[RELENG-7422] 📝 Add documentation

scality · Jun 22, 2023 · 9abb542 · 9abb542
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diff --git a/docs/metrics-analysis-prometheus/collected-reported-metrics.md b/docs/metrics-analysis-prometheus/collected-reported-metrics.md
@@ -107,14 +107,10 @@ to estimate the overall cost using the following formula:
 cost = (cost_per_flavor + cost_per_storage) * percentage_cost_of_bandwidth
 ```
 
-<!-- trunk:ignore
-
 !!! note
 
     GCP and AWS costs are quite the same for the same flavors.
 
--->
-
 ### The different tags and their associated cost
 
 | Provider | Runner               | Cost ($ per min) |
@@ -132,12 +128,8 @@ cost = (cost_per_flavor + cost_per_storage) * percentage_cost_of_bandwidth
 | GCP      | `n2-standard-4`      | 0.005            |
 | GCP      | `n2-standard-8`      | 0.01             |
 
-<!-- trunk:ignore
-
 !!! note
 
     Please note that the names of large GitHub hosted runners
     may not be explicitly the same as shown below, but this is
     the naming convention recommended by GitHub.
-
--->
diff --git a/docs/metrics-analysis-prometheus/prometheus.md b/docs/metrics-analysis-prometheus/prometheus.md
@@ -44,16 +44,12 @@ within a specified time range.
 5. The expression `> 0` filters the query results to only include
    repositories with a value greater than zero.
 
-<!-- trunk:ignore
-
 !!! info
 
-    Using Grafana enhances the visualization of Prometheus data and
-    provides powerful querying capabilities. Within Grafana, apply filters,
-    combine queries, and utilize variables for dynamic filtering. It's important
-    to understand `__interval` (time interval between data points) and `__range`
-    (selected time range) when working with Prometheus data in Grafana. This
-    integration enables efficient data exploration and analysis for better
-    insights and decision-making.
-
--->
+      Using Grafana enhances the visualization of Prometheus data and
+      provides powerful querying capabilities. Within Grafana, apply filters,
+      combine queries, and utilize variables for dynamic filtering. It's important
+      to understand `__interval` (time interval between data points) and `__range`
+      (selected time range) when working with Prometheus data in Grafana. This
+      integration enables efficient data exploration and analysis for better
+      insights and decision-making.