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One of the most common misconceptions about async programming is that it’s about improving performance. A lot of people think that using async makes their code run faster. However… Async does not necessarily make the operation itself any faster.

The distinction between async and parallelism is an important one. Async is primarily about allowing other work to make progress while something is waiting. Parallelism is about performing multiple pieces of work at the same time.

Modern applications spend a lot of time waiting for things to happen. They wait for APIs to respond, databases to return queries, files to be read or written, and data to travel across a network.

The exact mechanics vary between programming languages, but many modern languages provide the familiar async and await keywords to make asynchronous code easier to write and reason about. At a conceptual level, async identifies code that participates in asynchronous execution, while await marks a point where that code depends on the result of an asynchronous operation.

Async programming sounds great, but that doesn’t mean everything should be asynchronous. The distinction becomes clearer when we think about I/O-bound and CPU-bound work. I/O-bound operations spend a significant amount of their time waiting for something outside the CPU: network requests, database calls, file operations and similar tasks.

At its core, async programming allows your program to start an operation that may take some time and avoid blocking while it waits for that operation to complete. This contrasts with synchronous programming, where execution waits for an operation to finish before continuing past that point.