Most people think concurrency & parallelism are the same.

They’re not:

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Concurrency is about switching between different tasks at once.

Parallelism is about handling many tasks at once.

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Concurrency creates an illusion of parallel execution.

Yet the performance drops when the task count increases.

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Why?

Because each task has to wait for its turn.

The CPU-intensive tasks run efficiently with parallelism.

But parallelism doesn't guarantee performance because tasks need coordination for independent execution.

i.e., neither alone is enough to build scalable and high-performance systems.

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So how do you scale?

You combine them.

Here's how:

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1. Synchronization techniques:

→ Use synchronization primitives to coordinate access to a shared resource.

→ Mutex allows only 1 thread to access the shared resource at a time.

→ Semaphore limits the number of threads accessing a shared resource at a time.

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2. Lock-free mechanism:

→ Go programming language allows building concurrent and parallel solutions without explicit locking mechanisms.

→ Goroutines are lightweight threads that run functions concurrently or in parallel.

→ Channels are built-in thread-safe communication tools that let goroutines communicate without sharing memory.

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3. Software architecture:

→ It's necessary to architect the system for concurrency and parallelism.

→ A load balancer uses non-blocking I/O or a thread pool to avoid connection queuing.

→ A server uses event-driven or multi-threading models to handle requests efficiently.

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Remember, a system in which all tasks run in parallel and stay busy at all times has the highest throughput.

What else would you add?