bugfix/mv-ds-var-for-goldpinger #663
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WalkthroughThe pull request updates the goldpinger dashboard configuration by restructuring key sections in the JSON file. Notable changes include an update to the annotations section (switching the datasource uid to "grafana" and changing the id from 224 to 226), the addition of a new panel with a Prometheus datasource and enhanced field configurations, and modifications to existing panels with dynamic datasource references and metric expressions. The templating section is also reworked by introducing new variables for datasource, instance, and call_type, leading to improved consistency in the configuration. Changes
Sequence Diagram(s)sequenceDiagram
participant U as User
participant D as Grafana Dashboard
participant T as Templating Engine
participant P as Prometheus Datasource
U->>D: Open Goldpinger Dashboard
D->>T: Process template variables (datasource, instance, call_type)
T-->>D: Return dynamic configuration
D->>P: Query metrics using updated targets and expressions
P-->>D: Send metric data
D->>U: Render updated panels with annotations and thresholds
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🧹 Nitpick comments (2)
dashboards/goldpinger/goldpinger.json (2)
38-120: Goldpinger Nodes Panel Configuration:
This panel now uses a dynamic datasource reference via"uid": "${datasource}"and includes an updated field configuration. Note that the thresholds are defined with steps usingnull,31, and32values. Please verify that the visual thresholds (and color transitions) match the intended alerting or visual cues and that the expression(count(goldpinger_nodes_health_total{status='healthy'}) + count(goldpinger_nodes_health_total{status='unhealthy'})) /2accurately reflects the desired computation.
398-502: Percentage Unhealthy Nodes Reported Panel:
This panel calculates the fraction of unhealthy nodes by dividing the increase in unhealthy nodes by the total increase (healthy plus unhealthy). Consider edge cases (e.g. division by zero) and validate that the computed percentages reflect real-world conditions.
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🔇 Additional comments (14)
dashboards/goldpinger/goldpinger.json (14)
2-17: Annotations Block Update:
The annotations section now uses a standardized datasource configuration (with"type": "datasource"and"uid": "grafana") for built-in annotations. This promotes consistency across dashboards. Please verify that this change integrates correctly with other dashboards consuming these annotations.
22-23: Dashboard ID Update:
The"id"value has been updated to226. Ensure that any external references or bookmarks to this dashboard are aligned with the new id.
121-203: Increase in # Errors Panel:
This panel aggregates error counts over a 30‑minute window using the expression:sum(increase(goldpinger_errors_total{goldpinger_instance=~"$instance"}[30m]))Ensure that the chosen thresholds (with steps of
1and2) suit the typical error volumes and emphasize anomalies effectively.
204-292: Unhealthy Seen by Instance Panel:
The table panel groups unhealthy node counts by instance using:sum(goldpinger_nodes_health_total{status='unhealthy'}) by (goldpinger_instance)Confirm that the unit
"short"adequately formats these numbers and that the query returns data as expected.
293-397: Unhealthy Nodes Increase Panel:
This timeseries panel displays the increase in unhealthy nodes over 30 minutes. The field configuration and query settings appear to be correct. Verify that the chosen aggregation interval and visual settings provide clear insight into short‐term changes.
503-515: Connections to Peers Row:
The row panel “Connections to peers” serves as a layout divider and grouping header. Its configuration is minimal and follows dashboard design best practices.
516-618: 99% Response Time from Node Panel:
This panel leverageshistogram_quantile(0.99, …)to extract high-percentile response times from the node metrics. Confirm that the rate calculation and grouping by instance yield insights into worst-case performance.
619-721: 95% Response Time from Node Panel:
Similar to the 99% panel, this one useshistogram_quantile(0.95, …). Verify that having both 95% and 99% views gives a complementary perspective on performance and that the query parameters accurately capture the intended metrics.
722-837: Connections to Kubernetes API Row:
This row panel organizes subsequent API performance panels. Its simple configuration is appropriate for a grouping header; no changes are needed.
838-940: k8s API 99% Response Time Panel:
The panel useshistogram_quantile(0.99, …)to calculate the 99th percentile of Kubernetes API response times. Ensure that the Prometheus query on thegoldpinger_kube_master_response_time_s_bucketmetric and the applied aggregations deliver the expected performance insights.
941-1043: k8s API 95% Response Time Panel:
This panel mirrors the 99% panel but for the 95th percentile. Confirm that the differentiation between these two percentiles provides actionable information and that the query is optimized for performance monitoring in production.
1044-1146: k8s API 50% Response Time Panel:
Adding a median (50th percentile) view offers a balanced performance metric. Verify that the query (histogram_quantile(0.50, …)) and the table formatting (legend, tooltips) clearly communicate baseline response time data.
1152-1208: Templating Section Update:
The templating section now defines three variables:
•datasource– dynamically set to “prometheus” with a preselected value.
•instance– populated vialabel_values(goldpinger_instance)with multi-selection enabled.
•call_type– dynamically derived fromlabel_values(call_type).This enhances flexibility by enabling dynamic queries and data source assignment across panels. Verify that the variable values and query definitions accurately reflect the available data labels.
1209-1219: Global Dashboard Configuration:
The global settings—including time range, refresh interval, timezone, title, and UID—are set according to standard practices. Ensure that these settings match the deployment context and user expectations for data recency.
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Summary by CodeRabbit
New Features
Refactor