Observability in Microservices Architecture
In a monolithic architecture, debugging is relatively easy. You have one application, one log file, and one stack trace. In a microservices architecture, a single user request might touch twenty different services. If something goes wrong, how do you find the needle in the haystack? This is why we need Observability.
Monitoring vs. Observability
Let's clear this up. Monitoring tells you *when* something is wrong (e.g., "CPU is at 90%"). Observability allows you to ask *why* it is wrong (e.g., "Which customer's request caused the database lock that spiked the CPU?"). Monitoring is for known unknowns. Observability is for unknown unknowns.
The Three Pillars: Metrics
Metrics are aggregations over time. They are cheap to store and great for spotting trends. "Requests per second," "Error rate," "P99 Latency." Metrics answer the question: "Is the system healthy right now?" They are your first line of defense and what triggers your alerts.
The Three Pillars: Logs
Logs are discrete events. "Payment processed for user X," "Database connection failed." Logs provide the context. When your metrics show an error spike, you look at the logs to see the specific error messages. But in microservices, you need *structured* logs (JSON) so you can query and aggregate them effectively.
The Three Pillars: Traces
Tracing is the glue that holds it all together. A distributed trace follows a request as it hops from service A to service B to service C. It shows you exactly how long each step took. Without tracing, debugging latency issues in microservices is basically guessing.
Correlation is Key
The real power comes when you link these three together. Your metrics dashboard should link to the traces for that time period. Your traces should link to the logs for that specific request ID. This allows you to go from a high-level alert to the exact line of code causing the problem in minutes, not hours.
The Cost of Observability
Observability is not free. Storing every log and every trace can be incredibly expensive. You need to implement sampling. You don't need to trace 100% of successful requests. You might trace 1% of successes and 100% of failures. This gives you the visibility you need without breaking the bank.