Spring Micrometer: Tracing and Observability Made Easy

Observability is crucial for modern applications, allowing you to understand their behavior and performance. Spring Boot 3 introduces powerful observability features through Micrometer.

Understanding Observability

Observability involves collecting metrics, logs, and traces to provide insights into your running application. Micrometer is the core of this in Spring Boot, offering:

• Metrics: Numerical measurements about your application’s performance and resource usage.
• Tracing: Records the path of requests through your system, helping pinpoint bottlenecks and latency issues.
• Logging: Traditional text-based records for debugging and understanding application behavior.

Micrometer’s Role

Micrometer acts as a facade for various monitoring systems like Prometheus, Grafana, and Zipkin. It offers:

• Vendor-Neutral API: Instrument your code once, and Micrometer handles the integration with the backend system.
• Automatic Configuration: Spring Boot auto-configures Micrometer, making it easy to get started.
• Flexible Customization: Fine-tune metrics and traces collection to match your needs.

Tracing with Micrometer

Micrometer Tracing builds upon Micrometer’s metrics capabilities, providing distributed tracing support:

• Span Creation: Annotate your code to create spans representing units of work within a trace.
• Context Propagation: Spans are linked across service boundaries, enabling end-to-end tracing.
• Integration with Logging: Correlate logs with traces for enhanced troubleshooting.

Benefits of Micrometer Tracing

• Identify Performance Bottlenecks: Trace requests through your system to identify slowdowns.
• Understand Complex Interactions: Visualize how services interact to pinpoint issues.
• Improve Troubleshooting: Combine logs and traces for faster issue resolution.

Configuration and Setup in a Spring Project

1. Add Dependencies:

   • Include micrometer-core and micrometer-tracing dependencies in your pom.xml or build.gradle.
   • Choose a tracing implementation (e.g., Zipkin) and add its corresponding dependency.

2. Enable Tracing:

   • In your application.properties or application.yml, set management.tracing.enabled=true.
   • If using Zipkin, configure its endpoint: management.zipkin.tracing.endpoint=http://localhost:9411/api/v2/spans

3. Annotate Code:

   • Use @NewSpan to create new spans for methods or blocks of code you want to trace.
   • Use @ContinueSpan to continue an existing span in a new method.

4. Visualize Traces:

   • Access your tracing system’s UI (e.g., Zipkin) to view and analyze traces.

Tracing over Kafka

Spring Kafka 3.0 integrates seamlessly with Micrometer Tracing:

1. Enable Observation:

   • Set spring.kafka.listener.observation-enabled=true and spring.kafka.template.observation-enabled=true in your configuration.

2. Kafka Producer Example:

   @Service
   public class KafkaProducerService {
   
       private final KafkaTemplate<String, String> kafkaTemplate;
   
       public KafkaProducerService(KafkaTemplate<String, String> kafkaTemplate) {
           this.kafkaTemplate = kafkaTemplate;
       }
   
       @NewSpan("send-message")
       public void sendMessage(String topic, String message) {
           kafkaTemplate.send(topic, message);
       }
   }
   

3. Kafka Consumer Example:

   @Service
   public class KafkaConsumerService {
   
       @KafkaListener(topics = "my-topic")
       @ContinueSpan
       public void consumeMessage(String message) {
           // Process the message
       }
   }
   

Tracing MongoDB Operations

Spring Data MongoDB also integrates with Micrometer Tracing:

1. Enable Observation:

   • Configure your MongoClientSettings to include tracing.
   • Set necessary properties in your application.properties or application.yml:
     • management.tracing.mongo.enabled=true
     • spring.data.mongodb.tracing.enabled=true

2. MongoDB Repository Example:

   @Repository
   public interface UserRepository extends MongoRepository<User, String> {
   
       @NewSpan
       List<User> findByUsername(String username);
   }
   

Tracing SQL Databases (PostgreSQL, MariaDB, MySQL)

Spring Data JDBC and Spring Data JPA provide integration with Micrometer Tracing:

1. Enable Observation:

• Include DataSourceDecoratorAutoConfiguration: Add the following dependency to your project:

  <dependency>
      <groupId>com.github.gavlyukovskiy</groupId>
      <artifactId>datasource-decorator-spring-boot-autoconfigure</artifactId>
      <version>1.8.3</version> </dependency>
  

• Configure Datasource Proxy: In your application.properties or application.yml, enable datasource proxy:

  spring.datasource.decorator.enabled=true
  

2. JPA Repository Example:

   @Repository
   public interface ProductRepository extends JpaRepository<Product, Long> {
   
       @NewSpan
       List<Product> findByName(String name);
   }
   

Explanation:

• DataSourceDecoratorAutoConfiguration is a third-party library that enhances Spring Boot’s auto-configuration for datasources.
• It allows seamless integration with various datasource proxies like p6spy, datasource-proxy, and flexy-pool, enabling features like SQL logging, connection pooling enhancements, and most importantly, tracing support.
• By enabling spring.datasource.decorator.enabled, you instruct Spring Boot to use a proxy datasource that wraps your actual datasource, allowing the interception of SQL queries and their inclusion in traces.

Important Considerations:

• Choose a Compatible Tracing Implementation: Ensure your tracing implementation (e.g., Zipkin) is compatible with the DataSourceDecoratorAutoConfiguration library.
• Configure Tracing System: Set up your chosen tracing system to receive and visualize traces.
• Additional Configuration: Depending on your specific requirements and the chosen datasource proxy, you might need additional configuration. Refer to the DataSourceDecoratorAutoConfiguration documentation for details.

Spring Micrometer offers a robust framework for observability, with tracing playing a critical role. The integration with various data stores and messaging systems allows for seamless end-to-end tracing, enabling you to gain complete visibility into your applications. By leveraging Micrometer Tracing, you can identify performance bottlenecks, understand complex interactions, and improve troubleshooting across your entire system.