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You've finally reached the chapter where everything gets put into practice, or almost everything, mind you.

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In this chapter, I'm going to take into account everything covered so far, that means: functional programming, reactive programming, Node. You also have a working implementation of an FRP library to play with, so you might be wondering: What else is there? You made it to the final chapter of the book! So far you've received all the necessary information to add functional reactive logic on your Node. Real world applications, the ones that are actually out in production, ready for the end-users to access it, need to take other things into consideration. In some cases when the expected number of users is ever-growing, they need to be able to scale up to address the increasing demand.

Title Reactive Programming with Node. Print ISBN These functions are not exactly the same as ones defined on List , but are similar.

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The higher-order functions make it possible to compose functions returning futures using familiar functional programming patterns:. The call to Future.

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This code appears much cleaner than the callback example. However, it is important to note that each function in the above example is defined to take exactly output of the preceding function. We may not be so lucky with real-world APIs and we may need to process output before passing it to the next function. So, it is easy to imagine that real-world code could become much more complicated by additional processing steps.

Finally, reactive code composed with higher-order functions is often not purely functional—it typically involves side effects through network or file system IO 3. The Scala Async library provides async and await macros inspired by the similar constructs originally introduced by C.


The macros make it possible to write efficient asynchronous code in a direct style, very similar to how the synchronous code was written in the first example. The basic approach is to wrap each block of asynchronous code within an async block and each computation resulting in a future within an await block. The code above looks nearly identical to the synchronous code from the first example, reflects our intent more directly, and feels more natural.

The upcoming Coursera course Principles of Reactive Programming digs deeper into the details, incorporates interesting programming exercises, and covers two more reactive patterns that are particularly useful: observables and actors. Observables provide a powerful abstraction over event streams, similarly to how futures provide an abstraction over discrete events.

This library was used extensively at Netflix to redefine how they approach both back-end and front-end development. I decided to use the lodash utility library and Bacon. Turns your event spaghetti into clean and declarative feng shui bacon, by switching from imperative to functional The use of event streams makes Kafka an excellent fit here.

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It not only supports modeling the data as event streams but also has some very useful properties for managing those event streams. This is the final architecture that I implemented you can have a look at more details of the architecture and code here.

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Data flows from left to right. The hexagons each represent a Heroku app. Each app produces messages into Kafka, consumes messages out of Kafka, or both. The white rectangles are Kafka topics. Starting from the left, the first app ingests data as efficiently as possible. I perform as few operations on the data as possible here so that getting data into Kafka does not become a bottleneck in the overall pipeline. The next app fans the tweets out to keyword- or term-specific topics. In the example shown in the diagram above, there are three terms.

Reactive Programming with Node.js

The next two apps perform aggregate metric calculation and related term counting. This is called on every tweet. Implementing this in an imperative style would require many more lines of code and be much harder to understand i. In fact, this is the most complicated data processing step. The functional implementations for each of the other data processing apps are even shorter. Hopefully, this provided you with not only some tools but also the basic understanding of how to implement a data processing pipeline with Node.

We at Heroku are really excited about providing the tools to make evented architectures easier to build out, easier to manage, and more stable.

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