Reactive Programming

4 minute read

This note is about reactive programming with Quarkus and Mutiny on the app.

intro and resources

BMC_Anvil is a fully reactive application. From the Quarkus core, to the web container and even the database client and orm.

if a component was required, I would choose the reactive option.

why choosing a fully reactive stack from

there are two main reason for that decision.

  1. to learn a new tech and a whole new framework: I used Spring for years and project reactor was ok, but it was time to get out of the comfort zone and learn something new.
  2. build a full application in a reactive manner: I wanted to test for myself if the claims about the non-blocking io / data stream / message passing model were true under load.

resources on the web


Smallrye Mutiny:

Eclipse Vert.x:

some examples of reactive style


public class ProjectResource extends BasicOpsResource<ProjectDto, ProjectEntity> {
    private final ProjectService projectService;

    public ProjectResource(final ProjectService projectService) {
        this.projectService = projectService;

    public Uni<Response> findAllCreatedByUserId(final UUID userId,
                                                @QueryParam(value = "sortBy") @NotNull final String sortBy,
                                                @QueryParam(value = "sortDir") final String sortDir,
                                                @QueryParam(value = "pageIx") final Integer pageIx,
                                                @QueryParam(value = "pageSize") @NotNull final Integer pageSize) {
        return projectService.findAllByUserIdPaged(userId, new Pageable(sortBy, sortDir, pageIx, pageSize))
                .map(projectDtos -> Response.ok(projectDtos).build());

    public Uni<Response> findAllByAccountId(final UUID accountId,
                                            @QueryParam(value = "sortBy") @NotNull final String sortBy,
                                            @QueryParam(value = "sortDir") final String sortDir,
                                            @QueryParam(value = "pageIx") final Integer pageIx,
                                            @QueryParam(value = "pageSize") @NotNull final Integer pageSize) {
        return projectService.findAllByAccountIdPaged(accountId, new Pageable(sortBy, sortDir, pageIx, pageSize))
                .flatMap(projectDtos -> Uni.createFrom().item(Response.ok(projectDtos).build()));


let’s focus here on the 2 methods at play in this REST resource class.

both return a Uni<Response>.

we can think of a Uni as our main “reactive unit” from the Mutiny library, it represents a lazy stream emitting an item or failure.
If you want to develop an async op, Unis provide the tools to do so, from something very simple as our 2 methods above to a full reactive pipeline.

the two key concepts to understand how a Quarkus reactive app works are LAZY and STREAM.

by lazy we’re going to define that if we do not have a final subscriber to the Uni pipeline, nothing will execute.
by stream we’re going to define that Unis handle series of events to produce a final single result. (Multi’s behave similarly but with streams of data as result, instead of a single item)

observing our first example to get all projects created by a given userId we call the projectService.findAllByUserIdPaged(). That service also returns a Uni and as we want to return a Response and not the raw DTOs, we concatenate to the method to transform our service’s result into the desired Response. Our method returns itself a Uni of the mapping op. Even if we did not call explicitly on subscribe(), returning Uni<Response> by our REST resource acts as an implicit subscription.

teaching note:
it is completely valid to use subscribe() as unlike await() it resolves in an async manner. The gotcha with subscribe() is that flow of a method chain like above becomes “interrupted” given that you need to do something with the now resolved result of your computation.
if you can avoid subscribing when there is a round-trip op, do it so. If it is an event / broker related code, subscribing might be the way to go.

the second method has a small difference only shown here for demonstration’s sake.
we are concatenating .flatMap(). In mutiny’s API, flatMap() means that the transformation is also reactive / asynchronous, whereas .map() transformations are synchronous.

in our example generating a Response from the service’s result is a sync op, there is no delayed op of any kind, but if you can picture that the result is a projectId, and we call another remote REST endpoint to fetch data from that projectId, then wrapping that new network call in a Uni makes total sense.


complexity will depend a lot on the ops chained into the reactive pipeline. We will examine a few difficult ones when touching reactive hibernate sections.

let’s see here a more elaborated example of a REST resource chaining more than one op.

public abstract class BasicOpsResource<D, E> {
    public Uni<Response> update(@NotNull final UUID idToUpdate, @FormParam("field") final String field, @FormParam("value") final String value) {
        return basicPersistenceService.update(idToUpdate, field, value)

this update() method from the BasicOpsResource class not only calls on a service but instead of transforming the result as before it replaces it fully by calling .replaceWith(). As many things can go wrong when trying to update records… from trying to update a non-existing id, to trying to persist incorrect data or plain db errors, we can concatenate failure events emitted by the Uni pipeline and gracefully respond to each case.