Metrics and Trace Data: Fundamentals of Understanding System Issues
The foundation of understanding whether a system is running healthily is to be able to observe it. By observation, I don't just mean the binary query "is it working or not?"; I mean gaining detailed insight into the system's internal world. At this point, metric and trace data become our most powerful tools for solving problems at their root. Especially after the 2010s, with the widespread adoption of cloud computing and distributed systems, these two data types have become an integral part of system architecture. From systems I've set up myself to complex infrastructures in large corporate projects I've worked on, I actively use metric and trace data everywhere. In this post, I will explain why this data is so important, how it is analyzed, and practical use cases based on my own experiences.
Understanding the importance of this data not only allows our systems to respond but also enables us to proactively identify potential problems and perform performance optimizations. Today, systems are becoming increasingly complex. Monolithic structures are giving way to microservices, and virtual machines to containers. This makes troubleshooting more difficult. While it used to be possible to find a problem by looking at a log file on a single server, now, in an environment where hundreds of services interact, accessing the right data is vital. Metrics show us the overall health, while traces allow us to follow a request's journey step-by-step through the system.
Metrics: The Overall Health Status of the System
Metrics are numerical data that summarize the performance and status of our system over a specific period. Values such as CPU usage, memory usage, network traffic, disk I/O, request count, and error rates are all metrics. This data is used to understand the overall health of our system and detect anomalies. For example, a sudden spike in CPU usage might indicate that a service is overloaded or has entered an error loop. Low disk I/O, on the other hand, could be a sign of a performance bottleneck.
âšī¸ The Importance of Metrics
Metrics are like a "snapshot" of the system. They are typically stored in Time Series Databases (TSDB) and monitored with visualization tools (e.g., Grafana). This allows us to perform historical analyses, identify trends, and set up alerts. Continuously monitoring a system's performance enables us to adopt a proactive approach. For example, a service's memory usage consistently increasing over time might indicate a memory leak. If we don't regularly track these metrics, we might not realize the problem until the system crashes.
In my own projects, especially for services hosted on my VPS, I collect basic system metrics using tools like node_exporter and postgres_exporter. These metrics include CPU, memory, disk usage, network statistics, and PostgreSQL's WAL (Write-Ahead Log) activity. By visualizing this data on Grafana, I can immediately spot any anomalies. For instance, last month, while developing a production ERP, I noticed an unexpected increase in WAL write rates on the database server. This metric helped me understand that the problem was on the database side, and after a detailed investigation, I found that a query was generating an excessive amount of WAL logs unnecessarily. Such situations demonstrate how critical metrics are.
Traces: The Journey of a Request
While metrics provide a general picture, traces show the complete journey of a request through our system in detail, including how it passes from service to service, how much time it spends at each step, and potential errors. In distributed systems, a single request can pass through multiple services. Each service in this chain takes a piece of responsibility for processing the request. Trace data helps us follow each link in this chain, understanding where the request got stuck and which step took longer than expected.
đĄ The Power of Traces
Traces are typically collected via "distributed tracing" systems. Tools like Jaeger, Zipkin, and OpenTelemetry are popular in this field. A unique "trace ID" is generated at the beginning of a request, and this ID is recorded as a "span" for each step as the request is passed between different services within the system. This allows us to see the total duration of a request and how that duration is distributed across various services. This is incredibly effective for finding bottlenecks.
A few years ago, we were experiencing significant slowdowns in the order processing flow of a large e-commerce site. Users couldn't complete their orders, and the system was constantly timing out. Metrics generally showed that the system wasn't overloaded, but we couldn't find the source of the problem. When we deployed a distributed tracing system and traced the order placement request, we saw that the request was getting stuck in a payment service. That payment service, in turn, was communicating with an external bank API in the background. Looking at the trace details, we realized that the bank API was responding very slowly, and this delay was blocking the entire order flow. Once we identified this, we were able to contact the bank and resolve the API performance issue. This incident once again showed me the power of trace data in uncovering hidden problems in complex systems.
Combining Metric and Trace Data: Root Cause Analysis
While metrics and traces are powerful tools on their own, to see the complete picture, these two data types must be combined. When you observe a general slowdown or an increase in error rates in a system, the first step is to examine the metrics. Which metric is abnormal? Is it CPU, memory, or the number of requests? Metrics give you a clue about where the problem might be. However, the answer to the "why" question usually comes from traces.
For example, when you see a sudden spike in CPU usage, you can understand which services are consuming the CPU from the metrics. But to understand why that service is consuming so much CPU, you need to examine the traces of the requests coming to that service. Perhaps a query has become too complex, or an operation is repeating more than expected. Traces reveal these kinds of details.
â ī¸ Metric and Trace Correlation
Combining metric and trace data is crucial not only for troubleshooting but also for performance optimization. When you see a long-running step in a particular trace, you can find optimization opportunities by analyzing which metrics this step affects. For example, if you see a database query taking a long time in a trace, you can examine how this situation affects the database server's disk I/O metrics.
In a client project, we observed that the memory usage of application servers was continuously increasing. Metrics showed that a specific service's memory consumption was growing day by day. This raised suspicion of a memory leak. When we examined the service's traces, we saw that memory usage rapidly increased when a specific workflow was triggered, and this memory was never released. Thanks to this trace data, we were able to pinpoint the source of the problem precisely to that workflow. A code-level investigation revealed that an object's lifecycle was mismanaged and it was being unnecessarily held in memory. With the correction of this error, memory usage returned to normal, and system stability was ensured.
Advanced Techniques: Sampling and Anomaly Detection
Tracing every request can be costly, especially in high-traffic systems. Therefore, a technique called "sampling" is often used. Sampling means tracing only a certain percentage of incoming requests. For example, you might trace 1 out of every 100 requests. This reduces costs and makes the amount of data to be analyzed manageable. However, the disadvantage of this approach is the possibility of missing infrequent but critical errors.
đĄ Sampling and Risks
Care must be taken when choosing a sampling strategy. If you are monitoring a critical flow (e.g., a payment transaction), you should ensure that this flow is always traced. Some sampling methods track all requests with a specific trace ID as a group, so when an error is triggered, you can see all instances of that error. This is possible with techniques like "rate-limiting" or "error-based" sampling.
Anomaly detection is a method of automatically detecting abnormal situations using metric data. Machine learning algorithms learn the system's normal operating trends and generate alarms when there is a deviation from these trends. This allows us to identify potential problems faster without having to manually monitor every metric graph. For example, a server's CPU usage suddenly spiking at night, when it should normally be low, can be flagged as an anomaly. Such automatic detections significantly reduce the workload of operators, especially in large and complex systems.
In my own systems, in addition to basic metric monitoring, I have also created simple anomaly detection rules for some critical metrics. For example, I receive an automatic alert if a service's response time doubles its normal level. This ensures that anomalies are reported to me automatically, rather than me having to constantly check graphs manually. These types of automatic alerts help me catch hidden performance issues in systems early.
Conclusion: Manage with Observation, Understand with Data
Metric and trace data are indispensable tools for both understanding the health and solving problems in modern systems. Effectively collecting, analyzing, and correlating this data is key to improving the reliability and performance of our systems. Regularly examining this data, not just when errors occur, but also during the system's normal operation, helps prevent potential future problems.
It should not be forgotten that even the best system architecture remains blind without proper observation mechanisms. Metrics and traces are the windows that eliminate this blindness, showing us the inner workings of our system. When we learn to use these windows correctly, we can manage our systems more effectively, optimize their performance, and provide a better experience for our users. My experiences in my own projects and in corporate environments have repeatedly proven how powerful these two data types can be when used correctly. Therefore, adopting an observability culture in your systems and effectively using metric/trace data will greatly benefit you on your technical journey.
