generatorbased

Generatorbased is a design approach that relies on generator constructs to produce values on demand rather than computing and returning a full collection upfront. This enables lazy evaluation, streaming data processing, and asynchronous-style coordination within a single program flow.

Many modern languages support generator-based patterns. In Python, generator functions defined with yield create iterators that emit values as they are consumed; in JavaScript, generator functions declared with function* yield values and can be chained into pipelines through cooperative control flow. Async generators extend this pattern to produce values asynchronously, integrating with await and promise-based code.

The primary advantages are reduced memory usage when handling large data sets, the ability to compose simple stages into complex data-processing pipelines, and natural backpressure when a consumer lags behind a producer. Generators can also simplify code that streams data from files, network sources, or other slow I/O.

However, generatorbased designs introduce debugging and control-flow challenges, since data production and consumption are separated across yields. They can limit random access to previously produced items and may incur overhead from repeatedly suspending and resuming execution. They are not always the best choice for CPU-bound tasks that would benefit from eager evaluation or parallel processing.

Common use cases include streaming data pipelines, log and event processing, real-time analytics, and interfaces that read large files or network streams piece by piece. The concept is closely related to generator pipelines, coroutine-style programming, and asynchronous generators in Python and similar patterns in other languages.