feat(server): add product analytics dashboard row
This commit is contained in:
RainbowBird
@@ -60,6 +60,14 @@ OTel SDK 在导出到 Prometheus 时做两件事：
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| `ws.messages.sent` | Counter | 同上 | — |
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| `ws.messages.sent` | Counter | 同上 | — |
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| `ws.messages.received` | Counter | [services/domain/chats.ts](../../src/services/domain/chats.ts) | — |
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| `ws.messages.received` | Counter | [services/domain/chats.ts](../../src/services/domain/chats.ts) | — |
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## Product Analytics
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| Metric | 类型 | 落点 | Labels |
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|---|---|---|---|
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| `airi.product.events` | Counter | [services/domain/product-events.ts](../../src/services/domain/product-events.ts) `track` | `feature`、`action`、`status`、`source`（可选） |
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> **Prometheus 只看事件量，不看独立用户**：`airi.product.events` 的 labels 必须保持低基数，不能加 `user_id`、`session_id`、request id、raw error message 或 prompt。要回答"每个功能有多少独立用户"，查 Postgres `product_events`：`count(distinct user_id)`。
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## Revenue & Billing
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## Revenue & Billing
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### Stripe lifecycle
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### Stripe lifecycle
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@@ -134,12 +142,15 @@ OTel SDK 在导出到 Prometheus 时做两件事：
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| Row | viz | 关键 metric |
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| Row | viz | 关键 metric |
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|---|---|---|
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|---|---|---|
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| Service Health | stat / gauge | `user.active_sessions`（`max()`）、`ws.connections.active`（`sum()`）、`http.server.request.duration_count`（req/s + 5xx%）、`gen_ai.client.operation.count`、`airi.email.{send,failures}` 失败率 |
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| Service Health | stat / gauge / heatmap | `user.total`（`max()`）、`user.active_sessions`（`avg()`）、`ws.connections.active`（`sum()`）、`http.server.request.duration_count`（req/s + 5xx%）、`gen_ai.client.operation.count` |
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| Distribution (now) | donut | HTTP methods / LLM models / HTTP status codes — `increase([5m])` |
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| User Engagement | stat | `user.active_rolling`（DAU / WAU / MAU，`max()`） |
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| Traffic Trends | timeseries | 同 distribution 的数据 over time |
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| Product Analytics | stat / gauge / bargauge / timeseries | `airi.product.events`（Prom-safe event volume by `feature` / `action` / `status`；distinct users 仍查 Postgres `product_events`） |
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| Latency | timeseries | `http.server.request.duration_bucket`（P95 by route）、`gen_ai.client.first_token.duration_bucket`（P95 by model） |
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| HTTP | heatmap / bargauge / timeseries | `http.server.request.duration_count`（status mix、top routes、route errors）、`http.server.request.duration_bucket`（P95 by route） |
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| Errors / Quality | mix | 4xx/5xx stacked area、`airi.gen_ai.stream.interrupted`、`airi.rate_limit.blocked` |
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| LLM Gateway | timeseries | `gen_ai.client.operation.count`（by model）、`gen_ai.client.first_token.duration_bucket` / `gen_ai.client.operation.duration_bucket`（TTFB + end-to-end P95） |
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| Business | stat / gauge / donut | `airi.stripe.revenue`（by currency）、checkout conversion %、`stripe.events` 分布 |
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| Provider Upstreams | timeseries | `gen_ai.client.operation.count` / `gen_ai.client.operation.duration_bucket` by `provider`、`airi.billing.tts.chars` |
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| LLM Tokens & Quality | stat / timeseries | `gen_ai.client.token.usage.{input,output}`、`airi.billing.flux.unbilled`、`airi.gen_ai.stream.interrupted` |
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| LLM Router Health | stat / gauge / timeseries | `airi.gen_ai.gateway.{key.exhausted,decrypt.failures,fallback.count,upstream.errors}` |
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| Business | stat / gauge | `airi.stripe.revenue`（by currency）、checkout conversion %、`stripe.events` 分布 |
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| Infrastructure (collapsed, **by `service_instance_id`**) | timeseries | `db_client_operation_duration` P95（cluster）、`db_client_connection_count`、`v8js_memory_heap_used_bytes` %、`nodejs_eventloop_delay_p99_seconds` |
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| Infrastructure (collapsed, **by `service_instance_id`**) | timeseries | `db_client_operation_duration` P95（cluster）、`db_client_connection_count`、`v8js_memory_heap_used_bytes` %、`nodejs_eventloop_delay_p99_seconds` |
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| Logs | logs | Loki，不是 Prometheus |
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| Logs | logs | Loki，不是 Prometheus |
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@@ -904,6 +904,376 @@
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}
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}
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}
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}
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},
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},
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"panel-95": {
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"kind": "Panel",
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"spec": {
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"data": {
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"kind": "QueryGroup",
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"spec": {
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"queries": [
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{
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"kind": "PanelQuery",
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"spec": {
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"hidden": false,
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"query": {
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"datasource": {
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"name": "grafanacloud-projairi-prom"
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},
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"group": "prometheus",
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"kind": "DataQuery",
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"spec": {
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"expr": "sum(increase(airi_product_events_total{service_name=~\"$service\", deployment_environment=~\"$env\", feature!=\"\", action!=\"\"}[$__range]))",
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"legendFormat": "events"
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},
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"version": "v0"
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},
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"refId": "A"
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}
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}
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],
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"queryOptions": {},
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"transformations": []
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}
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},
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"description": "Total first-party product analytics events over the dashboard range. This is event volume, not distinct users — distinct-user counts come from the Postgres `product_events` table.",
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"id": 95,
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"links": [],
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"title": "Product Events (range)",
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"vizConfig": {
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"group": "stat",
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"kind": "VizConfig",
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"spec": {
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"fieldConfig": {
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"defaults": {
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"color": {
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"mode": "fixed",
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"fixedColor": "blue"
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},
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"fieldMinMax": false,
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"thresholds": {
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"mode": "absolute",
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"steps": [
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{
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"color": "blue",
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"value": 0
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}
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]
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},
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"unit": "short",
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"noValue": "0"
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},
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"overrides": []
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},
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"options": {
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"colorMode": "none",
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"graphMode": "none",
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"justifyMode": "auto",
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"orientation": "auto",
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"percentChangeColorMode": "standard",
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"reduceOptions": {
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"calcs": [
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"lastNotNull"
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],
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"fields": "",
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"values": false
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},
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"showPercentChange": true,
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"textMode": "value_and_name",
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"wideLayout": true
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}
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},
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"version": "13.0.0-23630096546"
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}
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}
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},
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"panel-96": {
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"kind": "Panel",
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"spec": {
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"data": {
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"kind": "QueryGroup",
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"spec": {
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"queries": [
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{
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"kind": "PanelQuery",
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"spec": {
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"hidden": false,
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"query": {
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"datasource": {
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"name": "grafanacloud-projairi-prom"
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},
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"group": "prometheus",
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"kind": "DataQuery",
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"spec": {
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"expr": "100 * sum(increase(airi_product_events_total{service_name=~\"$service\", deployment_environment=~\"$env\", feature!=\"\", action!=\"\", status=\"failed\"}[$__range])) / clamp_min(sum(increase(airi_product_events_total{service_name=~\"$service\", deployment_environment=~\"$env\", feature!=\"\", action!=\"\"}[$__range])), 1)",
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"legendFormat": "failed %"
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},
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"version": "v0"
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},
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"refId": "A"
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}
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}
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],
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"queryOptions": {},
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"transformations": []
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}
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},
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"description": "Failed product events ÷ all product events over the dashboard range. Uses only bounded labels (`feature`, `action`, `status`, `source`); no user/session/request identifiers are present in Prometheus.",
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"id": 96,
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"links": [],
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"title": "Product Failure %",
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"vizConfig": {
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"group": "gauge",
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"kind": "VizConfig",
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"spec": {
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"fieldConfig": {
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"defaults": {
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"color": {
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"mode": "thresholds"
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},
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"thresholds": {
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"mode": "absolute",
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"steps": [
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{
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"color": "green",
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"value": 0
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},
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{
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"color": "yellow",
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"value": 2
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},
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{
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"color": "red",
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"value": 10
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}
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]
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},
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"unit": "percent",
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"decimals": 2,
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"noValue": "0",
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"min": 0,
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"max": 20
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},
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"overrides": []
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},
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"options": {
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"minVizHeight": 75,
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||||||
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"minVizWidth": 75,
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||||||
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"orientation": "auto",
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||||||
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"reduceOptions": {
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||||||
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"calcs": [
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"lastNotNull"
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],
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||||||
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"fields": "",
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||||||
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"values": false
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},
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"showThresholdLabels": false,
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"showThresholdMarkers": true,
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||||||
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"sizing": "auto"
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||||||
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}
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},
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"version": "13.0.0-23630096546"
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}
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}
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},
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"panel-97": {
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||||||
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"kind": "Panel",
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||||||
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"spec": {
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||||||
|
"data": {
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||||||
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"kind": "QueryGroup",
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||||||
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"spec": {
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||||||
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"queries": [
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||||||
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{
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||||||
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"kind": "PanelQuery",
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||||||
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"spec": {
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||||||
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"hidden": false,
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||||||
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"query": {
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||||||
|
"datasource": {
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||||||
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"name": "grafanacloud-projairi-prom"
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},
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"group": "prometheus",
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"kind": "DataQuery",
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"spec": {
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"expr": "topk(12, sum by (feature, action, status) (increase(airi_product_events_total{service_name=~\"$service\", deployment_environment=~\"$env\", feature!=\"\", action!=\"\"}[$__range])))",
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"legendFormat": "{{feature}} · {{action}} · {{status}}",
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"instant": true,
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"range": false
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},
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||||||
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"version": "v0"
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||||||
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},
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||||||
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"refId": "A"
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||||||
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}
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||||||
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}
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],
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"queryOptions": {},
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"transformations": []
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}
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},
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"description": "Top product actions by event count over the dashboard range. Use this to see which features are actually being exercised after deployment; pair with DB `count(distinct user_id)` for user counts.",
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"id": 97,
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"links": [],
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"title": "Top Product Actions (range)",
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"vizConfig": {
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"group": "bargauge",
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||||||
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"kind": "VizConfig",
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||||||
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"spec": {
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||||||
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"fieldConfig": {
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||||||
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"defaults": {
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||||||
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"color": {
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"mode": "thresholds"
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||||||
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},
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"thresholds": {
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||||||
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"mode": "absolute",
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||||||
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"steps": [
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{
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||||||
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"color": "green",
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"value": 0
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||||||
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}
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]
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},
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||||||
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"unit": "short",
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||||||
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"noValue": "0"
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||||||
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},
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||||||
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"overrides": []
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||||||
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},
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||||||
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"options": {
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||||||
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"displayMode": "gradient",
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||||||
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"maxVizHeight": 300,
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||||||
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"minVizHeight": 12,
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"minVizWidth": 8,
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||||||
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"namePlacement": "auto",
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||||||
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"orientation": "horizontal",
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||||||
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"reduceOptions": {
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||||||
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"calcs": [
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||||||
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"lastNotNull"
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||||||
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],
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||||||
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"fields": "",
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||||||
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"values": false
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||||||
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},
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||||||
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"showUnfilled": true,
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"sizing": "auto",
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||||||
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"valueMode": "color"
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||||||
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}
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||||||
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},
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||||||
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"version": "13.0.0-23630096546"
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||||||
|
}
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||||||
|
}
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||||||
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},
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||||||
|
"panel-98": {
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||||||
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"kind": "Panel",
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||||||
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"spec": {
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||||||
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"data": {
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||||||
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"kind": "QueryGroup",
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||||||
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"spec": {
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||||||
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"queries": [
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||||||
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{
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||||||
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"kind": "PanelQuery",
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||||||
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"spec": {
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||||||
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"hidden": false,
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||||||
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"query": {
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||||||
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"datasource": {
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"name": "grafanacloud-projairi-prom"
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},
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"group": "prometheus",
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"kind": "DataQuery",
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"spec": {
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"expr": "sum by (feature, action, status) (rate(airi_product_events_total{service_name=~\"$service\", deployment_environment=~\"$env\", feature!=\"\", action!=\"\"}[$__rate_interval]))",
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||||||
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"legendFormat": "{{feature}} · {{action}} · {{status}}"
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||||||
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},
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||||||
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"version": "v0"
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||||||
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},
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||||||
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"refId": "A"
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||||||
|
}
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||||||
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}
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||||||
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],
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||||||
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"queryOptions": {},
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||||||
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"transformations": []
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}
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},
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"description": "Product event rate by feature/action/status. This is the Prometheus-safe trend view; user-level analysis remains in Postgres `product_events`.",
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||||||
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"id": 98,
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||||||
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"links": [],
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"title": "Product Event Rate",
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"vizConfig": {
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||||||
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"group": "timeseries",
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||||||
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"kind": "VizConfig",
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||||||
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"spec": {
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||||||
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"fieldConfig": {
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"defaults": {
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||||||
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"color": {
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||||||
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"mode": "palette-classic"
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||||||
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},
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||||||
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"custom": {
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||||||
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"axisBorderShow": false,
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||||||
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"axisCenteredZero": false,
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"axisColorMode": "text",
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"axisLabel": "",
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"axisPlacement": "auto",
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||||||
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"barAlignment": 0,
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||||||
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"barWidthFactor": 0.6,
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||||||
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"drawStyle": "line",
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||||||
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"fillOpacity": 15,
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||||||
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"gradientMode": "none",
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||||||
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"hideFrom": {
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||||||
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"legend": false,
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||||||
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"tooltip": false,
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||||||
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"viz": false
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||||||
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},
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||||||
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"insertNulls": false,
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||||||
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"lineInterpolation": "smooth",
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||||||
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"lineWidth": 1,
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||||||
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"pointSize": 5,
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||||||
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"scaleDistribution": {
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||||||
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"type": "linear"
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||||||
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},
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||||||
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"showPoints": "auto",
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||||||
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"showValues": false,
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||||||
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"spanNulls": false,
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||||||
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"stacking": {
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||||||
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"group": "A",
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||||||
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"mode": "none"
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||||||
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},
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||||||
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"thresholdsStyle": {
|
||||||
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"mode": "off"
|
||||||
|
}
|
||||||
|
},
|
||||||
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"thresholds": {
|
||||||
|
"mode": "absolute",
|
||||||
|
"steps": [
|
||||||
|
{
|
||||||
|
"color": "green",
|
||||||
|
"value": 0
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"unit": "eps"
|
||||||
|
},
|
||||||
|
"overrides": []
|
||||||
|
},
|
||||||
|
"options": {
|
||||||
|
"annotations": {
|
||||||
|
"clustering": -1,
|
||||||
|
"multiLane": false
|
||||||
|
},
|
||||||
|
"legend": {
|
||||||
|
"calcs": [
|
||||||
|
"lastNotNull",
|
||||||
|
"max"
|
||||||
|
],
|
||||||
|
"displayMode": "table",
|
||||||
|
"placement": "right",
|
||||||
|
"showLegend": true
|
||||||
|
},
|
||||||
|
"tooltip": {
|
||||||
|
"hideZeros": false,
|
||||||
|
"mode": "multi",
|
||||||
|
"sort": "desc"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"version": "13.0.0-23630096546"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
"panel-16": {
|
"panel-16": {
|
||||||
"kind": "Panel",
|
"kind": "Panel",
|
||||||
"spec": {
|
"spec": {
|
||||||
@@ -3808,6 +4178,72 @@
|
|||||||
"title": "User Engagement"
|
"title": "User Engagement"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
|
{
|
||||||
|
"kind": "RowsLayoutRow",
|
||||||
|
"spec": {
|
||||||
|
"collapse": false,
|
||||||
|
"layout": {
|
||||||
|
"kind": "GridLayout",
|
||||||
|
"spec": {
|
||||||
|
"items": [
|
||||||
|
{
|
||||||
|
"kind": "GridLayoutItem",
|
||||||
|
"spec": {
|
||||||
|
"element": {
|
||||||
|
"kind": "ElementReference",
|
||||||
|
"name": "panel-95"
|
||||||
|
},
|
||||||
|
"height": 5,
|
||||||
|
"width": 6,
|
||||||
|
"x": 0,
|
||||||
|
"y": 0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"kind": "GridLayoutItem",
|
||||||
|
"spec": {
|
||||||
|
"element": {
|
||||||
|
"kind": "ElementReference",
|
||||||
|
"name": "panel-96"
|
||||||
|
},
|
||||||
|
"height": 5,
|
||||||
|
"width": 6,
|
||||||
|
"x": 6,
|
||||||
|
"y": 0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"kind": "GridLayoutItem",
|
||||||
|
"spec": {
|
||||||
|
"element": {
|
||||||
|
"kind": "ElementReference",
|
||||||
|
"name": "panel-97"
|
||||||
|
},
|
||||||
|
"height": 9,
|
||||||
|
"width": 12,
|
||||||
|
"x": 12,
|
||||||
|
"y": 0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"kind": "GridLayoutItem",
|
||||||
|
"spec": {
|
||||||
|
"element": {
|
||||||
|
"kind": "ElementReference",
|
||||||
|
"name": "panel-98"
|
||||||
|
},
|
||||||
|
"height": 4,
|
||||||
|
"width": 12,
|
||||||
|
"x": 0,
|
||||||
|
"y": 5
|
||||||
|
}
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"title": "Product Analytics"
|
||||||
|
}
|
||||||
|
},
|
||||||
{
|
{
|
||||||
"kind": "RowsLayoutRow",
|
"kind": "RowsLayoutRow",
|
||||||
"spec": {
|
"spec": {
|
||||||
|
|||||||
@@ -39,6 +39,7 @@ const SCHEMA_VERSION = '13.0.0-23630096546'
|
|||||||
// Service / env filter applied to every Prom query. Pulled into a helper so
|
// Service / env filter applied to every Prom query. Pulled into a helper so
|
||||||
// the variable name only appears once.
|
// the variable name only appears once.
|
||||||
const SERVICE_FILTER = 'service_name=~"$service", deployment_environment=~"$env"'
|
const SERVICE_FILTER = 'service_name=~"$service", deployment_environment=~"$env"'
|
||||||
|
const PRODUCT_EVENT_FILTER = `${SERVICE_FILTER}, feature!="", action!=""`
|
||||||
|
|
||||||
// Build-script local types. Kept loose — Grafana owns the schema, and we
|
// Build-script local types. Kept loose — Grafana owns the schema, and we
|
||||||
// validate the rendered JSON by re-importing it into Grafana, not by typing.
|
// validate the rendered JSON by re-importing it into Grafana, not by typing.
|
||||||
@@ -526,6 +527,56 @@ elements['panel-92'] = timeseriesPanel(
|
|||||||
{ unit: 'short', fillOpacity: 30 },
|
{ unit: 'short', fillOpacity: 30 },
|
||||||
)
|
)
|
||||||
|
|
||||||
|
// --- Product Analytics — event-volume view only ---------------------------
|
||||||
|
// Prometheus deliberately does not carry user_id. These panels answer
|
||||||
|
// "which product actions are happening and failing"; DB-side product_events
|
||||||
|
// queries answer "how many distinct users used each feature".
|
||||||
|
elements['panel-95'] = statPanel(
|
||||||
|
95,
|
||||||
|
'Product Events (range)',
|
||||||
|
'Total first-party product analytics events over the dashboard range. This is event volume, not distinct users — distinct-user counts come from the Postgres `product_events` table.',
|
||||||
|
[
|
||||||
|
query(`sum(increase(airi_product_events_total{${PRODUCT_EVENT_FILTER}}[$__range]))`, 'events'),
|
||||||
|
],
|
||||||
|
{ unit: 'short', variant: 'count', noValue: '0', graphMode: 'none' },
|
||||||
|
)
|
||||||
|
|
||||||
|
elements['panel-96'] = gaugePanel(
|
||||||
|
96,
|
||||||
|
'Product Failure %',
|
||||||
|
'Failed product events ÷ all product events over the dashboard range. Uses only bounded labels (`feature`, `action`, `status`, `source`); no user/session/request identifiers are present in Prometheus.',
|
||||||
|
[query(
|
||||||
|
`100 * sum(increase(airi_product_events_total{${PRODUCT_EVENT_FILTER}, status="failed"}[$__range])) / clamp_min(sum(increase(airi_product_events_total{${PRODUCT_EVENT_FILTER}}[$__range])), 1)`,
|
||||||
|
'failed %',
|
||||||
|
)],
|
||||||
|
{ steps: [{ color: 'green', value: 0 }, { color: 'yellow', value: 2 }, { color: 'red', value: 10 }], max: 20, decimals: 2, noValue: '0' },
|
||||||
|
)
|
||||||
|
|
||||||
|
elements['panel-97'] = barGaugePanel(
|
||||||
|
97,
|
||||||
|
'Top Product Actions (range)',
|
||||||
|
'Top product actions by event count over the dashboard range. Use this to see which features are actually being exercised after deployment; pair with DB `count(distinct user_id)` for user counts.',
|
||||||
|
[query(
|
||||||
|
`topk(12, sum by (feature, action, status) (increase(airi_product_events_total{${PRODUCT_EVENT_FILTER}}[$__range])))`,
|
||||||
|
'{{feature}} · {{action}} · {{status}}',
|
||||||
|
'A',
|
||||||
|
PROM,
|
||||||
|
{ instant: true },
|
||||||
|
)],
|
||||||
|
{ unit: 'short', noValue: '0' },
|
||||||
|
)
|
||||||
|
|
||||||
|
elements['panel-98'] = timeseriesPanel(
|
||||||
|
98,
|
||||||
|
'Product Event Rate',
|
||||||
|
'Product event rate by feature/action/status. This is the Prometheus-safe trend view; user-level analysis remains in Postgres `product_events`.',
|
||||||
|
[query(
|
||||||
|
`sum by (feature, action, status) (rate(airi_product_events_total{${PRODUCT_EVENT_FILTER}}[$__rate_interval]))`,
|
||||||
|
'{{feature}} · {{action}} · {{status}}',
|
||||||
|
)],
|
||||||
|
{ unit: 'eps', fillOpacity: 15 },
|
||||||
|
)
|
||||||
|
|
||||||
// --- Row 2: HTTP — traffic ranking, error trend, latency trend -------------
|
// --- Row 2: HTTP — traffic ranking, error trend, latency trend -------------
|
||||||
elements['panel-16'] = barGaugePanel(
|
elements['panel-16'] = barGaugePanel(
|
||||||
16,
|
16,
|
||||||
@@ -857,6 +908,15 @@ const rows = [
|
|||||||
item('panel-81', 8, 0, 8, 4),
|
item('panel-81', 8, 0, 8, 4),
|
||||||
item('panel-82', 16, 0, 8, 4),
|
item('panel-82', 16, 0, 8, 4),
|
||||||
]),
|
]),
|
||||||
|
// Row 3: Product Analytics — Prom-safe event volume and failure trend.
|
||||||
|
// Distinct-user analytics stay in Postgres `product_events`; this row
|
||||||
|
// intentionally never uses user_id/session/request labels.
|
||||||
|
row('Product Analytics', [
|
||||||
|
item('panel-95', 0, 0, 6, 5),
|
||||||
|
item('panel-96', 6, 0, 6, 5),
|
||||||
|
item('panel-97', 12, 0, 12, 9),
|
||||||
|
item('panel-98', 0, 5, 12, 4),
|
||||||
|
]),
|
||||||
// Row 3: HTTP — full-width error breakdown on top, then traffic ranking +
|
// Row 3: HTTP — full-width error breakdown on top, then traffic ranking +
|
||||||
// latency trend side by side.
|
// latency trend side by side.
|
||||||
row('HTTP', [
|
row('HTTP', [
|
||||||
@@ -979,14 +1039,15 @@ const variables = [
|
|||||||
* 1. Service Health — signup/sessions/WS counts, req-rate, 5xx, status-code
|
* 1. Service Health — signup/sessions/WS counts, req-rate, 5xx, status-code
|
||||||
* heatmap, live WS trend: "is anything broken right now?"
|
* heatmap, live WS trend: "is anything broken right now?"
|
||||||
* 2. User Engagement — rolling DAU/WAU/MAU from user.last_seen_at
|
* 2. User Engagement — rolling DAU/WAU/MAU from user.last_seen_at
|
||||||
* 3. HTTP — error breakdown by route, request ranking, latency by route
|
* 3. Product Analytics — Prom-safe product event volume + failure trend
|
||||||
* 4. LLM Gateway — per-model request rate + latency (TTFB + end-to-end)
|
* 4. HTTP — error breakdown by route, request ranking, latency by route
|
||||||
* 5. Provider Upstreams — per-provider rate/latency/failure + TTS chars
|
* 5. LLM Gateway — per-model request rate + latency (TTFB + end-to-end)
|
||||||
* 6. LLM Tokens & Quality — token totals/throughput, revenue-leak alerts
|
* 6. Provider Upstreams — per-provider rate/latency/failure + TTS chars
|
||||||
* 7. LLM Router Health — key/decrypt/fallback "wake someone up" signals
|
* 7. LLM Tokens & Quality — token totals/throughput, revenue-leak alerts
|
||||||
* 8. Business — Stripe / Flux money flow
|
* 8. LLM Router Health — key/decrypt/fallback "wake someone up" signals
|
||||||
* 9. Infrastructure (collapsed) — DB / runtime health for triage
|
* 9. Business — Stripe / Flux money flow
|
||||||
* 10. Logs — Loki for live debugging
|
* 10. Infrastructure (collapsed) — DB / runtime health for triage
|
||||||
|
* 11. Logs — Loki for live debugging
|
||||||
*
|
*
|
||||||
* One metric, one panel: we deliberately do not duplicate a metric across
|
* One metric, one panel: we deliberately do not duplicate a metric across
|
||||||
* stat/trend/bar/pie forms. Counter conventions: rate() for "now" trends,
|
* stat/trend/bar/pie forms. Counter conventions: rate() for "now" trends,
|
||||||
|
|||||||
Reference in New Issue
Block a user