Concurrency

Goroutines are cheap, but the resources they call are often not. Starting one goroutine for every item in a large batch can overwhelm a database connection pool, trigger API rate limits, exhaust file descriptors, or create avoidable memory pressure. Bounded concurrency solves this by allowing only a fixed number of operations to run at the same time. A worker pool is one of the simplest standard-library patterns for implementing that limit in Go.

When many requests ask for the same expensive resource at the same time, running identical work for every caller can overload a database, API, or filesystem. A cache can help after a result exists, but it does not necessarily prevent several concurrent cache misses from triggering the same backend operation. Request coalescing solves a different problem: while one operation for a key is already running, later callers wait for that operation and share its result. After the operation finishes, the result is forgotten. The next request starts fresh work.