This is the eighth part of my series of articles about Spring Boot and AI. It is worth reading the following posts before proceeding with the current one. Here’s a list of articles about Spring AI on my blog with a short description:

1. https://piotrminkowski.com/2025/01/28/getting-started-with-spring-ai-and-chat-model: The first tutorial introduces the Spring AI project and its support for building applications based on chat models like OpenAI or Mistral AI.
2. https://piotrminkowski.com/2025/01/30/getting-started-with-spring-ai-function-calling: The second tutorial shows Spring AI support for Java function calling with the OpenAI chat model.
3. https://piotrminkowski.com/2025/02/24/using-rag-and-vector-store-with-spring-ai: The third tutorial shows Spring AI support for RAG (Retrieval Augmented Generation) and vector store.
4. https://piotrminkowski.com/2025/03/04/spring-ai-with-multimodality-and-images: The fourth tutorial shows Spring AI support for a multimodality feature and image generation
5. https://piotrminkowski.com/2025/03/10/using-ollama-with-spring-ai: The fifth tutorial shows Spring AI support for interactions with AI models run with Ollama
6. https://piotrminkowski.com/2025/03/13/tool-calling-with-spring-ai: The sixth tutorial shows Spring AI support for the Tool Calling feature.
7. https://piotrminkowski.com/2025/03/17/using-model-context-protocol-mcp-with-spring-ai: The seventh tutorial shows Spring AI support for MCP (Model Context Protocol) in Spring Boot server-side and client-side applications

Source Code

Feel free to use my source code if you’d like to try it out yourself. To do that, you must clone my sample GitHub repository. Then you should only follow my instructions.

Enable and Configure Azure OpenAI

You need to begin the exercise by creating an instance of the Azure OpenAI service. The most crucial element here is the service’s name since it is part of the exposed Open AI endpoint. My service’s name is piomin-azure-openai.

The Azure OpenAI service should be exposed without restrictions to allow easy access to the Spring AI app.

After creating the service, go to its main page in the Azure Portal. It provides information about API keys and an endpoint URL. Also, you have to deploy an Azure OpenAI model to start making API calls from your Spring AI app.

Copy the key and the endpoint URL and save them for later usage.

You must create a new deployment with an AI model in the Azure AI Foundry portal. There are several available options. The Spring AI Azure OpenAI starter by default uses the gpt-4o model. If you choose another AI model, you will have to set its name in the spring.ai.azure.openai.chat.options.deployment-name Spring AI property. After selecting the preferred model, click the “Confirm” button.

Finally, you can deploy the model on the Azure AI Foundry portal. Choose the most suitable deployment type for your needs.

Azure allows us to deploy multiple models. You can verify a list of model deployments here:

That’s all on the Azure Portal side. Now it’s time for the implementation part in the application source code.

Enable Azure OpenAI for Spring AI

Spring AI provides the Spring Boot starter for the Azure OpenAI Chat Client. You must add the following dependency to your Maven pom.xml file. Since the sample Spring Boot application is portable across various AI models, it includes the Azure OpenAI starter only if the azure-ai profile is active. Otherwise, it uses the spring-ai-openai-spring-boot-starter library.

<profile>
  <id>azure-ai</id>
  <dependencies>
    <dependency>
      <groupId>org.springframework.ai</groupId>
      <artifactId>spring-ai-azure-openai-spring-boot-starter</artifactId>
    </dependency>
  </dependencies>
</profile>

It’s time to use the key you previously copied from the Azure OpenAI service page. Let’s export it as the AZURE_OPENAI_API_KEY environment variable.

export AZURE_OPENAI_API_KEY=<YOUR_AZURE_OPENAI_API_KEY>

Here are the application properties dedicated to the azure-ai Spring Boot profile. The previously exported AZURE_OPENAI_API_KEY environment variable is set as the spring.ai.azure.openai.api-key property. You also must set the OpenAI service endpoint. This address depends on your Azure OpenAI service name.

spring.ai.azure.openai.api-key = ${AZURE_OPENAI_API_KEY}
spring.ai.azure.openai.endpoint = https://piomin-azure-openai.openai.azure.com/

To run the application and connect to your instance of the Azure OpenAI service, you must activate the azure-ai Maven profile and the Spring Boot profile under the same name. Here’s the required command:

mvn spring-boot:run -Pazure-ai -Dspring-boot.run.profiles=azure-ai

Test Spring AI Features with Azure OpenAI

I described several Spring AI features in the previous articles from this series. In each section, I will briefly mention the tested feature with a fragment of the sample source code. Please refer to my previous posts for more details about each feature and its sample implementation.

Chat Client with Memory and Structured Output

Here’s the @RestController containing endpoints we will use in these tests.

@RestController
@RequestMapping("/persons")
public class PersonController {

    private final ChatClient chatClient;

    public PersonController(ChatClient.Builder chatClientBuilder,
                            ChatMemory chatMemory) {
        this.chatClient = chatClientBuilder
                .defaultAdvisors(
                        new PromptChatMemoryAdvisor(chatMemory),
                        new SimpleLoggerAdvisor())
                .build();
    }

    @GetMapping
    List<Person> findAll() {
        PromptTemplate pt = new PromptTemplate("""
                Return a current list of 10 persons if exists or generate a new list with random values.
                Each object should contain an auto-incremented id field.
                The age value should be a random number between 18 and 99.
                Do not include any explanations or additional text.
                Return data in RFC8259 compliant JSON format.
                """);

        return this.chatClient.prompt(pt.create())
                .call()
                .entity(new ParameterizedTypeReference<>() {});
    }

    @GetMapping("/{id}")
    Person findById(@PathVariable String id) {
        PromptTemplate pt = new PromptTemplate("""
                Find and return the object with id {id} in a current list of persons.
                """);
        Prompt p = pt.create(Map.of("id", id));
        return this.chatClient.prompt(p)
                .call()
                .entity(Person.class);
    }
}

First, you must call the endpoint that generates a list of ten persons from different countries. Then choose one person by ID to pick it up from the chat memory. Here are the results.

The interesting part happens in the background. Here’s a fragment of advice context added to the prompt by Spring AI.

Tool Calling

Here’s the @RestController containing endpoints we will use in these tests. There are two tools injected into the chat client: StockTools and WalletTools. These tools interact with a local H2 database to get a sample stock wallet structure and with the stock online API to load the latest share prices.

@RestController
@RequestMapping("/wallet")
public class WalletController {

    private final ChatClient chatClient;
    private final StockTools stockTools;
    private final WalletTools walletTools;

    public WalletController(ChatClient.Builder chatClientBuilder,
                            StockTools stockTools,
                            WalletTools walletTools) {
        this.chatClient = chatClientBuilder
                .defaultAdvisors(new SimpleLoggerAdvisor())
                .build();
        this.stockTools = stockTools;
        this.walletTools = walletTools;
    }

    @GetMapping("/with-tools")
    String calculateWalletValueWithTools() {
        PromptTemplate pt = new PromptTemplate("""
        What’s the current value in dollars of my wallet based on the latest stock daily prices ?
        """);

        return this.chatClient.prompt(pt.create())
                .tools(stockTools, walletTools)
                .call()
                .content();
    }

    @GetMapping("/highest-day/{days}")
    String calculateHighestWalletValue(@PathVariable int days) {
        PromptTemplate pt = new PromptTemplate("""
        On which day during last {days} days my wallet had the highest value in dollars based on the historical daily stock prices ?
        """);

        return this.chatClient.prompt(pt.create(Map.of("days", days)))
                .tools(stockTools, walletTools)
                .call()
                .content();
    }
}

You must have your API key for the Twelvedata service to run these tests. Don’t forget to export it as the STOCK_API_KEY environment variable before running the app.

export STOCK_API_KEY=<YOUR_STOCK_API_KEY>

The GET /wallet/with-tools endpoint calculates the current stock wallet value in dollars.

The GET /wallet/highest-day/{days} computes the value of the stock wallet for a given period in days and identifies the day with the highest value.

Multimodality and Images

Here’s a part of the @RestController responsible for describing image content and generating a new image with a given item.

@RestController
@RequestMapping("/images")
public class ImageController {

    private final static Logger LOG = LoggerFactory.getLogger(ImageController.class);
    private final ObjectMapper mapper = new ObjectMapper();

    private final ChatClient chatClient;
    private ImageModel imageModel;

    public ImageController(ChatClient.Builder chatClientBuilder,
                           Optional<ImageModel> imageModel) {
        this.chatClient = chatClientBuilder
                .defaultAdvisors(new SimpleLoggerAdvisor())
                .build();
        imageModel.ifPresent(model -> this.imageModel = model);
    }
        
    @GetMapping("/describe/{image}")
    List<Item> describeImage(@PathVariable String image) {
        Media media = Media.builder()
                .id(image)
                .mimeType(MimeTypeUtils.IMAGE_PNG)
                .data(new ClassPathResource("images/" + image + ".png"))
                .build();
        UserMessage um = new UserMessage("""
        List all items you see on the image and define their category.
        Return items inside the JSON array in RFC8259 compliant JSON format.
        """, media);
        return this.chatClient.prompt(new Prompt(um))
                .call()
                .entity(new ParameterizedTypeReference<>() {});
    }
    
    @GetMapping(value = "/generate/{object}", produces = MediaType.IMAGE_PNG_VALUE)
    byte[] generate(@PathVariable String object) throws IOException, NotSupportedException {
        if (imageModel == null)
            throw new NotSupportedException("Image model is not supported");
        ImageResponse ir = imageModel.call(new ImagePrompt("Generate an image with " + object, ImageOptionsBuilder.builder()
                .height(1024)
                .width(1024)
                .N(1)
                .responseFormat("url")
                .build()));
        String url = ir.getResult().getOutput().getUrl();
        UrlResource resource = new UrlResource(url);
        LOG.info("Generated URL: {}", url);
        dynamicImages.add(Media.builder()
                .id(UUID.randomUUID().toString())
                .mimeType(MimeTypeUtils.IMAGE_PNG)
                .data(url)
                .build());
        return resource.getContentAsByteArray();
    }
    
}

The GET /images/describe/{image} returns a structured list of items identified on a given image. It also categorizes each detected item. In this case, there are two available categories: fruits and vegetables.

By the way, here’s the image described above.

The image generation feature requires a dedicated model on Azure AI. The DALL-E 2 and DALL-E 3 models on Azure support a text-to-image feature.

The application must be aware of the model name. That’s why you must add a new property to your application properties with the following value.

spring.ai.azure.openai.image.options.deployment-name = dall-e-3

Then you must restart the application. After that, you can generate an image by calling the GET /images/generate/{object} endpoint. Here’s the result for the pineapple.

Enable Azure CosmosDB Vector Store

Dependency

By default, the sample Spring Boot application uses Pinecone vector store. However, SpringAI supports two services available on Azure: Azure AI Search and CosmosDB. Let’s choose CosmosDB as the vector store. You must add the following dependency to your Maven pom.xml file:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-azure-cosmos-db-store-spring-boot-starter</artifactId>
</dependency>

Configuration on Azure

Then, you must create an instance of CosmosDB in your Azure account. The name of my instance is piomin-ai-cosmos.

Once it is created, you will obtain its address and API key. To do that, go to the “Settings -> Keys” menu and save both values visible below.

Then, you have to create a dedicated database and container for your application. To do that, go to the “Data Explorer” tab and provide names for the database and container ID. You must also set the partition key.

All previously provided values must be set in the application properties. Export your CosmosDB API key as the AZURE_VECTORSTORE_API_KEY environment variable.

spring.ai.vectorstore.cosmosdb.endpoint = https://piomin-ai-cosmos.documents.azure.com:443/
spring.ai.vectorstore.cosmosdb.key = ${AZURE_VECTORSTORE_API_KEY}
spring.ai.vectorstore.cosmosdb.databaseName = spring-ai
spring.ai.vectorstore.cosmosdb.containerName = spring-ai
spring.ai.vectorstore.cosmosdb.partitionKeyPath = /id

Unfortunately, there are still some issues with the Azure CosmosDB support in the Spring AI M6 milestone version. I see that they were fixed in the SNAPSHOT version. So, if you want to test it by yourself, you will have to switch from milestones to snapshots.

<properties>
  <java.version>21</java.version>
  <spring-ai.version>1.0.0-SNAPSHOT</spring-ai.version>
</properties>
  
<repositories>
  <repository>
    <name>Central Portal Snapshots</name>
    <id>central-portal-snapshots</id>
    <url>https://central.sonatype.com/repository/maven-snapshots/</url>
    <releases>
      <enabled>false</enabled>
    </releases>
    <snapshots>
      <enabled>true</enabled>
    </snapshots>
  </repository>
  <repository>
    <id>spring-snapshots</id>
    <name>Spring Snapshots</name>
    <url>https://repo.spring.io/snapshot</url>
    <releases>
      <enabled>false</enabled>
    </releases>
    <snapshots>
      <enabled>true</enabled>
    </snapshots>
  </repository>
</repositories>

Run and Test the Application

After those changes, you can start the application with the following command:

mvn spring-boot:run -Pazure-ai -Dspring-boot.run.profiles=azure-ai

Once the application is running, you can test the following @RestController that offers RAG functionality. The GET /stocks/load-data endpoint obtains stock prices of given companies and puts them in the vector store. The GET /stocks/v2/most-growth-trend uses the RetrievalAugmentationAdvisor instance to retrieve the most suitable data and include it in the user query.

@RestController
@RequestMapping("/stocks")
public class StockController {

    private final ObjectMapper mapper = new ObjectMapper();
    private final static Logger LOG = LoggerFactory.getLogger(StockController.class);
    private final ChatClient chatClient;
    private final RewriteQueryTransformer.Builder rqtBuilder;
    private final RestTemplate restTemplate;
    private final VectorStore store;

    @Value("${STOCK_API_KEY:none}")
    private String apiKey;

    public StockController(ChatClient.Builder chatClientBuilder,
                           VectorStore store,
                           RestTemplate restTemplate) {
        this.chatClient = chatClientBuilder
                .defaultAdvisors(new SimpleLoggerAdvisor())
                .build();
        this.rqtBuilder = RewriteQueryTransformer.builder()
                .chatClientBuilder(chatClientBuilder);
        this.store = store;
        this.restTemplate = restTemplate;
    }

    @GetMapping("/load-data")
    void load() throws JsonProcessingException {
        final List<String> companies = List.of("AAPL", "MSFT", "GOOG", "AMZN", "META", "NVDA");
        for (String company : companies) {
            StockData data = restTemplate.getForObject("https://api.twelvedata.com/time_series?symbol={0}&interval=1day&outputsize=10&apikey={1}",
                    StockData.class,
                    company,
                    apiKey);
            if (data != null && data.getValues() != null) {
                var list = data.getValues().stream().map(DailyStockData::getClose).toList();
                var doc = Document.builder()
                        .id(company)
                        .text(mapper.writeValueAsString(new Stock(company, list)))
                        .build();
                store.add(List.of(doc));
                LOG.info("Document added: {}", company);
            }
        }
    }

    @RequestMapping("/v2/most-growth-trend")
    String getBestTrendV2() {
        PromptTemplate pt = new PromptTemplate("""
                {query}.
                Which {target} is the most % growth?
                The 0 element in the prices table is the latest price, while the last element is the oldest price.
                """);

        Prompt p = pt.create(Map.of("query", "Find the most growth trends", "target", "share"));

        Advisor retrievalAugmentationAdvisor = RetrievalAugmentationAdvisor.builder()
                .documentRetriever(VectorStoreDocumentRetriever.builder()
                        .similarityThreshold(0.7)
                        .topK(3)
                        .vectorStore(store)
                        .build())
                .queryTransformers(rqtBuilder.promptTemplate(pt).build())
                .build();

        return this.chatClient.prompt(p)
                .advisors(retrievalAugmentationAdvisor)
                .call()
                .content();
    }

}

Finally, you can call the following two endpoints.

$ curl http://localhost:8080/stocks/load-data
$ curl http://localhost:8080/stocks/v2/most-growth-trend

Final Thoughts

This exercise shows how to modify an existing Spring Boot AI application to integrate it with the Azure OpenAI service. It also gives a recipe on how to include Azure CosmosDB as a vector store for RAG scenarios and similarity searches.

The post Spring AI with Azure OpenAI appeared first on Piotr's TechBlog.

If you need an introduction to Spring Cloud read my article about microservices with Spring Boot 3 and Spring Cloud available here. It is worth at least taking a look at the Spring Cloud Azure docs for a basic understanding of the main concepts.

Architecture

Our architecture is pretty simple. As I mentioned before, we have a single Spring Boot app (account-service in the diagram) that runs on Azure and connects to Cosmos DB. It exposes some REST endpoints for adding, deleting, or searching accounts backed by Cosmos DB. It also stores the whole required configuration (like Cosmos DB address and credentials) in the Azure App Configuration service. The app is managed by the Azure Spring Apps service. Here’s the diagram illustrating our architecture.

Source Code

If you would like to try it by yourself, you may always take a look at my source code. In order to do that you need to clone my GitHub repository. The Spring Boot app used in the article is located in the microservices/account-service directory. After you go to that directory you should just follow my further instructions.

There are some prerequisites before you start the exercise. You need to install JDK17+ and Maven on your local machine. You also need to have an account on Azure and az CLI to interact with that account. In order to deploy the app on Azure we will use azure-spring-apps-maven-plugin, which requires az CLI.

Dependencies

Firstly, let’s take a look at the list of required Maven dependencies. Of course, we need to add the Spring Boot Web starter to enable REST support through the Spring MVC module. In order to integrate with Cosmos DB, we will use the Spring Data repositories. Spring Cloud Azure provides a dedicated starter spring-cloud-azure-starter-data-cosmos for it. The spring-cloud-azure-starter-actuator module is optional. It will enable a health indicator for Cosmos DB in the /actuator/health endpoint. After that, we will include the starter providing integration with the Azure App Configuration service. Finally, we can add the Springdoc OpenAPI project responsible for generating REST API documentation.

<dependencies>
  <dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
  </dependency>
  <dependency>
    <groupId>com.azure.spring</groupId>
    <artifactId>spring-cloud-azure-starter-actuator</artifactId>
  </dependency>
  <dependency>
    <groupId>com.azure.spring</groupId>
    <artifactId>spring-cloud-azure-starter-data-cosmos</artifactId>
  </dependency>
  <dependency>
    <groupId>com.azure.spring</groupId>
    <artifactId>spring-cloud-azure-starter-appconfiguration-config</artifactId>
  </dependency>
  <dependency>
    <groupId>org.springdoc</groupId>
    <artifactId>springdoc-openapi-starter-webmvc-ui</artifactId>
    <version>2.2.0</version>
  </dependency>
</dependencies>

Spring Cloud with Azure Cosmos DB

After including the Spring Data module with Cosmos DB support we may define the model class. The Account class contains three String fields: id (primary key), number, and customerId (partition key). The partition key is responsible for dividing data into distinct subsets called logical partitions. The model must be annotated with @Container. The containerName parameter inside the annotation corresponds to the name of the Cosmos DB container created in Azure.

@Container(containerName = "accounts")
public class Account {
   @Id
   @GeneratedValue
   private String id;
   private String number;
   @PartitionKey
   private String customerId;

   // GETTERS AND SETTERS ...
}

Now, let’s prepare our environment in Azure. After logging in with the az login CLI command we create the resource group for our exercise. The name of the group is sample-spring-cloud. The location depends on your preferences. For me it is eastus.

$ az group create -l eastus -n sample-spring-cloud

Then, we are going to create a new Azure Cosmos DB database account. The name of my account is sample-pminkows-cosmosdb. It is placed inside our sample-spring-cloud resource group. I’ll leave the default values in all other parameters. But you can consider overriding some parameters to decrease the instance cost. For example, we can set the Local backup redundancy type using the --backup-redundancy parameter.

$ az cosmosdb create -n sample-pminkows-cosmosdb -g sample-spring-cloud

Once we enable a database account we can create a database instance. The name of our database is sampled. Of course, it has to be placed in the previously created sample-pminkows-cosmosdb Cosmos DB account.

$ az cosmosdb sql database create \
    -a sample-pminkows-cosmosdb \
    -n sampledb \
    -g sample-spring-cloud

Finally, we need to create a container inside our database. The name of the container should be the same as the value of the containerName field declared in the model class. We also have to set the partition key path. As you probably remember, we are using the customerId field in the Account class for that.

$ az cosmosdb sql container create \
    -a sample-pminkows-cosmosdb \
    -g sample-spring-cloud \
    -n accounts \
    -d sampledb \
    -p /customerId

Everything is ready on the Azure side. Let’s back for a moment to the source code. In order to interact with the database, we will create the Spring Data repository interface. It has to extend the CosmosRepository interface provided within Spring Cloud Azure. It defines one additional method for searching by the customerId field.

public interface AccountRepository extends CosmosRepository<Account, String> {
   List<Account> findByCustomerId(String customerId);
}

Finally, we can create @RestController with the endpoints implementation. It injects and uses the AccountRepository bean.

@RestController
@RequestMapping("/accounts")
public class AccountController {

   private final static Logger LOG = LoggerFactory
      .getLogger(AccountController.class);
   private final AccountRepository repository;

   public AccountController(AccountRepository repository) {
      this.repository = repository;
   }

   @PostMapping
   public Account add(@RequestBody Account account) {
      LOG.info("add: {}", account.getNumber());
      return repository.save(account);
   }

   @GetMapping("/{id}")
   public Account findById(@PathVariable String id) {
      LOG.info("findById: {}", id);
      return repository.findById(id).orElseThrow();
   }

   @GetMapping
   public List<Account> findAll() {
      List<Account> accounts = new ArrayList<>();
      repository.findAll().forEach(accounts::add);
      return accounts;
   }

   @GetMapping("/customer/{customerId}")
   public List<Account> findByCustomerId(@PathVariable String customerId) {
      LOG.info("findByCustomerId: {}", customerId);
      return repository.findByCustomerId(customerId);
   }
}

Azure App Configuration with Spring Cloud

Once we finish the app implementation, we can run it and connect with Cosmos DB. Of course, we need to set the connection URL and credentials. Let’s switch to the Azure Portal. We need to find the “Azure Cosmos DB” service in the main menu. Then click your database account. You will see the address of the endpoint as shown below. You should also see the previously created container in the “Containers” section.

In order to obtain the connection key, we need to go to the “Data Explorer” item in the left-side menu. Then choose the “Connect” tile. You will find the key in the target window.

We could easily set all the required connection parameters using the spring.cloud.azure.cosmos.* properties. However, I would like to store all the configuration settings on Azure. Spring Cloud comes with built-in support for Azure App Configuration service. We have already included the required Spring Cloud starter. So now, we need to enable the Azure App Configuration service and put our properties into the store. Here’s the command for creating an App Configuration under the sample-spring-cloud-config name:

$ az appconfig create \
    -g sample-spring-cloud \
    -n sample-spring-cloud-config \
    -l eastus \
    --sku Standard

Once we create the App Configuration we can put our configuration settings in the key/value form. By default, Spring Cloud Azure is loading configurations that start with the key /application/. We need to add three Spring Cloud properties: spring.cloud.azure.cosmos.key, spring.cloud.azure.cosmos.database, and spring.cloud.azure.cosmos.endpoint.

$ az appconfig kv set \
    -n sample-spring-cloud-config \
    --key /application/spring.cloud.azure.cosmos.key \
    --value <YOUR_PRIMARY_KEY>

$ az appconfig kv set \
    -n sample-spring-cloud-config \
    --key /application/spring.cloud.azure.cosmos.database \
    --value sampledb

$ az appconfig kv set \
    -n sample-spring-cloud-config \
    --key /application/spring.cloud.azure.cosmos.endpoint \
    --value <YOUR_ENDPOINT_URI>

Let’s switch to the Azure Portal to check the configuration settings. We need to find the “App Configuration” service in the main dashboard. Then go to the sample-spring-cloud-config details and choose the “Configuration explorer” menu item. You should have all your application properties prefixed by the /application/. I also overrode some Spring Actuator settings to enable health check details and additional management endpoints.

That’s all. Now, we are ready to run our app. We just need to connect it to the Azure App Configuration service. In order to do that, we need to obtain its connection endpoint and credentials. You can go to the “Access keys” menu item in the “Settings” section. Then you should copy the value from the “Connection string” field as shown below. Alternatively, you can obtain the same information by executing the following CLI command: az appconfig credential list --name sample-spring-cloud-config.

Let’s save the value inside the APP_CONFIGURATION_CONNECTION_STRING environment variable. After that, we just need to create the Spring bootstrap.properties file in the src/main/resources directory containing the spring.cloud.azure.appconfiguration.stores[0].connection-string property.

spring.cloud.azure.appconfiguration.stores[0].connection-string=${APP_CONFIGURATION_CONNECTION_STRING}

Running Spring Boot App Locally

Finally, we can run our sample Spring Boot app. For now, we will just run it locally. As a result, it will connect to the Azure App Configuration and Cosmos DB deployed on the cloud. We can execute the following Maven command to start the app:

$ mvn clean spring-boot:run

Once you start the app you should see that it loads property sources from the Azure store:

If everything works fine your app is loading settings from Azure App Configuration and connects to the Cosmos DB instance:

Once you start the app you can access it under the 8080 local port. The Swagger UI is available under the /swagger-ui.html path:

We can some data using e.g. the curl command as shown below:

$ curl -X 'POST' 'http://localhost:8080/accounts' \
    -H 'Content-Type: application/json' \
    -d '{"number": "1234567893","customerId": "1"}'
{"id":"5301e9dd-0556-40b7-9ea3-96975492f00c","number":"1234567893","customerId":"1"}

Then, we can e.g. find accounts owned by a particular customer:

$ curl http://localhost:8080/accounts/customer/1

We can also delete an existing account by calling the DELETE /account/{id} endpoint. In that case, I received the HTTP 404 Not Found error. Interesting?

Let’s see what happened. If you take a look at the implementation of AccountController you will find the method for the DELETE endpoint, right? In the meantime, I added one method annotated with @FeatureGate. This annotation is provided by Spring Cloud Azure. The following fragment of code shows the usage of feature management with Azure App Configuration. In fact, I’m using the “Feature Gate” functionality, which allows us to call the endpoint only if a feature is enabled on the Azure side. The name of our feature is delete-account.

@DeleteMapping("/{id}")
@FeatureGate(feature = "delete-account")
public void deleteById(@PathVariable String id) {
   repository.deleteById(id);
}

Now, the only thing we need to do is to add a new feature to the sample-spring-cloud-config App Configuration.

$ az appconfig feature set -n sample-spring-cloud-config --feature test-2

Let’s switch to the Azure Portal. You should go to the “Feature manager” menu item in the “Operations” section. As you see, by default the feature flag is disabled. It means the feature is not active and the endpoint is disabled.

You can enable the feature by clicking the checkbox button and then restart the app. After that, the DELETE endpoint should be available.

Deploy Spring Cloud App on Azure

We can deploy our sample app to Azure in several different ways. I’ll choose the service dedicated especially to Spring Boot – Azure Spring Apps.

The installation from Azure Portal is pretty straightforward. I won’t get into the details. The name of our instance (cluster) is sample-spring-cloud-apps. We don’t need to know anything more to be able to deploy our app there.

Azure provides several Maven plugins for deploying apps. For Azure Spring Apps we should use azure-spring-apps-maven-plugin. We need to set the Azure Spring Apps instance in the clusterName parameter. The name of our app is account-service. We should also choose SKU and set the Azure subscription ID (loaded from the SUBSCRIPTION environment variable). In the deployment section, we need to define the required resources (RAM and CPU), number of running instances, Java version, and a single environment variable containing the connection string to the Azure App Configuration instance.

<plugin>
  <groupId>com.microsoft.azure</groupId>
  <artifactId>azure-spring-apps-maven-plugin</artifactId>
  <version>1.19.0</version>
  <configuration>
    <subscriptionId>${env.SUBSCRIPTION}</subscriptionId>
    <resourceGroup>sample-spring-cloud</resourceGroup>
    <clusterName>sample-spring-cloud-apps</clusterName>
    <sku>Consumption</sku>
    <appName>account-service</appName>
    <isPublic>true</isPublic>
    <deployment>
      <cpu>0.5</cpu>
      <memoryInGB>1</memoryInGB>
      <instanceCount>1</instanceCount>
      <runtimeVersion>Java 17</runtimeVersion>
      <environment>
        <APP_CONFIGURATION_CONNECTION_STRING>
          ${env.APP_CONFIGURATION_CONNECTION_STRING}
        </APP_CONFIGURATION_CONNECTION_STRING>
      </environment>
      <resources>
        <resource>
          <directory>target/</directory>
          <includes>
            <include>*.jar</include>
          </includes>
        </resource>
      </resources>
    </deployment>
  </configuration>
</plugin>

Then we need to build the app and deploy it on Azure Spring Apps with the following command:

$ mvn clean package azure-spring-apps:deploy

You should have a similar result as shown below:

Does the name of the instance sound familiar? Under the hood it’s Kubernetes. The Azure Spring Apps service uses Azure Container Apps for running containers. On the other hand, Azure Container Apps is hosted on the Kubernetes cluster. But these are the details. What is important here – our app has already been deployed on Azure.

We can display the account-service app details. The app is exposed under the public URL. We just need to copy the link.

Let’s take a look at the configuration section. As you see it contains the connection string to the App Configuration endpoint.

We can display the Swagger UI and perform some test calls.

Final Thoughts

That’s all in this article, but I’m planning to create several others about Spring Boot and Azure soon! Azure seems to be a friendly platform for the Spring Boot developer I showed you how to easily integrate your Spring Boot app with the most popular Azure services like Cosmos DB. We also covered such topics as configuration management and feature flags (gates) with the App Configuration service. Finally, we deployed the app on… Kubernetes through the Azure Spring Apps service

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