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Activity Patterns: Examining logs over a time range can help spot patterns in system usage, traffic, or performance. For example, a system might experience higher load during specific hours of the day, and analyzing log volumes over a set period (like a day or week) can reveal predictable trends.
Scaling Decisions: If logs show consistent spikes in traffic or resource usage during certain time ranges, teams may be able to predict when the system will need additional capacity (e.g., servers, storage, network bandwidth) and plan ahead to scale appropriately.
Impact of Changes or Deployments: After deploying a new feature or making a system update, teams often analyze logs from a time range around the deployment to ensure that the change did not cause any unexpected issues (e.g., errors, performance degradation). For example, reviewing logs from the past 48 hours can reveal any issues arising from a recent deployment.
Count of All Fingerprints
Track System Behavior Changes: The count of different fingerprints over time helps track the diversity of behaviors or events in your system. If the number of distinct fingerprints increases, this could mean that new behaviors or error types are emerging. For example, if a feature is added or updated, it might introduce new types of log patterns, and tracking these patterns over time helps you understand how the system is evolving.
Count of all logs
Count of all logs group by level
Count of all fingerprints
Count of all fingerprints group by source
avg of Duration facet
avg of duration facet by source
error rate formula example
advanced functions
anomaly
outlier
forecast
arithmetic operator(log) to scale down y axis values
trig (skip for now)