effect-concurrency

Effect concurrency uses fibers to manage concurrent tasks. Fork effects to run them concurrently, join to collect results, and interrupt to stop tasks when needed. This gives you fine-grained control over lifecycle and coordination.

Yes. Effect provides parallel execution patterns like `all` and `forEach` to run effects concurrently with controlled concurrency levels. These compose multiple effects and coordinate their results automatically.

Use the race pattern to compete a timeout against a long operation. Effect's race logic interrupts the slower effect, allowing you to cancel operations that exceed your time limit.

Fibers provide lifecycle operations: fork to launch, join to await results, await to block, poll to check status, and interrupt to stop. This enables coordinated workflows with exact control over when tasks start, complete, and terminate.

Yes. Fork multiple data-fetch effects concurrently, join their results, and apply race or timeout patterns as needed. Effect's fiber model scales from simple data fetches to complex coordinated workflows.