Mastering Event-Driven Microservices: A Comprehensive Guide

Mastering Event-Driven Microservices: A Comprehensive Guide

Event-Driven Microservices
Event-Driven Microservices

Event-Driven Microservices: Revolutionizing Scalability and Flexibility

Imagine a busy city where people are always talking to each other, sharing information, and reacting to what’s going on. Each person represents a microservice, and the events are the changes happening in the system. Imagine these interactions happening at lightning speed in real time while maintaining order and resilience. It is the world of event-driven microservices.

Event-driven microservices unlock a new level of communication and flexibility in software development. This architectural style allows developers to build robust, scalable, and adaptable applications by breaking them down into smaller, independent components that communicate through events. Keep on reading if you want to have a thorough understanding of this powerful approach and be ready to tackle the challenges of modern software development with event-driven microservices in your toolkit.

By combining the ideas of microservices and event-driven architecture (EDA), this architectural style makes it easier and faster for businesses to meet changing business needs. Today, we’ll talk about the advantages, ideas, design patterns, and real-world uses of event-driven microservices.

Defining Microservices

The microservices method is a software development approach that structures applications as a group of small, autonomous services. Each service is responsible for a specific business capability and can be developed, deployed, and scaled independently. Key benefits of microservices include:

  • Improved scalability
  • Enhanced flexibility
  • Faster development cycles

Understanding Event-Driven Architecture in Microservices

Event-driven architectures (EDA) are software systems built around event production, consumption, and processing. Events are records of changes in state, such as a new order being placed or an item being shipped. EDA allows for the following:

  • Real-time processing
  • Decoupled components
  • Increased resiliency

The Power of Combining Microservices and Event-Driven Design

By combining the principles of microservices and EDA, event-driven microservices offer a powerful approach for developing applications that are:

  • Scalable
  • Flexible
  • Resilient
  • Capable of real-time data processing

Let’s dive deeper into the world of event-driven microservices, discussing key concepts, patterns, and real-world use cases.

The Evolution of Software Architecture

The software industry has evolved from monolithic applications to microservices and event-driven architectures to meet growing scalability, flexibility, and real-time data processing demands.

Monolithic Applications

Traditionally, developers were building software applications as large, monolithic systems. These applications took a lot of work to scale and adapt to changing business requirements, leading to a need for more flexible and modular architectures.

The Rise of Microservices

As modern software applications continued to grow in complexity, it became increasingly clear that the traditional monolithic architecture was no longer sufficient. In response, a new approach known as microservices emerged, offering a solution to the limitations of monolithic applications.

By breaking applications down into smaller, autonomous services, developers gained the ability to update and scale individual components with ease. This increased flexibility and agility enabled them to respond more quickly to changing business needs, leading to faster innovation and greater competitive advantage.

The Need for Event-Driven Architectures

In today’s fast-paced world, the demand for real-time data processing has reached unprecedented levels, fueled by the explosive growth of modern applications. As a result, traditional monolithic architectures have become increasingly inadequate, unable to keep up with the speed and complexity of modern software systems.

Enter event-driven architectures – a dynamic and innovative approach that leverages asynchronous communication and component decoupling to provide unmatched flexibility and resiliency in software systems. By breaking applications down into smaller, autonomous services, event-driven architectures allow developers to process and respond to data in real time, improving scalability and fault tolerance while minimizing resource consumption.

The benefits of event-driven architectures are many, including lightning-fast response times, improved system reliability, and enhanced overall performance. These advantages make event-driven architectures the go-to choice for businesses looking to build software applications that can keep up with the ever-increasing demands of the market.

Key Concepts of Event-Driven Microservices

To understand event-driven microservices, we need to grasp the following key concepts:

  • Events
  • Event Producers
  • Event Consumers
  • Event Streams
  • Event Aggregators
Event-Driven Architectures


An event is a record of a state change within a system. In the context of event-driven microservices, events are messages describing a specific action or occurrence, such as a user logging in or placing an order.

Event Producers

Event producers are components within a system that generate events. These can include user interfaces, devices, or other services. In a microservices architecture, each service can act as an event producer.

Event Consumers

Event consumers are components that listen for and process events. They react to events produced by other services, performing tasks such as updating databases, sending notifications, or triggering additional events.

Event Streams

An event stream is a sequence of events ordered by their production time. Event streams can be persisted for later processing or used in real-time to drive application behavior. In an event-driven microservices architecture, event streams enable communication between services and act as a source of truth for the system state.

Event Streams

Event Aggregators

Event aggregators power the event-driven microservices architecture. These advanced tools collect, filter, and disseminate events to the right consumers, ensuring seamless system flow. 

Event aggregators regulate data flow, facilitate communication between components, and deliver events to the correct consumers at the right time by serving as a single hub for event processing. An event-driven microservices architecture without event aggregators would quickly become chaotic, with data flowing indiscriminately and critical events missed. 

In short, event aggregators provide the critical infrastructure needed to ensure that applications run smoothly, efficiently, and reliably.

Benefits of Event-Driven Microservices

Event-driven microservices offer a range of benefits that stem from the combination of microservices and event-driven architecture principles. These benefits include such factors as:

Scalability: The ability to independently scale individual services helps to ensure that the entire application can adapt to changes in workload.

Flexibility: By decoupling services, event-driven microservices allow for easier updates and adjustments to individual components, making the overall system more adaptable to changing business requirements.

Resilience: The decoupled nature of event-driven microservices means that failures in one part of the system are less likely to impact other components, increasing overall system stability.

Real-Time Data Processing: Event-driven microservices enable real-time processing of events as they are generated, allowing for faster and more responsive applications.

Decoupled Architecture: The separation of concerns in an event-driven microservices architecture leads to more maintainable and modular code, making it easier to understand and modify.

The Power of Flexibility: How Event-Driven Microservices Transform Business Operations

In the constantly evolving business landscape, the need for flexibility and adaptability is more crucial than ever. The implementation of event-driven microservices has proven effective in facilitating this much-needed adaptability. Let’s explore how this approach has revolutionized the way businesses operate without diving into technical jargon.

Adapting to Change: Embracing the Unpredictable

The ability to adapt to change is vital for businesses that want to stay ahead of the competition. Unexpected market shifts, new technologies, and consumer trends can drastically alter the playing field. Event-driven microservices provide businesses with a way to respond to these changes more quickly and efficiently. This agile approach allows companies to reconfigure their operations in response to new events or changing conditions, ensuring they remain nimble and competitive.

Streamlining Communication: Breaking Down Silos

In many organizations, departments, and teams operate in isolation, leading to a lack of effective communication and collaboration. It can result in disjointed decision-making and inefficiencies. Event-driven microservices help to break down these silos by facilitating communication between different parts of the organization. This improved communication leads to better collaboration, faster decision-making, and ultimately more successful outcomes.

Scalability: Growing with Your Business

As businesses grow, they often face challenges in scaling their operations to meet increased demand. The addition of new team members, customers, and processes can put a strain on existing systems and infrastructure. Event-driven microservices allow businesses to scale more efficiently by enabling them to add or modify components without impacting the entire system. This flexibility helps organizations grow smoothly, minimizing growing pains and ensuring a seamless transition as they expand.


The Power of Real-Time: Making Data-Driven Decisions

In today’s fast-paced world, businesses need access to real-time data to make informed decisions. Traditional systems often struggle to process and analyze data in real time, resulting in delays and missed opportunities. Event-driven microservices can facilitate real-time data processing and analysis, enabling businesses to respond quickly to new information and make smarter decisions. The implementation of this technology leads to increased efficiency, better customer experiences, and improved performance overall.


Reducing Complexity: Simplifying Complex Processes

Managing complex business processes can be a challenging task for organizations. The need to coordinate various tasks, teams, and resources often leads to confusion, delays, and inefficiencies. Event-driven microservices can help simplify and streamline these processes by breaking them down into smaller, more manageable components. This approach allows businesses to tackle each component separately, making it easier to manage, monitor, and optimize their operations.

By embracing the power of event-driven microservices, businesses can adapt to changes quickly and seize new opportunities, ensuring they remain competitive and successful in the ever-changing landscape of the modern business world.

Popular Messaging Systems for Event-Driven Microservices

Various messaging systems can be used to facilitate communication between services in an event-driven microservices architecture. Some popular messaging systems include:


Apache Kafka: A distributed streaming platform designed for high-throughput, fault-tolerant, and scalable event streaming.

RabbitMQ: A robust and widely-used message broker that supports multiple messaging protocols and provides reliable message delivery.

Amazon Kinesis: A managed streaming service from AWS that enables real-time processing of streaming data at scale.

Google Cloud Pub/Sub: A messaging service from Google Cloud Platform that provides at-least-once delivery of messages and supports both push and pull-based message consumption.

NATS: A lightweight, high-performance messaging system that offers simple publish/subscribe and request/response patterns for event-driven communication.

Popular Messaging Systems for Event-Driven Microservices

Various messaging systems can be used to facilitate communication between services in an event-driven microservices architecture. Some popular messaging systems include:


Apache Kafka: A distributed streaming platform designed for high-throughput, fault-tolerant, and scalable event streaming.

RabbitMQ: A robust and widely-used message broker that supports multiple messaging protocols and provides reliable message delivery.

Amazon Kinesis: A managed streaming service from AWS that enables real-time processing of streaming data at scale.

Google Cloud Pub/Sub: A messaging service from Google Cloud Platform that provides at-least-once delivery of messages and supports both push and pull-based message consumption.

NATS: A lightweight, high-performance messaging system that offers simple publish/subscribe and request/response patterns for event-driven communication.

Design Patterns for Event-Driven Microservices

Several design patterns can be used to build robust and efficient event-driven microservices. These patterns include:

Event Sourcing

Unlike traditional data storage methods that rely on a single source of truth, event sourcing derives the state of an application from a sequence of events.

This approach has a number of key benefits, including greater traceability, auditing, and the ability to rebuild the system state at any point in time. By capturing every change that occurs in the system as a separate event, event sourcing allows developers to track and analyze the evolution of data over time, providing valuable insights into the behavior of the system and enabling more informed decision-making.

In addition, event sourcing enhances the resilience and fault tolerance of applications by allowing them to recover quickly and easily from errors or failures. By simply replaying the sequence of events that led up to a particular point in time, developers can recreate the system state exactly as it was, making it possible to address issues quickly and effectively.

Command Query Responsibility Segregation (CQRS)

CQRS is a pattern that separates the “read” and “write” operations of a system. This separation allows for independent scaling and optimization of each operation, improving overall system performance.

Design Patterns for Event-Driven Microservices

Saga Pattern

The saga pattern is used to manage distributed transactions in a microservices architecture. It involves breaking a transaction into a series of smaller, isolated steps, each of which is coordinated by an orchestrator service. This pattern helps to ensure data consistency across services while maintaining the benefits of a decoupled architecture.

Materialized Views

Materialized views are precomputed views of data that are updated when the underlying data changes. They can be used in event-driven microservices to create projections of data that are tailored to specific use cases, improving query performance and reducing the load on primary data stores.

Implementing Event-Driven Microservices

To successfully implement event-driven microservices, you can follow these steps:

1. Identify Domain Events

Domain events are the events that represent meaningful changes within your application’s domain. Start by identifying the key domain events that your system should handle and define their structure.

2. Define Event Contracts

Event contracts are the agreed-upon format and schema for events within your system. Establishing event contracts helps ensure consistency across services and makes it easier to evolve the system over time.

3. Design Event Consumers and Producers

With domain events and contracts defined, design the event consumers and producers for each microservice. Consider factors such as scalability, performance, and fault tolerance when designing these components.

4. Handle Data Consistency

One of the challenges in event-driven microservices is ensuring data consistency across services. Utilize patterns such as the Saga pattern or CQRS to manage distributed transactions and maintain data consistency.

5. Monitoring and Logging

Monitoring and logging are crucial for understanding the health and performance of your event-driven microservices. Implement logging and monitoring solutions to track events, detect issues, and analyze system performance.

Testing Event-Driven Microservices

Thorough testing is essential for building reliable and robust event-driven microservices. Key testing strategies include:

1. Unit Testing

Unit testing involves testing individual components or functions within your services. Ensure that all core functionality is covered by unit tests to verify correct behavior and catch potential issues early in the development process.

2. Integration Testing

Integration testing verifies the correct interaction between services and their dependencies, such as messaging systems or databases. Test the behavior of your services as they consume and produce events to ensure proper communication and data handling.

3. End-to-End Testing

End-to-end testing validates the entire system by simulating real-world usage scenarios. This type of testing helps to uncover issues that may not be apparent during unit or integration testing, such as problems with data consistency or event order.

4. Load and Stress Testing

Load and stress testing involves subjecting your system to high levels of traffic or workload to ensure that it remains stable and performs well under pressure. This testing is especially important for event-driven microservices, which often need to handle large volumes of events and real-time data processing.

Deployment and Infrastructure Considerations

When deploying event-driven microservices, consider the following aspects of infrastructure and deployment:

Containerization and Orchestration

Leveraging containerization and orchestration tools, like Docker and Kubernetes, streamline the deployment, scalability, and administration of microservices in a proficient and technical manner.

CI/CD Pipelines

The implementation of continuous integration and continuous deployment (CI/CD) workflows streamlines the automation of building, testing, and deploying microservices. Implementing CI/CD pipelines ensures that your services are always up to date and reduces the risk of deployment-related issues.

Security and Compliance

Ensure that your event-driven microservices adhere to security best practices and comply with relevant regulations. This may include securing communication between services, implementing access controls, and monitoring for potential security threats.

Observability and Monitoring

Implement observability and monitoring solutions to gain insight into the health and performance of your event-driven microservices. This can include monitoring event streams, tracking system metrics, and implementing distributed tracing to understand the flow of events through your system.

Challenges and Trade-offs

While event-driven microservices offer many benefits, they also come with some challenges and trade-offs:

Increased Complexity

Event-driven microservices can introduce additional complexity due to the asynchronous nature of event processing and the need to manage distributed transactions. Developers must carefully design and implement their services to handle these complexities effectively.

Eventual Consistency

Event-driven microservices often rely on eventual consistency, meaning the system state may be temporarily inconsistent during event processing. While this can improve performance and scalability, it may also require additional logic to handle temporary inconsistencies within the system.

Debugging and Troubleshooting

Debugging and troubleshooting can be more challenging in event-driven microservices due to the distributed nature of the system and the potential for events to be processed out of order or delayed. Implementing robust monitoring, logging, and tracing solutions can help mitigate these challenges.

Real-World Applications of Event-Driven Microservices

Event-driven microservices are used in a wide range of industries and applications, including:


In the fast-paced world of e-commerce, where customer satisfaction is key, event-driven microservices have emerged as a game-changer. With their impressive capacity to handle large volumes of data and process requests in real-time, event-driven microservices enable businesses to deliver fast and reliable service that meets the expectations of today’s tech-savvy customers.

The days of frustrating delays and system downtimes that led to lost sales and dissatisfied customers are gone. With event-driven microservices, businesses can seamlessly handle complex tasks, such as processing orders and checking inventory, without breaking a sweat. This level of agility and responsiveness is crucial in the highly competitive e-commerce landscape, where customers expect nothing less than lightning-fast performance and impeccable service.

In addition, event-driven microservices can help e-commerce businesses personalize the customer experience. By tracking customer behavior and preferences, microservices can trigger events and messages that deliver personalized recommendations, promotions, and content to customers in real time. This not only improves customer satisfaction but also helps businesses drive sales and increase customer loyalty.

Another advantage of event-driven microservices is their ability to integrate with third-party services and APIs. This allows e-commerce businesses to easily connect with external partners and providers, such as payment gateways, shipping providers, and social media platforms, to provide a seamless end-to-end experience for customers. Microservices can also be used to monitor and analyze customer data, enabling businesses to make data-driven decisions and optimize their operations.


Financial institutions deal with massive amounts of data daily, including market data, customer information, and transaction records. With event-driven microservices, businesses can process and respond to this data quickly and efficiently, which allows them to make informed decisions in real time.

Event-driven microservices allow organizations to connect with other financial institutions, trading platforms, and regulatory bodies. Microservices can be used to process and transmit data between these systems, making it easier for businesses to comply with regulations, execute trades, and manage risk.

Event-driven microservices can also be used to enhance the customer experience in finance. For example, microservices can be used to trigger personalized messages and alerts to customers based on their behavior or transaction history. This can improve customer satisfaction and help businesses build stronger relationships with their clients.

Finally, event-driven microservices can be used to automate and streamline internal processes in finance. This includes tasks such as account management, fraud detection, and transaction processing. By automating these processes, businesses can reduce the risk of errors and free up their employees to focus on more complex tasks.

The Internet of Things (IoT)

IoT has revolutionized the way we live and work, connecting devices and sensors to the internet and enabling new levels of automation and efficiency. IoT devices generate massive amounts of data every second, including sensor readings, location data, and user behavior. With event-driven microservices, businesses can process and respond to this data quickly and efficiently, which allows them to make informed decisions in real time.

Event-driven microservices can also be used to enhance the functionality and intelligence of IoT devices. For example, microservices can be used to trigger events based on certain conditions or user interactions, enabling devices to respond in real time to changing environments. This can improve the efficiency of IoT systems and enable new levels of automation and control.

Finally, event-driven microservices can be used to improve the security and reliability of IoT systems. Microservices can be used to monitor device activity and detect anomalies or suspicious behavior. This can help identify and mitigate security threats before they cause harm.

Social Media

Social media has become an integral part of our daily lives, connecting us with friends, family, and the world around us. To keep up with the demands of users and stay competitive, social media platforms have turned to event-driven microservices to power their notification systems, content recommendations, and user activity tracking.

One of the advantages of event-driven microservices is their ability to personalize content recommendations for users. By tracking user behavior and preferences, microservices can trigger events and messages that deliver personalized recommendations and content to users.

Event-driven microservices can also be used to track user activity on social media platforms. It includes tracking user behavior, such as liking, sharing, and commenting on posts, as well as monitoring user-generated content for inappropriate or harmful content. By using microservices to automate these tasks, social media platforms can improve their moderation and content management capabilities.

Microservices can be used to power notification systems on social media platforms. It includes notifications for new followers, likes, comments, and messages. Microservices can be used to trigger events based on user activity, enabling social media platforms to deliver notifications to users.


Event-driven microservices offer a powerful approach to building scalable, flexible, and resilient software applications. By combining the principles of microservices and event-driven architecture, this architectural style enables organizations to adapt rapidly to changing business requirements while ensuring high performance and reliability. As a result, event-driven microservices are becoming an increasingly popular choice for modern software development.

Chat GPT Coding in Programming – How ChatGPT Writes Code

Chat GPT Coding in Programming – How ChatGPT Writes Code

ChatGPT coding
ChatGPT coding

How ChatGPT Generates Code and ChatGPT Coding Examples

One of the main events in the information technology market this year is the release of artificial intelligence technology Chat GPT. Also, Chat GPT can write code and this is starting to gain popularity among developers as the ability to use ChatGPT for coding. This technology immediately conquered the market and brought the concept of AI to a completely new level. The technology turned the concept of data search on the Internet upside down and made it possible not just to search for information on indexed pages and keywords, but to immediately find a detailed solution and, not least, a description of how to come to this solution. Immediately after the publication of ChatGPT, other AI systems appeared that have similar functionality somewhere.

Chat GPT Coding as a Programmer’s Assistant

The AI ​​that writes code is what allowed IT professionals to also not be left behind when using this technology. It made it possible to speed up the writing of routine qc operations when writing complex code and also acts as a knowledge base to help novice programmers write code. As mentioned earlier, a good feature of ChatGPT coding is the ability to describe an action or algorithm, that is, when asking a question, the user not only receives a ready-made solution or piece of code but also a description of what this code does and why, as well as a description of the algorithm itself. At the initial stages of Chat GPT coding training this AI can be compared to a more experienced mentor with an impossible patience for help and hints:).

 Chat GPT Coding Examples

Let’s take a look at some examples where ChatGPT helps you like a program that writes code.

For example, let’s take a list of users who have multiple dates. We need to get the maximum date for each user.

Let’s ask:

C# There is a List with PersonID and Date fields. Get Lise with max Date for each PersonID

We get solution with chat bot code:

To get a list with the maximum date for each PersonID, you can use a LINQ query like this:

using System.Linq;

/ /Defining class andDatefields
class Person
  public int PersonID { get; set; }
  public DateTime Date { get; set; }

// Source List of Person
List<Person> Persons = new List<Person>
  new Person { PersonID = 1, Date = new DateTime(2022, 1, 1) },
  new Person { PersonID = 1, Date = new DateTime(2022, 2, 1) },
  new Person { PersonID = 2, Date = new DateTime(2022, 3, 1) },
  new Person { PersonID = 2, Date = new DateTime(2022, 4, 1) },
  new Person { PersonID = 2, Date = new DateTime(2022, 5, 1) },

/ /LINQrequest for getting a list with the maximum date for eachPersonID
List<Person> result = Persons
.GroupBy(Person => Person.PersonID) // Grouping by PersonID
.Select(group => group.OrderByDescending(Person => Person.Date).First()).ToList();
As a result of executing this code, the variable resultwill contain a list consisting of two elements:

result[0]— list element Personswith maximum date for PersonID = 1;
result[1]— list element Personswith the maximum date for PersonID = 2.

As we can see, we got a solution in one second that solves our problem in just two lines of code.

Let’s take the second example. For searching databases, it is advisable to use well-tested engines, one of which is Elasticsearch. Writing a query for this engine can sometimes be difficult, and getting the right result takes a lot of trial and error. What was the surprise when asking a question in ChatGPT, you can not only get the correct query for Elasticsearch but also translate it into the desired programming language. Consider example:


ElasticSearch There are Person objects with an expiryDate field. Print all Persons where expiryDate is null

Answer – AI writing code:

To accomplish this task, you need to use a query on the selection (query) in ElasticSearch with a filter on the expiryDate field.

An example of a select query that you can use to get all Person objects with an empty expiryDate field:

GET /Person/_search
           "field": "expiryDate"

In this query, we use a must_not bool filter to exclude from the results all Person objects that have an expiryDate field that exists (i.e., is not null).

If you only want to retrieve those Person objects whose expiryDate field is explicitly null, you can use the following select query:

GET /Person/_search
         "expiryDate": null

In this query, we use a bool filter with must and match to select only those Person objects whose expiryDate field is explicitly null. Note that this will not select objects where the expiryDate field does not exist.

Rewrite Code in C#

To execute a query in C#, you need to use the official .NET Elasticsearch client Elasticsearch.Net and the Nest query library.

An example query to select all Person objects with an empty expiryDate field in C# using Nest:

using Nest;

var settings = new ConnectionSettings(new Uri("http://localhost:9200"))
var client = new ElasticClient(settings);

var searchResponse = client.Search<Person>(s => s
.Query(q => q
.Bool(b => b
.MustNot(mn => mn
.Exists(e => e.Field(f => f.ExpiryDate))

In this example, we create an instance ElasticClientwith Elasticsearch connection settings, set a default index name, and send a fetch request.

Note that in this example we are using the type Personas the document type we want to select. This assumes that you have an appropriate index mapping for the Person.

If you only want to get those Person objects whose expiryDate field is explicitly null, you can use the following code:

var searchResponse = client.Search<Person>(s => s
.Query(q => q
.Bool(b => b
.Must(m => m
.Match(ma => ma
.Field(f => f.ExpiryDate)

In this example, we used .Match()to select only those objects whose field expiryDate is explicitly equal to null.


In this article, we have reviewed only a small part of the ChatGPT coding usage examples. It should also be noted that now additions or extensions have been released for almost all development environments, which allows you to use the functionality of artificial intelligence directly when writing code. With proper skill and learning ability, over time, more and more programming tasks will pass under AI that can write code. Looking at all the areas where AI is rapidly being implemented, and whether it will replace specialists from your field of activity, or you. I think it is difficult to answer this question, but I think that as long as there are customers with non-trivial desires, we will be needed in our field.

Efficiently Save Form Data on Web Page to File Using Python with Flask

Efficiently Save Form Data on Web Page to File Using Python with Flask

Save data from form Python
Save data from form Python

How to Save Data From a Form on a Web Page to a File Using the Python?

Building a web application that requires users to fill out a form can be challenging. You need to ensure that the data they submit is saved somewhere secure. Fortunately, saving data to a file using the Python programming language is an efficient solution. In this guide, we’ll walk you through the process of saving form data to a file using Python with some helpful tips.

Create Form

Firstly, you need to create a form on your web page using HTML, which can be a bit overwhelming, especially for beginners. Make sure to give each input field a name attribute, as we’ll be using these names to identify the data later. You can use any method for creating the form, but for this example, we’ll use a simple HTML form with two input fields: name and email.

<form method="POST" action="/submit">
    <label for="name">Name:</label>
    <input type="text" name="name" id="name">
    <label for="email">Email:</label>
    <input type="email" name="email" id="email">
    <button type="submit">Submit</button>

Use Flask

Once you’ve created your form, it’s time to create a Python script that will handle the data when the form is submitted. You can use any web framework that supports Python, but we’ll use Flask for this example. We’ll assume you already have Flask installed and configured on your system.

In your Flask application, you’ll need to create a route that will handle the form submission. This route should be configured to accept POST requests, as the form data will be submitted using this method. You’ll use the request object to access the form data within the route function. This process can be complicated, especially for beginners, but with practice, it becomes easy.

from flask import Flask, request, render_template, redirect

app = Flask(__name__)

@app.route('/submit', methods=['POST'])
def submit_form():
name = request.form['name']
email = request.form['email']
save_data(name, email)
return render_template('success.html', message='Your data has been saved!')

def save_data(name, email):
with open('formdata.txt', 'a') as f:

Save Data

To save the form data to a file, you’ll need to open a file object using Python’s built-in open() function. You can choose any filename and file extension that you like, but for this example, we’ll use “formdata.txt”. You’ll need to specify the file mode as “a” to append the data to the end of the file rather than overwrite any existing data. This step requires a bit of attention to detail, but it’s an essential part of the process.

Once you’ve opened the file, you can use Python’s write() method to add the form data to the file. You’ll want to format the data in a way that makes it easy to read later. For example, you could separate each field with a comma and each submission with a newline character. This step requires some creativity and experimentation to determine the best format for your specific needs.

After writing the data to the file, you’ll need to close the file object using the close() method. This ensures that the data is safely saved to the file and that no other processes can access the file while you’re writing to it. It’s essential to remember this step to avoid data loss or corruption.

Finally, you’ll want to display a message to the user letting them know that their data has been successfully saved. You can do this by returning an HTML template with the message embedded in it. You can also redirect the user to a new page if you prefer. This step ensures that your user knows their data is safe and can continue using your application with confidence.

<!DOCTYPE html>
<h1>{{ message }}</h1>
<p>Thank you for submitting your data.</p>

That’s it! You’ve successfully saved form data to a file using Python. With this knowledge, you can expand your web application to handle more complex forms and save data in different formats. Remember to keep practicing, experimenting, and learning to perfect your coding skills.

Wet vs Dry Principles of Coding: Application and Main Differences

Wet vs Dry Principles of Coding: Application and Main Differences

Dry wet principles
Dry wet principles

Dry Concept and Wet Principles of Coding in Software Engineering

“Dry code” and “wet code” are concepts applied in the development of the software, which serves as a description of a different approach to writing code. The dry principle of code writing assumes that code “Does not repeat itself” or is known as dry programming.

The dry principle foresees no duplication of functionality or logic in writing code but applies components, libraries, and functions of multiple usages. Dry principles of code writing are more often considered more effective, simple in maintenance, and less subject to errors.

Wet code, on the other hand, refers to code that is “Write Everything Twice” or WET. This means that the code is written with a lot of repeat code cases and redundancy, which can make it harder to read, understand, and maintain. Wet code is often the result of copy-pasting or manually duplicating code without refactoring it to remove the repetition. Wet software development is mainly a result of “copy-pasting” and hand code duplication without making its refactoring to avoid repeats.

As a rule, a dry software design is preferable for developers as it allows them to create more effective, convenient, and scalable software systems. Although, sometimes wet code may be useful or even necessary when performance optimization or customization is required.

Wet and Dry Principles of Code Writing

The principles of the dry and wet code are fundamentally different. Here are some of the key principles of each approach:

The dry and wet principles are different. Here are some features of the main wet vs dry concept.

Dry Principles of Code Writing:

  1. Don’t Repeat Yourself means trying not to duplicate logic functions in the code.
  2. You should encapsulate changing elements, separate parts of your code that may change from that one, which probably will not change.
  3. Use higher-level abstraction to present complicated logic in easier and clear way.
  4. Write module code, and split your code into smaller repeatable components, which you can use in different parts of your software.
  5. Avoid tight connections. Avoid dependencies between different parts of the code you create as it may complicate future changes or support.

Wet Code Writing Principles:

  1. Write everything twice (WET) means duplicate code free, even though it leads to superfluity.
  2. Prefer readability to efficiency. Write code that is easy to understand and read, even if it is less effective.
  3. It is better to use easier structures. Give a privilege to the simple structures, than to abstractive ones as they are subject to easy understanding and change.
  4. Avoid excessive engineering, excessive complexity, and code abstraction.
  5. Do not optimize your code beforehand until you find a problem with productivity.

In general, dry runs in programming underline the possibility of repeatable use, maintainability, and code scalability, while wet programming principles give a privilege to simplicity and readability.

What is Wet and Dry Programming Difference?

We would like to consider it in the form of a comparative analysis, provided below.

Look at this table and find the difference between wet and dry methods of software development.


Principles of coding in software engineering

Duplication of the code elements

Code readability

Efficiency of the code

Code abstraction

Modularity in software coding principles




Code writing flexibility


Code optimization

Dry Code

Do not repeat yourself

Avoiding functionality and logic duplication

Underline readability due to repeated usage and abstraction

Underline efficiency by the repeated use and optimization

Encapsulates everything that changes

Better to write modular codes

Tries to avoid tight connections

Underline scalability due to the repeated use and abstraction

Dry code can be easier to maintain due to abstraction and repeated usage

More flexibility is offered because of the repeated use and abstraction

Makes an accent on creating simple solutions to complicated problems

Can optimize a code, when necessary

Wet Code

Write everything twice

Free duplication of the code, even though it cases a superfluity

Underline readability due to simplicity

Makes an emphasizes a simplicity, rather, than the efficiency

Give a privilege to simple structures rather, than to abstractions

Prefers linear method of code writing

Wet software development principle can cause tight connections and dependencies

Wet code can be more complicated to scale due to the tight connection and superfluity

Wet programming can be harder to maintain due to the superfluity the absence of abstraction

Lack of repeated usage and abstraction leads to the lower flexibility

Unnecessary complexity can be created

Avoids optimization until it becomes necessary

Dry concepts, in general, underline repeat code principles, scalability, and maintainability, and wet code emphasize readability and simplicity. These two approaches have their own pros and cons. A choice will depend on certain requirements and limitations of the project development. Both principles of coding in software engineering, wet and dry can be applied in any project, regardless of the language of the programming or using technology.

Dry principles of coding are mostly suitable for larger and more complicated projects, where repeat code is a key principle as well as maintainability and scalability. Dry code also suits flexible methods of software development, where iterations and code base changes are often used.

Wet code, although, can more fit small projects or can be used in cases, where simplicity and readability are the main priorities. Wet software coding, for example, may be more suitable for the project of testing prototype or concept, when the main focus is a quick launch of something, rather than the creation of a scalable and maintainable system.

Finally, choosing between wet and dry methods depends on certain project needs and goals as well as on the experience of the project developer team. It is important to weigh thoroughly all advantages and disadvantages of each approach and choose one that meets better the project requirements.

What are Wet and Dry Methods of Coding Examples?

Here are some examples of wet and dry code written in Javascript.

Wet code example on Javascript:

function calculatePrice(quantity, price) {
  let totalPrice = 0;
  for (let i = 0; i < quantity; i++) {
    totalPrice += price;
  return totalPrice;

This is the wet code example as it repeats and is redundant. The function calculates the total product value by multiplication of the quality and product price. However, it uses a cycle “For” for enumeration and adds the price to the amount for each iteration. This is a redundant approach as it repeats several times the same logic.

Javascript dry code example:

Javascript dry code example: 
function calculatePrice(quantity, price) {
return quantity * price;

This is a dry code example, as it allows for avoiding duplication of logic and functionality. The functions calculate the total value of the product by multiplication directly price and quantity, without using a cycle “For” or any other unnecessary structures.
Another wet principle of coding example:

function getFullName(firstName, lastName) {
let fullName = '';
if (firstName) {
fullName += firstName;
if (lastName) {
fullName += ' ' + lastName;
return fullName;

This wet coding example, because it is redundant and repeats. The function joints the name and the surname to create a full name, However, it uses two operators “If” to check whether the name and the surname are empty before joint them. This is a redundant approach as it repeats the same checking twice.

Finally, the second dry concept example:

function getFullName(firstName, lastName) {
return `${firstName} ${lastName}`.trim();

This is the dry programming example, as the code allows for avoiding duplication of logic and functionality. The function joints the name and the surname for the creation of the full name using the literals of a template and then removes all excessive spaces from the resulting line using “Trim()” method.

In summary, both dry and wet code is necessary for software development and each one plays its own certain role.

Dry code follows the principle of avoiding functionality duplication. A high level of abstraction and modularity is typical for it when each fragment of the code serves to a certain goal and can be used repeatedly in the whole application. Dry code is considered to be more maintainable, testable, and extensible as all changes made in one place reflect automatically in the whole application.

Wet code is an often-duplicable code type and leads to more lines in a code, than required. This code type is harder to maintain, and it may lead to errors and bags. Wet code can make modifications and extensions of the application more complicated as it will be necessary to make changes in several places throughout the whole coding base.