Distributed Tracing and Telemetry in Microservices

Niraj Kumar
4 min readDec 16, 2024

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Distributed Tracing and Telemetry are key pillars of observability in microservices. Together, they provide a comprehensive view of how applications perform and behave across a distributed system, enabling better debugging, performance optimization, and monitoring.

1. Distributed Tracing

Definition

Distributed tracing tracks the lifecycle of a request as it propagates through a distributed system. It provides a holistic view of the interactions between services, measuring performance and identifying bottlenecks.

How Distributed Tracing Works in Microservices

  1. Trace Context Propagation:
  • Every incoming request is assigned a Trace ID and a Span ID.
  • The Trace ID identifies the entire transaction (e.g., a user’s HTTP request), while the Span IDs are unique for each operation within the trace.
  • These IDs are propagated across services using headers (e.g., traceparent in the W3C Trace Context standard).

2. Spans:

  • Each microservice creates spans for significant operations (e.g., HTTP requests, database queries, or external API calls).
  • Spans include metadata like:
  • Start time and duration
  • Operation name
  • Tags (e.g., status codes, error details)

3. Collection and Visualization:

  • Spans from all services are collected in a tracing backend like Jaeger, Zipkin, or Datadog.
  • These tools provide a visualization of the entire trace, showing the request’s journey and latency for each span.

Why Distributed Tracing is Critical in Microservices

  • Understand Request Flow: Visualize how a single user request interacts with multiple services.
  • Performance Monitoring: Identify slow services or operations.
  • Root Cause Analysis: Pinpoint where errors or performance bottlenecks occur.
  • Dependency Analysis: Map service dependencies and their interactions.

Example Distributed Tracing Workflow

A user sends a request to Service A.

  1. Service A:
  • Creates a root span (e.g., “HTTP GET /order”).
  • Calls Service B.

2. Service B:

  • Creates a child span (e.g., “Process Payment”).
  • Calls Service C for a database query.

3. Service C:

  • Creates a child span (e.g., “SQL Query”).
  • Responds to Service B, which then responds to Service A.

This trace might look like:

Trace ID: 12345
Root Span: Service A (HTTP GET /order) -> 200ms
├── Child Span: Service B (Process Payment) -> 150ms
│ └── Sub-Span: Service C (SQL Query) -> 50ms

By visualizing this, you can see that the database query in Service C is the slowest part of the transaction.

Popular Tools for Distributed Tracing

  1. Jaeger: Distributed tracing and service dependency analysis.
  2. Zipkin: Lightweight tracing tool inspired by Google’s Dapper.
  3. OpenTelemetry: Open-source observability framework for tracing, metrics, and logs.
  4. Datadog APM, AWS X-Ray, New Relic, and Google Cloud Trace: Managed solutions for tracing.

2. Telemetry

Definition

Telemetry in microservices refers to the automated collection, transmission, and analysis of data (e.g., metrics, logs, and traces) from services to monitor their health, performance, and behavior.

Types of Telemetry

  1. Metrics:
  • Quantitative data points about the system’s state.
  • Examples: CPU usage, memory consumption, request latency, error rates.

2. Logs:

  • Records of discrete events or messages within a service.
  • Examples: Error messages, debug logs, state transitions.

3. Traces:

  • Sequence of spans that represent the lifecycle of a request in a distributed system.
  • Examples: Request propagation across services, performance bottlenecks.

How Telemetry Works

  1. Data Collection:
  • Metrics, logs, and traces are collected from microservices using libraries or agents.
  • Examples: Prometheus for metrics, ELK stack for logs, OpenTelemetry for tracing.

2. Data Transmission:

  • Telemetry data is sent to a centralized system (e.g., Prometheus for metrics or Jaeger for traces).
  • Instrumentation libraries like Micrometer, Logback, and OpenTelemetry can help.

3. Storage and Analysis:

  • Collected telemetry data is stored in time-series databases (e.g., Prometheus, InfluxDB) or tracing backends (e.g., Jaeger).
  • Observability tools (e.g., Grafana, Kibana) analyze and visualize this data.

Why Telemetry is Critical in Microservices

Proactive Monitoring:

  • Detect performance degradation or unusual patterns.

Anomaly Detection:

  • Identify errors or failures early.

Debugging and Root Cause Analysis:

  • Use logs and traces to diagnose issues.

Capacity Planning:

  • Use metrics to forecast resource needs and optimize scaling.

Distributed Tracing vs Telemetry

  1. Focus: Distributed Tracing tracks a request’s lifecycle across services. Telemetry monitors system-wide metrics, logs, and traces.

2. Key Data: Distributed Tracing spans, trace IDs, request flow. Telemetry metrics (CPU, memory), logs, traces.

3. Tools: Distributed Tracing Jaeger, Zipkin, OpenTelemetry. Telemetry Prometheus, Grafana, ELK, OpenTelemetry.

Purpose: Distributed Tracing: Debugging, latency analysis. Telemetry: Health monitoring, capacity planning.

Bringing Distributed Tracing and Telemetry Together

In microservices, distributed tracing is a subset of telemetry. Here’s how they complement each other:

  1. Trace-Aware Metrics:
  • Combine traces with metrics to see which services contribute the most to overall latency.

2. Trace-Aware Logs:

  • Include trace IDs in logs to correlate specific logs with requests.

3. End-to-End Observability:

  • Use traces to track request flow and telemetry for system health and performance.

Example Implementation in Microservices

To implement distributed tracing and telemetry:

  1. Integrate OpenTelemetry:
  • Use OpenTelemetry SDKs to collect traces, metrics, and logs.

2. Add Dependencies:

For Spring Boot:

<dependency>     
<groupId>io.opentelemetry</groupId>
<artifactId>opentelemetry-exporter-jaeger</artifactId>
<version>1.28.0</version>
</dependency>

<dependency>
<groupId>io.micrometer</groupId>
<artifactId>micrometer-registry-prometheus</artifactId>
</dependency>

3. Configure Backends:

  • Set up Jaeger (for traces) and Prometheus/Grafana (for metrics).
  • Example application.yml:
spring:   
sleuth:
sampler:
probability: 1.0

management:
metrics:
export:
prometheus:
enabled: true

4. Visualize Data:

  • Use Grafana for metrics and Jaeger for tracing to monitor your system.

Conclusion

Distributed tracing provides visibility into the flow of requests across microservices, while telemetry ensures system-wide monitoring of metrics, logs, and traces. Together, they form the foundation of observability, enabling teams to build, debug, and maintain robust microservices architectures.

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Niraj Kumar
Niraj Kumar

Written by Niraj Kumar

Architect | Lead Developer | Cloud Computing | Microservices | Java | React | Angular | Kafka | AI/ML

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