reactive-programming

Kotlin coroutines handle async request/response logic, while reactive streams like Flow manage continuous data flow with back-pressure. Evaluate your data consumption needs to distinguish non-blocking concurrency from true stream processing.

Non-blocking request response in Kotlin uses suspend functions for single asynchronous values, whereas reactive stream processing uses Flow or Rx for emitting multiple values over time, requiring specific back-pressure handling.

Transition from suspend functions to Kotlin Flow when your system must consume continuous data streams rather than single request-response values. Evaluate if your data flow requires back-pressure handling and cold stream capabilities.

Validate reactive programming architectural decisions by checking if the implemented reactive libraries match actual data consumption needs. Ensure true reactive streams are not incorrectly applied to simple non-blocking request-response logic.

Cold Flows in Kotlin execute per collector and are suited for on-demand data, whereas hot streams broadcast data independently. Evaluate your specific data shape and concurrency requirements to select the appropriate stream type.

Non-blocking concurrent code is often mislabeled as reactive stream processing because both use asynchronous execution. Distinguish them by checking if the logic handles single request-response or continuous multi-value data flow with back-pressure.