Improve Your Code Before Diving into Domain Driven Design or the Clean Architecture

This is some principles to improve your developer skills before diving to DDD / Clean Architecture.

Think on paper about use-cases and core domain events before diving into code and data modelization
Write clean code
Reduce code coupling think about abstraction

#javascript

The Concept of Domain-Driven Design Explained

Using microservices means creating applications from loosely coupling services. The application consists of several small services, each representing a separate business goal. They can be developed and easily maintained individually, after what they are joint in a complex application.

Microservices is an architecture design model with a specific bounded context, configuration, and dependencies. These result from the architectural principles of the domain-driven design and DevOps. Domain-driven design is the idea of solving problems of the organization through code.

The business goal is important to the business users, with a clear interface and functions. This way, the microservice can run independently from other microservices. Moreover, the team can also work on it independently, which is, in fact, the point of the microservice architecture.

Many developers claim microservices have made them more efficient. This is due to the ability to work in small teams. This allows them to develop different small parts that will later be merged as a large app.

They spend less time coordinating with other developers and more time on developing the actual code. Eventually, this creates more value for the end-user.

The Complexity Challenge

Complexity is a relative term. What’s complex for one person is simple for another. However, complexity is the problem that domain-driven design should solve. In this context, complexity means interconnectedness, many different data sources, different business goals, etc.

The domain-driven approach is here to solve the complexity of software development. On the other hand, you can use emergent design when the challenge is simple. However, when your application is complex, the complexity will only grow, and so will your problems.

Domain-driven design bases on the business domain. Modern business environments are very complex and wrong moves can lead to fatal outcomes. Domain-driven design solves complex domain models, connecting to the core business concepts.

Eric Evans, introduced the concept in 2004, in his book Domain-Driven Design: Tackling Complexity in the Heart of Software. According to the book, it focuses on three principles:

The primary focus of the project is the core domain and domain logic.
Complex designs are based on models of the domain.
Collaboration between technical and domain experts is crucial to creating an application model that will solve particular domain problems.

Important Terms in Domain-Driven Design

In DDD, it’s important to pay attention to the following terms:

Domain logic

Domain logic is the purpose of your modeling. Most commonly, it’s referred to as the business logic. This is where your business rules define the way data gets created, stored, and modified.

Domain model

Domain model includes the ideas, knowledge, data, metrics, and goals that revolve around that problem you’re trying to solve. It contains all the rules and patterns that will help you deal with complex business logic. Moreover, they will be useful to meet the requirements of your business.

Subdomain

A domain consists of several subdomains that refer to different parts of the business logic. For example, an online retail store could have a product catalog, inventory, and delivery as its subdomains.

Design patterns

Design patternsare all about reusing code. No matter the complexity of the problem you encounter, someone who’s been doing object-oriented programming has probably already created a pattern that will help you solve it. Breaking down your problem into its initial elements will lead you to its solution. Everything you learn through patterns, you can later use for any object-oriented language you start to program in.

#devops #microservices #domain driven design #microservices adoption #microservices archiecture #domain driven design introduction

Brain Crist

1594741500

Clean Code — 4 Rules of Simple Design

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What if there were simple rules that could help you create a good design as you worked and help you gain insight into the structure and design of your code?
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Kent Beck came up with four rules of Simple Design while he was developing ExtremeProgramming in the late 1990s. Until now, many developers feel that these rules are of significant help in creating simple yet well-designed software, and also helps in maintaining code to be clean. According to Kent, a design is “simple” when it follows these rules, in order of the importance:
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Image for post
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https://www.martinfowler.com/bliki/BeckDesignRules.html
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Passes the tests (It Works)
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Reveals intention
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No duplication
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Fewest elements
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The rules are in priority order, so “passes the tests” takes priority over “reveals intention”.
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Without further delay, let’s dig in each of every rule and talks about what they mean in the full extent:
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1st Rule: Passes All Tests (It Works!)

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Image for post
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https://blogs.egu.eu/divisions/gd/files/2019/07/Untitled-4-e1560239780934-1400x800.png
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First of all, of course, we need to make a working application to be delivered, that’s why it’s the number one priority. One of the many things that could make us sure that our app is working fine (and know exactly why it’s working) is to have tests for the particular code we wrote. Writing tests (comprehensive tests) actually could lead us to better designs. The more tests we write, the more we’ll continue to push towards things that are simpler to test, thus pushing us towards making our classes small and single purpose.
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_Once the tests are passed, we are empowered to keep our code clean. We do this by refactoring the code, and it’s better to do it frequently and incrementally after each few lines of code we add. After making a single feature work and pass all the tests, take time to review your code. Did others not understand what I actually write here? Am I just degrading it? If the answers are yes, then we clean it up and run our tests. _The fact that we have these tests eliminates the fear that cleaning up the code will break it!
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The next 3 rules could help you to check the quality of your code.
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2nd Rule: Reveals Intention

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Image for post
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https://cdn.wrytin.com/images/wrytup/r/1024/under-jx0fxee8.jpeg
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When we are communicating with other people, we need to make sure other parties understand what we want to convey in our conversation. It’s also the same with code. As coders, we were working as a team in a project and there are lots of other coders that will see, touch, or even need to modify the existing code, and it’s very important for them to understand what you write in there.
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To make our code reveals our intentions, we need to be expressive, and the most important way to be expressive is to try. Too often we get our code working and then move on to the next problem without giving a second thought to make that code easy to read. Place ourselves as other people that will become the next person to read our code. Care is a precious resource.
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Some things that we can do to make our code more expressive are:
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Choose a good name that represents the things. Use predictable names so when other people read the class, function, or variable names, they don’t get a wrong idea or surprised when discovering the responsibilities.
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**_Keep your functions and classes small. _**It’s easier to name, write, and understand it.
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Challenge and commit yourself to write code so that it reads as documentation. Well-written unit tests are expressive and act as documentation by example. Someone that reads the tests should be able to get a quick understanding of what a class is all about.
What if there were simple rules that could help you create a good design as you worked and help you gain insight into the structure and design of your code?

Kent Beck came up with four rules of Simple Design while he was developing ExtremeProgramming in the late 1990s. Until now, many developers feel that these rules are of significant help in creating simple yet well-designed software, and also helps in maintaining code to be clean. According to Kent, a design is “simple” when it follows these rules, in order of the importance:

Image for post

https://www.martinfowler.com/bliki/BeckDesignRules.html

Passes the tests (It Works)
Reveals intention
No duplication
Fewest elements
The rules are in priority order, so “passes the tests” takes priority over “reveals intention”.

Without further delay, let’s dig in each of every rule and talks about what they mean in the full extent:

1st Rule: Passes All Tests (It Works!)
Image for post

https://blogs.egu.eu/divisions/gd/files/2019/07/Untitled-4-e1560239780934-1400x800.png

First of all, of course, we need to make a working application to be delivered, that’s why it’s the number one priority. One of the many things that could make us sure that our app is working fine (and know exactly why it’s working) is to have tests for the particular code we wrote. Writing tests (comprehensive tests) actually could lead us to better designs. The more tests we write, the more we’ll continue to push towards things that are simpler to test, thus pushing us towards making our classes small and single purpose.

_Once the tests are passed, we are empowered to keep our code clean. We do this by refactoring the code, and it’s better to do it frequently and incrementally after each few lines of code we add. After making a single feature work and pass all the tests, take time to review your code. Did others not understand what I actually write here? Am I just degrading it? If the answers are yes, then we clean it up and run our tests. _The fact that we have these tests eliminates the fear that cleaning up the code will break it!

The next 3 rules could help you to check the quality of your code.

2nd Rule: Reveals Intention
Image for post

https://cdn.wrytin.com/images/wrytup/r/1024/under-jx0fxee8.jpeg

When we are communicating with other people, we need to make sure other parties understand what we want to convey in our conversation. It’s also the same with code. As coders, we were working as a team in a project and there are lots of other coders that will see, touch, or even need to modify the existing code, and it’s very important for them to understand what you write in there.

To make our code reveals our intentions, we need to be expressive, and the most important way to be expressive is to try. Too often we get our code working and then move on to the next problem without giving a second thought to make that code easy to read. Place ourselves as other people that will become the next person to read our code. Care is a precious resource.

Some things that we can do to make our code more expressive are:

Choose a good name that represents the things. Use predictable names so when other people read the class, function, or variable names, they don’t get a wrong idea or surprised when discovering the responsibilities.
**_Keep your functions and classes small. _**It’s easier to name, write, and understand it.
Challenge and commit yourself to write code so that it reads as documentation. Well-written unit tests are expressive and act as documentation by example. Someone that reads the tests should be able to get a quick understanding of what a class is all about.

#clean-code #simple-design #visual studio code #coding

Houston Sipes

1604088000

How to Find the Stinky Parts of Your Code (Part II)

There are more code smells. Let’s keep changing the aromas. We see several symptoms and situations that make us doubt the quality of our development. Let’s look at some possible solutions.

Most of these smells are just hints of something that might be wrong. They are not rigid rules.

This is part II. Part I can be found here.

Code Smell 06 - Too Clever Programmer

The code is difficult to read, there are tricky with names without semantics. Sometimes using language’s accidental complexity.

_Image Source: NeONBRAND on _Unsplash

Problems

Readability
Maintainability
Code Quality
Premature Optimization

Solutions

Refactor the code
Use better names

Examples

Optimized loops

Exceptions

Optimized code for low-level operations.

Sample Code

Wrong

function primeFactors(n){
	  var f = [],  i = 0, d = 2;  

	  for (i = 0; n >= 2; ) {
	     if(n % d == 0){
	       f[i++]=(d); 
	       n /= d;
	    }
	    else{
	      d++;
	    }     
	  }
	  return f;
	}

Right

function primeFactors(numberToFactor){
	  var factors = [], 
	      divisor = 2,
	      remainder = numberToFactor;

	  while(remainder>=2){
	    if(remainder % divisor === 0){
	       factors.push(divisor); 
	       remainder = remainder/ divisor;
	    }
	    else{
	      divisor++;
	    }     
	  }
	  return factors;
	}

Detection

Automatic detection is possible in some languages. Watch some warnings related to complexity, bad names, post increment variables, etc.

#pixel-face #code-smells #clean-code #stinky-code-parts #refactor-legacy-code #refactoring #stinky-code #common-code-smells

DataIT Solutions

1609840501

Landscapes Website Design | Nature Landscapes Website Designer

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DataIT Solutions specializes in landscape website designing that are not only beautiful but also rank well in search engine results and convert your visitors into customers. We’ve specialized in the landscaping industry for over 10 years, and we look at your business from an owner’s perspective.

Why use our Landscapes for your landscape design?

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If you are a gardener or have a gardening company please do not hesitate to contact us for a quote.
Need help with your website? Get in touch

#nature landscapes website design #landscapes website design #website design #website designing #website designer #designer

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7, Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

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import io

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import tokenize

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code = b"color = input('Enter your favourite color: ')"

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for token in tokenize.tokenize(io.BytesIO(code).readline):

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    print(token)