FuseQL Cheatsheet

A list of useful FuseQL queries and their use cases.

Count of All Logs

Use Cases:

• 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 logs

1. Count of all logs group by level

2. Count of all fingerprints

3. Count of all fingerprints group by source

4. avg of Duration facet

5. avg of duration facet by source

6. error rate formula example

7. advanced functions

   1. anomaly

   2. outlier

   3. forecast

8. arithmetic operator(log) to scale down y axis values

9. trig (skip for now)