Understand your backend’s behavior with essential monitoring and observability.

Monitoring vs. Observability

While often used interchangeably, monitoring and observability serve distinct purposes.

• Monitoring focuses on collecting specific, known metrics and logs to track the health of your system. You monitor for known unknowns, such as CPU usage, memory, or error rates. It answers “what is happening?”
• Observability is a broader concept, allowing you to debug and understand the internal state of your system from external outputs. It helps you explore unknown unknowns, providing the ability to ask arbitrary questions about your system’s behavior without prior knowledge. It answers “why is it happening?”

Key pillars of observability include logs, metrics, and traces:

• Logs are records of events that occur within your application or infrastructure. They provide detailed insights into specific actions or errors.
• Metrics are numerical measurements collected over time, representing the

  Understanding how your backend applications behave is fundamental for stability and performance. As developers, we build systems, but without insight into their runtime characteristics, we operate in the dark. Implementing proper monitoring and observability provides the necessary visibility to identify issues, troubleshoot problems, and ensure our services perform as expected. This approach helps us move from reactive firefighting to proactive system management.

  Monitoring vs. Observability
  

  While often used interchangeably, monitoring and observability serve distinct purposes.
  Monitoring focuses on collecting specific, known metrics and logs to track the health of your system. You monitor for known unknowns, such as CPU usage, memory, or error rates. It answers “what is happening?”
  Observability is a broader concept, allowing you to debug and understand the internal state of your system from external outputs. It helps you explore unknown unknowns, providing the ability to ask arbitrary questions about your system’s behavior without prior knowledge. It answers “why is it happening?”

  Key Pillars of Observability
  

  Observability is typically built upon three pillars: logs, metrics, and traces.

  Logs
  

  Logs are records of events that occur within your application or infrastructure. They provide detailed insights into specific actions or errors.

  • What to log:
    • Application events: User actions, business logic steps, state changes.
    • Error events: Exceptions, failed API calls, database connection issues.
    • Access logs: HTTP requests, response status, duration, client IP.
  • Why they matter:
    • Debugging specific issues.
    • Auditing system activity for security or compliance.
    • Post-mortem analysis to understand incident root causes.

  • How to implement:

    • Structured logging: Always log in a structured format, like JSON. This makes logs machine-readable and easier to query and analyze.

      Log::info('User login attempt', [
          'user_id' => $user->id,
          'ip_address' => $request->ip(),
          'status' => 'success'
      ]);
      
      try {
          // ... some operation
      } catch (\Exception $e) {
          Log::error('Operation failed', [
              'exception' => $e->getMessage(),
              'file' => $e->getFile(),
              'line' => $e->getLine(),
              'context_id' => $someIdentifier // For correlation
          ]);
      }
      

    • Centralized logging: Ship logs from all services and servers to a central system (e.g., ELK Stack, Grafana Loki, AWS CloudWatch Logs, Splunk). This allows for unified searching, filtering, and aggregation.

  Metrics
  

  Metrics are numerical measurements collected over time, representing the health and performance of your system components.

  • What to measure:
    • System metrics: CPU utilization, memory usage, disk I/O, network throughput.
    • Application metrics: Request latency, error rates (HTTP 5xx), request per second (RPS), queue sizes, database connection pool usage, cache hit ratios.
    • Business metrics: Number of registered users, successful transactions, items in shopping carts.
  • Why they matter:
    • Tracking trends and identifying performance degradation.
    • Setting up alerts for critical thresholds.
    • Capacity planning.
    • Creating dashboards for a high-level overview of system health.
  • How to implement:
    • Instrumentation: Use client libraries (e.g., Prometheus client for PHP) or built-in frameworks to expose custom metrics.
    • Collection: Use agents (e.g., Prometheus node_exporter, php-fpm_exporter) or SDKs to collect and push metrics to a time-series database (e.g., Prometheus, InfluxDB, CloudWatch Metrics).
    • Visualization: Use tools like Grafana to create dashboards that visualize metric trends.

  Traces (Distributed Tracing)
  

  Traces represent the end-to-end journey of a request or transaction as it propagates through various services in a distributed system.

  • What they show:
    • The sequence of operations across multiple services.
    • The duration of each operation (span).
    • Dependencies between services.
  • Why they matter:
    • Pinpointing performance bottlenecks in microservice architectures.
    • Understanding the flow of complex requests.
    • Debugging latency issues across multiple service calls.
  • How to implement:
    • Instrumentation: Use OpenTelemetry SDKs or specific tracing libraries (e.g., Jaeger client, Zipkin client) to instrument your code. Each service adds “span” information and passes context (trace ID, span ID) to subsequent services via HTTP headers.
    • Collection: Agents collect and send trace data to a tracing backend (e.g., Jaeger, Zipkin, AWS X-Ray, New Relic).
    • Visualization: Tracing backends visualize the full trace, showing the duration of each span and service involved.

  Implementing Observability in Practice
  

  1. Instrument Your Code Early: Integrate logging, metrics, and tracing into your application development lifecycle, not as an afterthought. Use libraries and frameworks that simplify instrumentation.
  2. Choose a Centralized Platform: Select a platform for each pillar (or an all-in-one solution) that allows for centralized collection, storage, and analysis of data. Consistency is key.
  3. Create Actionable Dashboards: Build dashboards that provide immediate insights into the health of your services. Focus on key performance indicators (KPIs) and potential failure points.
  4. Set Up Intelligent Alerting: Define thresholds for critical metrics and logs that trigger alerts. Avoid alert fatigue by making alerts specific, actionable, and routed to the correct teams. Examples: high error rate (5xx), low disk space, elevated request latency.
  5. Correlate Data: Ensure your logs, metrics, and traces can be correlated. A common pattern is to include a unique request_id or trace_id in logs and metrics associated with a specific trace. This links all related data points for a single operation.

  Tips and Tricks
  

  • Context in Logs: Always add relevant context to your logs (e.g., request_id, user_id, tenant_id, correlation_id). This makes debugging much easier.
  • Semantic Naming for Metrics: Use clear, consistent, and semantically meaningful names for your metrics (e.g., http_requests_total, database_query_duration_seconds).
  • Avoid High Cardinality Metrics: Be cautious with metrics that have many unique labels (e.g., a label for every user ID). This can lead to high storage costs and performance issues in your monitoring system.
  • Alerting Philosophy: Aim for alerts that indicate a real problem requiring immediate attention, not just minor deviations. Tune your thresholds.
  • Cost Management: Observability tools, especially in the cloud, can incur significant costs. Regularly review your data retention policies and sampling strategies.
  • Start Simple: You do not need to implement every tool or feature at once. Begin with essential logs and metrics, then expand as your understanding and needs grow.

  Takeaways
  

  • Monitoring and observability are vital for understanding and maintaining healthy backend systems.
  • Logs provide detailed event records, metrics offer numerical trends, and traces show end-to-end request flows.
  • Implement structured logging and comprehensive instrumentation for metrics and traces.
  • Centralize data collection and use dashboards for clear visualization.
  • Establish actionable alerts to proactively address issues.
  • Correlate data across all three pillars for a holistic view of system behavior.