6 minute read

Do you have some kind of streaming source of data in your application? Notifications? Tweets? Messages? What happens if that source starts to produce data faster than your application can handle?

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In Reactive programming when the client cannot handle the amount of data produced by the observable it is called Backpressure. If these concepts are not familiar, head to What is Reactive Programming by Keval Patel to get you up to speed.

Let’s visualize backpressure with an Angular application that connects to a Spring back end using RSockets. They allow you to get a reactive stream through the network layer with backpressure support.

Then we’ll create a Rx operator that can control the data flow during backpressure so the client, in this case the browser, receives less data because of it.

Our example: a News Ticker.

The Ticker

Let’s say there’s a news ticker that contains some very important news. It chugs along at the speed events unfold in the world. The user looks at the stream and pauses the ticker and reads some item in detail. When the user is done, the ticker resumes where it left off.

OK this is the spec. I know you have some clarifying questions but the PM is long gone. This is all we have.

To the drawing board. First a data object for the news items:

News items has some short description (headline), the article text and whether it is breaking news.

A minimal API for the provider could look something like this if we were to implement this non reactively:

interface NewsProvider {
    fun getNews(fromId: Int?, 
                breakingOnly: Boolean, 
                limit: Int = 100): List<News>

This API could be implemented RESTfully for the client. The client can then use this to get the initial list, and then poll for more when the ticker runs out.

On a slow news day it polls hopelessly at the end of the list for more items. If something crazy happens like an orange sociopath is given nuclear launch codes without restraint and the ticker has always more data that the client can handle. Then it’s up to the client to either leave forever behind or always simply fetch the latest data and perhaps filter by priority.

Going reactive

While this would probably work, it has one glaring problem: polling. If it’s critical that the feed is delivered as fast as possible, we would need to set the polling time interval short and strain the server.

So let’s get reactive. Let’s provide the same data, but this time with a reactive interface:

interface NewsProvider {
 fun news(): Flowable<News>

The client uses the Observable (Flowable type is the backpressure supported type in RxJava2) to read News at the pace they appear from the server and they’re immediately shown on the ticker.

But we can’t just push the feed through the socket. What happens if the ticker is paused for longer and the feed is swamped by the orange menace? This is where backpressure kicks in. If the server has news to give but the client can’t handle them at the moment, we need to specify a strategy to handle this. In this case a reasonable solution would be to buffer some of them, and if that’s filled start dropping them.

Lets’s create a more dynamic solution so that when backpressure occurs the feed is filtered to only contain breaking news for a period of time.

Ok enough with the rambling, show me the code!

Backend Implementation

Let’s start by creating the RSocket endpoint for the client (if your not familiar with Kotlin, a Java version without the operators and in Reactor 3 can be found here):

RSocket endpoint for news

Here we create the RSocket end point (line 10) and start listening to a port on a WebSocket and relay the NewsProvider news Flowable directly into the socket as JSON (line 30).

We need an implementation for the NewsProvider interface we defined above, so let’s implement a FakeNewsGenerator generator source for it:

Here we have a source that simulates some external source where the news are coming from. In our case source is simply an interval Flowable that churns news fake news at defined intervals.

If you look at the news()method (line 33) there’s a custom operator on the Flowable called onBackPressureFilter . It has a duration parameter and a lambda parameter for the filter in case of backpressure — show only breaking news.

This is our own extension function:

We first lift our own operator BackPressureFilterOperator into the stream and then use the standard Rx Operator onBackpressureBuffer(capacity, onOverflowAction, overflowStrategy) ** to invoke our handleBackPressure method if backpressure occurs.

Writing operators for RxJava 2 is a daunting task. You get to jump into the deep end of the pool if you do it. There’s sharks with lasers in the pool too. I read that guide multiple times and I may have even understood it, who knows.

I opted not to implement the drain algorithm (or use stuff inside the internal package) to detect backpressure in my own operator and opted to use the default onBackpressureBuffer ** instead to invoke the action. Influencing an operator from another feels like the sharks have their laser sights on me, but it should work.

Here’s the operator in its entirety for the curious:

It simply uses the backpressure action signal to enable the filter. The rest is to conform (I hope!) to the rules required for Rx operators. In our case this means remember to request more data from upstream if we filter elements and to stop doing anything if the stream has completed.

If you want your own strategy for backpressure, you might be better of to simply use the onBackPressureBuffer action call and act accordingly in your observable producer code.

The Client

The client is rather straight forward, it merely connects to the WebSocket and implements the Ticker requirements.

Here is the UI in all it’s glory (Dilbert would be proud):

Sorry, I didn’t have time to make it pretty. The UI allows us to connect to the back end and view the news stream. You can also use the slider to modify the fake news generator speed. If you click on any item and the ticker stops and resumes if you click again.

Depending on the speed of your computer sliding down the generator interval at some point backpressure will kick in and only red breaking news are let through, or if you pause the ticker for long enough.

The most relevant part is the ticker service that connects to the back end, I’ll put that in it’s entirety here as well:

On line 43 we create the RSocket for the connection and define the connection to use WebSockets and request a stream (line 68).

The most interesting parts are perhaps the subscription phase (line 81) for the RSocket — to control the flow speed we only request one item from upstream and then another when we receive data (onNext, line 75). Usually this number would be much higher or even unbounded (MAX_STREAM_ID).

I noticed that if the speed is too much for the client (browser) to handle, the WebSocket blocks and the connection dies on the back end side to a OutOfDirectMemoryError.

Final Thoughts

RSockets look really promising in general, but here I just highlighted the backpressure aspects of them. Reactive streams through the network layer feel rather awesome.

The full source can be found at github. The java version here (without the operator, simply filtering in the producer).

I’m really interested in your thoughts about this, so leave them below, thanks!

Originally posted on Medium

Tagged under: rxjava