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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchArgo Rollouts controls how a Kubernetes release reaches users; Datadog supplies the metric evidence that can let the rollout proceed, pause, or abort. To automate the decision, define a Rollout and an AnalysisTemplate, configure the template’s Datadog query and pass conditions, then connect the analysis outcome to the rollout’s promotion or rollback behavior. The gate is only as reliable as the query’s tags, time window, aggregation, and threshold.
How Argo Rollouts and Datadog work together
Argo Rollouts is a Kubernetes controller with custom resources for progressive delivery. It adds canary and blue-green strategies, traffic shaping integrations, analysis runs, and automated promotion or rollback based on metrics. In this arrangement, Argo Rollouts provides the deployment state machine and traffic-control mechanics; Datadog provides the measurements used by an analysis gate.
An AnalysisTemplate defines what to measure and how to judge the result. An AnalysisRun executes that template. Its outcome informs the Rollout: successful analysis can allow progression, failed analysis can abort it, and an inconclusive result can pause it for human judgment. The timing of the analysis matters: it can run during a traffic ramp, before promotion, or after promotion.
Choose a traffic strategy and verification point
| Strategy | Traffic model | What analysis can verify | Operational consideration |
|---|---|---|---|
| Canary | Gradually expose a portion of traffic to the new ReplicaSet while the release progresses. | Metric behavior as exposure changes, with checks during the ramp or before promotion. | Evaluation can require multiple ReplicaSets or traffic paths to remain active while the candidate is assessed. |
| Blue-green | An active Service continues to send normal traffic to the stable ReplicaSet while a preview Service routes to the new ReplicaSet. Argo Rollouts uses ReplicaSet hashes in Service selectors and can switch traffic after verification. | Behavior of the preview version before switching traffic, or the promoted version in post-promotion analysis. | Switching back relies on retaining the previous stable ReplicaSet and having Services routed so traffic can return to it. |
Decide when a metric should block a release before configuring the template. A pre-promotion check protects the stable service from an unverified candidate; a post-promotion check can detect regressions after traffic has switched; a canary check can inform progression as exposure increases. These are different control points, so choose the one that matches the risk you want the gate to manage.
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Configure a Datadog analysis gate
- Define the Rollout. Use an Argo Rollouts
Rolloutresource and select canary or blue-green behavior for the workload. - Define the AnalysisTemplate. Specify the metric, sampling interval, success condition, and failure limit that determine whether the rollout may progress. Attach analysis at the intended point in the rollout strategy.
- Configure the Datadog provider. The provider configuration uses an API version, query, interval, success condition, and failure limit. Store Datadog API and application credentials in a Kubernetes Secret rather than embedding them in the template.
- Run and inspect the AnalysisRun. Confirm that the query returns data for the service and that the observed result is being evaluated as expected before relying on the gate to control production traffic.
- Define the failure response. Decide whether the rollout should pause or abort when analysis fails, and make sure the rollout strategy’s promotion and abort behavior matches that decision.
The Argo Rollouts documentation’s Datadog example uses the query sum:requests.error.rate{service:{{args.service-name}}} and a success condition of result <= 0.01. These are example settings, not a recommended universal error threshold. The documentation also shows a five-minute analysis interval as an example; choose the interval and threshold for the service’s traffic volume, metric behavior, and acceptable risk.
Make the Datadog query a trustworthy gate
A query that returns a number is not automatically a useful release signal. Before using its result to control a rollout, check the following:
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- Tags: Make sure the query selects the intended service and distinguishes the candidate release when the metric needs to isolate it. A broad query can combine stable and candidate traffic and hide a regression.
- Window and interval: Align the query’s time window and the analysis sampling interval with how quickly the service can produce a meaningful signal. A short window may be noisy or sparse; a long one may delay a decision.
- Aggregation: Check that the aggregation answers the operational question. A rate, count, or aggregate over multiple instances can tell a different story from a per-instance measurement.
- Empty results: Decide how the analysis should behave when a query has no matching data. Missing measurements should not be mistaken for evidence that a candidate is healthy.
- Threshold: Set the success condition from the service’s own baseline and release tolerance. The example condition is illustrative, not a standard for every application.
- Failure limit: Choose how many unsuccessful measurements can occur before the analysis fails, taking account of transient noise and the time a bad release could remain exposed.
What happens when analysis fails
A failed AnalysisRun is a control signal, not just an alert: it can abort the Rollout. In a blue-green release, the active Service can be switched back to the previous stable ReplicaSet if that ReplicaSet is retained and the Service routing is configured for the switch. In a canary, configure the rollout’s abort behavior so that a failed gate stops further exposure and returns traffic to the stable version where the strategy supports it.
An inconclusive run is different from a successful run: it can pause progression for judgment rather than authorize promotion. Define how operators should respond to that pause, including checking whether the query has data, its tags select the intended release, and the Datadog credentials and provider configuration are valid. Do not treat “no usable result” as equivalent to a passing result.
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Monitor the rollout controller separately from the gate
Datadog’s Argo Rollouts integration can collect the controller’s Prometheus-formatted metrics through OpenMetrics. The controller exposes metrics at /metrics on port 8090; documented examples include rollout phase and updated replicas. These metrics help show controller and rollout state, but they do not replace the Datadog query that decides whether application behavior meets the release condition.
Use Datadog deployment tracking and version tags alongside the gate to compare error rates, traces, and service behavior for canary releases. Deployment tracking provides context for interpreting a change; the AnalysisTemplate query remains the configured evidence used by the rollout decision.
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Operational checks before automating promotion
- Verify the Datadog query against the correct service and candidate release tags.
- Confirm that the query produces representative data at the selected sampling interval.
- Review success conditions, failure limits, and the handling of empty or inconclusive results.
- Check that the intended analysis point—during the ramp, before promotion, or after promotion—matches the release risk.
- Confirm that the abort path can restore traffic to the stable ReplicaSet and that it remains available during evaluation.
- Use controller metrics to spot rollout-state or controller issues, and use deployment tracking to interpret service behavior; do not substitute either for a correctly configured gate query.
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