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Add dashboards and alerts as code, mark Phase 6 done

Browse Source 
Dashboards: observability/dashboards/generate.py (Grafana Foundation SDK)
writes k8s/grafana/dashboards/*.json -- a RED dashboard per HTTP service
and a system overview -- shipped through a file provider and
configMapGenerators, so `kubectl apply -k k8s/` rebuilds them. Verified by
deleting Grafana and its volume and re-applying. Adds tracesToMetrics to
the Tempo datasource.

Alerts: Prometheus rule files delivering to Alertmanager, under a
labels/annotations contract enforced by observability/alerts/check_contract.py,
with runbooks in docs/runbooks/.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
This commit is contained in:
Movses Aslanyan
2026-10-25 20:01:52 +03:30
co-authored by Claude Sonnet 5.5
parent b50f8c3b24
commit fef735c922
19 changed files with 1314 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files
k8s/grafana/dashboards-provider.yml
+13
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@@ -0,0 +1,13 @@
apiVersion: 1
providers:
- name: ledgerline
orgId: 1
type: file
folder: Ledgerline
options:
path: /etc/grafana/dashboards/
allowUiUpdates: false
disableDeletion: false
updateIntervalSeconds: 15
foldersFromFilesStructure: false
k8s/grafana/dashboards/overview.json
+115
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@@ -0,0 +1,115 @@
{
"annotations": {},
"editable": false,
"fiscalYearStartMonth": 0,
"graphTooltip": 0,
"panels": [
{
"datasource": {
"type": "tempo",
"uid": "tempo"
},
"gridPos": {
"h": 12,
"w": 24,
"x": 0,
"y": 0
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "tempo",
"uid": "tempo"
},
"filters": [],
"queryType": "serviceMap",
"refId": "graph"
}
],
"title": "Service graph",
"transparent": false,
"type": "nodeGraph"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 12
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "ledgerline_stream_lag",
"legendFormat": "{{consumer_group}}",
"refId": "lag"
}
],
"title": "Stream consumer lag",
"transparent": false,
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 12,
"y": 12
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "sum by (service, pool) (ledgerline_db_pool_in_use)",
"legendFormat": "{{service}} / {{pool}}",
"refId": "pool"
}
],
"title": "DB pool connections in use",
"transparent": false,
"type": "timeseries"
}
],
"refresh": "15s",
"schemaVersion": 41,
"tags": [
"generated",
"overview"
],
"templating": {},
"time": {
"from": "now-1h",
"to": "now"
},
"timezone": "browser",
"title": "System overview",
"uid": "overview"
}
k8s/grafana/dashboards/red-fraud.json
+140
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@@ -0,0 +1,140 @@
{
"annotations": {},
"editable": false,
"fiscalYearStartMonth": 0,
"graphTooltip": 0,
"panels": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "reqps"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 0
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "sum by (http_route) (rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"fraud\"}[5m]))",
"legendFormat": "{{http_route}}",
"refId": "rate"
}
],
"title": "Request rate",
"transparent": false,
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"min": 0,
"unit": "percentunit"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 12,
"y": 0
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "(sum(rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"fraud\",http_response_status_code=~\"5..\"}[5m])) or vector(0)) / sum(rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"fraud\"}[5m]))",
"legendFormat": "5xx ratio",
"refId": "errors"
}
],
"title": "Error ratio",
"transparent": false,
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "s"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 9
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.5, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"fraud\"}[5m])))",
"legendFormat": "p50",
"refId": "p50"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.95, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"fraud\"}[5m])))",
"legendFormat": "p95",
"refId": "p95"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.99, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"fraud\"}[5m])))",
"legendFormat": "p99",
"refId": "p99"
}
],
"title": "Request duration",
"transparent": false,
"type": "timeseries"
}
],
"refresh": "15s",
"schemaVersion": 41,
"tags": [
"generated",
"red"
],
"templating": {},
"time": {
"from": "now-1h",
"to": "now"
},
"timezone": "browser",
"title": "RED: fraud",
"uid": "red-fraud"
}
k8s/grafana/dashboards/red-gateway.json
+140
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@@ -0,0 +1,140 @@
{
"annotations": {},
"editable": false,
"fiscalYearStartMonth": 0,
"graphTooltip": 0,
"panels": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "reqps"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 0
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "sum by (http_route) (rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"gateway\"}[5m]))",
"legendFormat": "{{http_route}}",
"refId": "rate"
}
],
"title": "Request rate",
"transparent": false,
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"min": 0,
"unit": "percentunit"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 12,
"y": 0
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "(sum(rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"gateway\",http_response_status_code=~\"5..\"}[5m])) or vector(0)) / sum(rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"gateway\"}[5m]))",
"legendFormat": "5xx ratio",
"refId": "errors"
}
],
"title": "Error ratio",
"transparent": false,
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "s"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 9
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.5, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"gateway\"}[5m])))",
"legendFormat": "p50",
"refId": "p50"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.95, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"gateway\"}[5m])))",
"legendFormat": "p95",
"refId": "p95"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.99, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"gateway\"}[5m])))",
"legendFormat": "p99",
"refId": "p99"
}
],
"title": "Request duration",
"transparent": false,
"type": "timeseries"
}
],
"refresh": "15s",
"schemaVersion": 41,
"tags": [
"generated",
"red"
],
"templating": {},
"time": {
"from": "now-1h",
"to": "now"
},
"timezone": "browser",
"title": "RED: gateway",
"uid": "red-gateway"
}
k8s/grafana/dashboards/red-insights.json
+140
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@@ -0,0 +1,140 @@
{
"annotations": {},
"editable": false,
"fiscalYearStartMonth": 0,
"graphTooltip": 0,
"panels": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "reqps"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 0
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "sum by (http_route) (rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"insights\"}[5m]))",
"legendFormat": "{{http_route}}",
"refId": "rate"
}
],
"title": "Request rate",
"transparent": false,
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"min": 0,
"unit": "percentunit"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 12,
"y": 0
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "(sum(rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"insights\",http_response_status_code=~\"5..\"}[5m])) or vector(0)) / sum(rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"insights\"}[5m]))",
"legendFormat": "5xx ratio",
"refId": "errors"
}
],
"title": "Error ratio",
"transparent": false,
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "s"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 9
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.5, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"insights\"}[5m])))",
"legendFormat": "p50",
"refId": "p50"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.95, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"insights\"}[5m])))",
"legendFormat": "p95",
"refId": "p95"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.99, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"insights\"}[5m])))",
"legendFormat": "p99",
"refId": "p99"
}
],
"title": "Request duration",
"transparent": false,
"type": "timeseries"
}
],
"refresh": "15s",
"schemaVersion": 41,
"tags": [
"generated",
"red"
],
"templating": {},
"time": {
"from": "now-1h",
"to": "now"
},
"timezone": "browser",
"title": "RED: insights",
"uid": "red-insights"
}
k8s/grafana/dashboards/red-ledger.json
+140
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@@ -0,0 +1,140 @@
{
"annotations": {},
"editable": false,
"fiscalYearStartMonth": 0,
"graphTooltip": 0,
"panels": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "reqps"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 0
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "sum by (http_route) (rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"ledger\"}[5m]))",
"legendFormat": "{{http_route}}",
"refId": "rate"
}
],
"title": "Request rate",
"transparent": false,
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"min": 0,
"unit": "percentunit"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 12,
"y": 0
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "(sum(rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"ledger\",http_response_status_code=~\"5..\"}[5m])) or vector(0)) / sum(rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"ledger\"}[5m]))",
"legendFormat": "5xx ratio",
"refId": "errors"
}
],
"title": "Error ratio",
"transparent": false,
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "s"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 9
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.5, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"ledger\"}[5m])))",
"legendFormat": "p50",
"refId": "p50"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.95, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"ledger\"}[5m])))",
"legendFormat": "p95",
"refId": "p95"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.99, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"ledger\"}[5m])))",
"legendFormat": "p99",
"refId": "p99"
}
],
"title": "Request duration",
"transparent": false,
"type": "timeseries"
}
],
"refresh": "15s",
"schemaVersion": 41,
"tags": [
"generated",
"red"
],
"templating": {},
"time": {
"from": "now-1h",
"to": "now"
},
"timezone": "browser",
"title": "RED: ledger",
"uid": "red-ledger"
}
k8s/grafana/dashboards/red-sanctions.json
+140
View File
@@ -0,0 +1,140 @@
{
"annotations": {},
"editable": false,
"fiscalYearStartMonth": 0,
"graphTooltip": 0,
"panels": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "reqps"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 0
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "sum by (http_route) (rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"sanctions\"}[5m]))",
"legendFormat": "{{http_route}}",
"refId": "rate"
}
],
"title": "Request rate",
"transparent": false,
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"min": 0,
"unit": "percentunit"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 12,
"y": 0
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "(sum(rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"sanctions\",http_response_status_code=~\"5..\"}[5m])) or vector(0)) / sum(rate(http_server_request_duration_seconds_count{job=\"ledgerline\",service=\"sanctions\"}[5m]))",
"legendFormat": "5xx ratio",
"refId": "errors"
}
],
"title": "Error ratio",
"transparent": false,
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"fieldConfig": {
"defaults": {
"unit": "s"
},
"overrides": []
},
"gridPos": {
"h": 9,
"w": 12,
"x": 0,
"y": 9
},
"repeatDirection": "h",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.5, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"sanctions\"}[5m])))",
"legendFormat": "p50",
"refId": "p50"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.95, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"sanctions\"}[5m])))",
"legendFormat": "p95",
"refId": "p95"
},
{
"datasource": {
"type": "prometheus",
"uid": "prometheus"
},
"expr": "histogram_quantile(0.99, sum by (le) (rate(http_server_request_duration_seconds_bucket{job=\"ledgerline\",service=\"sanctions\"}[5m])))",
"legendFormat": "p99",
"refId": "p99"
}
],
"title": "Request duration",
"transparent": false,
"type": "timeseries"
}
],
"refresh": "15s",
"schemaVersion": 41,
"tags": [
"generated",
"red"
],
"templating": {},
"time": {
"from": "now-1h",
"to": "now"
},
"timezone": "browser",
"title": "RED: sanctions",
"uid": "red-sanctions"
}
k8s/grafana/datasources.yml
+16
View File
@@ -38,6 +38,22 @@ datasources:
filterByTraceID: true
spanStartTimeShift: -1m
spanEndTimeShift: 1m
# QUESTION (answer in your own words; verify by reading the LogQL that
# "Logs for this span" generates): why is `filterByTraceID: true` enough
# with no `tags` mapping here, and when would it not be?
tracesToMetrics:
datasourceUid: prometheus
# Wider than the span: rate() needs a few scrape intervals of data.
spanStartTimeShift: -2m
spanEndTimeShift: 2m
# span attribute -> Prometheus label on traces_spanmetrics_*
tags:
- key: service.name
value: service
queries:
# `$$` is the provisioning-file escape for a literal `$`.
- name: Request rate
query: sum(rate(traces_spanmetrics_calls_total{$$__tags}[5m]))
serviceMap:
datasourceUid: prometheus
nodeGraph:
k8s/grafana/deployment.yaml
+10
View File
@@ -51,6 +51,10 @@ spec:
mountPath: /var/lib/grafana
- name: datasources
mountPath: /etc/grafana/provisioning/datasources
- name: dashboards-provider
mountPath: /etc/grafana/provisioning/dashboards
- name: dashboards
mountPath: /etc/grafana/dashboards/
volumes:
- name: data
persistentVolumeClaim:
@@ -58,3 +62,9 @@ spec:
- name: datasources
configMap:
name: grafana-datasources
- name: dashboards-provider
configMap:
name: grafana-dashboards-provider
- name: dashboards
configMap:
name: grafana-dashboards
k8s/grafana/kustomization.yaml
+11 -2
View File
@@ -7,9 +7,18 @@ resources:
- service.yaml
- ingress.yaml
# Datasources only, as in the Ansible role. Dashboard provisioning is the
# Phase 6 deliverable and belongs in this directory.
configMapGenerator:
- name: grafana-datasources
files:
- datasources.yml
- name: grafana-dashboards-provider
files:
- dashboards-provider.yml
- name: grafana-dashboards
files:
- dashboards/overview.json
- dashboards/red-fraud.json
- dashboards/red-gateway.json
- dashboards/red-ledger.json
- dashboards/red-sanctions.json
- dashboards/red-insights.json
k8s/prometheus/kustomization.yaml
+1
View File
@@ -14,3 +14,4 @@ configMapGenerator:
- name: prometheus-rules
files:
- rules/host.rules.yml
- rules/ledgerline.rules.yml
k8s/prometheus/rules/ledgerline.rules.yml
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# Ledgerline alerting rules.
#
# ALERTING CONTRACT -- every alert in this directory (except scratch.*) must carry:
#
# labels (machine-readable; Alertmanager routes and groups on these)
# severity critical | warning | info
# critical = a human is needed now; warning = look today.
# team who owns responding: platform | payments | risk | data
# service which Ledgerline service, taken from the series' own label
# (aggregations must keep it: `by (service)`)
#
# annotations (text for humans; never used for routing)
# summary one line, no templating beyond the service name
# description what is wrong, with the measured value and the threshold
# runbook_url what to do about it (docs/runbooks/<kebab-alert-name>.md)
# dashboard where to look first (the dashboard that explains the alert)
#
# observability/alerts/check_contract.py enforces this; run it before applying.
#
# `team` is static per alert rule, not per service: it says who answers this
# *class* of alert. Per-service ownership would need a join against an
# ownership metric or one rule per team.
groups:
- name: ledgerline.rules
rules:
# All seven targets are scraped through one Traefik address, so a proxy
# failure takes every target down at once and fires seven alerts.
- alert: LedgerlineTargetDown
expr: up{job="ledgerline"} == 0
for: 2m
labels:
severity: critical
team: platform
annotations:
summary: "{{ $labels.service }} is not being scraped"
description: "Prometheus has failed to scrape {{ $labels.service }} for more than two minutes."
runbook_url: "https://registry.mohsen-hassani.com/mohsen_hassani/LedgerLine/src/branch/main/docs/runbooks/ledgerline-target-down.md"
dashboard: "https://grafana.vione.cloud/d/overview"
# 5xx only (a 404 is the client's mistake). The second condition keeps a
# service with almost no traffic from alerting on one failed request:
# below 0.05 req/s (about 15 requests per 5m) the ratio is noise. When
# there is no traffic at all the ratio is NaN and nothing fires.
- alert: LedgerlineHighErrorRatio
expr: |
(
sum by (service) (rate(http_server_request_duration_seconds_count{job="ledgerline",http_response_status_code=~"5.."}[5m]))
/
sum by (service) (rate(http_server_request_duration_seconds_count{job="ledgerline"}[5m]))
) > 0.05
and
sum by (service) (rate(http_server_request_duration_seconds_count{job="ledgerline"}[5m])) > 0.05
for: 5m
labels:
severity: warning
team: payments
annotations:
summary: "{{ $labels.service }} is returning 5xx errors"
description: "{{ $labels.service }} has had a 5xx ratio of {{ $value | humanizePercentage }} for five minutes (threshold 5%)."
runbook_url: "https://registry.mohsen-hassani.com/mohsen_hassani/LedgerLine/src/branch/main/docs/runbooks/ledgerline-high-error-ratio.md"
dashboard: "https://grafana.vione.cloud/d/red-{{ $labels.service }}"