Observability in Distributed Systems

Imagine you're the captain of a large ship navigating through stormy waters, with no radar or compass to guide you. You'd be lost, unable to make sense of the chaos around you. In 2025, distributed systems are like those ships—complex and vast, requiring robust tools for navigation and troubleshooting.

As businesses increasingly rely on distributed architectures to scale applications, observability becomes critical for maintaining performance, reliability, and security. This blog post will guide you through understanding, implementing, and managing observability in distributed systems.

What you'll learn:

The importance of observability
Key components: logs, metrics, and traces
Tools and technologies for observability
Best practices for setting up observability

Understanding Observability

Observability is the ability to understand a system's internal state based on its external outputs. In distributed systems, this means monitoring multiple interconnected services.

Without observability, diagnosing issues can be like searching for a needle in a haystack, leading to prolonged downtime and frustrated users.

# Example of a simple Kubernetes deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: my-app-container
        image: my-app-image:latest

This YAML snippet defines a basic Kubernetes deployment. Without observability, you'd struggle to track the health and performance of my-app-container across three replicas.

Key Components of Observability

Logs

Logs are textual records that capture what happens inside an application. They provide historical data about events and can be invaluable for debugging issues.

# Example log entry from a web server
2023-10-05T14:48:00Z [INFO] User login successful: user=johndoe

Each log entry includes a timestamp, severity level, and message. Analyzing these logs helps identify patterns and root causes.

Metrics

Metrics are numerical values that measure system performance or behavior over time. They can be aggregated to provide insights into system health.

# Example metric for request latency
import prometheus_client as prom
REQUEST_LATENCY = prom.Histogram('request_latency_seconds', 'Request latency in seconds')

Prometheus, a popular metrics tool, uses histograms like REQUEST_LATENCY to track how long requests take. This helps in understanding performance bottlenecks.

Traces

Traces are used to monitor and troubleshoot transactions as they travel through a distributed system. They provide end-to-end visibility into request flows.

# Example Jaeger trace configuration for an application
service_name: my-service
collector_endpoint: http://jaeger-collector:14268/api/traces

Jaeger, an open-source tracing system, collects traces from your services. This helps in pinpointing slow or failed requests.

Tools and Technologies

Several tools and technologies enable observability in distributed systems:

Logging: ELK Stack (Elasticsearch, Logstash, Kibana), Fluentd
Metrics: Prometheus, Grafana, Datadog
Traces: Jaeger, Zipkin

Each tool has its strengths and can be chosen based on specific needs.

Setting Up Observability

Step 1: Collect Logs

Start by setting up a logging pipeline. ELK Stack is a popular choice for centralized log management.

# Install Elasticsearch
docker run -d --name elasticsearch -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" elasticsearch:7.10.2

# Install Kibana
docker run -d --name kibana -p 5601:5601 --link elasticsearch:elasticsearch kibana:7.10.2

Elasticsearch stores logs, while Kibana provides a dashboard for visualization.

Step 2: Gather Metrics

Prometheus is widely used for collecting and storing metrics. Set up Prometheus to scrape your application's metrics.

# Example Prometheus configuration file (prometheus.yml)
scrape_configs:
  - job_name: 'my-app'
    static_configs:
      - targets: ['localhost:8080']

Prometheus collects metrics from the specified target and stores them in its time-series database.

Step 3: Enable Tracing

Integrate Jaeger for tracing. Add a tracing agent to your services to capture traces.

// Example Node.js code using OpenTelemetry
const opentelemetry = require('@opentelemetry/sdk-node');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');
const { JaegerExporter } = require('@opentelemetry/exporter-jaeger');

const jaegerExporter = new JaegerExporter({
  endpoint: 'http://localhost:14268/api/traces',
});

const sdk = new opentelemetry.NodeSDK({
  traceExporter: jaegerExporter,
  instrumentations: [getNodeAutoInstrumentations()],
});

sdk.start();

This Node.js code sets up OpenTelemetry to send traces to Jaeger.

Best Practices

Use Contextual Data

Always include contextual data in your logs and metrics. This helps in understanding the context of an event or metric.

💡 Tip: Add metadata like user IDs, session IDs, and timestamps to your logs and metrics for better analysis.

Centralize Observability Tools

Centralizing your observability tools simplifies management and provides a unified view of your system.

⚠️ Warning: Avoid siloed solutions that require separate dashboards for different types of data.

Troubleshooting

Common Issues

Log Overload: Too many logs can overwhelm your logging system. Use log levels to filter out less important messages.
Metric Inaccuracy: Ensure metrics are accurate and relevant. Use labels to differentiate between similar metrics.
Trace Loss: Network issues or misconfigurations can lead to lost traces. Verify that all services are correctly instrumented.

Resolutions

Log Management: Implement log rotation and retention policies. Use filters to capture only necessary logs.
Metric Validation: Regularly review and validate your metrics configuration. Remove unused or redundant metrics.
Trace Debugging: Check network connectivity and instrumentation setup. Use tools like Jaeger UI to analyze trace data.

Conclusion

Observability is crucial for managing complex distributed systems in 2025. By collecting logs, metrics, and traces, you gain deep insights into your system's behavior. Using the right tools and following best practices ensures that you can quickly identify and resolve issues, maintaining high performance and reliability.

Key Takeaways:

Understand the importance of observability in distributed systems.
Implement logging, metrics, and tracing for comprehensive monitoring.
Centralize observability tools for easier management and analysis.
Follow best practices to ensure accurate and actionable data collection.