Building a Distributed Tracing Platform on AWS using OpenTelemetry and Grafana Tempo

Source: Dev.to

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. 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. 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.

A distributed tracing platform on AWS using OpenTelemetry and Grafana Tempo follows a layered architecture where telemetry is generated, processed, stored, and visualized. ┌───────────────────────────────┐ │ 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 │ └───────────────────────────────┘

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 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();

java -javaagent:opentelemetry-javaagent.jar
-Dotel.service.name=checkout-service
-jar app.jar

receivers: otlp: protocols: grpc: http:

processors: batch:

exporters: tempo: endpoint: tempo:4317

service: pipelines: traces: receivers: [otlp] processors: [batch] exporters: [tempo]

Grafana enables: Trace search

Latency analysis

Service dependency visualization

Bottleneck detection

Captures all traces
Captures percentage of traces
Example: 10% of traffic

Captures important traces (errors, slow requests) 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. Trace volume

Storage cost

Sampling strategy

Tempo uses object storage (e.g., S3), making it cost-efficient. 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.