9/20/2023 0 Comments Flux windowdoOnNext(flux -> llectList().subscribe(l -> ("Received :: " + l))) To split the original Flux into Flux of containing 5 items. Windowing behavior would be like opening a separate Flux for every 3 items. Reactor Window is more or less same like Reactor Buffer – but it creates a branch in the Flux instead of collecting them as list as buffering does.įor example, buffering behavior would be like this. buffer(3, 5) // collect 3 for every 5 items buffer(3, 2) // collect 3, skip 2 then collect 3 Sometimes we might to have overlapping buffers as shown below. bufferTimeout(5, Duration.ofSeconds(3)) // collect 5 items every 3 seconds Collecting 5 elements / every 3 seconds:.subscribe(l -> ("Received :: " + l)) // output buffer(Duration.ofSeconds(3)) // collect the items every 3 seconds subscribe(l -> ("Received :: " + l)) // output: buffer(5) // collect the items in batches of 5 Project Reactor provides 2 high level options with many method overloading options. Let’s see how we can accomplish this using reactive programming using project Reactor. In that case, something is wrong! It could be a fraudulent activity and the card should be blocked! Even though this requirement sounds very simple, it would be very challenging to implement! This sounds a lot better.Īnother example could be to detect credit cards which are used 3 times within 2 seconds (just an example!). So we might want something like either 100 items or 10 seconds – whichever come first, we will do the processing and store them in the DB. what will happen in the case? Our application will wait forever for the 100th item to arrive. What if we had received only 99 items? The 100th item has not yet arrived for a while. Let’s assume that we would like to collect 100 items at a time and store them. Instead we might want to collect them in batches and do a bulk insert. For an extremely high throughput application, we can not process and store these events in the DB one by one as it could affect the overall processing time. Let’s consider a service in an application which is supposed to collect all the audit-events happening among all other systems in the application, process them and store them in a Database. Reactor Window: to emit a collection of items as a Flux within the Flux.Reactor Buffer: to collect the items as a list within the Flux.This is where Reactor Buffer & Window options would be very helpful. Sometimes, instead of executing the operations for every item one by one, we could collect the item periodically and execute the operations for all the collected items at once or we might want to perform some aggregate operations on the set. The data stream could be unbounded / never ending stream! In the reactive pipeline, we could have some operations which need to be executed for every item. Reactive programming is a style of programming which observes on the data streams, reacting to the changes and propagating them! The data stream will be closed when there is no more data for source to emit or when there is an un-handled exception! If you are new to reactive programming or project reactor, take a look at this entire series to get a good idea on that. Miller, Computational Methods of Neutron Transport, American Nuclear Society, 1993, ISBN: 2-4.In this tutorial, I would like to demo Reactor Buffer vs Window options for collecting the items in a reactive pipeline & doing operations in batches. Hetrick, Dynamics of Nuclear Reactors, American Nuclear Society, 1993, ISBN: 3-2. Neuhold, Introductory Nuclear Reactor Dynamics, American Nuclear Society, 1985, ISBN: 9-4. Bezella, Introductory Nuclear Reactor Statics, American Nuclear Society, Revised edition (1989), 1989, ISBN: 3-2. Department of Energy, Nuclear Physics and Reactor Theory. DOE Fundamentals Handbook, Volume 1 and 2. January 1993.
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