Learn Ways in Which DTOs are Shared Between Microservices

1. Overview

Microservices have become popular in recent years. One of the essential characteristics of microservices is that they are modular, isolated, and easy to scale. The microservices need to work together and exchange data. To achieve this, we create a shared data transfer objects called as DTOs.

In this article, we will present ways in which DTOs are shared between microservices.

2. Exposing Domain Objects as DTO

Models that represent the application domain are managed using microservices. Domain models are different concerns, and we separate them from data models in the DAO layer.

The main reason for this is that we don’t want to expose the complexity of our domain through the services to the clients. Instead, we expose DTOs between our services that serve application clients through REST APIs. While DTOs pass between these services, we convert them to domain objects.

The service-oriented architecture above schematically shows the components and flow of DTO to Domain objects.

3. DTO Sharing Between Microservices

Take, as an example, the process of a customer ordering a product. This process is based on the Customer-Order model. Let’s look at the process from the side of the service architecture.

Let’s say that the Customer service sends request data to the Order service as:

"order": {
    "customerId": 1,
    "itemId": "A152"
}

The Customer and Order services communicate with each other using contracts_._The contract, which is otherwise a service request, is displayed in JSON format. As a Java model, the OrderDTO class represents a contract between the Customer service and the Order service:

public class OrderDTO {
    private int customerId;
    private String itemId;
 
    // constructor, getters, setters
}

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Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

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Testing Microservices Applications

The shift towards microservices and modular applications makes testing more important and more challenging at the same time. You have to make sure that the microservices running in containers perform well and as intended, but you can no longer rely on conventional testing strategies to get the job done.

This is where new testing approaches are needed. Testing your microservices applications require the right approach, a suitable set of tools, and immense attention to details. This article will guide you through the process of testing your microservices and talk about the challenges you will have to overcome along the way. Let’s get started, shall we?

A Brave New World

Traditionally, testing a monolith application meant configuring a test environment and setting up all of the application components in a way that matched the production environment. It took time to set up the testing environment, and there were a lot of complexities around the process.

Testing also requires the application to run in full. It is not possible to test monolith apps on a per-component basis, mainly because there is usually a base code that ties everything together, and the app is designed to run as a complete app to work properly.

Microservices running in containers offer one particular advantage: universal compatibility. You don’t have to match the testing environment with the deployment architecture exactly, and you can get away with testing individual components rather than the full app in some situations.

Of course, you will have to embrace the new cloud-native approach across the pipeline. Rather than creating critical dependencies between microservices, you need to treat each one as a semi-independent module.

The only monolith or centralized portion of the application is the database, but this too is an easy challenge to overcome. As long as you have a persistent database running on your test environment, you can perform tests at any time.

Keep in mind that there are additional things to focus on when testing microservices.

• Microservices rely on network communications to talk to each other, so network reliability and requirements must be part of the testing.
• Automation and infrastructure elements are now added as codes, and you have to make sure that they also run properly when microservices are pushed through the pipeline
• While containerization is universal, you still have to pay attention to specific dependencies and create a testing strategy that allows for those dependencies to be included

Test containers are the method of choice for many developers. Unlike monolith apps, which lets you use stubs and mocks for testing, microservices need to be tested in test containers. Many CI/CD pipelines actually integrate production microservices as part of the testing process.

Contract Testing as an Approach

As mentioned before, there are many ways to test microservices effectively, but the one approach that developers now use reliably is contract testing. Loosely coupled microservices can be tested in an effective and efficient way using contract testing, mainly because this testing approach focuses on contracts; in other words, it focuses on how components or microservices communicate with each other.

Syntax and semantics construct how components communicate with each other. By defining syntax and semantics in a standardized way and testing microservices based on their ability to generate the right message formats and meet behavioral expectations, you can rest assured knowing that the microservices will behave as intended when deployed.

#testing #software testing #test automation #microservice architecture #microservice #test #software test automation #microservice best practices #microservice deployment #microservice components

What is the Best Way to Learn Java?

Let’s face it: the web today is full of educational materials for every taste and budget. On the one hand, it is good for learners as they can gain the needed knowledge without spending a fortune and years in university. On the other hand, this whole lot of courses sometimes makes the newbies confused and stumbled upon a tricky choice. There is an endless number of courses, books, websites, video tutorials, and other resources to get in-depth knowledge about any programming language.

But, what is the best way to learn Java? That is the question.

In this post, I’ve put together a few of the most effective ways that will be a good starting point in your path to mastering Java. So, stay tuned and get ready to write them down.

#programming #coding #java #learn-to-code #what is the best way to learn java? #best way to learn java

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Visit Blog- https://www.xplace.com/article/8743

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Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

• Temporal difference learning (TD learning)
• Parameters
• QL & SARSA
• Comparison
• Implementation
• Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning