+++ title = "Datadog" availability = "v2.6+" maintainer = "Community" category = "Metrics" description = "Scale applications based on Datadog." go_file = "datadog_scaler" +++ > 💡 **NOTE:** Take into account [API Datadog endpoints rate limits](https://docs.datadoghq.com/api/latest/rate-limits/) when defining polling interval. For more detailed information about polling intervals check [the Polling intervals and Datadog rate limiting section](#polling-intervals-and-datadog-rate-limiting). There are two ways to poll Datadog for a query value using the Datadog scaler: using the REST API endpoints, or using the [Datadog Cluster Agent](https://docs.datadoghq.com/containers/cluster_agent/) as proxy. Using the Datadog Cluster Agent as proxy reduces the chance of reaching rate limits. As both types are different in terms of usage and authentication, this documentation handles them separately. ## Using the Datadog Cluster Agent (Experimental) With this method, the Datadog scaler will be connecting to the Datadog Cluster Agent to retrieve the query values that will be used to drive the KEDA scaling events. This reduces the risk of reaching rate limits for the Datadog API, as the Cluster Agent retrieves metric values in batches. ### Deploy the Datadog Cluster Agent with enabled external metrics First, deploy the Datadog Cluster Agent enabling the external metrics provider, but without registering it as an `APIService` (to avoid clashing with KEDA). If you are using Helm to deploy the Cluster Agent, set: * `clusterAgent.metricsProvider.enabled` to `true` * `clusterAgent.metricsProvider.registerAPIService` to `false` * `clusterAgent.metricsProvider.useDatadogMetrics` to `true` * `clusterAgent.env` to `[{name: DD_EXTERNAL_METRICS_PROVIDER_ENABLE_DATADOGMETRIC_AUTOGEN, value: "false"}]` If you are using the Datadog Operator, add the following options to your `DatadogAgent` object: ``` apiVersion: datadoghq.com/v2alpha1 kind: DatadogAgent metadata: name: datadog spec: features: externalMetricsServer: enabled: true useDatadogMetrics: true registerAPIService: false override: clusterAgent: env: [{name: DD_EXTERNAL_METRICS_PROVIDER_ENABLE_DATADOGMETRIC_AUTOGEN, value: "false"}] [...] ``` NOTE: Using the Datadog Operator for this purpose requires version 1.8.0 of the operator or later. ### Create a DatadogMetric object for each scaling query To use the Datadog Cluster Agent to retrieve the query values from Datadog, first, create a [`DatadogMetric`](https://docs.datadoghq.com/containers/guide/cluster_agent_autoscaling_metrics/?tab=helm#create-the-datadogmetric-object) object with the query to drive your scaling events. You will need to add the `external-metrics.datadoghq.com/always-active: "true"` annotation, to ensure the Cluster Agent retrieves the query value. Example: ```yaml apiVersion: datadoghq.com/v1alpha1 kind: DatadogMetric metadata: annotations: external-metrics.datadoghq.com/always-active: "true" name: nginx-hits spec: query: sum:nginx.net.request_per_s{kube_deployment:nginx} ``` ### Trigger Specification This specification describes the `datadog` trigger that scales based on a Datadog query, using the Datadog Cluster Agent as proxy. ```yaml triggers: - type: datadog metricType: Value metadata: useClusterAgentProxy: "true" datadogMetricName: "nginx-hits" datadogMetricNamespace: "default" targetValue: "7.75" activationQueryValue: "1.1" type: "global" # Deprecated in favor of trigger.metricType metricUnavailableValue: "1.5" timeout: "10s" # Optional. Custom timeout for the HTTP client used in this scaler ``` **Parameter list:** - `useClusterAgentProxy` - Whether to use the Cluster Agent as proxy to get the query values. (Values: true, false, Default: false, Optional) - `datadogMetricName` - The name of the `DatadogMetric` object to drive the scaling events. - `datadogMetricNamespace` - The namespace of the `DatadogMetric` object to drive the scaling events. - `targetValue` - Value to reach to start scaling (This value can be a float). - `activationQueryValue` - Target value for activating the scaler. Learn more about activation [here](./../concepts/scaling-deployments.md#activating-and-scaling-thresholds).(Default: `0`, Optional, This value can be a float) - `type` - Whether to start scaling based on the value or the average between pods. (Values: `average`, `global`, Default:`average`, Optional) - `age`: The time window (in seconds) to retrieve metrics from Datadog. (Default: `90`, Optional) - `lastAvailablePointOffset`: The offset to retrieve the X to last data point. The value of last data point of some queries might be inaccurate [because of the implicit rollup function](https://docs.datadoghq.com/dashboards/functions/rollup/#rollup-interval-enforced-vs-custom), try to adjust to `1` if you encounter this issue. (Default: `0`, Optional) - `metricUnavailableValue`: The value of the metric to return to the HPA if Datadog doesn't find a metric value for the specified time window. If not set, an error will be returned to the HPA, which will log a warning. (Optional, This value can be a float) - `timeout` - Timeout (in a Duration string format) **for this specific trigger**. This value will override the value defined in `KEDA_HTTP_DEFAULT_TIMEOUT`. (Optional) > 💡 **NOTE:** The `type` parameter is deprecated in favor of the global `metricType` and will be removed in a future release. Users are advised to use `metricType` instead. > 💡 **NOTE:** Datadog may return HTTP 422 (Unprocessable Entity) or empty metric series. Both scenarios are treated as "no data" and will use `metricUnavailableValue` if set, otherwise an error is returned to the HPA. ### Authentication The Datadog scaler with Cluster Agent supports one type of authentication - Bearer authentication. You can use `TriggerAuthentication` CRD to configure the authentication. Specify `authMode` and other trigger parameters in `TriggerAuthentication` as mentioned below: **Common to all authentication types** - `authMode` - The authentication mode to connect to the Cluster Agent. (Values: bearer, Default: bearer, Optional) - `datadogNamespace` - The namespace where the Datadog Cluster Agent is deployed. - `datadogMetricsService` - The service name for the Cluster Agent metrics server. To find the name of the service, check the available services in the Datadog namespace and look for the `*-cluster-agent-metrics*` name pattern. - `datadogMetricsServicePort` - The port of the service for the Cluster Agent Metrics API. (Default: 8443, Optional) - `unsafeSsl` - Skip certificate validation when connecting over HTTPS. (Values: true, false, Default: false, Optional) **Bearer authentication:** - `token` - The ServiceAccount token to connect to the Datadog Cluster Agent. The service account needs to have permissions to `get`, `watch`, and `list` all `external.metrics.k8s.io` resources. Instead of manually creating long-lived tokens stored in Secrets, it is recommended to use [Bound Service Account Tokens](https://kubernetes.io/docs/reference/access-authn-authz/service-accounts-admin/#bound-service-account-tokens) via the `boundServiceAccountToken` parameter, which are more secure as they are time-bound and automatically rotated. ### Example ```yaml apiVersion: v1 kind: ConfigMap metadata: name: datadog-config namespace: my-project data: datadogNamespace: # Required: namespace where the Datadog Cluster Agent is deployed datadogMetricsService: # Required: Cluster Agent metrics server service name unsafeSsl: # Optional: set to "true" to skip SSL certificate validation authMode: # Required: authentication mode (bearer) --- apiVersion: keda.sh/v1alpha1 kind: TriggerAuthentication metadata: name: datadog-cluster-agent-creds namespace: my-project spec: boundServiceAccountToken: - parameter: token serviceAccountName: my-service-account # Required: service account with permissions to get, watch, list external.metrics.k8s.io configMapTargetRef: - parameter: datadogNamespace name: datadog-config key: datadogNamespace - parameter: unsafeSsl name: datadog-config key: unsafeSsl - parameter: authMode name: datadog-config key: authMode - parameter: datadogMetricsService name: datadog-config key: datadogMetricsService --- apiVersion: keda.sh/v1alpha1 kind: ScaledObject metadata: name: datadog-scaledobject namespace: my-project spec: scaleTargetRef: name: nginx maxReplicaCount: 3 minReplicaCount: 1 pollingInterval: 60 triggers: - type: datadog metadata: useClusterAgentProxy: "true" datadogMetricName: "nginx-hits" datadogMetricNamespace: "default" targetValue: "2" type: "global" timeout: "10s" authenticationRef: name: datadog-cluster-agent-creds ``` ## Using the Datadog REST API ### Trigger Specification This specification describes the `datadog` trigger that scales based on a Datadog query, using the Datadog REST API. ```yaml triggers: - type: datadog metricType: Value metadata: useClusterAgentProxy: "false" query: "sum:trace.redis.command.hits{env:none,service:redis}.as_count()" queryValue: "7.75" activationQueryValue: "1.1" queryAggregator: "max" type: "global" # Deprecated in favor of trigger.metricType age: "120" timeWindowOffset: "30" lastAvailablePointOffset: "1" metricUnavailableValue: "1.5" timeout: "10s" ``` **Parameter list:** - `useClusterAgentProxy` - Whether to use the Cluster Agent as proxy to get the query values. (Default: false) - `query` - The Datadog query to run. - `queryValue` - Value to reach to start scaling (This value can be a float). - `activationQueryValue` - Target value for activating the scaler. Learn more about activation [here](../concepts/scaling-deployments#activating-and-scaling-thresholds).(Default: `0`, Optional, This value can be a float) - `queryAggregator` - When `query` is multiple queries, comma-separated, this sets how to aggregate the multiple results. (Values: `max`, `average`, Required only when `query` contains multiple queries) - `type` - Whether to start scaling based on the value or the average between pods. (Values: `average`, `global`, Default:`average`, Optional) - `age`: The time window (in seconds) to retrieve metrics from Datadog. (Default: `90`, Optional) - `timeWindowOffset`: The delayed time window offset (in seconds) to wait for the metric to be available. The values of some queries might be not available at now and need a small delay to become available, try to adjust `timeWindowOffset` if you encounter this issue. (Default: `0`, Optional) - `lastAvailablePointOffset`: The offset to retrieve the X to last data point. The value of last data point of some queries might be inaccurate [because of the implicit rollup function](https://docs.datadoghq.com/dashboards/functions/rollup/#rollup-interval-enforced-vs-custom), try to adjust to `1` if you encounter this issue. (Default: `0`, Optional) - `metricUnavailableValue`: The value of the metric to return to the HPA if Datadog doesn't find a metric value for the specified time window. If not set, an error will be returned to the HPA, which will log a warning. (Optional, This value can be a float) - `timeout` - Timeout (in a Duration string format) **for this specific trigger**. This value will override the value defined in `KEDA_HTTP_DEFAULT_TIMEOUT`. (Optional) > 💡 **NOTE:** The `type` parameter is deprecated in favor of the global `metricType` and will be removed in a future release. Users are advised to use `metricType` instead. > 💡 **NOTE:** Datadog may return HTTP 422 (Unprocessable Entity) or empty metric series. Both scenarios are treated as "no data" and will use `metricUnavailableValue` if set, otherwise an error is returned to the HPA. ### Authentication Datadog requires both an API key and an APP key to retrieve metrics from your account. You should use `TriggerAuthentication` CRD to configure the authentication: **Parameter list:** - `apiKey` - Datadog API key. - `appKey` - Datadog APP key. - `datadogSite` - Datadog site where to get the metrics from. This is commonly referred as DD_SITE in Datadog documentation. (Default: `datadoghq.com`, Optional) ### Example The example below uses the default KEDA polling interval (30 seconds). Take into account that [API Datadog endpoints are rate limited](https://docs.datadoghq.com/api/latest/rate-limits/) and reducing the polling interval can accelerate reaching it. If your account has reached its rate limit, a relevant error will be logged in KEDA. ```yaml apiVersion: v1 kind: Secret metadata: name: datadog-secrets namespace: my-project type: Opaque data: apiKey: # Required: base64 encoded value of Datadog apiKey appKey: # Required: base64 encoded value of Datadog appKey datadogSite: # Optional: base64 encoded value of Datadog site --- apiVersion: keda.sh/v1alpha1 kind: TriggerAuthentication metadata: name: keda-trigger-auth-datadog-secret namespace: my-project spec: secretTargetRef: # Required: API key for your Datadog account - parameter: apiKey name: datadog-secrets key: apiKey # Required: APP key for your Datadog account - parameter: appKey name: datadog-secrets key: appKey # Optional: Datadog site. Default: "datadoghq.com" - parameter: datadogSite name: datadog-secrets key: datadogSite --- apiVersion: keda.sh/v1alpha1 kind: ScaledObject metadata: name: datadog-scaledobject namespace: my-project spec: scaleTargetRef: name: worker triggers: - type: datadog # Optional: (Value or AverageValue). Whether the target value is global or average per pod. Default: AverageValue metricType: "Value" metadata: # Required: datadog metric query query: "sum:trace.redis.command.hits{env:none,service:redis}.as_count()" # Required: according to the number of query result, to scale the TargetRef queryValue: "7" # Optional: The time window (in seconds) to retrieve metrics from Datadog. Default: 90 age: "120" # Optional: The metric value to return to the HPA if a metric value wasn't found for the specified time window metricUnavailableValue: "0" # Optional: the HTTP timeout for this trigger timeout: "10s" authenticationRef: name: keda-trigger-auth-datadog-secret ``` ### Polling intervals and Datadog rate limiting [API Datadog endpoints are rate limited](https://docs.datadoghq.com/api/latest/rate-limits/). Depending on the state of the `ScaledObject` there are two different parameters to control how often (per `ScaledObject`) we query Datadog for a metric. When scaling from 0 to 1, the polling interval is controlled by KEDA, using [the `spec.pollingInterval` parameter in the `ScaledObject` definition](../reference/scaledobject-spec/#pollinginterval). For example, if this parameter is set to `60`, KEDA will poll Datadog for a metric value every 60 seconds while the number of replicas is 0. While scaling from 1 to N, on top of KEDA, the HPA will also poll regularly Datadog for metrics, based on [the `--horizontal-pod-autoscaler-sync-period` parameter to the `kube-controller-manager`](https://kubernetes.io/docs/reference/command-line-tools-reference/kube-controller-manager/#options), which by default is 15 seconds. For example, if the `kube-controller-manager` was started with `--horizontal-pod-autoscaler-sync-period=30`, the HPA will poll Datadog for a metric value every 30 seconds while the number of replicas is between 1 and N. ### Multi-Query Support To reduce issues with API rate limiting from Datadog, it is possible to send a single query, which contains multiple queries, comma-separated. When doing this, the results from each query are aggregated based on the `queryAggregator` value (eg: `max` or `average`). > 💡 **NOTE:** Because the average/max aggregation operation happens at the scaler level, there won't be any validation or errors if the queries don't make sense to aggregate. Be sure to read and understand the two patterns below before using Multi-Query. #### Example 1 - Aggregating Similar Metrics Simple aggregation works well, when wanting to scale on more than one metric with similar return values/scale (ie. where multiple metrics can use a single `queryValue` and still make sense). ```yaml apiVersion: keda.sh/v1alpha1 kind: ScaledObject metadata: name: datadog-scaledobject namespace: my-project spec: scaleTargetRef: name: worker triggers: - type: datadog metricType: "AverageValue" metadata: # Comma-separated queries count as a single API call: query: "per_second(sum:http.requests{service:myservice1}).rollup(max, 300)),per_second(sum:http.requests{service:myservice1}).rollup(avg, 600)" # According to aggregated results, how to scale the TargetRef queryValue: "100" # How to aggregate results from multi-query queries. Default: 'max' queryAggregator: "average" # Optional: The time window (in seconds) to retrieve metrics from Datadog. Default: 90 age: "600" # Optional: The metric value to return to the HPA if a metric value wasn't found for the specified time window metricUnavailableValue: "0" authenticationRef: name: keda-trigger-auth-datadog-secret ``` The example above looks at the `http.requests` value for a service; taking two views of the same metric (max vs avg, and different time windows), and then uses a scale value which is the average of them both. This works particularly well when scaling against the same metric, but with slightly different parameters, or methods like ```week_before()``` for example. #### Example 2 - Driving scale directly When wanting to scale on non-similar metrics, whilst still benefiting from reduced API calls with multi-query support, the easiest way to do this is to make each query directly return the desired scale (eg: number of pods), and then `max` or `average` the results to get the desired target scale. This can be done by adding arithmetic to the queries, which makes them directly return the number of pods that should be running. Following this pattern, and then setting `queryValue: 1` and `metricType: AverageValue` results in the desired number of pods being spawned directly from the results of the metric queries. ```yaml apiVersion: keda.sh/v1alpha1 kind: ScaledObject metadata: name: datadog-scaledobject namespace: my-project spec: scaleTargetRef: name: worker triggers: - type: datadog # `AverageValue` tracks the query results divided by the number of running containers metricType: "AverageValue" metadata: # Comma-separated queries count as a single API call: ## This example returns "http.requests" @ 180 requests-per-second per-pod, ## and "http.backlog" size of 30 per-pod query: "per_second(sum:http.requests{service:myservice1}).rollup(max, 300))/180,per_second(sum:http.backlog{service:myservice1}).rollup(max, 300)/30" # Setting query value to 1 and metricType to "AverageValue" allows the metric to dictate the number of pods from it's own arthimetic. queryValue: "1" # How to aggregate results from multi-query queries. Default: 'max' queryAggregator: "max" authenticationRef: name: keda-trigger-auth-datadog-secret ``` Using the example above, if we assume that `http.requests` is currently returning `360`, dividing that by `180` in the query, results in a value of `2`; if `http.backlog` returns `90`, dividing that by `30` in the query, results in a value of `3`. With the `max` Aggregator set, the scaler will set the target scale to `3` as that is the higher value from all returned queries. ### Cases of unexpected metrics value in DataDog API response #### Latest data point is unavailable By default, Datadog scaler retrieves the metrics with time window from `now - metadata.age (in seconds)` to `now`, however, some kinds of queries need a small delay (usually 30 secs - 2 mins) before data is available when querying from the API. In this case, adjust `timeWindowOffset` to ensure that the latest point of your query is always available. ```yaml apiVersion: keda.sh/v1alpha1 kind: ScaledObject metadata: name: datadog-scaledobject namespace: my-project spec: scaleTargetRef: name: worker triggers: - type: datadog metricType: "AverageValue" metadata: query: "sum:trace.express.request.hits{*}.as_rate()" queryValue: "100" age: "90" metricUnavailableValue: "0" # Optional: The delayed time window offset (in seconds) to wait for the metric to be available. The values of some queries might be not available at now and need a small delay to become available, try to adjust it if you encounter this issue. Default: 0 timeWindowOffset: "30" # Optional: The offset to retrieve the X to last data point. The value of last data point of some queries might be inaccurate, try to adjust to 1 if you encounter this issue. Default: 0 lastAvailablePointOffset: "1" authenticationRef: name: keda-trigger-auth-datadog-secret ``` Check [here](https://github.com/kedacore/keda/pull/3954#discussion_r1042820206) for the details of this issue #### The value of last data point is inaccurate Datadog implicitly rolls up data points automatically with the `avg` method, effectively displaying the average of all data points within a time interval for a given metric. Essentially, there is a rollup for each point. The values at the end attempt to have the rollup applied. When this occurs, it looks at a X second bucket according to your time window, and will default average those values together. Since this is the last point in the query, there are no other values to average with in that X second bucket. This leads to the value of last data point that was not rolled up in the same fashion as the others, and leads to an inaccurate number. In these cases, adjust `lastAvailablePointOffset` to 1 to use the second to last points of an API response would be the most accurate. ```yaml apiVersion: keda.sh/v1alpha1 kind: ScaledObject metadata: name: datadog-scaledobject namespace: my-project spec: scaleTargetRef: name: worker triggers: - type: datadog metricType: "AverageValue" metadata: query: "sum:trace.express.request.hits{*}.as_rate()" queryValue: "100" age: "90" metricUnavailableValue: "0" # Optional: The delayed time window offset (in seconds) to wait for the metric to be available. The values of some queries might be not available at now and need a small delay to become available, try to adjust it if you encounter this issue. Default: 0 timeWindowOffset: "30" # Optional: The offset to retrieve the X to last data point. The value of last data point of some queries might be inaccurate, try to adjust to 1 if you encounter this issue. Default: 0 lastAvailablePointOffset: "1" authenticationRef: name: keda-trigger-auth-datadog-secret ``` Check [here](https://github.com/kedacore/keda/pull/3954#discussion_r1042820206) for the details of this issue.