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OpenTelemetry, Prometheus & Grafana with Spring Boot

A high-level guide to implementing a complete observability platform using OpenTelemetry, Prometheus, Grafana, and Spring Boot for monitoring distributed applications.

Introduction

Modern cloud-native applications generate massive amounts of operational data. Monitoring only CPU or application logs is no longer sufficient for troubleshooting distributed systems. Organizations need a unified observability platform that provides visibility into application health, infrastructure, and business transactions.

OpenTelemetry, Prometheus, and Grafana form one of the most popular open-source observability stacks for monitoring Spring Boot applications.

Together they enable teams to:

  • Collect metrics
  • Capture distributed traces
  • Correlate logs
  • Visualize dashboards
  • Detect failures
  • Reduce Mean Time to Resolution (MTTR)

What is Observability?

Observability is the ability to understand the internal state of a system using telemetry data.

The three pillars of observability are:

  • Metrics – Numerical measurements over time (CPU, memory, request rate)
  • Logs – Detailed event records
  • Traces – End-to-end request flow across services

When combined, they provide complete visibility into an application's behavior.

Why OpenTelemetry?

OpenTelemetry (OTel) is the CNCF standard for collecting telemetry data. Instead of using vendor-specific SDKs, applications emit telemetry in a standard format that can be exported to multiple backends.

Benefits include:

  • Vendor-neutral instrumentation
  • Unified metrics, traces, and logs
  • Automatic and manual instrumentation
  • Support for Java, Go, Python, .NET, Node.js, and more
  • Integration with cloud providers and open-source tools

High-Level Architecture

flowchart LR
    USER[Client]
    APP[Spring Boot Application]
    OTEL[OpenTelemetry SDK]
    COLLECTOR[OpenTelemetry Collector]
    PROM[Prometheus]
    GRAFANA[Grafana]
    TRACE[Tracing Backend]
    LOGS[Log Platform]

    USER --> APP
    APP --> OTEL
    OTEL --> COLLECTOR
    COLLECTOR --> PROM
    COLLECTOR --> TRACE
    COLLECTOR --> LOGS
    PROM --> GRAFANA

Core Components

Spring Boot Application

Generates business requests and application telemetry.

Examples:

  • REST APIs
  • Database calls
  • Kafka producers/consumers
  • Scheduled jobs

OpenTelemetry SDK

Embedded inside the application.

Responsibilities:

  • Capture metrics
  • Record traces
  • Collect contextual information
  • Export telemetry

OpenTelemetry Collector

Acts as a centralized telemetry pipeline.

Functions:

  • Receive telemetry
  • Process data
  • Filter unwanted signals
  • Enrich metadata
  • Export to multiple destinations

The Collector decouples applications from monitoring backends.

Prometheus

Prometheus is a time-series database designed for metrics.

It periodically scrapes metrics exposed by applications and stores historical metric data.

Common metrics include:

  • CPU usage
  • JVM heap
  • Request count
  • Error rate
  • Response time
  • Active threads

Grafana

Grafana provides interactive dashboards for visualizing telemetry.

Typical dashboards display:

  • System health
  • JVM performance
  • Business KPIs
  • API latency
  • Error trends
  • Infrastructure utilization

End-to-End Request Flow

sequenceDiagram
    participant User
    participant App
    participant OTel
    participant Collector
    participant Prometheus
    participant Grafana

    User->>App: REST Request
    App->>OTel: Generate Metrics & Traces
    OTel->>Collector: Export Telemetry
    Collector->>Prometheus: Store Metrics
    Prometheus->>Grafana: Query Metrics
    Grafana-->>User: Dashboard Visualization

Types of Telemetry

Metrics

Metrics answer questions such as:

  • How many requests per second?
  • What is CPU utilization?
  • How much JVM memory is used?
  • How many errors occurred?

Examples:

  • HTTP request count
  • JVM heap usage
  • Active database connections
  • Cache hit ratio

Traces

Traces follow a single request across multiple services.

Example flow:

Client
 ↓
API Gateway
 ↓
Order Service
 ↓
Payment Service
 ↓
Inventory Service
 ↓
Database
 ↓
Response

Traces help identify bottlenecks and latency.

Logs

Logs provide detailed event information.

Examples:

  • Authentication success
  • Order created
  • Payment failed
  • SQL exception
  • External API timeout

Logs complement metrics and traces during troubleshooting.

Spring Boot Integration

Spring Boot integrates with OpenTelemetry using the Java agent or SDK.

Telemetry can include:

  • HTTP requests
  • Database queries
  • Kafka messaging
  • Scheduled tasks
  • Cache operations
  • Custom business metrics

No business logic changes are required for many common frameworks when auto-instrumentation is used.

Metrics Collected

A production Spring Boot application should monitor:

JVM Metrics

  • Heap memory
  • Non-heap memory
  • Garbage collection
  • Thread count
  • Class loading

HTTP Metrics

  • Request count
  • Response status
  • Latency
  • Throughput

Infrastructure Metrics

  • CPU utilization
  • Memory utilization
  • Disk usage
  • Network traffic

Business Metrics

  • Orders created
  • Payments processed
  • Login success rate
  • Failed transactions
  • Revenue
  • Inventory updates

Distributed Tracing

Each incoming request generates a trace.

A trace contains:

  • Trace ID
  • Span ID
  • Parent span
  • Child spans
  • Duration
  • Status
  • Attributes

This enables complete request visualization across microservices.

Dashboard Design

A typical Grafana dashboard contains:

  • Application availability
  • Request rate
  • Error rate
  • Average response time
  • JVM memory
  • CPU utilization
  • Database latency
  • Active users
  • Kafka consumer lag
  • Business KPIs

Alerting

Monitoring without alerts is incomplete.

Create alerts for:

  • High CPU
  • Memory threshold
  • Slow APIs
  • Increased error rate
  • Database connection failures
  • Disk space
  • Service downtime

Alerts can be sent via:

  • Email
  • Slack
  • Microsoft Teams
  • PagerDuty
  • Webhooks

Enterprise Architecture

flowchart TD
    CLIENT[Users]

    CLIENT --> LB[Load Balancer]

    LB --> APP1[Order Service]
    LB --> APP2[Payment Service]
    LB --> APP3[Inventory Service]

    APP1 --> DB[(PostgreSQL)]
    APP2 --> REDIS[(Redis)]
    APP3 --> KAFKA[Kafka]

    APP1 --> OTEL
    APP2 --> OTEL
    APP3 --> OTEL

    OTEL --> COLLECTOR[OpenTelemetry Collector]

    COLLECTOR --> PROM[Prometheus]
    COLLECTOR --> TRACE[Tracing Backend]
    COLLECTOR --> LOGS[Log Backend]

    PROM --> GRAFANA[Grafana Dashboards]

    GRAFANA --> DEVOPS[Operations Team]

Kubernetes Deployment

In Kubernetes, the Collector typically runs as:

  • Deployment
  • DaemonSet
  • Sidecar

Prometheus scrapes metrics from application pods, while Grafana connects to Prometheus for visualization.

AWS Deployment

Applications running on:

  • Amazon EC2
  • Amazon ECS
  • Amazon EKS
  • AWS Lambda

can all export telemetry through the OpenTelemetry Collector to AWS-managed or self-hosted monitoring solutions.

Security Considerations

Protect telemetry by:

  • Encrypting communication
  • Limiting dashboard access
  • Removing sensitive data
  • Masking personal information
  • Applying retention policies
  • Enforcing least-privilege IAM permissions

Best Practices

  • Instrument applications early in development.
  • Monitor infrastructure and business metrics together.
  • Use consistent metric naming.
  • Add meaningful trace attributes.
  • Correlate logs with trace IDs.
  • Build reusable Grafana dashboards.
  • Create actionable alerts with appropriate thresholds.
  • Regularly review telemetry costs and retention.

Common Challenges

Challenge Solution
Missing metrics Verify instrumentation and scraping configuration
High telemetry volume Filter unnecessary metrics and adjust sampling
Slow dashboards Optimize Prometheus queries
Alert fatigue Fine-tune thresholds and routing
Incomplete traces Ensure context propagation across services

OpenTelemetry vs Traditional Monitoring

Feature Traditional Monitoring OpenTelemetry
Vendor Neutral No Yes
Metrics Yes Yes
Traces Limited Yes
Logs Correlation Limited Yes
Multi-cloud Support Limited Yes
Open Standard No Yes

Typical Production Workflow

flowchart LR
    REQUEST[User Request]
    APP[Spring Boot]
    OTEL[OpenTelemetry]
    COLLECTOR[Collector]

    COLLECTOR --> PROM[Prometheus]
    COLLECTOR --> TRACE[Tracing Backend]
    COLLECTOR --> LOGS[Log Storage]

    PROM --> GRAFANA
    TRACE --> GRAFANA
    LOGS --> GRAFANA

Real-World Use Cases

  • Monitor microservices in e-commerce platforms.
  • Track payment transaction latency in banking systems.
  • Observe healthcare API performance.
  • Analyze Kafka processing throughput.
  • Measure order processing times.
  • Detect infrastructure bottlenecks before users are impacted.
  • Correlate application failures with infrastructure events.

Summary

OpenTelemetry, Prometheus, and Grafana together provide a comprehensive observability platform for modern Spring Boot applications.

  • OpenTelemetry standardizes telemetry collection.
  • Prometheus stores and queries metrics efficiently.
  • Grafana visualizes operational and business data through rich dashboards.
  • Combined with centralized logging and distributed tracing, they enable faster troubleshooting, proactive monitoring, and improved application reliability.

This stack is widely adopted in enterprise environments because it is open, extensible, cloud-native, and integrates well with Kubernetes, AWS, and other modern deployment platforms.

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