- Newest
- Most votes
- Most comments
Based on the description you could use a Metric Math 'AVG' function with p90 statistic (you may utilize appropriate function that best fits your model). See sample dashboard source which uses p90 over a 1 hour (3600) period. When approaching to build an 'overall' latency you'd want to ensure the Step functions have a degree of correlation that fulfills a common business goal (otherwise you may have to re-evaluate the effectiveness for such a combined aggregation). This blog post could be useful: https://aws.amazon.com/blogs/mt/what-is-observability-and-why-does-it-matter-part-1/
{ "metrics": [ [ { "expression": "AVG(METRICS())", "label": "Expression1", "id": "e1", "stat": "p90" } ], [ "AWS/States", "ExecutionTime", "StateMachineArn", "StateMachineARN1", { "id": "m1" } ], [ "...", "ExecutionTime", "StateMachineArn", "StateMachineARN1", { "id": "m2" } ], [ "...", "ExecutionTime", "StateMachineArn", "StateMachineARN1", { "id": "m3" } ] ], "view": "timeSeries", "stacked": false, "region": "us-east-2", "stat": "p90", "period": 3600 }
Relevant content
- asked 5 years ago
- asked 4 months ago
- asked 4 months ago
- AWS OFFICIALUpdated 3 years ago
- AWS OFFICIALUpdated a month ago
- AWS OFFICIALUpdated a year ago
- AWS OFFICIALUpdated 2 years ago