Modern cloud-native applications are typically built using microservices architectures, where a single user request can travel through multiple services before returning a response. While this architecture improves scalability and development speed, it also introduces a major challenge: observability.
When a request fails or becomes slow, it becomes difficult to understand where exactly the problem occurred across multiple services.
This is where distributed tracing becomes critical.
In this blog, we will explore how to build a production-ready distributed tracing platform on AWS using OpenTelemetry and Grafana Tempo. We'll cover the architecture, implementation, and best practices.
Why Distributed Tracing Matters
In microservices environments, a single request may pass through multiple services such as:
- API Gateway
- Authentication service
- Product service
- Payment service
- Database
Without tracing, engineers cannot easily determine:
- Which service introduced latency
- Where failures occurred
- How requests propagate across services
Distributed tracing solves this by tracking every request across services and visualizing the entire request path.
Architecture Overview
A distributed tracing platform typically consists of:
- Instrumentation – Applications generate trace data
- Collection Pipeline – Telemetry data is collected
- Storage & Visualization – Trace data is stored and visualized
Architecture Flow
- Applications emit traces using OpenTelemetry SDKs
- Traces are sent to OpenTelemetry Collector
- Collector processes and exports traces to Grafana Tempo
- Grafana visualizes traces
Distributed Tracing Architecture
High-level distributed tracing architecture using OpenTelemetry, Collector, and Grafana Tempo.
Architecture Diagram
A distributed tracing platform on AWS using OpenTelemetry and Grafana Tempo follows a layered architecture where telemetry is generated, processed, stored, and visualized.
High-Level Architecture
┌───────────────────────────────┐
│ End Users │
└──────────────┬────────────────┘
│
▼
┌───────────────────────────────┐
│ Application Layer │
│ (EKS / ECS / EC2 Services) │
│ │
│ - frontend-service │
│ - checkout-service │
│ - payment-service │
└──────────────┬────────────────┘
│
│ (OTel SDK / Auto-Instrumentation)
▼
┌───────────────────────────────┐
│ OpenTelemetry Collector │
│ │
│ Receivers → Processors → │
│ Exporters │
└──────────────┬────────────────┘
│
│ (OTLP gRPC / HTTP)
▼
┌───────────────────────────────┐
│ Grafana Tempo │
│ (Trace Storage Backend) │
│ │
│ Uses Object Storage (S3) │
└──────────────┬────────────────┘
│
▼
┌───────────────────────────────┐
│ Grafana │
│ (Visualization Layer) │
│ │
│ - Trace Search │
│ - Service Map │
│ - Latency Analysis │
└───────────────────────────────┘
Component Interaction Flow
- Applications are instrumented using OpenTelemetry SDKs or auto-instrumentation
- Requests generate spans which form traces
- Telemetry is sent to OpenTelemetry Collector
- Collector processes and batches data
- Data is exported to Grafana Tempo
- Tempo stores traces in S3
- Grafana visualizes traces
Core Components
OpenTelemetry
OpenTelemetry is an open-source observability framework used for collecting:
- traces
- metrics
- logs
Key benefits:
- Vendor-neutral
- Supports multiple languages
- Enables auto-instrumentation
OpenTelemetry Collector
Acts as a centralized telemetry pipeline:
- Receives data
- Processes data
- Exports data
Benefits:
- Decouples apps from backend
- Enables scaling
- Reduces overhead
OpenTelemetry Collector pipeline showing receivers, processors, and exporters.
Grafana Tempo
Grafana Tempo is a scalable tracing backend with:
- Object storage-based design
- Minimal indexing
- High scalability
- Low cost
Deploying on AWS
Typical setup:
- Amazon EKS – application workloads
- OpenTelemetry Operator – auto instrumentation
- OpenTelemetry Collector – telemetry pipeline
- Grafana Tempo – storage
- Grafana – visualization
Instrumentation
Manual Instrumentation (Node.js)
const { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node');
const provider = new NodeTracerProvider();
provider.register();
Auto Instrumentation (Java)
java -javaagent:opentelemetry-javaagent.jar \
-Dotel.service.name=checkout-service \
-jar app.jar
OpenTelemetry Collector Configuration
receivers:
otlp:
protocols:
grpc:
http:
processors:
batch:
exporters:
tempo:
endpoint: tempo:4317
service:
pipelines:
traces:
receivers: [otlp]
processors: [batch]
exporters: [tempo]
Visualizing with Grafana
Grafana enables:
- Trace search
- Latency analysis
- Service dependency visualization
- Bottleneck detection
Sampling Strategies
Always-On
Captures all traces
Probabilistic
Captures percentage of traces
Example:
10% of traffic
Tail Sampling
Captures important traces (errors, slow requests)
Best Practices
- Use collectors instead of direct ingestion
- Implement sampling
- Monitor collector performance
- Separate pipelines for metrics, logs, traces
Real-World Example
Example flow:
User Request
↓
Frontend
↓
Product Service
↓
Cart Service
↓
Checkout Service
↓
Payment Gateway
Tracing helps identify latency or failure at any step.
Cost Considerations
- Trace volume
- Storage cost
- Sampling strategy
Tempo uses object storage (e.g., S3), making it cost-efficient.
Final Thoughts
Distributed tracing is essential for modern cloud-native systems.
By combining:
- OpenTelemetry
- OpenTelemetry Collector
- Grafana Tempo
you can build a scalable, vendor-neutral tracing platform on AWS.
This enables:
- Faster debugging
- Better system visibility
- Improved reliability
Distributed tracing is no longer optional—it is a critical part of modern DevOps practices.
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