Demystifying Log Analysis:
Simple Tips for Effective
Log Monitoring

In today's world, where applications are integral to customer satisfaction, it is necessary to ensure they perform seamlessly without any disruptions. However, even the best-designed systems encounter errors and fail to perform optimally due to various reasons such as bugs or misconfigurations. This is where log monitoring plays a critical role in detecting and resolving issues before they have a significant impact on end-user experience. The primary purpose of logs is to offer an overall view of events and errors taking place while software is running, or when a failure occurs. Log monitoring solutions, as provided by Rakuten SixthSense, collect activity generated by applications, services, and components of the operating systems stack and index them effectively- thereby, improving the overall stability and reliability of application performance.

However, configuring logs for an entire infrastructure and application stack can be overwhelming due to the sheer volume of data generated. Almost every system event can generate a log entry, and modern application stacks can produce millions or billions of events each day. Collecting irrelevant information can cause log files to become enormous, making it difficult for humans or automated solutions to spot anomalies. On the other hand, collecting too little information can cause important events to be missed.

To derive maximum benefits from logs, it is essential to carefully plan what data needs to be collected, how to best visualise it and to deeply understand the context in which entries are created. To ensure that log analytics yield the most value, one of the most essential best practices is to select the information most fundamental to understanding system performance based on team-wide input. This helps to avoid collecting irrelevant information that can cause log files to become too large to spot errors. Furthermore, sensitive information such as passwords or personal data should be treated with extra care. If it is necessary to capture this data, ensure your logging solution supports encryption.

Another best-practice is to establish a performance benchmark. Having a baseline is key to distinguish between false-positives and actionable alerts. It also helps in identifying when the system deviates from its expected course, making it easier to diagnose issues and reduce MTTR (mean-time to resolution). Further, selecting messages that support data-driven decisions is crucial. Only messages that provide specific information about errors and are directly related to the conditions being monitored should be used. When formatting logs, make sure to specify information required to debug an error, while also keeping messages concise to avoid pitfalls from overly technical message constructs. However, to gain the most value from logs, teams need to have a comprehensive understanding of the entire system, including how different components interact with each other. Full-stack observability provides this level of understanding, enabling teams to correlate data from different sources and identify the root cause of issues rapidly in real-time. This approach goes beyond traditional monitoring, which only looks at individual components, to provide a holistic deep-dive into application behaviour.