Threads can be tricky:

Safety: Shared memory means one thread can accidentally overwrite another’s data.
Complexity: Coordinating threads requires careful planning (locking, synchronization).
Debugging: Bugs can be subtle and hard to reproduce.

That’s why we avoid threads unless they’re truly needed. But sometimes, they’re the best solution.

When Should You Use Threads?

Threads shine in three main scenarios:

I/O Blocking Tasks
Heavy CPU-Bound Work
Large Volume of Small Tasks

Let’s explore each with analogies, kernel behavior, and Rust examples.

1. I/O Blocking Tasks

Analogy

Imagine a chef waiting for bread to bake. If the chef stands idle, the kitchen’s productivity drops. Instead, a sous-chef (thread) can wait for the oven, while the main chef keeps cooking.

Diagram: Offloading Blocking I/O to a Thread

graph LR
    MainThread["Main Thread"] -- "Spawns" --> WorkerThread["Worker Thread"]
    WorkerThread -- "Performs Blocking I/O" --> IO["Disk/Network I/O"]
    MainThread -- "Continues Processing" --> OtherWork["Other Work"]

Technical Explanation

When a process performs I/O (like reading from disk), the kernel may block it—pausing execution until the operation completes. This is called blocking I/O.

Kernel Behavior: The process is put to sleep, freeing up the CPU for other tasks.
Memory Management: The process’s memory stays in RAM, but its execution is paused.

Rust Example

use std::thread;
use std::fs::File;
use std::io::Read;

fn main() {
    let handle = thread::spawn(|| {
        let mut file = File::open("data.txt").unwrap();
        let mut contents = String::new();
        file.read_to_string(&mut contents).unwrap();
        println!("File contents: {}", contents);
    });

    // Main thread can do other work here
    handle.join().unwrap();
}

Note

If you use asynchronous I/O (like Rust’s tokio with async/await), you might not need threads at all.

2. Heavy CPU-Bound Work

Analogy

Suppose a chef needs to knead dough for 20 minutes. Rather than tying up the main chef, a sous-chef can handle the kneading, letting the main chef prepare other dishes.

Diagram: Threads on Multiple CPU Cores

graph TD
    MainThread["Main Thread (Core 1)"]
    WorkerThread["Worker Thread (Core 2)"]
    MainThread -- "Runs Main Logic" --> MainTask["Main Task"]
    WorkerThread -- "Runs Heavy Computation" --> HeavyTask["Heavy Task"]

Technical Explanation

CPU-bound tasks consume lots of processor time. If left on the main thread, they can starve other operations.

Kernel Behavior: Modern CPUs have multiple cores. Threads can run in parallel on different cores.
Memory Management: Threads share memory, so data can be exchanged quickly, but synchronization is needed.

Rust Example

use std::thread;

fn heavy_computation() -> u64 {
    (0..1_000_000_000).sum()
}

fn main() {
    let handle = thread::spawn(|| {
        let result = heavy_computation();
        println!("Computation result: {}", result);
    });

    // Main thread can handle other tasks
    handle.join().unwrap();
}

3. Large Volume of Small Tasks

Analogy

If a restaurant suddenly gets hundreds of orders, one chef can’t keep up. Multiple sous-chefs (threads) can each handle a few orders, increasing throughput.

Diagram: Multiple Threads Handling Connections

graph TD
    Listener["Listener Thread"] -- "Accepts" --> Conn1["Connection 1"]
    Listener -- "Accepts" --> Conn2["Connection 2"]
    Listener -- "Accepts" --> ConnN["Connection N"]
    Conn1 -- "Handled by" --> Worker1["Worker Thread 1"]
    Conn2 -- "Handled by" --> Worker2["Worker Thread 2"]
    ConnN -- "Handled by" --> WorkerN["Worker Thread N"]

Technical Explanation

When you have many small, independent tasks (like handling network connections), a single thread can become a bottleneck. Spawning multiple threads allows you to process more tasks in parallel.

Kernel Behavior: The kernel schedules threads across available CPU cores.
Memory Management: Threads can share data structures, but must coordinate access.

Rust Example

use std::thread;

fn handle_connection(id: usize) {
    println!("Handling connection {}", id);
}

fn main() {
    let mut handles = vec![];

    for i in 0..10 {
        handles.push(thread::spawn(move || {
            handle_connection(i);
        }));
    }

    for handle in handles {
        handle.join().unwrap();
    }
}

Conclusion

Threads are powerful, but come with trade-offs. Use them when:

You’re waiting on blocking I/O.
You have CPU-intensive tasks that can run in parallel.
You need to handle a high volume of small, independent tasks.

Always weigh the complexity and risks of threading against the benefits. Sometimes, asynchronous programming or process-based concurrency is a better fit.

Happy coding! And remember: with great concurrency comes great responsibility.