Monitor and Debug Spring Boot Microservices Using ELK Stack

Debugging and monitoring microservices is a complex yet crucial task for ensuring system reliability. Distributed applications often struggle with scattered logs, incomplete information, and nontrivial tracing of user requests across multiple services. The ELK Stack (Elasticsearch, Logstash, Kibana) solves many of these pain points by centralizing logs, providing insightful visualizations, and enabling detailed trace analysis.

This blog will guide you through setting up an observability strategy for Spring Boot microservices using the ELK stack, covering logging best practices, real-life debugging scenarios, and building dashboards in Kibana. By the end, you’ll understand how ELK can streamline your debugging process and keep your services stable in production.

Table of Contents

1. How the ELK Stack Helps in Distributed Environments
2. Logging Microservice Names and Correlation IDs
3. Use Case: Trace an Error Across Services
4. Creating Dashboards in Kibana
5. Summary

How the ELK Stack Helps in Distributed Environments

Distributed systems, such as microservices, involve multiple services that handle different parts of a user request. Monitoring these interactions and debugging issues quickly becomes overwhelming when each service maintains separate log files. The ELK Stack centralizes logging and provides necessary tools for troubleshooting.

Benefits of Using ELK for Microservices

1. Log Centralization: ELK collects logs from all microservices, stores them in Elasticsearch, and makes them searchable across services.
2. Efficient Debugging: By correlating logs using IDs (like traceId), you can debug and trace issues across dependent services effortlessly.
3. Powerful Search: Elasticsearch provides fault-tolerant storage and supports advanced queries like filtering logs by specific fields (e.g., service name, error codes).
4. Insightful Visualization: Dashboards in Kibana provide aggregated views of microservice performance and logs, enabling trend analysis and faster decision-making.
5. Scalability: The ELK stack handles massive amounts of log data, making it suitable for high-volume enterprise systems.

With the features above, the ELK stack has become an essential part of observability pipelines for microservice architectures.

Logging Microservice Names and Correlation IDs

For effective monitoring and debugging, every logged event should contain essential identifiers such as the microservice name, traceId, and spanId. This ensures logs can be organized and correlated across services.

Step 1. Adding Service-Specific Identifiers

Every log entry should identify from which microservice it originates. Update logback-spring.xml to include your service name:

<configuration>
    <appender name="LOGSTASH" class="net.logstash.logback.appender.LogstashTcpSocketAppender">
        <destination>localhost:5044</destination>
        <encoder class="net.logstash.logback.encoder.LoggingEventCompositeJsonEncoder">
            <providers>
                <timestamp />
                <message />
                <loggerName />
                <mdc /> <!-- Important for traceId and spanId -->
                <customFields>{"serviceName":"order-service"}</customFields>
            </providers>
        </encoder>
    </appender>

    <root level="INFO">
        <appender-ref ref="LOGSTASH" />
    </root>
</configuration>

Step 2. Implementing Correlation IDs with Spring Cloud Sleuth

Spring Cloud Sleuth automatically propagates traceId and spanId across services. These IDs allow you to track user requests as they flow through your system.

Add the dependency in your pom.xml:

<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-starter-sleuth</artifactId>
</dependency>

Sleuth will enrich your logs with these fields:

• traceId: A unique identifier for the entire request lifecycle, shared across microservices.
• spanId: A unique identifier for a single operation within the trace.

Example Enriched Log Output:

{
  "timestamp": "2025-06-13T12:45:32.123",
  "serviceName": "order-service",
  "traceId": "12345abcde",
  "spanId": "67890fghij",
  "message": "Order created successfully"
}

This structure allows you to correlate logs at any point in the request cycle.

Use Case: Trace an Error Across Services

To illustrate the power of the ELK stack, consider this real-world debugging scenario.

Problem:

A user reports that their order is not being processed. This request flows from the Frontend Service → Order Service → Payment Service, any of which could be failing.

Steps to Trace and Resolve the Issue

Step 1. Isolate the Issue with Kibana

1. Open Kibana’s Discover tab.
2. Query logs by the user’s traceId: traceId:"12345abcde"
3. Review all logs related to this trace to identify which service encountered an error.

Example Findings:

Logs indicate that the Payment Service threw a NullPointerException during payment processing:

{
  "serviceName": "payment-service",
  "level": "ERROR",
  "traceId": "12345abcde",
  "message": "NullPointerException at PaymentProcessor.java"
}

Step 2. Drill Down into the Payment Service

Switch back to Kibana’s Discover and filter Payment Service logs:

serviceName:"payment-service" AND traceId:"12345abcde"

This query narrows down relevant logs, showing that an API dependency returned an unexpected null.

Step 3. Fix the Issue

With the root cause identified, implement and deploy a code fix.

The ability to trace user activity and service interactions across the entire stack is a critical advantage of centralized logging with the ELK stack.

Creating Dashboards in Kibana

Kibana enhances troubleshooting by translating log data into meaningful visual insights. Dashboards can reveal patterns, identify anomalies, and display key performance metrics.

Step 1. Create a Kibana Index

1. Navigate to Management > Data Views (Index Patterns).
2. Add an index pattern for spring-logs-* and set @timestamp as the time field.

Step 2. Build Visualizations

1. Errors Over Time

• Visualization Type: Line chart.
• Metrics: Count logs where level:"ERROR".
• Buckets: Time-based aggregation (@timestamp).

2. Top Services by Logs

• Visualization Type: Pie chart.
• Metrics: Count logs.
• Buckets: Split by serviceName.

3. Latency Distribution

• Visualization Type: Histogram.
• Metrics: Average duration of requests.
• Buckets: Group by latency ranges.

Step 3. Save and Share

Bundle visualizations into a single dashboard and set it to auto-refresh for real-time monitoring. Share dashboards with your team for collaborative debugging sessions.

Example Kibana Dashboard:

Your dashboard might include:

• A real-time view of error occurrences.
• Average latency per service.
• Top services emitting the most logs.

This level of observability keeps your team proactive in identifying and resolving potential issues.

Summary

Monitoring and debugging Spring Boot microservices with the ELK stack transforms how you oversee distributed architectures. Here are the key takeaways:

1. Log Centralization: Combine logs from all services into an Elasticsearch cluster for centralized monitoring.
2. Enhanced Observability: Use traceId and spanId to correlate logs across services and reconstruct the request lifecycle.
3. Efficient Debugging: Resolve errors faster by isolating and analyzing logs specific to a problem.
4. Kibana Visualizations: Build dashboards in Kibana to monitor trends, analyze errors, and gain real-time insights.

By implementing these practices, your microservices will be better equipped to handle failures, debug issues rapidly, and maintain high performance at scale. Start using the ELK stack today to take control over your application’s observability!