Reactive Programming with Reactor 3

Previous: Reactive To Blocking

Blocking to Reactive


The big question is "How to deal with legacy, non reactive code?".

Say you have blocking code (eg. a JDBC connection to a database), and you want to integrate that into your reactive pipelines while avoiding too much of an impact on performance.

The best course of action is to isolate such intrinsically blocking parts of your code into their own execution context via a Scheduler, keeping the efficiency of the rest of the pipeline high and only creating extra threads when absolutely needed.

In the JDBC example you could use the fromIterable factory method. But how do you prevent the call to block the rest of the pipeline?


The subscribeOn method allow to isolate a sequence from the start on a provided Scheduler. For example, the Schedulers.boundedElastic() will create a pool of threads that grows on demand, releasing threads that haven't been used in a while automatically. In order to avoid too many threads due to abusing of this easy option, the boundedElastic Scheduler places an upper limit to the number of threads it can create and reuse (unlike the now deprecated elastic() one).

Use that trick to slowly read all users from the blocking repository in the first exercise. Note that you will need to wrap the call to the repository inside a Flux.defer lambda.

Slow publisher

For slow subscribers (eg. saving to a database), you can isolate a smaller section of the sequence with the publishOn operator. Unlike subscribeOn, it only affects the part of the chain below it, switching it to a new Scheduler.

As an example, you can use doOnNext to perform a save on the repository, but first use the trick above to isolate that save into its own execution context. You can make it more explicit that you're only interested in knowing if the save succeeded or failed by chaining the then() operator at the end, which returns a Mono<Void>.

Slow subscriber
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