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fix(batch): more high cardinality metric attribute fixes #2846
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,53 @@ | ||
| --- | ||
| description: Guidelines for creating OpenTelemetry metrics to avoid cardinality issues | ||
| globs: | ||
| - "**/*.ts" | ||
| --- | ||
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| # OpenTelemetry Metrics Guidelines | ||
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| When creating or editing OTEL metrics (counters, histograms, gauges), always ensure metric attributes have **low cardinality**. | ||
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| ## What is Cardinality? | ||
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| Cardinality refers to the number of unique values an attribute can have. Each unique combination of attribute values creates a new time series, which consumes memory and storage in your metrics backend. | ||
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| ## Rules | ||
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| ### DO use low-cardinality attributes: | ||
| - **Enums**: `environment_type` (PRODUCTION, STAGING, DEVELOPMENT, PREVIEW) | ||
| - **Booleans**: `hasFailures`, `streaming`, `success` | ||
| - **Bounded error codes**: A finite, controlled set of error types | ||
| - **Shard IDs**: When sharding is bounded (e.g., 0-15) | ||
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| ### DO NOT use high-cardinality attributes: | ||
| - **UUIDs/IDs**: `envId`, `userId`, `runId`, `projectId`, `organizationId` | ||
| - **Unbounded integers**: `itemCount`, `batchSize`, `retryCount` | ||
| - **Timestamps**: `createdAt`, `startTime` | ||
| - **Free-form strings**: `errorMessage`, `taskName`, `queueName` | ||
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| ## Example | ||
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| ```typescript | ||
| // BAD - High cardinality | ||
| this.counter.add(1, { | ||
| envId: options.environmentId, // UUID - unbounded | ||
| itemCount: options.runCount, // Integer - unbounded | ||
| }); | ||
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| // GOOD - Low cardinality | ||
| this.counter.add(1, { | ||
| environment_type: options.environmentType, // Enum - 4 values | ||
| streaming: true, // Boolean - 2 values | ||
| }); | ||
| ``` | ||
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| ## Reference | ||
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| See the schedule engine (`internal-packages/schedule-engine/src/engine/index.ts`) for a good example of low-cardinality metric attributes. | ||
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| High cardinality metrics can cause: | ||
| - Memory bloat in metrics backends (Axiom, Prometheus, etc.) | ||
| - Slow queries and dashboard timeouts | ||
| - Increased costs (many backends charge per time series) | ||
| - Potential data loss or crashes at scale | ||
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