Tutorial by Examples

Backpressure is when in an Observable processing pipeline, some asynchronous stages can't process the values fast enough and need a way to tell the upstream producer to slow down. The classic case of the need for backpressure is when the producer is a hot source: PublishSubject<Integer> sour...

Most developers encounter backpressure when their application fails with MissingBackpressureException and the exception usually points to the observeOn operator. The actual cause is usually the non-backpressured use of PublishSubject, timer() or interval() or custom operators created via create(). ...

Creating backpressured data sources is the relatively easier task when dealing with backpressure in general because the library already offers static methods on Observable that handle backpressure for the developer. We can distinguish two kinds of factory methods: cold "generators" that ei...