Observability Design Patterns

Introduction

Observability is essential for understanding and monitoring complex systems. It provides insight into the internal workings of a system through metrics, logs, and traces. This lesson discusses key observability design patterns that facilitate effective monitoring and troubleshooting.

Key Concepts

• **Metrics**: Quantitative measures of system performance.
• **Logs**: Detailed records of events occurring within the system.
• **Traces**: Track the flow of requests through a system to understand latency and bottlenecks.

Observability Design Patterns

                // Example of sending logs to a centralized service
                const logEvent = (message) => {
                    fetch('https://centralized-logging-service.com/log', {
                        method: 'POST',
                        body: JSON.stringify({ message }),
                        headers: { 'Content-Type': 'application/json' },
                    });
                };
                logEvent('User logged in');
                

                // Example of instrumenting a function for tracing
                const traceFunction = (fn, traceId) => {
                    const start = performance.now();
                    const result = fn();
                    const end = performance.now();
                    console.log(`Trace ID: ${traceId}, Duration: ${end - start}ms`);
                    return result;
                };
                traceFunction(() => { /* some operation */ }, 'trace-12345');
                

                // Example of collecting metrics
                const collectMetric = (metricName, value) => {
                    fetch('https://metrics-collection-service.com/metrics', {
                        method: 'POST',
                        body: JSON.stringify({ metricName, value }),
                        headers: { 'Content-Type': 'application/json' },
                    });
                };
                collectMetric('request_latency', 150);
                

Best Practices

1. Use structured logging for better machine readability.
2. Implement proper sampling strategies for traces to avoid performance overhead.
3. Regularly review and update metrics to ensure relevance.
4. Correlate logs, metrics, and traces to gain comprehensive insights.

FAQ