reactor-core

Design a Reactor pipeline by identifying your data boundary, selecting a Flux or Mono factory like fromIterable, chaining operators such as map and flatMap, and applying backpressure and Context propagation for predictable data transformation workflows.

Backpressure in reactive streams controls data flow rates between upstream producers and downstream consumers. You need it when building Reactor pipelines with Flux to prevent overwhelming subscribers during streaming data processing.

Yes, Context propagation passes contextual metadata through a Reactor pipeline without mutating stream signatures. It enables explicit access to contextual data across Flux and Mono operator composition boundaries for reactive services.

Handle errors in a Flux chain by applying explicit error semantics and retry policies during operator composition. This ensures robust failure handling by validating error boundaries and leveraging contextual metadata for reactive service recovery.

Combining operators in Reactor pipelines differ by ordering semantics: merge interleaves multiple Flux streams concurrently, concat preserves upstream sequence order, and zip pairs elements. Choose based on backpressure handling and data flow requirements.

A Mono pipeline returns empty when source boundaries or operator composition fail to emit values. Validate cardinality contracts and explicit empty semantics to ensure your source factory correctly emits the expected value during pipeline execution.