NEWS

July 01, 2025

by Tega Akuruli

• #errorindistributedsystems

Error Handling and Logging in Distributed Systems 

Software systems today more and more rely on distributed architecture between many services, servers, networks, and even geographies. As great as this architecture is from the point of view of scalability, flexibility, and fault tolerance, it also makes error handling and logging more complex. 

Contrasted with monolithic applications where everything occurs within one context, distributed systems run across independent components that may crash in unexpected ways. For stability and the capacity for monitoring, there must be effective error handling and logging at every level. 

This article addresses the unique challenges of error handling in distributed systems and provides actionable best practices for good logging and fault management. 

Why Error Handling is Challenging in Distributed Systems 

In a distributed system, the subsystems exchange messages over the network and run independently. This brings along a variety of challenges: 

• Partial failures: One service may fail while others continue running.
• Network unreliability: Packet loss, timeouts, or sudden latency spikes are common.
• Eventual consistency: Data updates might not be immediately visible between services.
• Diverse failure modes: Crashes, memory leaks, exceptions, retries, and resource exhaustion. 

Basic try-catch blocks or status codes won’t do. You need a system-wide strategy with error classification, retries, fallbacks, logging, alerting, and recovery. 

Types of Errors in Distributed Systems 

Understanding the types of failures that occur helps to design the proper responses: 

Best Practices for Handling Errors 

1. Graceful Degradation

• Instead of crashing completely, design services to degrade service with a core user experience retained. 

Example: In the event that the product recommendation service crashes, render bestsellers instead of crashing the homepage. 

2. Retry with Exponential Backoff

For transient issues, retry the failed operation with increasing delays. Avoid retry storms by: 

• Avoiding retry count threshold
• Adding jitter (randomized delays)
• Making retries idempotent 

3. Use Circuit Breakers

• Circuit breakers prevent cascading failure by short-circuiting calls to failing services for a temporary period. This avoids overwhelming the system.
• Libraries like Hystrix, Resilience4j, and Istio provide this functionality. 

4. Time Out Wisely

• Set sensible timeouts for service calls. Infinite delays lead to thread exhaustion and bottlenecks.
• Set client-side and server-side timeouts to catch hung services early. 

5. Fail Fast, Fail Loud

When a failure is significant and non-recoverable, fail fast and notify the system. Silent failures or swallowed exceptions propagate latent bugs. 

Best Practices for Logging in Distributed Systems 

Logging becomes increasingly difficult in a system where various services and environments communicate with each other. Here’s how to make logging valuable: 

1. Centralize Your Logs

Utilize log aggregation tools such as: 

• ELK Stack (Elasticsearch, Logstash, Kibana)
• Fluentd + Grafana Loki
• AWS CloudWatch
• Datadog / Splunk / Sentry 

Centralization allows you to search, correlate, and analyze logs from all services in real time. 

2. Add Contextual Metadata

Each log should have: 

• Timestamp
• Service name
• Instance ID or pod name
• Trace ID or correlation ID
• Request ID
• Environment (dev, staging, prod)
• User ID (if required)   

This allows tracing an issue across services. 

3. Use Structured Logging

Log in JSON or key-value structures instead of plain text. Automated parsing, filtering, and analysis are possible. 

Example: 

 {
“level”: “error”,
“timestamp”: “2025-06-30T12:45:00Z”,
“service”: “user-service”,
“trace_id”: “abc123”,
“message”: “Failed to authenticate user”,
“user_id”: “12345”
}

4. Log at the Right Level

Avoid log spam and offer actionable insights by using proper severity levels: 

• DEBUG: Development/debugging information
• INFO: Operations with success
• WARN: Unexpected but not breaking conditions
• ERROR: Failures that require attention
• FATAL/CRITICAL: High severity failures that need to be addressed immediately 

5. Integrate with Monitoring and Alerts

Logs need to be piped into monitoring tools like: 

• Prometheus + Alertmanager
• PagerDuty
• Opsgenie
• Set error rate or pattern thresholds and alert teams in real time.
• Tracing and Observability 

Logging in isolation may not be enough. Supplement it with: 

• Distributed Tracing (e.g., Jaeger, OpenTelemetry, Zipkin)
• Metrics (e.g., Prometheus, Datadog)
• Dashboards (e.g., Grafana) 

All three together constitute a three-pillared observability strategy—metrics, logs, and traces—that speeds up debugging and performance tuning. 

Real-World Scenario: A Payment System 

Let’s consider a typical error flow: 

• A customer tries to pay.
• The frontend invokes checkout-service, which invokes payment-gateway.
• The external gateway times out. 

How error handling and logging ought to behave: 

• Checkout-service retries with exponential backoff (max 3 times).
• When all retries fail, it logs an error with trace ID and responds with 503 to the frontend.
• Payment-gateway logs have increasing latency moments before failure.
• Alert triggers on payment-gateway failure rate greater than 2% in 5 mins.
• The engineers follow the trace ID across services to identify the exact timeout point. 

With no distributed logging and error handling, this would virtually be impossible to debug. 

In distributed systems, errors aren’t exceptions something is going to go wrong. What does matter is how good you are at predicting, detecting, and reacting to them. 

By investing in thorough error handling mechanisms and intelligent, centralized logging, teams can: 

• Improve reliability and uptime
• Speed up debugging
• Deliver better user experiences
• Enable scalable growth 

Distributed systems are difficult but, with the right practices, their behavior don’t have to be mysterious.