Three Main Components in Reactive Programming

This article is about fundamental knowledge for components in reactive programming.

1. Producers
2. Consumers
3. Data pipeline

The graph below demonstrates how reactive programming works in the big picture.

generic model from upstream observable to downstream observer in reactive programming

Producers

Producers are the sources of your data.

A stream must always have a producer of data, which will be the starting point for any logic that you’ll perform in RxJS.
A stream is nothing more than a sequence of events over time

The observer pattern defines producers as the subject. We call them observables, as in something that’s able to be observed.

Observables are in charge of pushing notifications, so we refer to this behavior as fire-and-forget. This means that we’ll never expect the producer to be involved in the processing of events, only the emission of them.

Consumers

Consumers are the components that accept events from the producer and process them in some way. When the consumer begins listening to the producer for events, you now have a stream. It is at this point that the stream begins to push events; we’ll refer to a consumer as an observer.

Streams travel only from the producer to the consumer.

consumer sample in rxjs

From the example above, whenever the user clicks on the submit button, it will produce an event that flows down to consumers.

In nature, a stream will flow from upstream to downstream; this same thing happens in reactive programming. The streams will always flow from the upstream observable to the downstream observer.

The click action from the user would be the upstream observable because it would only produce events, not consume them. And the code that executes logic based on the click action would be the downstream observer.

Data pipeline

The biggest benefit of reactive programming is you can edit the data as it passes from producer to consumer.

From one of my recent article is Reactive Programming in a Modern Web Application, I mentioned:

It combines the Observer pattern with the Iterator pattern and functional programming with collections to fill the need as an ideal way of managing sequences of events.

Functional programming? Yes, that is why we can find many functional programming syntax implementations inside libraries like RxJS.

To create a data pipeline, we use the pipable operator and operators that are provided by the RxJS library to transform data from producer to consumer by using functional programming.

data pipeline sample in rxjs

The producer will emit an array of users with name and age information via the data pipeline and the **pipe** operator, use map to transform data from the producer, and return to the consumer with the name of the user only.

Summary

This article provided basic knowledge on components in reactive programming. We have 3 parts which are the producer, data pipeline, and consumer.

#angular #rxjs #javascript

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Diving Into Reactive Microservices

What are Monoliths?

To start explaining the microservices it’s useful to compare it to the monolithic application. An application is said to be a monolith when it is deployed as a single unit. Monoliths have a single shared database. They communicate with synchronous method calls where you send a message and expect a response immediately.

What are the cons of Monoliths?

• Monoliths are limited by the maximum size of a single physical machine. As the monolith grows, it acquires more and more system resources.
• They scale as far as the database allows because they often rely on a relational database which probably runs only a single instance in order to maintain consistency.

#reactive-systems #reactive-microservice #reactive-programming #reactive-architecture

Building Reactive Scalable Systems

Building a Reactive System is all about the balance between consistency and availability and the consequences of picking one over the other. This article mainly focuses on consistency and availability and how they impact the scalability of a system.

What are Scalability, Consistency, and Availability?

A system is considered scalable if it can meet the increase in demand while remaining responsive.

A system is considered consistent if all the nodes show the same data at the same time.

A system is considered available if it remains responsive despite any failures.

How Does the Scalability of a System Differ From the Performance of the System?

Scalability and performance are related but different concepts and we need to understand what the difference is.

Scalability is the number of requests a system can handle at a time, i.e. load. It’s about optimizing the ability to handle the load, which means improving how many requests a system can handle at a time. Performance on the other hand is the time system takes to complete a single request, i.e. latency. It’s about optimizing the response time, which means improving how quickly a system can handle a single request.

Performance has a limit on reducing the response time, and we will eventually reach that limit. Whereas, scalability has no theoretical limit. We may be restricted by the implementation, but in a perfectly scalable system, we could scale forever.

So when we build Reactive Micro-services we tend to focus on improving scalability than improving performance.

How Can We Measure Scalability and Performance of a System?

Measurement like requests-per-second measures both. This makes it a valuable metric because we can use it to see whether we have improved our scalability or our performance. But it also means that it is somewhat restrictive in the sense that if it improves we can’t tell which one changed. So if we want to know where that improvement came from then we have to track scalability and performance individually.

How Can We Explain Consistency in Distributed Systems?

Distributed systems are systems that are separated by space. This means the system could be deployed across multiple data centers or within the same data center, or just deployed to different hardware or the same hardware.

Even if it’s deployed to the same hardware, a distributed system is one where information has to be transferred between different parts of that system, and when that information is transferred it’s crossing some sort of space. It could be going over a local network, or it could be writing to a disk, or it could be writing to a database.

Information cannot be transferred instantaneously, it takes some time. Granted that time could be very small but there is an amount of time that elapses during the transfer of information. Within that time duration when the transfer of the information takes place, the state of the original sender may change.

The key here is to recognize that when we are dealing with a distributed system, we are always dealing with stale data. The reality_ is eventually consistent._

What Is Eventual Consistency?

When a system stops receiving updates at least for some time, we can guarantee that all parts of the system will eventually converge on the same state. Thus in this way, we can reach that level of consistency.

Common source control tools (Git, Subversion, etc) operate on an eventually consistent model. They rely on a later merge operation to bring things back into alignment. That’s how modern source control tools achieve consistency and it’s all an eventually consistent system.

Traditional monolithic architectures are usually based around strong consistency they use a strongly consistent database like a SQL database.

What Is Strong Consistency?

When all members of a system agree on the state, before it becomes available, then we reach the level of strong consistency.

We can achieve strong consistency by introducing mechanisms like locks. Distributed system problem occurs when we have multiple things which are responsible for the same piece of data. As long as only one thing is responsible for that data, as long as we only have one instance of the lock, it’s not a distributed system problem anymore. Thus in this way, we can resolve the distributed system problem by using a non distributed resource(lock).

But when we introduce a lock, it introduces overhead in the form of contention. That overhead has consequences to our ability to be elastic, to be resilient, and it has other consequences as well.

#scalability #reactive architecture #cap theorem #reactive systems #reactive microservices #reactive

Advanced Level for Angular Reactive Form

Have you ever wondered why Angular is so powerful with forms? In this article, I will walk you through this topic to understand why and how Angular is better with forms compared to React or Vuejs.

Contents:

1. What Reactive Form is
2. Three Basic Blocks
3. FormBuilder
4. Validators
5. The Magic

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What Reactive Form is

Angular provides 2 ways to work with forms which are template-driven form and reactive form.

While template-driven form is dealing with form by using directives, Reactive form allows Angular developers to handle forms at the Angular template component.

Reactive Form will bring more benefits compared to template-driven from such as:

• The code will be testable.
• Keep all form logic in the same place.
• Easy to handle multi-level form (I will highlight this at The Magicsection).
• This approach is friendly with reactive programming.

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Three Basic Blocks

There are 3 fundamental building blocks of Angular Reactive Form:

1. FormControl
2. FormGroup
3. FormArray

When we build reactive forms in Angular, they all come from these three basic blocks.

In order to use Reactive Form, import [_ReactiveFormsModule_](https://angular.io/api/forms/ReactiveFormsModule) from the_@angular/forms__ package and add it to your NgModule’s _imports_ array._

1. FormControl

Initializing Form Controls

Here is how we create form control in Angular with 4 different syntaxes.

First, we can init form control with value only.

#angular #javascript #reactive-programming #programming #reactive-forms

Reactive Streams in JavaScript with RSocket Flowable

When programming async operations, JavaScript developers are generally at least familiar with Promises, async/await, and callbacks. However, newer async models such as ReactiveX (RxJS in JavaScript), are not quite as popular. In this article, we dig into something similar but slightly different to RxJS; a Reactive Streams implementation called RSocket Flowable (rsocket-flowable on npm).

#rsocket #javascript #reactive-streams #reactive-programming #programming

Coding 101: Programming Language Building Blocks

This article will introduce the concepts and topics common to all programming languages, that beginners and experts must know!

Do you want to learn a programming language for the first time?

Do you want to improve as a Programmer?

Well, then you’re in the right place to start. Learn any programming language without difficulty by learning the concepts and topics common to all programming languages.

Let me start by answering the following questions:

• Why learn Programming?
• What is Programming?
• How to Learn a Programming Language?

Why learn Programming❔

Programming develops creative thinking

Programmers solve a problem by breaking it down into workable pieces to understand it better. When you start learning to program, you develop the habit of working your way out in a very structured format. You analyze the problem and start thinking logically and this gives rise to more creative solutions you’ve ever given.

Whether you want to uncover the secrets of the universe, or you just want to pursue a career in the 21st century, basic computer programming is an essential skill to learn.

_– _Stephen Hawking

Everybody in this country should learn how to program a computer… because it teaches you how to think.

_- _Steve Jobs

Programming Provides Life-Changing Experiences

Programming always provides you with a new challenge to take risks every time and that teaches you to take risks in your personal life too. The world is filled up with websites, apps, software and when you build these yourself you’ll feel more confident. When a programmer solves a problem that no one has ever solved before it becomes a life-changing experience for them.

What is Programming🤔?

A program is a set of instructions to perform a task on a computer.

Programming is the process of designing and building an executable computer program to accomplish a specific task.

Well, according to me programming is like raising a baby. We provide knowledge (data) to help understand a baby what’s happening around. We teach a baby to be disciplined (and much more) by making rules.

Similarly, a computer is like a baby. We set rules and provide data to the computer through executable programs with the help of a Programming Language.

(Photo by Clément H on Unsplash)

That’s it👍. If you can understand this basic concept of programming, you’re good to go. Pick up a programming language and start learning. Read the following section to get an idea of where to start.

My recommendation is to choose Python Programming Language as a start, because it’s beginner-friendly.

#programming #programming-tips #programming-language #programming-top-story #computer-science #data-structures-and-algorithms #tips-for-programmers #coding