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Copy file name to clipboardExpand all lines: articles/container-apps/dapr-keda-scaling.md
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# Scale Dapr applications with KEDA scalers
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Using [KEDA scalers](https://keda.sh/), you can scale your application and its [Dapr](https://docs.dapr.io/) sidecar when it has scaled to zero with inbound events and messages. In this guide, we demonstrate scaling a Dapr pub/sub application by setting up a KEDA scaler to watch for messages coming into the queue and triggers the Dapr sidecar to spin up.
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Using [KEDA scalers](https://keda.sh/), you can scale your application and its [Dapr](https://docs.dapr.io/) sidecar when it has scaled to zero with inbound events and messages. In this guide, we demonstrate scaling a Dapr pub/sub application by setting up a KEDA scaler to watch for messages coming into the queue and triggers the app to scale up and down as needed.
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In the following scenario, we examine the Bicep for:
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1. A `checkout` publisher container app continuously publishes messages via the Dapr pub/sub API to the `orders` topic in Azure Service Bus.
In the `order-processor` subscriber, we've added a custom scale rule on the resource for the type `azure-servicebus`. With this scale rule, KEDA can scale up the container app and its Dapr sidecar, allowing incoming messages to be processed.
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By default, [Container Apps assigns an http-based scale rule to applications](./scale-app.md), scaling apps based on the number of incoming HTTP requests. In the `order-processor` subscriber, we've added a custom scale rule on the resource for the type `azure-servicebus`. With this scale rule, KEDA can scale up the container app and its Dapr sidecar, allowing incoming messages to be processed.
By default, [Container Apps assigns an http-based scale rule to applications](./scale-app.md), scaling apps based on the number of incoming http requests. However, since the `order-processor` application is using the Dapr pub/sub abstraction to pull messages from a queue, it needs to define a custom scaler based on the number of messages waiting to be processed.
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Behind the scenes, KEDA scales our `order-processor` appropriately (even down to zero). Notice the `messageCount` property on the scaler's configuration:
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