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Built-in Metrics

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.

Namespace Name Type Description
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

Use the metrics/collect task in an experiment to collect built-in metrics for your app/ML model versions.

Example

For an example of an experiment that uses built-in metrics, look inside the Knative experiment in this tutorial.

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