Metric Analysis
Last updated
Last updated
Note: Before going through this article, we recommend you to know how to create a metric template.
The goal of metric analysis is to score/compare all the metrics from baseline with the canary to get the user a good idea of whether the canary is a fail or a pass based on the overall average metric score given to the system. You can make a decision overall to have the application in production or not based on this metric score. The goal here is to carry out the canary analysis for old and new releases by comparing both versions' of metrics. This is carried out structurally by comparing each metric of the old release to the new release and finally scoring the metrics analysis by averaging out the individual score of the metrics.
From the “Analysis History” page click on “Metric Analysis” to view the scores of each metric. Refer to the image below.
To view the comparison between Baseline and New Release, Observed significant change in characteristics of a metric, click on the drop-down arrow of that particular metric and then click on the text as shown in the image below.
User-defined metrics that represent the KPI of service behavior. The continuous Verification test will fail if any of the metrics tagged as critical fail in the canary test. If critical metrics are not tagged by the user, the system will treat all metrics equally and will assign rank based on algorithms.
User-defined metrics that represent intuitive performance measures. These metrics are used for filtering when presenting results with a large number of analyzed metrics.
Metrics are grouped based on the service as well as system-level metrics based on network, compute, disk and memory. This grouping allows users to identify high-ranking groups and low-scoring groups to diagnose the issues.
Selecting an individual metric in the list shows details of the metric with its statistics, box plots, and behavior throughout canary. Each of the metrics rows has additional details on bucket scores. Each bucket is a timeslice that is used in comparison that allows users to identify trends or a subset of load that could be causing a problem with the new build.
Every metric is ranked based on its particular effect on the service behavior as well as the difference between canary and baseline versions.
The following plot shows the box plot that allows easy detection of trends in the metric's behavior over the canary run's duration.
