Built-in latency/error metrics
Iter8 ships with a set of nine built-in metrics that measure your application's performance in terms of latency and errors. You can collect and use these metrics in experiments without the need to configure any external databases.
This feature enables you to get started with Iter8 experiments, especially, SLO validation experiments, quickly. As part of metrics collection, Iter8 will also generate HTTP requests to the application end-point.
List of built-in metrics¶
The following are the set of built-in Iter8 metrics.
|iter8-system||request-count||Counter||Number of requests|
|iter8-system||error-count||Gauge||Number of responses with HTTP status code 4xx or 5xx|
|iter8-system||error-rate||Gauge||Fraction of responses with HTTP status code 4xx or 5xx|
|iter8-system||mean-latency||Gauge||Mean response latency|
|iter8-system||latency-50th-percentile||Gauge||50th percentile (median) response latency|
|iter8-system||latency-75th-percentile||Gauge||75th percentile response latency|
|iter8-system||latency-90th-percentile||Gauge||90th percentile response latency|
|iter8-system||latency-95th-percentile||Gauge||95th percentile response latency|
|iter8-system||latency-99th-percentile||Gauge||99th percentile response latency|
Collecting built-in metrics¶
metrics/collect task in an experiment to collect built-in metrics for your app/ML model versions.
For an example of an experiment that uses built-in metrics, look inside the Knative experiment in this tutorial.