A single `subscribe()` call completely changes how your Java app runs. Learn how this reactive approach builds more resilient systems that can handle heavy loads.
#1about 6 minutes
Understanding the core principles of the reactive manifesto
Reactive systems are designed to be responsive, elastic, and resilient by using a message-driven architecture.
#2about 2 minutes
Visualizing blocking vs non-blocking request models
Sequence diagrams illustrate how non-blocking, fire-and-forget requests improve resource utilization compared to traditional blocking calls.
#3about 3 minutes
An analogy for reactive task distribution
A story about cleaning rooms illustrates how reactive programming breaks large workloads into small, independent tasks for efficient parallel execution.
#4about 2 minutes
Exploring reactive Java frameworks and adoption challenges
Despite numerous frameworks like WebFlux and RxJava, reactive programming faces a steep learning curve and debugging complexities.
#5about 3 minutes
Live code demonstration of imperative Java
A walkthrough of a standard, non-reactive Java method shows a straightforward, blocking approach to fetching and processing API data.
#6about 3 minutes
Live code demonstration of reactive Java
The same API data fetching task is implemented using a reactive, stream-based approach with Spring WebFlux and Project Reactor's Flux.
#7about 3 minutes
Understanding the unique reactive debugging experience
Debugging reactive code reveals its declarative nature, where execution is non-linear and only begins after a subscription is made.
#8about 3 minutes
Q&A on Project Loom and integration challenges
The discussion covers whether Project Loom makes reactive programming obsolete and how to handle integration with non-reactive components like JDBC.
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Comparing reactive and imperative code performance
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Contrasting imperative, declarative, and reactive code styles
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Q&A on virtual threads, reactive programming, and Java 21
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