Solution ideas
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This article describes a variation of a serverless event-driven architecture that runs on Azure Kubernetes Service (AKS) with KEDA scaler. The solution ingests a stream of data, processes the data, and then writes the results to a back-end database.
Architecture
Download a Visio file of this architecture.
Dataflow
- AKS with the KEDA scaler is used to autoscale Azure Functions containers based on the number of events needing to be processed.
- Events arrive at the Input Event Hub.
- The De-batching and Filtering Azure Function is triggered to handle the event. This step filters out unwanted events and de-batches the received events before submitting to the Output Event Hub.
- If the De-batching and Filtering Azure Function fails to store the event successfully, the event is submitted to the Deadletter Event Hub 1.
- Events arriving at the Output Event Hub trigger the Transforming Azure Function. This Azure Function transforms the event into a message for the Azure Cosmos DB instance.
- The event is stored in an Azure Cosmos DB database.
Components
- Azure Kubernetes Service (AKS) simplifies deploying a managed Kubernetes cluster in Azure by offloading the operational overhead to Azure. As a hosted Kubernetes service, Azure handles critical tasks, like health monitoring and maintenance.
- KEDA is an event-driven autoscaler used to scale containers in the Kubernetes cluster based on the number of events needing to be processed.
- Event Hubs ingests the data stream. Event Hubs is designed for high-throughput data streaming scenarios.
- Azure Functions is a serverless compute option. It uses an event-driven model, where a piece of code (a function) is invoked by a trigger.
- Azure Cosmos DB is a multi-model database service that is available in a serverless, consumption-based mode. For this scenario, the event-processing function stores JSON records, using the Azure Cosmos DB for NoSQL.
Note
For Internet of Thing (IoT) scenarios, we recommend Azure IoT Hub. IoT Hub has a built-in endpoint that's compatible with the Azure Event Hubs API, so you can use either service in this architecture with no major changes in the back-end processing. For more information, see Connecting IoT Devices to Azure: IoT Hub and Event Hubs.
Scenario details
This article describes a serverless event-driven architecture that runs on AKS with KEDA scaler. The solution ingests a stream of data, processes the data, and then writes the results to a back-end database.
To learn more about the basic concepts, considerations, and approaches for serverless event processing, see the Serverless event processing reference architecture.
Potential use case
A popular use case for implementing an end-to-end event stream processing pattern includes the Event Hubs streaming ingestion service to receive and process events per second using a de-batching and transformation logic implemented with highly scalable, event hub-triggered functions.
Contributors
This article is maintained by Microsoft. It was originally written by the following contributors.
Principal author:
- Rajasa Savant | Senior Software Development Engineer
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Next steps
- Introduction to Azure Kubernetes Service
- Azure Event Hubs documentation
- Introduction to Azure Functions
- Azure Functions documentation
- Overview of Azure Cosmos DB
- Choose an API in Azure Cosmos DB
Related resources
- Serverless event processing is a reference architecture detailing a typical architecture of this type, with code samples and discussion of important considerations.
- Monitoring serverless event processing provides an overview and guidance on monitoring serverless event-driven architectures like this one.
- De-batching and filtering in serverless event processing with Event Hubs describes in more detail how these portions of the architecture work.
- Private link scenario in event stream processing is a solution idea for implementing a similar architecture in a virtual network with private endpoints, in order to enhance security.