Introduction
When we think about database optimization, the topic is usually associated with index creation, execution plan analysis, internal statistics, and query tuning. Logs, on the other hand, are often treated merely as debugging or auditing tools, mainly used after a problem has already occurred.
However, records generated by the database itself also represent a valuable source of information about the real behavior of applications in production. Analyzing this data can reveal access patterns, recurring queries, and performance bottlenecks that are not always visible through the system’s initial modeling alone.
This article discusses how query logs can support advanced indexing and optimization decisions, presenting Grafana Loki as a supporting infrastructure capable of organizing and analyzing these records continuously.
Database Optimization in Practice
Modern database management systems use cost-based optimizers to decide how queries should be executed. These mechanisms analyze internal statistics and estimate which execution plan is likely to be more efficient. This process works well in most scenarios but depends heavily on the information available within the database itself.
In practice, system performance is not defined only by data structure, but mainly by application behavior over time. Real-world applications exhibit specific access patterns: some queries are executed thousands of times per day, certain filters appear frequently, and particular periods concentrate higher processing loads.
Indexing decisions made only during development do not always reflect the system’s real usage. Over time, new features are added, data volume grows, and query patterns change. Without continuous observation of this behavior, performance adjustments tend to be reactive, occurring only after performance degradation becomes noticeable.
Understanding the Real Behavior of the Database
Databases such as PostgreSQL and MySQL allow logging of executed queries, especially those that exceed specific time thresholds. These records form a detailed history of system activity.
Over time, logs begin to represent the database’s real usage pattern, known in the literature as workload. This term describes the set of operations executed by the database — including queries, inserts, updates, and the frequency with which these actions occur — reflecting how the application truly uses data in production.
This information reveals which operations are most frequent, which commands present higher latency, and when the system experiences greater pressure. In this way, logs complement the optimizer’s internal statistics by providing a perspective closer to operational reality.
Grafana Loki as Supporting Infrastructure
Grafana Loki is an open-source log aggregation system developed by Grafana Labs. Unlike traditional tools that index the entire textual content of logs, Loki indexes only metadata called labels. This design reduces storage costs and makes the system more efficient when handling large volumes of records.
According to Grafana Labs’ official documentation, Loki is inspired by Prometheus and organizes logs into streams based on labels. This approach simplifies separating records by application, environment, or specific instance.
In a database scenario, PostgreSQL logs can be sent to Loki through collection agents. Each instance can be identified using labels that describe the context of the record, such as execution environment or responsible server.
Example:
{app="database", env="prod", instance="db01"}
With this organization, it becomes possible to analyze only the events related to a specific instance and observe its behavior over time. Loki does not replace internal database mechanisms but provides an external observability layer that increases system visibility.
Analyzing Patterns with LogQL
Loki uses a query language called LogQL, inspired by PromQL. Through it, logs can be filtered and analyzed similarly to time-series metrics.
For example, it is possible to locate records associated with query duration:
{job="postgres"} |= "duration:"
It is also possible to observe the frequency of these events over time intervals:
sum(rate({job="postgres"}[5m]))
This type of analysis enables performance trend monitoring. A gradual increase in slow queries, for instance, may indicate data growth without proper indexing adjustments. Peaks during specific hours may reveal usage patterns that justify targeted optimizations.
Thus, logs help guide where investigation should begin, reducing reliance on trial-and-error approaches.
How Logs Support Optimization
Index creation is one of the primary optimization strategies in relational databases. Structures such as B-Trees significantly improve read operations but introduce additional costs for inserts and updates. Excessive indexing can degrade overall system performance.
Analyzing aggregated logs makes it possible to observe which queries truly impact the database. Instead of creating indexes preventively, decisions can be based on concrete workload evidence.
Recurring queries that consistently use the same filters indicate clear optimization opportunities. Conversely, rare and isolated queries may not justify structural changes. This approach brings database administration closer to data-driven practices, reducing unnecessary adjustments.
A New Way to View Performance
Observability tools are often associated with monitoring applications and infrastructure. However, their application in the database context significantly expands performance analysis capabilities.
By centralizing logs and analyzing them historically, it becomes possible to track system behavior evolution. Trends can be identified before becoming critical problems. Optimization stops being purely reactive and becomes guided by continuous observation.
In this scenario, Grafana Loki acts as a complementary tool. It does not perform optimizations automatically but provides the visibility necessary for more informed decisions.
Conclusion
Database logs do not need to be seen only as technical records used during failures. When analyzed in a structured way, they become a valuable source of information about the system’s real behavior.
Using Grafana Loki as a log aggregation infrastructure allows large volumes of logs to be transformed into analyzable data over time, supporting decisions related to indexing and query tuning. This integration brings observability practices closer to database engineering, showing that optimization can benefit not only from theory and internal statistics but also from continuous analysis of real system usage.
Top comments (0)