Catalog
concept#Software Engineering#Architecture#Observability#Reliability

Reactive Programming

Reactive programming is a declarative paradigm for handling asynchronous data streams and events with emphasis on backpressure, composition and fault tolerance.

Reactive programming is a declarative paradigm for handling asynchronous data streams and events with explicit backpressure, composition and error propagation.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Event brokers (e.g. Kafka, RabbitMQ)Reactive client libraries (e.g. RxJS, Reactor)Monitoring and tracing systems (e.g. Prometheus, Jaeger)

Principles & goals

Prioritize non-blocking processing and responsivenessBackpressure and demand-driven controlCompose streams instead of central state management
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Incorrect backpressure design can cause resource bottlenecks
  • Composition bugs can cause subtle performance issues
  • Excessive asynchrony increases operational effort and monitoring needs
  • Use and test explicit backpressure protocols
  • Compose small, testable stream operators
  • Ensure observability for every relevant stream path

I/O & resources

  • Sources of event streams (WebSockets, Kafka, HTTP streams)
  • Definitions for flow control and backpressure
  • Fault tolerance and timeout strategies
  • Controlled, predictable processing pipelines
  • Metrics on latency, throughput and errors
  • Cleanly isolated error paths and fallbacks

Description

Reactive programming is a declarative paradigm for handling asynchronous data streams and events with explicit backpressure, composition and error propagation. It encourages non-blocking, scalable architectures and improves responsiveness in distributed systems. Libraries and specifications enable interoperability across implementations and common runtime semantics.

✔

  • Improved scalability via non-blocking resource usage
  • Finer control over throughput and latency
  • Clear models for error propagation and resilience

✖

  • Higher cognitive complexity for developers
  • Debugging and traceability can be harder
  • Not always appropriate for simple synchronous workloads

  • Throughput (events/s)

    Measures processed events per second as an indicator of capacity.

  • End-to-end latency

    Time from arrival to processing of an event including queuing.

  • Error and drop rate

    Proportion of failed or dropped events in the stream.

Akka Streams for high-throughput pipelines

Akka Streams demonstrates reactive flow control, backpressure and complex topologies on the JVM.

RxJS in modern frontends

RxJS is commonly used for event streams, form validation and asynchronous UI logic in browser apps.

Project Reactor in reactive microservices

Project Reactor provides a Reactive Streams-compliant foundation for non-blocking server backends on the JVM.

1

Conceptual training of the team on reactive principles and backpressure.

2

Build and measure a prototype pipeline using reactive libraries.

3

Implement observability and metrics for asynchronous flows.

4

Iteratively introduce into production systems with alerts and SLOs.

⚠️ Technical debt & bottlenecks

  • Ad-hoc adapters for legacy sync code without robust error handling
  • Unclear ownership of stream operators and pipelines
  • Missing standardized metrics for reactive flows
Thread and scheduler contentionI/O latency to remote servicesUncontrolled buffer usage
  • Putting UI rendering entirely into complex stream topologies instead of simple state management
  • Uncontrolled buffering of large data volumes in memory
  • Suppressing error handling and thereby causing inconsistencies
  • Hidden thread contexts when bridging blocking code
  • Missing backpressure tests in load testing
  • Insufficient instrumentation of reactive paths
Knowledge of asynchronous programming models and Reactive StreamsExperience debugging asynchronous flowsUnderstanding of backpressure mechanisms and resource management
Scalability under variable loadLow latency and high responsivenessResilience to failures
  • • Existing libraries must support Reactive Streams
  • • Monitoring and tracing tooling must represent asynchronous flows
  • • Legacy synchronous code requires adapters or context switches