동시성과 병렬성
리소스를 더 효율적으로 활용하기 위해 동시성과 병렬성으로 여러 작업을 동시에 실행하는 방법을 이해합니다.
동시성과 병렬성은(는) CoddyKit의 무료 System Design Basics for Backend Developers 강의입니다. 이것은 4개 중 2번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 System Design Basics for Backend Developers 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. System Design Basics for Backend Developers 강의에는 총 4개의 강의가 포함되어 있습니다.
이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.
Multi-Tasking Systems
Ever notice how your computer can play music, download files, and browse the web all at the same time? This isn't magic; it's the power of multi-tasking!
In system design, we often need our applications to handle many operations efficiently. This is where the concepts of concurrency and parallelism become crucial.
Understanding Concurrency
Concurrency is about dealing with many things at once. Think of a chef juggling multiple cooking tasks in a single kitchen.
- The chef might chop vegetables, then stir a pot, then check an oven.
- They're not doing everything simultaneously, but they're making progress on several tasks by switching between them quickly.
- This gives the illusion of simultaneous execution.
Concurrency in Software
In software, concurrency often means a single CPU core rapidly switches between different tasks or threads. This is called context switching.
- One task runs for a short period.
- The CPU saves its state and switches to another task.
- This happens so fast that users perceive tasks running "at the same time."
It improves responsiveness and allows a system to make progress on multiple operations.
Concurrent Task Demo
Here's a simple Java example showing two "tasks" running concurrently. The main thread starts two new threads, and the operating system or JVM schedules them to run.
Notice how their output might interleave, showing that they are making progress without necessarily finishing one before starting the other.
public class ConcurrencyDemo {
public static void main(String[] args) {
Runnable task1 = () -> {
for (int i = 0; i < 3; i++) {
System.out.println("Task A: " + i);
try { Thread.sleep(50); } catch (InterruptedException e) {}
}
};
Runnable task2 = () -> {
for (int i = 0; i < 3; i++) {
System.out.println("Task B: " + i);
try { Thread.sleep(50); } catch (InterruptedException e) {}
}
};
new Thread(task1).start();
new Thread(task2).start();
System.out.println("Main thread done.");
}
}True Parallelism
Parallelism is about doing many things at once, literally simultaneously. Imagine having multiple chefs, each with their own kitchen, working on different dishes at the exact same time.
- Each chef (or CPU core) executes a task independently.
- This requires multiple processing units (like multiple cores in a CPU).
- It's about increasing throughput by truly executing multiple instructions at the same instant.
Parallelism in Action
For true parallelism, your system needs multiple processing units. Modern CPUs have multiple cores, allowing multiple threads to run simultaneously.
If you run the previous Java example on a multi-core processor, the operating system might schedule Task A on one core and Task B on another, leading to actual simultaneous execution.
This is different from concurrency on a single core, which simulates simultaneous execution through rapid switching.
Concurrency vs. Parallelism
Let's clarify the key difference:
- Concurrency: Deals with many tasks at once, often by switching between them. (e.g., one CPU core handling multiple threads).
- Parallelism: Does many tasks at once, literally simultaneously. (e.g., multiple CPU cores each handling a thread).
A system can be concurrent without being parallel (single-core CPU). A parallel system is always concurrent (it's dealing with multiple tasks).
Boosting System Performance
Both concurrency and parallelism are vital for high-performance systems:
- Improved Responsiveness: Concurrent systems can keep the user interface active while background tasks run.
- Higher Throughput: Parallel systems can process more requests or data in a given time, utilizing all available CPU power.
- Better Resource Utilization: They make efficient use of CPU cores, especially in servers handling many client connections.
Managing the Complexity
While powerful, concurrency and parallelism introduce challenges:
- Race Conditions: When multiple threads access shared resources, the final outcome depends on their execution order, leading to unpredictable results.
- Deadlocks: Two or more threads get stuck waiting for each other to release resources, causing the system to halt.
- Complexity: Designing and debugging concurrent/parallel systems is harder due to non-deterministic behavior.
Careful synchronization and design patterns are needed to mitigate these issues.
Concurrency vs. Parallelism Check
Consider a web server running on a single-core CPU that handles multiple client requests by rapidly switching between them. Which of the following best describes this scenario?
Recap: Concurrency & Parallelism
We've explored concurrency, which is about managing multiple tasks by switching between them, and parallelism, which is about executing multiple tasks truly simultaneously using multiple processing units.
Both are fundamental for designing high-performance, responsive, and scalable backend systems, though they introduce complexities like race conditions and deadlocks that require careful handling.
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네 — “동시성과 병렬성” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 System Design Basics for Backend Developers 강의 전체를 잠금 해제할 수 있습니다. System Design Basics for Backend Developers 강의에는 총 4개의 강의가 포함되어 있습니다.
“동시성과 병렬성”에서 뭘 배우나요?
리소스를 더 효율적으로 활용하기 위해 동시성과 병렬성으로 여러 작업을 동시에 실행하는 방법을 이해합니다. 브라우저에서 직접 실행하는 실습 코드로 System Design Basics for Backend Developers을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.
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사전 경험은 필요하지 않습니다. CoddyKit의 System Design Basics for Backend Developers은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 2번째 강의입니다.
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