Performance Monitoring and Root Cause Analysis Using ELK Spring Boot

Monitoring the performance of microservices and uncovering the root cause of system inefficiencies are critical tasks in achieving a reliable and high-performing distributed architecture. One of the most effective approaches is leveraging the ELK Stack (Elasticsearch, Logstash, Kibana). By consolidating and analyzing logs, teams can pinpoint bottlenecks, track the performance of APIs, and identify areas for improvement.

This guide provides a step-by-step walkthrough of using the ELK stack for performance monitoring and root cause analysis. We’ll explore how to identify performance bottlenecks from logs, configure Spring Boot applications to log key metrics such as response time and status, aggregate slow endpoints in Kibana, and filter logs for high-latency events.

Table of Contents

1. Why Use ELK for Performance Monitoring?
2. Identifying Performance Bottlenecks from Logs
3. Logging Response Time, Endpoint, and Status in Spring Boot
4. Aggregating Slow Endpoints in Kibana
5. Filtering Logs by High-Latency Operations
6. Summary

Why Use ELK for Performance Monitoring?

The ELK Stack provides a centralized platform for capturing, searching, and visualizing logs. It simplifies performance monitoring by enabling you to analyze logs at scale, identify trends, and quickly zero in on potential bottlenecks.

Advantages of ELK for Performance Monitoring:

1. High Query Speed: Elasticsearch indexes log data, making it easy to search and analyze large datasets quickly.
2. Powerful Aggregation: Kibana aggregates logs by metrics like response time, endpoints, and request volume.
3. Custom Dashboards: Visualize key performance indicators (KPIs) like latency trends or error counts with interactive dashboards.
4. Root Cause Analysis: Combine filtering and query features in Kibana to correlate slow requests with downstream failures.

With ELK, even complex distributed architectures become observable and manageable.

Identifying Performance Bottlenecks from Logs

Performance bottlenecks are often hidden in data such as long response times, frequent retries, or high resource consumption by specific services. ELK helps surface these issues by analyzing your application logs.

Symptoms of Performance Bottlenecks:

1. High Latencies: Requests to specific endpoints consistently take longer than expected.
2. Error Prone Operations: Frequent retries or HTTP 500 responses indicate underlying inefficiencies.
3. Skewed Traffic Distribution: Some services or endpoints handle disproportionately high loads.
4. Resource Contention: High CPU or memory usage correlates with slow performance.

Identifying bottlenecks begins with structured logging, where key metrics like response times, endpoints, and statuses are captured for every transaction.

The next section details how to log this data in Spring Boot.

Logging Response Time, Endpoint, and Status in Spring Boot

Spring Boot makes it easy to log critical performance metrics out of the box. By recording response times, endpoints, and status codes, you can capture the data needed for analysis in Elasticsearch.

Step 1. Add Dependencies for Structured Logging

To produce JSON logs compatible with ELK, include the following dependency:

<dependency>
    <groupId>net.logstash.logback</groupId>
    <artifactId>logstash-logback-encoder</artifactId>
    <version>7.3</version>
</dependency>

Step 2. Configure Logback for Logging Performance Metrics

Update logback-spring.xml to include relevant fields such as responseTime, endpoint, and status in JSON format:

<configuration>
    <appender name="LOGSTASH" class="net.logstash.logback.appender.LogstashTcpSocketAppender">
        <destination>localhost:5044</destination>
        <encoder class="net.logstash.logback.encoder.LoggingEventCompositeJsonEncoder">
            <providers>
                <timestamp />
                <loggerName />
                <message />
                <mdc />
                <customFields>{"application":"my-spring-app"}</customFields>
            </providers>
        </encoder>
    </appender>
    
    <root level="INFO">
        <appender-ref ref="LOGSTASH"/>
    </root>
</configuration>

Step 3. Log Request Data with an Interceptor

Use a Spring HandlerInterceptor to capture response times for each request:

@Component
public class PerformanceLoggingInterceptor extends HandlerInterceptorAdapter {
    
    @Override
    public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler) {
        request.setAttribute("startTime", System.currentTimeMillis());
        return true;
    }
    
    @Override
    public void afterCompletion(HttpServletRequest request, HttpServletResponse response, Object handler, Exception ex) {
        long startTime = (Long) request.getAttribute("startTime");
        long duration = System.currentTimeMillis() - startTime;
        
        String endpoint = request.getRequestURI();
        int status = response.getStatus();
        
        Logger logger = LoggerFactory.getLogger("PerformanceLogger");
        logger.info("Response Time={}ms Endpoint={} Status={}", duration, endpoint, status);
    }
}

Output Example:

2025-06-13 14:30:00 INFO PerformanceLogger - Response Time=85ms Endpoint=/api/orders Status=200

These logs, once ingested by Elasticsearch, enable deeper analysis.

Aggregating Slow Endpoints in Kibana

Kibana’s aggregation features make it easy to visualize and analyze slow endpoints.

Step 1. Create an Index Pattern in Kibana

1. Open Kibana and go to Management > Data Views (Index Patterns).
2. Create an index pattern, e.g., performance-logs-*.
3. Use @timestamp as the time field.

Step 2. Visualize Slow Endpoints

1. Create a new dashboard and add a Bar Chart.
2. Configure:
   • Metrics: Select Average of responseTime for the y-axis.
   • Buckets: Split data by endpoint on the x-axis.
   • Filters: Add a condition to limit results to slow responses, e.g.: responseTime > 1000ms

Step 3. Identify Outliers

Use heatmaps or sortable tables to quickly identify endpoints with unusually high response times. This data helps prioritize optimizations for specific APIs.

Filtering Logs by High-Latency Operations

Filtering log data makes it easier to focus on high-latency operations in large-scale systems.

Step 1. Search for High Response Times in Kibana

Use Kibana’s Discover view to find high-latency requests:

responseTime:[1000 TO *]

Step 2. Combine Filters to Narrow Scope

To drill down by endpoint or status code:

responseTime:[1000 TO *] AND status:"200" AND endpoint:"/api/orders"

Step 3. Save Filters and Automate Alerts

1. Save frequently used filters for reuse in Kibana’s Discover view.
2. Configure alert rules to notify your team when performance issues are detected. For example:
   • Condition: WHEN average(responseTime) > 1000
   • Notification: Send alerts to email, Slack, or PagerDuty.

Proactively monitoring high-latency operations prevents bottlenecks from impacting users.

Summary

The ELK stack empowers teams to monitor application performance and diagnose root causes of inefficiencies. Here’s a recap of key insights:

1. Structured Logging: Log response times, endpoints, and statuses from Spring Boot applications to provide actionable metrics.
2. Centralized Analysis: Use Elasticsearch to index logs and Kibana to aggregate and visualize performance trends.
3. Proactive Performance Monitoring: Filter logs for high-latency events and automate alerts for ongoing observability.
4. Root Cause Analysis: Trace slow endpoints or high-latency operations back to their source for targeted optimizations.

By implementing this setup, you’ll gain deeper visibility into your systems, enabling faster problem resolution and better system performance. Start using ELK to transform your logs into actionable insights today!