Flight of the Flux 1 - Assembly vs Subscription

Engineering | Simon Baslé | March 06, 2019 | ...

This blog post is the first in a series of posts that aim at providing a deeper look into Reactor's more advanced concepts and inner workings.

It is derived from my Flight of the Flux talk, which content I found to be more adapted to a blog post format.

I'll update the table below with links when the other posts are published, but here is the planned content:

  1. Assembly vs Subscription (this post)
  2. Debugging caveats
  3. Hopping Threads and Schedulers
  4. Inner workings: work stealing
  5. Inner workings: operator fusion

If you're missing an introduction to Reactive Streams and the basic concepts of Reactor, head out to the site's learning section and the reference guide.

Without further ado, let's jump in:

Assembly Time

When you first learn about Reactive Streams and reactive programming on the JVM, the first thing you learn is the high-level relationship between Publisher and Subscriber: one produces data, the other consumes it. Simple right? Furthermore, it appears that the Publisher pushes data to the Subscriber.

But when working with Reactive Streams libraries like Reactor (or RxJava2), you quickly come across the following mantra:

Nothing Happens Until You Subscribe

Sometimes, you might read that both libraries implement a "push-pull hybrid model". Hang on a minute! pull?

We'll get back to it, but to understand that sentence you first need to realize that, by default, Reactor's reactive types are lazy.

Calling methods on a Flux or Mono (the operators) doesn't immediately trigger the behavior. Instead, a new instance of Flux (or Mono) is returned, on which you can continue composing further operators. You thus create a chain of operators (or an operator acyclic graph), which represents your asynchronous processing pipeline.

This declarative phase is called assembly time.

Let's take an example where a client side application makes an HTTP request to a server, expecting an HttpResponse:

Mono<HttpResponse> httpSource = makeHttpRequest();
Mono<Json> jsonSource = httpSource.map(req -> parseJson(req));
Mono<String> quote = jsonSource.map(json -> json.getString("quote"));
//at this point, no HTTP request has been made

This can be simplified using the fluent API:

Mono<String> quote = makeHttpRequest()
    .map(req -> parseJson(req))
    .map(json -> json.getString("quote"));

Once you are done declaring your pipeline, there are two situations: either you pass the Flux/Mono representing the processing pipeline down to another piece of code or you trigger the pipeline.

The former means that the code to which you return the Mono might apply other operators, resulting in a derived new pipeline. Since the operators create new instances (it's like an onion), your own Mono is not mutated, so it could be further decorated several times with widely different results:

//you could derive a `Mono<String>` of odd-length strings vs even-length ones
Mono<String> evenLength = quote.filter(str -> str.length() % 2 == 0);
Mono<String> oddLength = quote.filter(str -> str.length() % 2 == 1);

//or even a `Flux<String>` of words in a quote
Flux<String> words = quote.flatMapMany(quote -> Flux.fromArray(quote.split(" ")));

//by this point, none of the 3 "pipelines" have triggered an HTTP request

Compare that with a CompletableFuture, which is not lazy in nature: once you have a reference to the CompletableFuture, it means the processing is already ongoing...

With that in mind, let's look into how to trigger the reactive pipeline.

Subscription Time

So far, we've assembled an asynchronous pipeline. That is, we've instantiated Flux and Mono variables through the use of operators, that results other Flux/Mono with behavior layered like an onion.

But the data hasn't started flowing through each of these declared pipelines yet.

That's because the trigger for the data to flow is not the declaration of the pipeline, but rather the subscription to it. Remember:

Nothing Happens Until You Subscribe

Subscribing is the act of saying "ok, this pipeline represent a transformation of data, and I'm interested in the final form of that data". The most common way of doing so is by calling Flux.subscribe(valueConsumer, errorConsumer).

That signalling of interest is propagated backwards through the chain of operators, up until the source operator, the Publisher that actually produces the initial data:

makeHttpRequest() //<5>
    .map(req -> parseJson(req)) //<4>
    .map(json -> json.getString("quote")) //<3>
    .flatMapMany(quote -> Flux.fromArray(quote.split(" "))) //<2>
    .subscribe(System.out::println, Throwable::printStackTrace); //<1>
  1. we subscribe to the words Flux, stating that we want to print each word to the console (and print the stack trace of any error)
  2. that interest is signalled to the flatMapMany step...
  3. ...which signals it up the chain to the json map step...
  4. ...then the request map step...
  5. ...to finally reach the makeHttpRequest() (which we'll consider our source)

At this point, the source is triggered. It generates the data in the appropriate way: here it would make an HTTP request to a JSON-producing endpoint and then emit the HTTP response.

From there on, we're in execution time. The data has started flowing through the pipeline (in the more natural top-to-bottom order, or upstream to downstream):

  1. The HttpResponse is emitted to the parseJson map
  2. It extracts the JSON body and emits it to the getString map
  3. Which extracts the quote and passes it to the flatMapMany
  4. The flatMapMany splits the quote into words and emit each word individually
  5. The value handler in the subscribe is notified of each word, printing these to the console, one per line

Hopefully that helps you understand the difference between assembly time and subscription/execution time!

Cold vs Hot

Right after explaining the difference and introducing this mantra is probably a good time to introduce an exception :laughing:

Nothing happens until you subscribe... until something does


So far, we've been dealing with a flavor of Flux and Mono sources called a Cold Publisher. As we've explained, these Publishers are lazy and only generate data when there is a Subscription. Furthermore, they generate the data anew for each individual Subscription.

In our example of an HTTP response Mono, the HTTP request would be performed for each subscription:

Mono<String> evenLength = quote.filter(str -> str.length() % 2 == 0);
Mono<String> oddLength = quote.filter(str -> str.length() % 2 == 1);
Flux<String> words = quote.flatMapMany(quote -> Flux.fromArray(quote.split(" ")));

evenLength.subscribe(); //this triggers an HTTP request
oddLength.subscribe(); //this triggers another HTTP request
words.subscribe(); //this triggers a third HTTP request

On a side note, some operators' behavior imply multiple subscriptions. For example retry re-subscribe to its source in case of an error (onError signal), while repeat does the same for the onComplete signal.

So for a cold source like the HTTP request, something like retry would re-perform the request thus allowing to recover from a transient server-side error, for instance.


A Hot Publisher on the other hand isn't as clear-cut: it doesn't necessarily need a Subscriber to start pumping data. It doesn't necessarily re-generate dedicated data per each new Subscriber either.

To illustrate that, let's introduce a new cold publisher example, then we'll show how to turn that cold publisher into a hot one:

Flux<Long> clockTicks = Flux.interval(Duration.ofSeconds(1));

clockTicks.subscribe(tick -> System.out.println("clock1 " + tick + "s");


clockTicks.subscribe(tick -> System.out.println("\tclock2 " + tick + "s");

This prints:

clock1 1s
clock1 2s
clock1 3s
    clock2 1s
clock1 4s
    clock2 2s
clock1 5s
    clock2 3s
clock1 6s
    clock2 4s

We can turn the clockTicks source into a hot one by invoking share():

Flux<Long> coldTicks = Flux.interval(Duration.ofSeconds(1));
Flux<Long> clockTicks = coldTicks.share();

clockTicks.subscribe(tick -> System.out.println("clock1 " + tick + "s");


clockTicks.subscribe(tick -> System.out.println("\tclock2 " + tick + "s");

It yields the following result instead:

clock1 1s
clock1 2s
clock1 3s
    clock2 3s
clock1 4s
    clock2 4s
clock1 5s
    clock2 5s
clock1 6s
    clock2 6s

You see that the two subscriptions now share the same ticks of the clock. share() converts cold to hot by letting the source multicast elements to new Subscribers, but only the elements that are emitted after these new subscriptions. Since clock2 has subscribed 2 seconds later, it missed early emissions 1s and 2s.

So hot publishers can be less lazy, even though they generally require at least an initial Subscription to trigger data flow.


In this article, we've learned about the difference between instantiating a Flux / chaining operator (aka Assembly time), triggering it (aka Subscription time) and executing it (aka Execution time).

We've thus learned that Flux and Mono are mostly lazy (aka cold Publisher): nothing happens until you subscribe to them.

Finally, we've learned about an alternative flavor of Flux and Mono, dubbed the hot Publisher, which behaves a little differently and is less lazy.

In the next instalment, we'll see why these three phases make a major difference in how you as a developer would debug reactor-based code.

In the meantime, happy reactive coding!

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