A collection of working Alloy scenarios
C-Rank Grade: A (Welcoming) - 60/100
External PR Merge Rate: 88%
Response Time: 2d
First Timer Success: 100%
grafana/alloy-scenarios has a recorded first-timer success rate of 100.0%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in grafana/alloy-scenarios respond to incoming external pull requests in approximately 54.7 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 60.3 places grafana/alloy-scenarios in the A tier.
The external contributor pull request merge rate for grafana/alloy-scenarios is 88.4%, based on public PR activity from non-core contributors.
grafana/alloy-scenarios currently has 5 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Active maintainer team in grafana/alloy-scenarios welcoming community pull requests and bug fixes.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
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Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 10:00 UTC (10:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 10:00 to 14:00 UTC (10:00 - 14:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~36 hours (96% approval). Large PRs (>500 lines) take ~262 hours (40% approval). Keep your diff compact for rapid review.
What this scenario demonstrates A Datadog Agent → Alloy path using otelcol.receiver.datadog — Alloy accepts Datadog Agent metrics/traces on the Datadog intake protocol and forwards them to Prometheus/Tempo, letting teams put Alloy in front of (or in place of) the Datadog Agent. (The earlier #110 "datadog-receiver" issue was closed without a scenario; the receiver now exists.) Why users want it Datadog is a common vendor lock-in point; an evaluable "send your existing Datadog Agent traffic to Alloy" demo is high-demand. Components otelcol.receiver.datadog otelcol.exporter.otlp — traces to Tempo prometheus.remote_write — metrics to Prometheus Success criteria After docker compose up -d, a bundled sender emitting Datadog-format metrics/traces (e.g. a DogStatsD/Datadog Agent in a container, or a small script) lands data in Grafana → Prometheus/Tempo via Alloy — no Datadog account. References https://grafana.com/docs/alloy/latest/reference/components/otelcol/otelcol.receiver.
What this scenario demonstrates An OpenTelemetry Collector → Alloy migration playbook: take a representative otelcol config (OTLP in, batch, OTLP out) and show the equivalent Alloy otelcol.* pipeline, running a stock otel/opentelemetry-collector and Alloy side by side against the same OTLP app and confirming identical output in Tempo/Prometheus. Why users want it The OpenTelemetry Collector is the industry-standard collector; teams consolidating onto Alloy want a clear, verifiable mapping (and to see that Alloy is the Collector under the hood). Components otelcol.receiver.otlp otelcol.processor.batch otelcol.exporter.otlp plus a stock otel/opentelemetry-collector container with the equivalent YAML for comparison Success criteria After docker compose up -d, one OTLP demo app fans out to both collectors; Tempo/Prometheus show matching traces/metrics from each path, demonstrating parity. References https://grafana.com/docs/alloy/latest/set-up/migrate/ https://grafana.com/docs/alloy/l
What this scenario demonstrates Cut log volume and cost with loki.process sampling and rate limiting — keep all WARN/ERROR lines, sample DEBUG/INFO down to a fraction, and cap throughput per stream — before loki.write. Why users want it High-volume log sinks need per-stream sampling to control Loki cost; "how do I drop/sample noisy logs in Alloy" is a frequent cost-control question and complements the existing redaction scenario. Components loki.source.file (or loki.source.api) loki.process with stage.sampling and/or stage.limit, gated by level loki.write Success criteria After docker compose up -d, a high-volume generator emits mixed-severity logs; a Grafana panel of log ingest rate shows DEBUG/INFO sampled to ~10% while ERROR lines pass through at 100%. References https://grafana.com/docs/alloy/latest/reference/components/loki/loki.process/ Authoring notes Follow the scenario template and authoring conventions. Keep config.alloy ≤80 lines where possible. Pin image versions
What this scenario demonstrates Drive Alloy scraping from Prometheus Operator CRDs — PodMonitor and ServiceMonitor — with prometheus.operator.podmonitors / prometheus.operator.servicemonitors, so teams already using the operator's CRDs can adopt Alloy without rewriting scrape config. Kubernetes scenario (kind), under k8s/. Why users want it PodMonitor/ServiceMonitor are the de-facto standard for declaring scrape targets in Kubernetes; "does Alloy honor my existing ServiceMonitors?" is a common migration question (grafana/alloy#6010). Components prometheus.operator.servicemonitors prometheus.operator.podmonitors prometheus.remote_write Success criteria In a kind cluster with the Prometheus Operator CRDs installed and a sample app + ServiceMonitor, the app's metrics appear in Grafana → Prometheus without any static scrape config in Alloy. References https://grafana.com/docs/alloy/latest/reference/components/prometheus/prometheus.operator.servicemonitors/ https://grafana.com/docs
What this scenario demonstrates Enrich OTLP traces with Kubernetes metadata — pod name, namespace, node, workload, and labels — using otelcol.processor.k8sattributes, so traces in Tempo are filterable by k8s identity. Kubernetes scenario (kind), placed under k8s/. Why users want it k8s attribute decoration is essential for anyone running Alloy as a DaemonSet, and it is the single most common "how do I tie traces to pods" request. The repo's k8s/tracing scenario does not cover attribute enrichment. Components otelcol.receiver.otlp otelcol.processor.k8sattributes otelcol.processor.batch otelcol.exporter.otlp — to Tempo Success criteria In a kind cluster, an in-cluster OTLP demo app emits spans; Tempo shows them enriched with k8s.pod.name, k8s.namespace.name, and k8s.node.name. References https://grafana.com/docs/alloy/latest/reference/components/otelcol/otelcol.processor.k8sattributes/ Authoring notes Follow the k8s scenario template and the existing k8s/ scenarios (kind + Hel
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When evaluating whether to contribute to grafana/alloy-scenarios, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of grafana/alloy-scenarios acknowledge new external contributions in approximately 2 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 88.4% were successfully merged into the primary branch.
grafana/alloy-scenarios has a recorded first-timer success rate of 100.0%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~2 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 60.3 places grafana/alloy-scenarios in the Welcoming tier.
The external pull request merge rate is 88.4%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, grafana/alloy-scenarios currently has 5 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for grafana/alloy-scenarios are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.