Logging in Microservices: Best Practices for Scalable Observability

When working with microservices, logging becomes both more critical and more complex. In a monolithic system, finding errors often involves reading a single application log. But in a microservices architecture, a single user action can span dozens of services, each running in its own container or node. Without a strategy for logging, debugging becomes a nightmare.

In this article, we’ll cover:

• Why logging in microservices is different
• Key challenges
• Best practices for logging in microservices
• How to implement centralized, structured, and correlated logging
• Recommended tools and architectures

Why Logging in Microservices Is Different

In a microservices setup:

• Each service may be written in a different language.
• Logs are distributed across multiple containers, VMs, or pods.
• Requests often pass through proxies, gateways, and queues.
• There’s no single “log file” to tail.

As a result, logs must be:

• Centralized – from multiple services and platforms
• Structured – for filtering and analysis
• Correlated – to trace a single request across services
• Resilient – to scale with load and handle failures

Common Logging Challenges in Microservices

Logging Best Practices in Microservices

1. Use Structured Logs (JSON)

Structured logs are machine-readable. Use JSON or a consistent key=value format.

Bad:

Something went wrong when fetching order

Good:

{
  "level": "error",
  "service": "order-service",
  "request_id": "1a2b3c",
  "message": "Failed to fetch order",
  "order_id": 123
}

Use libraries like:

• Logback / Log4j2 (Java)
• Winston / Pino (Node.js)
• Zap / Zerolog (Go)
• Python’s structlog or loguru

2. Propagate a Request ID (e.g., X-Request-ID)

As discussed in our previous article, a correlation ID must travel with each request through all services, and be included in every log entry.

Make sure you:

• Generate the ID in the frontend, gateway, or entrypoint
• Inject it into HTTP headers or gRPC metadata
• Set it in the logging context (e.g., SLF4J’s MDC)

3. Centralize Logging

Use a log aggregation pipeline:

App Logs → Fluent Bit / Filebeat → Kafka / Loki / Elasticsearch → Grafana / Kibana

Popular tools:

4. Include Context in Every Log Entry

Each log should include:

• request_id
• timestamp
• service_name
• environment (e.g., dev, staging, prod)
• log_level (INFO, ERROR, WARN, DEBUG)
• message
• metadata: user ID, IP, route, database ID, etc.

5. Log at the Right Levels

Avoid log spamming:

6. Redact Sensitive Information

Automatically sanitize:

• Passwords
• Tokens
• Credit card numbers
• Personal identifiers (names, emails, IPs)

Use structured logging libraries with support for filters, or preprocess logs using tools like Fluent Bit or Logstash before sending them to a central system.

7. Correlate Logs Across Systems

Combine:

• Logs
• Metrics
• Traces (OpenTelemetry, Jaeger)

All tied together via:

• X-Request-ID
• trace_id, span_id (if using OpenTelemetry)

This gives you end-to-end observability.

Example Logging Architecture

Here’s a scalable logging pipeline:

Docker / Kubernetes Logs
   │
   ▼
[Fluent Bit DaemonSet]
   │
   ▼
[Kafka] or [Loki]
   │
   ▼
[Elasticsearch]
   │
   ▼
[Kibana] or [Grafana]

For smaller setups, you can go directly from Fluent Bit → Loki → Grafana.

Conclusion

Logging in microservices is essential for maintaining system health, debugging issues, and auditing user activity. By adopting structured logging, centralized aggregation, and request correlation strategies, you ensure that your system remains observable, resilient, and developer-friendly.

Combined with X-Request-ID, this logging strategy makes debugging multi-service applications far more efficient.