Angular Optimizing client application size with lightweight injection tokens

Angular Optimizing client application size with lightweight injection tokens

In Angular, injection tokens are used to provide dependencies to components and services. These tokens can be used to configure the application with different values at runtime. By default, Angular uses Type as the token for a dependency, which can lead to a large bundle size if there are many different types of dependencies in the application.

One way to optimize the bundle size of an Angular application is to use lightweight injection tokens instead of Type for dependencies. Lightweight injection tokens are a way to reduce the size of the application by replacing the Type token with a smaller token.

Here’s an example of how to create a lightweight injection token for a dependency:

import { InjectionToken } from '@angular/core';

export const LIGHTWEIGHT_TOKEN = new InjectionToken('lightweight_token');

Once you’ve created the lightweight injection token, you can use it to provide the dependency in the @Inject decorator:

import { Component, Inject } from '@angular/core';
import { LIGHTWEIGHT_TOKEN } from './lightweight-token';

@Component({
  selector: 'app-example',
  template: `{{value}}`,
})
export class ExampleComponent {
  constructor(@Inject(LIGHTWEIGHT_TOKEN) public value: string) {}
}

You can also use the lightweight injection token to provide the dependency in the module’s providers array:

import { NgModule } from '@angular/core';
import { LIGHTWEIGHT_TOKEN } from './lightweight-token';

@NgModule({
  providers: [{ provide: LIGHTWEIGHT_TOKEN, useValue: 'lightweight' }],
})
export class AppModule {}

It’s worth mentioning that by using lightweight injection tokens, the token’s name is not included in the final bundle, which can significantly reduce the size of your application. Additionally, it’s also recommended to use the useValue or useExisting instead of useClass or useFactory when providing the dependency in the providers array, which will further reduce the size of your application.

Angular When tokens are retained

In Angular, when a token is retained, it means that the token is not released when it is no longer needed. This can lead to a memory leak, as the token continues to occupy memory even though it is no longer in use.

There are several situations in which tokens can be retained in an Angular application:

Singleton services: When a service is provided as a singleton, it means that the same instance is used throughout the application. If the service holds a reference to a token, the token will be retained until the service is destroyed.

Retained tokens in component constructors: If a component constructor holds a reference to a token, the token will be retained until the component is destroyed.

Retained tokens in observables: If an observable holds a reference to a token, the token will be retained until the observable is unsubscribed.

Retained tokens in subscriptions: If a subscription holds a reference to a token, the token will be retained until the subscription is unsubscribed.

To prevent tokens from being retained, it’s important to ensure that references to tokens are released when they are no longer needed. This can be done by unsubscribing from observables, unsubscribing from subscriptions, and properly destroying components and services.

It’s worth mentioning that Angular’s change detection mechanism can help with preventing retained tokens as well, as it allows the framework to automatically unsubscribe and destroy components that are no longer needed. Additionally, using Angular’s dependency injection mechanism to provide services and tokens can also help to prevent retained tokens.

When to use the lightweight injection token pattern example code

The lightweight injection token pattern is a technique that can be used to reduce the size of an Angular application by replacing the Type token with a smaller token. This pattern can be useful in several scenarios:

1. Large number of dependencies: If your application has a large number of dependencies, using lightweight injection tokens can help to reduce the size of the application by replacing the Type token with a smaller token for each dependency.

2. Large number of components: If your application has a large number of components, each component may have a reference to the Type token for its dependencies, which can lead to a large bundle size. Using lightweight injection tokens can help to reduce the size of the bundle by replacing the Type token with a smaller token for each component.

3. Third-party libraries: If your application uses third-party libraries, they may have a large number of dependencies and components. Using lightweight injection tokens can help to reduce the size of the application by replacing the Type token with a smaller token for each dependency and component in the third-party library.

4. Large applications : In general, for large applications, using lightweight injection tokens can help to reduce the bundle size and improve the application’s performance.

Here’s an example of how to use the lightweight injection token pattern in an Angular application:

import { InjectionToken } from '@angular/core';

export const LIGHTWEIGHT_TOKEN = new InjectionToken('lightweight_token');

@Injectable({
  providedIn: 'root',
  useValue: 'lightweight'
})
export class Service {
  constructor(@Inject(LIGHTWEIGHT_TOKEN) public value: string) {}
}

Angular Using lightweight injection tokens example code

Yes, here is an example of how to use lightweight injection tokens in an Angular application:

1. Create a new injection token for a dependency:

import { InjectionToken } from '@angular/core';

export const LIGHTWEIGHT_TOKEN = new InjectionToken('lightweight_token');

This creates an injection token with the name “lightweight_token” that can be used to provide the dependency.

2. Use the injection token to provide the dependency in a service:

import { Injectable, Inject } from '@angular/core';
import { LIGHTWEIGHT_TOKEN } from './lightweight-token';

@Injectable({
  providedIn: 'root',
  useValue: 'lightweight'
})
export class Service {
  constructor(@Inject(LIGHTWEIGHT_TOKEN) public value: string) {}
}

This service uses the LIGHTWEIGHT_TOKEN to inject the value of ‘lightweight’ into the service.

3. Use the service in a component

import { Component } from '@angular/core';
import { Service } from './service';

@Component({
  selector: 'app-example',
  template: `{{value}}`,
})

How to use Angular Use the lightweight injection token for API definition example

An example of using a lightweight injection token for an API definition in an Angular application might look like this:

1. Create a new injection token for the API definition:

import { InjectionToken } from '@angular/core';

export const API_URL = new InjectionToken('api_url');

This creates an injection token with the name “api_url” that can be used to provide the API definition.

2. Use the injection token to provide the API definition in the app.module.ts file:

import { NgModule, InjectionToken } from '@angular/core';
import { HttpClientModule } from '@angular/common/http';
import { API_URL } from './api-token';

@NgModule({
  imports: [
    HttpClientModule
  ],
  providers: [
    { provide: API_URL, useValue: 'https://api.example.com' }
  ]
})
export class AppModule { }

This configures the application to use the value “https://api.example.com” for the API_URL token.

3. Use the API_URL token in a service to make HTTP requests:

import { Injectable, Inject } from '@angular/core';
import { HttpClient } from '@angular/common/http';
import { API_URL } from './api-token';

@Injectable({
  providedIn: 'root'
})
export class ApiService {
  constructor(private http: HttpClient, @Inject(API_URL) private apiUrl: string) {}

  getData() {
    return this.http.get(`${this.apiUrl}/data`);
  }
}

In this example, the ApiService uses the API_URL token to construct the URL for making HTTP requests to the API.

This way, you can use the same injection token in any other service or component to make requests to the API, and you can change the URL of the API in a single place, which makes it easier to manage the application.

Angular Lazy-loading feature modules

What is Angular Lazy-loading feature modules?

Lazy-loading is a technique in Angular that allows the application to load feature modules only when they are needed, rather than loading all of the modules at the start of the application. This can improve the initial load time and performance of the application, as well as reduce the amount of memory used.

In Angular, lazy-loading is achieved by using the loadChildren property in the @NgModule decorator of the routing module. The loadChildren property is used to specify the location of the module that should be loaded lazily.

For example, if you have a feature module called ProductModule that should be loaded lazily, you would update your routing module as follows:

const routes: Routes = [
  { path: 'products', loadChildren: () => import('./product/product.module').then(m => m.ProductModule) }
];

This tells the router to load the ProductModule when the user navigates to the /products route.

It’s worth mentioning that lazy-loading feature modules can improve the performance of your application by reducing the initial bundle size and loading the necessary modules based on the user interactions.

How to step by step setup Angular Lazy-loading feature modules?

Here are the general steps to set up lazy-loading of feature modules in an Angular application:

1. Create a new feature module: Use the Angular CLI to create a new feature module using the ng generate module command. For example, to create a module called ProductModule, you would run ng generate module product.

2. Create routes for the feature module: In the new feature module, create a new routing module using the ng generate module command. For example, ng generate module product –routing. This will create a new product-routing.module.ts file in the product module folder.

3. Define the lazy-loaded routes: In the newly created routing module, import the RouterModule and Routes from @angular/router. Then define the routes for the feature module using the Routes type. For example:

const routes: Routes = [
  { path: '', component: ProductListComponent },
  { path: ':id', component: ProductDetailComponent },
];

4. Update the app-routing module: In the app-routing module, import the new feature module’s routing module and add it to the imports array. Use the loadChildren property to specify the location of the module that should be loaded lazily.

const routes: Routes = [
  { path: 'products', loadChildren: () => import('./product/product.module').then(m => m.ProductModule) }
];

5. Use the feature module: Once you’ve set up the lazy-loaded routes, you can use the components from the feature module in the app.component.html file or any other component.

6. Run the application: Once you’ve completed these steps, you should be able to run your application and see that the feature module is only loaded when the user navigates to the corresponding route.

It’s worth mentioning that this is a simple example, you can customize the routes based on your application requirements and keep in mind that the loadChildren property can accept a string or a function that returns a string or a promise that resolves to a string.

Angular forRoot() and forChild()

In Angular, the forRoot() and forChild() methods are used when configuring a module’s routing. These methods are used to set up the router service with the routes for a module.

forRoot() is used when configuring the routes for the root module of the application. It is typically used in the app.module.ts file and is used to set up the router service with the routes for the entire application. The forRoot() method takes an object that contains the routes for the application and returns a module with the router service configured with those routes.

forChild() is used when configuring the routes for a feature module. It is typically used in the feature module’s routing module and is used to set up the router service with the routes for that specific feature module. The forChild() method takes an object that contains the routes for the feature module and returns a module with the router service configured with those routes.

Here’s an example of how to use forRoot() and forChild() in an Angular application:

// app-routing.module.ts
import { NgModule } from '@angular/core';
import { RouterModule, Routes } from '@angular/router';

const routes: Routes = [
  { path: '', redirectTo: 'home', pathMatch: 'full' },
  { path: 'home', component: HomeComponent },
  { path: 'about', component: AboutComponent },
  { path: 'products', loadChildren: './product/product.module#ProductModule' },
];

@NgModule({
  imports: [RouterModule.forRoot(routes)],
  exports: [RouterModule]
})
export class AppRoutingModule { }

// product-routing.module.ts
import { NgModule } from '@angular/core';
import { RouterModule, Routes } from '@angular/router';

const routes: Routes = [
  { path: '', component: ProductListComponent },
  { path: ':id', component: ProductDetailComponent },
];

@NgModule({
  imports: [RouterModule.forChild(routes)],
  exports: [RouterModule]
})
export class ProductRoutingModule { }

In this example, forRoot() method is used to configure the router service with the routes for the entire application in the app-routing.module.ts file, while forChild() method is used to configure the router service with the routes for the ProductModule in the product-routing.module.ts file.

What is Angular Preloading?

In Angular, preloading is a technique that allows the application to load certain feature modules in the background while the user is interacting with the application. This can improve the overall performance of the application by reducing the time it takes for the feature modules to load when the user navigates to them.

Angular provides a preloading strategy called PreloadAllModules that preloads all feature modules as soon as the application starts. This strategy loads all feature modules in parallel with the application, which can improve the application’s start-up time.

To enable preloading in an Angular application, you need to import the RouterModule and call the forRoot() method with the PreloadAllModules strategy. Here’s an example of how to enable preloading in the app-routing.module.ts file:

import { NgModule } from '@angular/core';
import { RouterModule, Routes, PreloadAllModules } from '@angular/router';

const routes: Routes = [
  { path: '', redirectTo: 'home', pathMatch: 'full' },
  { path: 'home', component: HomeComponent },
  { path: 'about', component: AboutComponent },
  { path: 'products', loadChildren: './product/product.module#ProductModule' },
];

@NgModule({
  imports: [RouterModule.forRoot(routes, {preloadingStrategy: PreloadAllModules})],
  exports: [RouterModule]
})
export class AppRoutingModule { }

Angular Troubleshooting lazy-loading modules

Lazy-loading feature modules in Angular can be a powerful technique for improving the performance of your application, but it can also cause some issues if not implemented correctly. Here are some common issues that you might encounter when lazy-loading feature modules and how to troubleshoot them:

1. 404 error when navigating to a lazy-loaded route: This issue can occur if the path specified in the loadChildren property is incorrect. Make sure that the path to the module is correct and that the module is being exported correctly.

2. Error: Unexpected value ‘undefined’ imported by the module: This issue can occur if the module being imported is not being exported correctly. Make sure that the module is being exported in the correct way.

3. Error: Can’t resolve all parameters for X: (?): This issue can occur if the components or services in the lazy-loaded module have dependencies that are not being provided. Make sure that all dependencies are being provided correctly.

4. Error: No provider for X: This issue can occur if the services provided in the root module are not being propagated to the lazy-loaded modules. Make sure that the services are being provided correctly and that they are being imported correctly in the lazy-loaded module.

5. Error: Template parse errors: This issue can occur if the components in the lazy-loaded module are not being recognized by the Angular compiler. Make sure that the components are being imported correctly in the lazy-loaded module and that they are being declared in the declarations array of the @NgModule decorator.

If you run into any of these issues, it’s recommended to check the path of the module, the imports/exports, dependencies, services provided and the components declared in the modules. Also, it’s recommended to check the browser’s developer console for any errors that might give you more details about the issue.

Lambda AWS SageMaker example code: python, nodejs, Java

What is AWS SageMaker?

Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS) that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models.

SageMaker provides a variety of features and capabilities, including:

– Easy model creation: SageMaker provides pre-built algorithms and frameworks that can be used to quickly train and deploy machine learning models. It also supports custom algorithms and frameworks, such as TensorFlow, PyTorch, and MXNet.
– Scalable infrastructure: SageMaker automatically provisions and manages the infrastructure required to train and deploy models, including compute resources and storage.
– Model deployment: SageMaker makes it easy to deploy trained models to a variety of environments, including the SageMaker hosting environment, AWS Lambda, and EC2.
– Monitoring and management: SageMaker provides tools for monitoring and managing machine learning workflows, including tracking metrics, debugging, and troubleshooting.
– Collaboration and sharing: SageMaker provides features for collaboration and sharing, such as sharing notebooks and models with other users.
– SageMaker also provides the capability to use pre-trained models, such as ChatGPT, which can be fine-tuned to specific use cases. This allows developers and data scientists to quickly build and deploy models without having to start from scratch.

In summary, SageMaker is a comprehensive machine learning platform that provides an easy way to build, train, and deploy machine learning models with the scalability and security of the AWS infrastructure.

Python Lambda AWS SageMaker example code

Sure, here is an example of an AWS Lambda function that uses Amazon SageMaker to invoke a pre-trained model and return the result:

import json
import boto3

def lambda_handler(event, context):
    # Connect to SageMaker runtime
    sagemaker_runtime = boto3.client('runtime.sagemaker')

    # Get the input text from the event
    input_text = event['input_text']

    # Invoke the pre-trained model
    response = sagemaker_runtime.invoke_endpoint(
        EndpointName='chatgpt-endpoint',
        Body=input_text.encode('utf-8'),
        ContentType='application/json'
    )

    # Get the response from the model
    result = json.loads(response['Body'].read().decode('utf-8'))
    return result

This example assumes that you have already set up an endpoint for your pre-trained model in Amazon SageMaker and the endpoint name is ‘chatgpt-endpoint’.

In this example, the function takes an event and a context as input, the input text is extracted from the event using the key ‘input_text’ and passed as input to the sagemaker_runtime.invoke_endpoint method. The result of the model is returned by the function in form of a json object.

Please note that this is just an example and it may not work as is, it would require additional code and setup to make it work in your environment. Additionally, you may need to consider other factors, such as error handling, security and performance, depending on your specific use case.

Also, make sure that the IAM role associated with the lambda has the permission to access SageMaker and Lambda.

Java Lambda AWS SageMaker example code

Sure, here is an example of an AWS Lambda function written in Java that uses Amazon SageMaker to invoke a pre-trained model and return the result:

import com.amazonaws.services.lambda.runtime.Context;
import com.amazonaws.services.lambda.runtime.RequestHandler;
import com.amazonaws.services.sagemakerruntime.AmazonSageMakerRuntime;
import com.amazonaws.services.sagemakerruntime.AmazonSageMakerRuntimeClientBuilder;
import com.amazonaws.services.sagemakerruntime.model.InvokeEndpointRequest;
import com.amazonaws.services.sagemakerruntime.model.InvokeEndpointResult;
import java.nio.charset.StandardCharsets;

public class LambdaFunctionHandler implements RequestHandler {

    @Override
    public String handleRequest(Object input, Context context) {
        // Connect to SageMaker runtime
        AmazonSageMakerRuntime sagemakerRuntime = AmazonSageMakerRuntimeClientBuilder.defaultClient();

        // Get the input text from the input object
        String inputText = (String) input;

        // Invoke the pre-trained model
        InvokeEndpointRequest request = new InvokeEndpointRequest()
            .withEndpointName("chatgpt-endpoint")
            .withContentType("application/json")
            .withAccept("application/json")
            .withBody(inputText);
        InvokeEndpointResult response = sagemakerRuntime.invokeEndpoint(request);

        // Get the response from the model
        return new String(response.getBody().array(), StandardCharsets.UTF_8);
    }
}

This example assumes that you have already set up an endpoint for your pre-trained model in Amazon SageMaker and the endpoint name is ‘chatgpt-endpoint’. In this example, the function takes an object as input, the input text is extracted from the object, and passed as input to the sagemakerRuntime.invokeEndpoint method. The result of the model is returned by the function in form of a string.

Please note that this is just an example and it may not work as is, it would require additional code and setup to make it work in your environment. Additionally, you may need to consider other factors, such as error handling, security and performance, depending on your specific use case.

Also, make sure that the IAM role associated with the lambda has the permission to access SageMaker and Lambda.

Nodejs Lambda AWS SageMaker example code

Sure, here is an example of an AWS Lambda function written in Node.js that uses Amazon SageMaker to invoke a pre-trained model and return the result:

const AWS = require('aws-sdk');
const sagemakerRuntime = new AWS.SageMakerRuntime({apiVersion: '2017-05-13'});

exports.handler = async (event) => {
    // Get the input text from the event
    const inputText = event.input_text;

    // Invoke the pre-trained model
    const params = {
        EndpointName: "chatgpt-endpoint",
        Body: inputText,
        ContentType: "application/json",
        Accept: "application/json"
    };
    const response = await sagemakerRuntime.invokeEndpoint(params).promise();

    // Get the response from the model
    const result = response.Body.toString('utf-8');

    return result;
};

This example assumes that you have already set up an endpoint for your pre-trained model in Amazon SageMaker and the endpoint name is ‘chatgpt-endpoint’. In this example, the function takes an event as input, the input text is extracted from the event using the key ‘input_text’ and passed as input to the sagemakerRuntime.invokeEndpoint method. The result of the model is returned by the function in form of a string.

Please note that this is just an example and it may not work as is, it would require additional code and setup to make it work in your environment. Additionally, you may need to consider other factors, such as error handling, security and performance, depending on your specific use case.

Also, make sure that the IAM role associated with the lambda has the permission to access SageMaker and Lambda.

AWS SageMaker cost

The cost of using Amazon SageMaker depends on several factors, including the type and number of instances used for training and inference, the amount of data stored, and the usage of additional services.

There are three main components that make up the cost of using SageMaker:

Compute: This includes the cost of running instances for training and inference, as well as the cost of using SageMaker’s built-in algorithms and frameworks.

Storage: This includes the cost of storing data in Amazon S3, as well as the cost of using SageMaker’s data management features.

Other Services: This includes the cost of using additional services such as data preparation, data labeling, and data management.

Additionally, you’ll be charged for data transfer out of the region, as well as for data stored by SageMaker.

You can use the AWS pricing calculator to estimate the cost of using SageMaker for your specific use case. The pricing calculator allows you to select different instance types, configure storage and data transfer options, and estimate the cost of using additional services.

It’s also important to note that if you choose to use a pre-trained model such as ChatGPT, you’ll be charged for the usage of the model, and if you fine-tune it, the cost of the instances used for fine-tuning.

In general, the cost of using SageMaker can vary greatly depending on the specific use case, so it’s important to monitor usage and adjust instances and other resources as needed to keep costs under control.

SageMaker with ChatGPT example code

Here is an example of using Amazon SageMaker to fine-tune a pre-trained ChatGPT model and deploy it as an endpoint:

import boto3

# Connect to SageMaker
sagemaker = boto3.client('sagemaker')

# Specify the S3 location of the pre-trained model and the location to save the fine-tuned model
model_data = 's3://path/to/pre-trained/model'
output_path = 's3://path/to/save/fine-tuned/model'

# Specify the training job name and the location of the training data
job_name = 'chatgpt-fine-tuning-job'
training_data = 's3://path/to/training/data'

# Create the training job
response = sagemaker.create_training_job(
    TrainingJobName=job_name,
    AlgorithmSpecification={
        'TrainingImage': 'openai/gpt-2:latest',
        'TrainingInputMode': 'File'
    },
    RoleArn='arn:aws:iam::ACCOUNT_ID:role/service-role/AmazonSageMaker-ExecutionRole-20210101T000000',
    InputDataConfig=[
        {
            'ChannelName': 'train',
            'DataSource': {
                'S3DataSource': {
                    'S3DataType': 'S3Prefix',
                    'S3Uri': training_data,
                    'S3DataDistributionType': 'FullyReplicated'
                }
            },
            'ContentType': 'text/plain',
            'CompressionType': 'None'
        }
    ],
    OutputDataConfig={
        'S3OutputPath': output_path
    },
    ResourceConfig={
        'InstanceType': 'ml.p3.2xlarge',
        'InstanceCount': 1,
        'VolumeSizeInGB': 50
    },
    StoppingCondition={
        'MaxRuntimeInSeconds': 86400
    },
    HyperParameters={
        'model_name': 'text-davinci-002',
        'overwrite_output_dir': 'True'
    }
)

# Wait for the training job to complete
sagemaker.get_waiter('training_job_completed_or_stopped').wait(TrainingJobName=job_name)

# Create an endpoint configuration
response = sagemaker.create_endpoint_config(
    EndpointConfigName='chatgpt-endpoint-config',
    ProductionVariants=[
        {
            'VariantName': 'chatgpt-variant',
            'ModelName': job_name,
            'InitialInstanceCount': 1,
            'InstanceType': 'ml.t2.medium'
        }
    ]
)

# Create the endpoint
response = sagemaker.create_endpoint(
    EndpointName='chatgpt-endpoint',
    EndpointConfigName='chatgpt-endpoint-config'
)

Create RestAPI with Go & Postgres: Post, Get, PUT, Delete example code

Create Post Api with Go example code

To create a “POST” API in Go, you can use the net/http package that is included in the Go standard library. Here’s an example of how you can create a simple “POST” API that listens on a specific port and responds with “Hello, World!” when a client makes a “POST” request to the endpoint:

package main

import (
	"fmt"
	"net/http"
)

func main() {
	http.HandleFunc("/", func(w http.ResponseWriter, r *http.Request) {
		if r.Method == http.MethodPost {
			fmt.Fprint(w, "Hello, World!")
			return
		}
		http.Error(w, "Invalid request method", http.StatusBadRequest)
	})

	http.ListenAndServe(":8080", nil)
}

In this example, the http.HandleFunc function is used to register a function that will be called whenever a client makes a request to the root endpoint (“/”). The function checks the request method to see if it is a “POST” request, and if it is, it responds with “Hello, World!”. If the request method is not “POST”, it sends an error response with status code “400 Bad Request”. Finally, the http.ListenAndServe function is used to start the server and listen for incoming requests on port 8080.

Create GET api with go example code

Here is an example of a basic GET API written in Go using the popular Gin web framework:

package main

import (
    "net/http"

    "github.com/gin-gonic/gin"
)

func main() {
    r := gin.Default()

    r.GET("/", func(c *gin.Context) {
        c.JSON(http.StatusOK, gin.H{
            "message": "Hello, World!",
        })
    })

    r.Run() // listen and serve on 0.0.0.0:8080
}

This code creates a new Gin router and sets up a route for the root URL (“/”) that responds to GET requests. The route handler is a function that takes a Gin context as its argument. This function sends a JSON response with a message of “Hello, World!” and a status code of 200 OK. Finally, the router is started by calling the Run() method.

You would run this code by running go run main.go, and then making a GET request to http://localhost:8080/ using any HTTP client.

Create PUT API with Go example code

Here is an example of a basic PUT API written in Go using the popular Gin web framework:

package main

import (
    "net/http"

    "github.com/gin-gonic/gin"
)

func main() {
    r := gin.Default()

    r.PUT("/user/:id", func(c *gin.Context) {
        // Get the ID from the URL parameters
        id := c.Param("id")

        // Bind the request body to a struct
        var user struct {
            Name string `json:"name" binding:"required"`
            Age  int    `json:"age" binding:"required"`
        }
        if err := c.ShouldBindJSON(&user); err != nil {
            c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
            return
        }

        // Update the user in the database
        // ...

        c.JSON(http.StatusOK, gin.H{
            "message": "User updated successfully",
        })
    })

    r.Run() // listen and serve on 0.0.0.0:8080
}

This code creates a new Gin router and sets up a route for the URL path /user/:id that responds to PUT requests. The :id in the route path is a URL parameter.
The route handler is a function that takes a Gin context as its argument. This function first extracts the “id” from the URL parameters, then binds the request body to a struct, in this case the struct is user which contains Name and Age field.
Then it checks if there is any error from the binding process, if there is, it will return a Bad Request with the error message.
If no error, it will update the user in the database (this part is left to you to implement) and then sends a JSON response with a message of “User updated successfully” and a status code of 200 OK. Finally, the router is started by calling the Run() method.

You would run this code by running go run main.go, and then making a PUT request to http://localhost:8080/user/ using any HTTP client with json payload of the user such as {“name”:”John”,”age”:25}

Create Delete RestAPI with Go example code

Here is an example of a basic DELETE REST API written in Go using the popular Gin web framework:

package main

import (
    "net/http"

    "github.com/gin-gonic/gin"
)

func main() {
    r := gin.Default()

    r.DELETE("/user/:id", func(c *gin.Context) {
        // Get the ID from the URL parameters
        id := c.Param("id")

        // Delete the user from the database
        // ...

        c.JSON(http.StatusOK, gin.H{
            "message": "User deleted successfully",
        })
    })

    r.Run() // listen and serve on 0.0.0.0:8080
}

This code creates a new Gin router and sets up a route for the URL path /user/:id that responds to DELETE requests. The :id in the route path is a URL parameter.
The route handler is a function that takes a Gin context as its argument. This function first extracts the “id” from the URL parameters, then deletes the user from the database (this part is left to you to implement) and then sends a JSON response with a message of “User deleted successfully” and a status code of 200 OK. Finally, the router is started by calling the Run() method.

You would run this code by running go run main.go, and then making a DELETE request to http://localhost:8080/user/ using any HTTP client.

Create Post RestAPI with Go and Postgresql example code

Here is an example of a basic POST REST API written in Go using the popular Gin web framework and Postgresql as a database:

package main

import (
    "database/sql"
    "net/http"

    "github.com/gin-gonic/gin"
    _ "github.com/lib/pq"
)

func main() {
    db, err := sql.Open("postgres", "postgres://user:password@host/dbname")
    if err != nil {
        panic(err)
    }
    defer db.Close()

    r := gin.Default()

    r.POST("/user", func(c *gin.Context) {
        // Bind the request body to a struct
        var user struct {
            Name string `json:"name" binding:"required"`
            Age  int    `json:"age" binding:"required"`
        }
        if err := c.ShouldBindJSON(&user); err != nil {
            c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
            return
        }

        // Insert the user into the database
        _, err := db.Exec("INSERT INTO users (name, age) VALUES ($1, $2)", user.Name, user.Age)
        if err != nil {
            c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
            return
        }

        c.JSON(http.StatusCreated, gin.H{
            "message": "User created successfully",
        })
    })

    r.Run() // listen and serve on 0.0.0.0:8080
}

This code creates a new Gin router and sets up a route for the URL path /user that responds to POST requests.
The route handler is a function that takes a Gin context as its argument. This function first binds the request body to a struct, in this case the struct is user which contains Name and Age field.
Then it checks if there is any error from the binding process, if there is, it will return a Bad Request with the error message.
If no error, it will insert the user into the Postgresql database by executing the SQL statement INSERT INTO users (name, age) VALUES ($1, $2) using the user’s name and age as the parameters and then sends a JSON response with a message of “User created successfully” and a status code of 201 Created.
It also checks if there is any error while executing the query, if so it will return a status code of 500 Internal Server Error with the error message.
Finally, the router is started by calling the Run() method.

You would run this code by running go run main.go, and then making a POST request to http://localhost:8080/user using any HTTP client with json payload of the user such as {“name”:”John”,”age”:25}

Please note that the database connection string is an example and you need to change it to match your database credentials and settings.
Also, it is important to note that you need to have the “github.com/lib/pq” package installed and imported in order to use postgres as a database with Go.
Also, it is assumed that you have the necessary table created in the database and ready to use.

Create Get RestAPI with Go and Postgresql example code

Here is an example of a basic GET REST API written in Go using the popular Gin web framework and Postgresql as a database:

package main

import (
    "database/sql"
    "net/http"

    "github.com/gin-gonic/gin"
    _ "github.com/lib/pq"
)

func main() {
    db, err := sql.Open("postgres", "postgres://user:password@host/dbname")
    if err != nil {
        panic(err)
    }
    defer db.Close()

    r := gin.Default()

    r.GET("/user/:id", func(c *gin.Context) {
        // Get the ID from the URL parameters
        id := c.Param("id")

        // Get the user from the database
        var user struct {
            Name string
            Age  int
        }
        err := db.QueryRow("SELECT name, age FROM users WHERE id = $1", id).Scan(&user.Name, &user.Age)
        if err != nil {
            c.JSON(http.StatusNotFound, gin.H{"error": "User not found"})
            return
        }

        c.JSON(http.StatusOK, gin.H{
            "user": user,
        })
    })

    r.Run() // listen and serve on 0.0.0.0:8080
}

This code creates a new Gin router and sets up a route for the URL path /user/:id that responds to GET requests. The :id in the route path is a URL parameter.
The route handler is a function that takes a Gin context as its argument. This function first extracts the “id” from the URL parameters, then it queries the Postgresql database for user with the corresponding id by executing the SQL statement SELECT name, age FROM users WHERE id = $1 and scan the result into user struct.
Then it checks if there is any error from the query, if there is and the error is not found, it will return a Not Found with the error message.
If no error, it will send a JSON response with the user data and a status code of 200 OK.
Finally, the router is started by calling the Run() method.

You would run this code by running go run main.go, and then making a GET request to http://localhost:8080/user/ using any HTTP client.
Please note that the database connection string is an example and you need to change it to match your database credentials and settings.
Also, it is important to note that you need to have the “github.com/lib/pq” package installed and imported in order to use postgres as a database with Go.
Also, it is assumed that you have the necessary table created in the database and ready to use.

Create Put RestAPI with Go and Postgresql example code

Here is an example of a basic PUT REST API written in Go using the popular Gin web framework and Postgresql as a database:

package main

import (
    "database/sql"
    "net/http"

    "github.com/gin-gonic/gin"
    _ "github.com/lib/pq"
)

func main() {
    db, err := sql.Open("postgres", "postgres://user:password@host/dbname")
    if err != nil {
        panic(err)
    }
    defer db.Close()

    r := gin.Default()

    r.PUT("/user/:id", func(c *gin.Context) {
        // Get the ID from the URL parameters
        id := c.Param("id")

        // Bind the request body to a struct
        var user struct {
            Name string `json:"name" binding:"required"`
            Age  int    `json:"age" binding:"required"`
        }
        if err := c.ShouldBindJSON(&user); err != nil {
            c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
            return
        }

        // Update the user in the database
        _, err := db.Exec("UPDATE users SET name = $1, age = $2 WHERE id = $3", user.Name, user.Age, id)
        if err != nil {
            c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
            return
        }

        c.JSON(http.StatusOK, gin.H{
            "message": "User updated successfully",
        })
    })

    r.Run() // listen and serve on 0.0.0.0:8080
}

This code creates a new Gin router and sets up a route for the URL path /user/:id that responds to PUT requests. The :id in the route path is a URL parameter.
The route handler is a function that takes a Gin context as its argument. This function first extracts the “id” from the URL parameters, then binds the request body to a struct, in this case the struct is user which contains Name and Age field.
Then it checks if there is any error from the binding process, if there is, it will return a Bad Request with the error message.
If no error, it will update the user in the Postgresql database by executing the SQL statement UPDATE users SET name = $1, age = $2 WHERE id = $3 using the user’s name, age and id as the parameters and then sends a JSON response with a message of “User updated successfully” and a status code of 200 OK.
It also checks if there is any error while executing the query, if so it will return a status code of 500 Internal Server Error with the error message.
Finally, the router is started by calling the Run() method.

You would run this code by running go run main.go, and then making a PUT request to http://localhost:8080/user/ using any HTTP client with json payload of the user such as {“name”:”John”,”age”:25}
Please note that the database connection string is an example and you need to change it to match your database credentials and settings.

Create Delete RestAPI with Go and Postgresql example code

Here is an example of a basic DELETE REST API written in Go using the popular Gin web framework and Postgresql as a database:

package main

import (
    "database/sql"
    "net/http"

    "github.com/gin-gonic/gin"
    _ "github.com/lib/pq"
)

func main() {
    db, err := sql.Open("postgres", "postgres://user:password@host/dbname")
    if err != nil {
        panic(err)
    }
    defer db.Close()

    r := gin.Default()

    r.DELETE("/user/:id", func(c *gin.Context) {
        // Get the ID from the URL parameters
        id := c.Param("id")

        // Delete the user from the database
        _, err := db.Exec("DELETE FROM users WHERE id = $1", id)
        if err != nil {
            c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
            return
        }

        c.JSON(http.StatusOK, gin.H{
            "message": "User deleted successfully",
        })
    })

    r.Run() // listen and serve on 0.0.0.0:8080
}

This code creates a new Gin router and sets up a route for the URL path /user/:id that responds to DELETE requests. The :id in the route path is a URL parameter.
The route handler is a function that takes a Gin context as its argument. This function first extracts the “id” from the URL parameters, then deletes the user from the Postgresql database by executing the SQL statement DELETE FROM users WHERE id = $1 using the user’s id as the parameter and then sends a JSON response with a message of “User deleted successfully” and a status code of 200 OK.
It also checks if there is any error while executing the query, if so it will return a status code of 500 Internal Server Error with the error message.
Finally, the router is started by calling the Run() method.

You would run this code by running go run main.go, and then making a DELETE request to http://localhost:8080/user/ using any HTTP client.

How to write unit-testcase for Go RestAPI

Unit testing in Go is typically done using the built-in testing package. You can use this package to create unit tests for your REST API by writing test functions that make HTTP requests to your API and assert that the responses are as expected.

Here’s an example of how you might write a unit test for a GET endpoint that retrieves a user by ID:

package main

import (
    "net/http"
    "net/http/httptest"
    "testing"

    "github.com/gin-gonic/gin"
)

func TestGetUser(t *testing.T) {
    // Set up the router and a test server
    router := gin.Default()
    router.GET("/user/:id", getUser)
    w := httptest.NewRecorder()
    req, _ := http.NewRequest("GET", "/user/1", nil)
    router.ServeHTTP(w, req)

    // Assert that the response has the expected status code and body
    if w.Code != http.StatusOK {
        t.Errorf("Expected status code %d, got %d", http.StatusOK, w.Code)
    }
    expectedBody := `{"user":{"name":"John","age":25}}`
    if w.Body.String() != expectedBody {
        t.Errorf("Expected body %s, got %s", expectedBody, w.Body.String())
    }
}

This test function creates a new Gin router, sets up a route for the /user/:id path that calls the getUser function, and then creates a test server using httptest.NewRecorder().
It creates an HTTP GET request to the endpoint and serves the request to the test server.
The test function uses the testing package’s Errorf function to check that the response has the expected status code (200 OK) and body ({“user”:{“name”:”John”,”age”:25}}).

To run this test, you can use the command go test in your project’s directory. The test function name should be in the format Test* to be recognized as a unit test.

You can also use testing frameworks like testify which provides a lot of useful testing utility functions like assert and require which makes it more readable and easy to use for testing.

It’s also important to note that you may want to consider testing your database interactions in isolation, by using a library like sqlmock which allows you to mock and test your database interactions without hitting an actual database.

Testing SpringBoot with Mockito.mock() vs @Mock vs @MockBean

How to test with SpringBoot

There are several ways to test a Spring Boot application, including unit tests, integration tests, and end-to-end tests.

Unit tests can be written using JUnit and test individual components of the application, such as service or repository classes. Spring Boot provides the @SpringBootTest annotation to enable loading of the application context for these tests.

Integration tests can be written using JUnit and the Spring Test and Spring Boot Test libraries. These tests run against the entire application and can test the interaction between multiple components. The @SpringBootTest annotation can also be used for integration tests.

End-to-end tests can be written using libraries such as Selenium or Cucumber and test the application from the perspective of an end user. These tests typically run against a deployed application and test the UI and user interactions.

You can also use mockMvc to test your controllers.
It is a powerful way to easily test Spring MVC controllers without starting a full HTTP server.

In summary, you can use JUnit and the Spring Test and Spring Boot Test libraries to write unit and integration tests, and use libraries such as Selenium or Cucumber for end-to-end tests. You can also use mockMvc to test your controllers.

Mockito.mock() example code

Here is an example of how to use Mockito to create a mock object:

import org.mockito.Mockito;

public class ExampleTest {
    @Test
    public void testExample() {
        // Create a mock object of the class you want to test
        MyService myService = Mockito.mock(MyService.class);

        // Set up the mock object's behavior
        Mockito.when(myService.doSomething()).thenReturn("Mocked result");

        // Use the mock object in your test
        Example example = new Example(myService);
        String result = example.useMyService();
        assertEquals("Mocked result", result);
    }
}

In this example, we are creating a mock object of the MyService class using the Mockito.mock() method. We are then using the when() method to specify the behavior of the mock object when the doSomething() method is called. In this case, we are telling the mock to return the string “Mocked result” when doSomething() is called.

In the test method, we are creating an instance of the class Example and passing it the mock object of MyService, then we are calling the useMyService() method of Example class and assert the result with the mocked result.

Mocking allows you to isolate the component you are testing, so that you can test its behavior independently of other components.

Please note that this is just an example and the actual implementation of the classes and methods used in this example might be different based on your use case.

@Mock example code

Here is an example of how to use the @Mock annotation to create a mock object:

import org.junit.jupiter.api.Test;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.junit.jupiter.MockitoExtension;

@ExtendWith(MockitoExtension.class)
public class ExampleTest {

    @Mock
    private MyService myService;

    @InjectMocks
    private Example example;

    @Test
    public void testExample() {
        // Set up the mock object's behavior
        Mockito.when(myService.doSomething()).thenReturn("Mocked result");

        // Use the mock object in your test
        String result = example.useMyService();
        assertEquals("Mocked result", result);
    }
}

In this example, we are using the @Mock annotation to create a mock object of the MyService class. We are then using the when() method to specify the behavior of the mock object when the doSomething() method is called. In this case, we are telling the mock to return the string “Mocked result” when doSomething() is called.

We also use the @InjectMocks annotation to inject the mock objects in the class we want to test Example.

In the test method, we are calling the useMyService() method of Example class and assert the result with the mocked result.

The @ExtendWith(MockitoExtension.class) is used to enable the usage of annotations in junit5.

Mocking allows you to isolate the component you are testing, so that you can test its behavior independently of other components.

@MockBean example code

Here is an example of how to use the @MockBean annotation to create a mock object in a Spring Boot application:

import org.junit.jupiter.api.Test;
import org.springframework.boot.test.mock.mockito.MockBean;
import org.springframework.test.context.junit.jupiter.SpringExtension;

@ExtendWith(SpringExtension.class)
@SpringBootTest
public class ExampleTest {

    @MockBean
    private MyService myService;

    @Autowired
    private Example example;

    @Test
    public void testExample() {
        // Set up the mock object's behavior
        Mockito.when(myService.doSomething()).thenReturn("Mocked result");

        // Use the mock object in your test
        String result = example.useMyService();
        assertEquals("Mocked result", result);
    }
}

In this example, we are using the @MockBean annotation to create a mock object of the MyService class in a Spring Boot application. The @MockBean annotation can only be used in the context of a Spring Boot test and is used to replace a bean in the ApplicationContext with a mock.

We also use the @Autowired annotation to inject the Example class in the test case.

In the test method, we are calling the useMyService() method of Example class and assert the result with the mocked result.

The @ExtendWith(SpringExtension.class) and @SpringBootTest is used to enable the usage of annotations in junit5 and to load the spring context.

Mocking allows you to isolate the component you are testing, so that you can test its behavior independently of other components.

Testing with Mock for SpringBoot RestAPI Controller

Here is an example of how to use mock objects to test a Spring Boot REST API controller:

import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.extension.ExtendWith;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.junit.jupiter.MockitoExtension;
import org.springframework.http.MediaType;
import org.springframework.test.web.servlet.MockMvc;
import org.springframework.test.web.servlet.setup.MockMvcBuilders;

import static org.mockito.Mockito.when;
import static org.springframework.test.web.servlet.request.MockMvcRequestBuilders.get;
import static org.springframework.test.web.servlet.result.MockMvcResultMatchers.content;
import static org.springframework.test.web.servlet.result.MockMvcResultMatchers.status;

@ExtendWith(MockitoExtension.class)
public class ExampleControllerTest {

    @Mock
    private MyService myService;

    @InjectMocks
    private ExampleController exampleController;

    private MockMvc mockMvc;

    @BeforeEach
    public void setUp() {
        mockMvc = MockMvcBuilders.standaloneSetup(exampleController).build();
    }

    @Test
    public void testGetData() throws Exception {
        // Set up the mock object's behavior
        when(myService.getData()).thenReturn("Mocked data");

        // Test the controller's GET endpoint
        mockMvc.perform(get("/data"))
                .andExpect(status().isOk())
                .andExpect(content().contentType(MediaType.APPLICATION_JSON_UTF8))
                .andExpect(content().string("Mocked data"));
    }
}

In this example, we are using the @Mock annotation to create a mock object of the MyService class, and the @InjectMocks annotation to inject the mock object into the ExampleController class.

We are also using the MockMvc class from the Spring Test framework to test the controller’s GET endpoint. MockMvcBuilders.standaloneSetup(controller) is used to set up a MockMvc instance to test the ExampleController.

In the test method, we are setting up the behavior of the mock object using when(myService.getData()).thenReturn(“Mocked data”); and then we are testing the /data endpoint by performing a GET request to the endpoint and asserting that the status code is 200 OK and the content type and body of the response are as expected.

This way you can test your RestAPI Controllers without starting a full HTTP server and without depending on any external resources.
Please note that this is just an example and the actual implementation of the classes and methods used in this example might be different based on your use case.

Testing Service Layer with SpringBoot and Mock

Here is an example of how to use mock objects to test a service layer in a Spring Boot application:

import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.extension.ExtendWith;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.junit.jupiter.MockitoExtension;

import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.mockito.Mockito.when;

@ExtendWith(MockitoExtension.class)
public class MyServiceTest {

    @Mock
    private MyRepository myRepository;

    @InjectMocks
    private MyService myService;

    @Test
    public void testGetData() {
        when(myRepository.findData()).thenReturn("Mocked data");
        String result = myService.getData();
        assertEquals("Mocked data", result);
    }
}

In this example, we are using the @Mock annotation to create a mock object of the MyRepository class, and the @InjectMocks annotation to inject the mock object into the MyService class.

In the test method, we are setting up the behavior of the mock object using when(myRepository.findData()).thenReturn(“Mocked data”); and then we are testing the getData() method of the MyService class by calling it and asserting that the result is equal to “Mocked data”.

This way you can test your Service Layer without starting a full HTTP server and without depending on any external resources.

Testing Repository Layer with SpringBoot and Mock

Here is an example of how to use mock objects to test a repository layer in a Spring Boot application:

import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.extension.ExtendWith;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.junit.jupiter.MockitoExtension;
import org.springframework.jdbc.core.JdbcTemplate;
import org.springframework.jdbc.core.RowMapper;

import java.sql.ResultSet;
import java.sql.SQLException;
import java.util.List;

import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.mockito.ArgumentMatchers.any;
import static org.mockito.ArgumentMatchers.anyString;
import static org.mockito.Mockito.when;

@ExtendWith(MockitoExtension.class)
public class MyRepositoryTest {
    @Mock
    private JdbcTemplate jdbcTemplate;

    @InjectMocks
    private MyRepository myRepository;

    @Test
    public void testFindData() {
        when(jdbcTemplate.query(anyString(), any(RowMapper.class))).thenReturn(List.of("Mocked data"));
        List result = myRepository.findData();
        assertEquals(List.of("Mocked data"), result);
    }
}

In this example, we are using the @Mock annotation to create a mock object of the JdbcTemplate class, and the @InjectMocks annotation to inject the mock object into the MyRepository class.

In the test method, we are setting up the behavior of the mock object using when(jdbcTemplate.query(anyString(), any(RowMapper.class))).thenReturn(List.of(“Mocked data”)); and then we are testing the findData() method of the MyRepository class by calling it and asserting that the result is equal to a list containing one element “Mocked data”.

Mocking Exception Throwing with Non-Void return

Here is an example of how to use mock objects to test a service layer that throws an exception:

import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.extension.ExtendWith;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.junit.jupiter.MockitoExtension;

import static org.junit.jupiter.api.Assertions.assertThrows;
import static org.mockito.Mockito.when;

@ExtendWith(MockitoExtension.class)
public class MyServiceTest {

    @Mock
    private MyRepository myRepository;

    @InjectMocks
    private MyService myService;

    @Test
    public void testGetData() {
        when(myRepository.findData()).thenThrow(new IllegalArgumentException("Mocked exception"));
        assertThrows(IllegalArgumentException.class, () -> myService.getData());
    }
}

In this example, we are using the @Mock annotation to create a mock object of the MyRepository class, and the @InjectMocks annotation to inject the mock object into the MyService class.

In the test method, we are setting up the behavior of the mock object using when(myRepository.findData()).thenThrow(new IllegalArgumentException(“Mocked exception”)); and then we are testing the getData() method of the MyService class by calling it inside an assertThrows block and asserting that it throws an IllegalArgumentException with the message “Mocked exception”.

This way you can test your service layer and the exception handling of it.

Mocking Exception Throwing with Void return

Here is an example of how to use mock objects to test a service layer that throws an exception when a void method is called:

import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.extension.ExtendWith;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.junit.jupiter.MockitoExtension;

import static org.junit.jupiter.api.Assertions.assertThrows;
import static org.mockito.Mockito.doThrow;

@ExtendWith(MockitoExtension.class)
public class MyServiceTest {

    @Mock
    private MyRepository myRepository;

    @InjectMocks
    private MyService myService;

    @Test
    public void testDeleteData() {
        doThrow(new IllegalArgumentException("Mocked exception")).when(myRepository).deleteData();
        assertThrows(IllegalArgumentException.class, () -> myService.deleteData());
    }
}

In this example, we are using the @Mock annotation to create a mock object of the MyRepository class, and the @InjectMocks annotation to inject the mock object into the MyService class.

In the test method, we are setting up the behavior of the mock object using doThrow(new IllegalArgumentException(“Mocked exception”)).when(myRepository).deleteData(); and then we are testing the deleteData() method of the MyService class by calling it inside an assertThrows block and asserting that it throws an IllegalArgumentException with the message “Mocked exception”.

Spy to throw an exception example code

Here is an example of how to use a spy object to test a service layer that throws an exception when a void method is called:

import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.extension.ExtendWith;
import org.mockito.InjectMocks;
import org.mockito.Spy;
import org.mockito.junit.jupiter.MockitoExtension;

import static org.junit.jupiter.api.Assertions.assertThrows;
import static org.mockito.Mockito.doThrow;

@ExtendWith(MockitoExtension.class)
public class MyServiceTest {

    @Spy
    private MyRepository myRepository;

    @InjectMocks
    private MyService myService;

    @Test
    public void testDeleteData() {
        doThrow(new IllegalArgumentException("Mocked exception")).when(myRepository).deleteData();
        assertThrows(IllegalArgumentException.class, () -> myService.deleteData());
    }
}

In this example, we are using the @Spy annotation to create a spy object of the MyRepository class, and the @InjectMocks annotation to inject the spy object into the MyService class.

AssertJ Exception Assertions example code

Here is an example of how to use AssertJ to assert that a specific exception is thrown in a test case:

import org.junit.jupiter.api.Test;
import static org.assertj.core.api.Assertions.assertThatThrownBy;

public class MyServiceTest {

    @Test
    public void testDeleteData() {
        MyService myService = new MyService();
        assertThatThrownBy(() -> myService.deleteData())
                .isInstanceOf(IllegalArgumentException.class)
                .hasMessage("Invalid argument");
    }
}

In this example, we are using the assertThatThrownBy method from AssertJ to assert that the deleteData() method of the MyService class throws an IllegalArgumentException with the message “Invalid argument”.

We are wrapping the method call inside the assertThatThrownBy block, this way AssertJ will check if any exception was thrown, and if so it will check the type and message of the exception.

This way you can use a more expressive and readable way to assert the exceptions thrown by your code.