Understanding the difference between monitoring and observability is essential in DevOps. While both help you keep track of your systems, they serve different purposes:

• Monitoring: lets you collect and display data about your system's state, such as CPU usage, memory, or error rates;
• Observability: goes deeper, allowing you to ask new questions about your system's behavior and troubleshoot unexpected issues.

In DevOps, monitoring and observability are crucial because they help you:

• Detect problems early, before they affect users;
• Respond quickly to incidents and outages;
• Understand system performance and usage patterns;
• Make informed decisions about scaling, improvements, and reliability.

To track system health and performance, you will use several key techniques and tools:

• Metrics collection: gather numerical data, like response times or request rates, using tools such as Prometheus or Datadog;
• Logging: record system events and errors for later analysis, often with tools like ELK Stack (Elasticsearch, Logstash, Kibana) or Splunk;
• Tracing: follow requests as they move through your system, using tools such as Jaeger or Zipkin;
• Dashboards and alerts: visualize data and set up notifications for unusual activity, with platforms like Grafana or CloudWatch.

By mastering these concepts and tools, you will be able to maintain healthy, reliable systems and support a fast-paced DevOps workflow.

Example: Detecting Issues Early with Logs, Metrics, and Traces

Imagine you are responsible for a web application that allows users to buy movie tickets online. To keep the service reliable, you use three main observability tools: logs, metrics, and traces.

Logs

• Each time a user tries to purchase a ticket, the application writes a log entry like INFO: User 1234 started checkout at 12:01:02;
• If something goes wrong, such as a payment failure, the application logs ERROR: Payment failed for User 1234 at 12:01:05.

Metrics

• You monitor how many successful purchases happen every minute (purchase_success_count);
• You track the average response time for the checkout process (checkout_response_time_ms);
• You count the number of failed payments per minute (payment_failure_count).

Traces

• When a user clicks "Buy Now," a trace follows the request as it moves through different services:
  • The frontend sends the request to the backend;
  • The backend checks seat availability;
  • The payment service processes the card;
  • Each step in the trace is recorded with timing and status.

How You Detect Issues Early

• You notice a sudden spike in the payment_failure_count metric;
• You check the logs and see multiple ERROR: Payment failed messages, all within the last 10 minutes;
• You look at traces for failed transactions and see they all get stuck at the payment service step, taking much longer than normal.

By collecting and analyzing logs, metrics, and traces together, you quickly identify that the payment service is experiencing problems. You can alert your team and start fixing the issue before many users are affected.