KEDA is an open-source cloud native project under CNCF that enables Kubernetes to scale applications based on the events they consume. By leveraging event-based scaling, KEDA empowers Kubernetes to automatically adjust the number of running instances of a containerized service in response to external events, such as messages in a queue or an incoming request from an event stream. This dynamic scaling approach optimizes resource utilization and ensures that applications remain highly available and responsive.
What is KEDA?
KEDA stands for Kubernetes Event-Driven Autoscaling. It is an extension of Kubernetes that enables autoscaling of workloads based on external events. Unlike traditional Kubernetes Horizontal Pod Autoscaler (HPA), which scales pods based on resource metrics such as CPU or memory usage, KEDA scales workloads based on custom metrics related to external systems.
KEDA can connect to various event sources, such as message queues (Kafka, RabbitMQ), cloud-native storage (Azure Blob Storage), databases (SQL Server), and even HTTP endpoints. It allows applications to scale up or down in response to metrics derived from these event sources, helping organizations handle unpredictable traffic patterns, burst workloads, and improve cost efficiency by scaling down when demand decreases.
How KEDA Works
KEDA operates by creating a new Kubernetes custom resource called ScaledObject (or ScaledJob in some use cases), which connects Kubernetes workloads to external event sources. These ScaledObjects define the conditions for scaling the application and are based on event-driven metrics, such as the number of messages in a queue or the rate of incoming requests.
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KEDA ScaledObject: A ScaledObject defines the scaling behavior of a Kubernetes deployment in response to external events. It specifies the event source and scaling rules. A KEDA ScaledObject might, for example, trigger a scale-up of a deployment when the number of messages in a queue exceeds a certain threshold.
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Scaler: A Scaler is the component that interacts with the external event source and fetches metrics. KEDA supports a wide variety of scalers, including those for message queues, HTTP triggers, and even custom metrics. These scalers pull information such as queue length, request count, or other external metrics and convert them into scaling decisions.
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Kubernetes Horizontal Pod Autoscaler (HPA): KEDA uses Kubernetes’ Horizontal Pod Autoscaler to trigger scaling actions. However, instead of relying on traditional metrics like CPU or memory, KEDA uses event-based metrics provided by scalers.
Key Features of KEDA
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Event-Driven Scaling: KEDA enables event-driven workloads to scale based on external signals, such as a number of messages in a queue or database records. This allows for greater flexibility compared to standard scaling mechanisms.
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Wide Range of Supported Event Sources: KEDA supports a wide range of event sources, such as:
- Message queues (Kafka, RabbitMQ, NATS)
- Azure Event Hubs and Azure Storage Queues
- AWS SQS and SNS
- Google Cloud Pub/Sub
- Prometheus metrics
- HTTP requests and custom metrics
By supporting these sources, KEDA can be integrated with a variety of cloud services and on-premises systems.
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Scalability and Efficiency: With KEDA, Kubernetes can scale workloads precisely based on demand. This dynamic scaling reduces infrastructure costs and ensures applications can handle traffic spikes efficiently. Kubernetes automatically adjusts the number of replicas in a deployment depending on the incoming events, ensuring that resources are used only when necessary.
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Customizable Metrics: KEDA allows users to create custom scalers based on specific metrics, enabling deep integration with both cloud-native and legacy systems. Custom metrics can come from a variety of sources, providing fine-grained control over scaling decisions.
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Declarative and Kubernetes-native: KEDA operates within the Kubernetes ecosystem, and its configuration is managed declaratively via Kubernetes resources like ScaledObjects and ScaledJobs. It leverages the Kubernetes API and integrates seamlessly with existing workflows.
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Works with Existing Kubernetes Tools: KEDA is designed to work alongside existing Kubernetes tools, including Helm and kubectl. It doesn’t require major architectural changes and can be easily integrated into existing Kubernetes clusters.
Use Cases for KEDA
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Event-Driven Microservices: Microservices that consume messages from event streams or message queues can benefit from KEDA’s ability to scale up or down based on the number of messages or events being processed. For instance, an e-commerce platform with a heavy spike in order placement can scale up the backend services to handle the burst of incoming events.
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Real-Time Data Processing: Applications that process real-time data, such as data pipelines or IoT solutions, can use KEDA to automatically scale based on the incoming event rate. For example, a data processing pipeline that consumes messages from Kafka can be scaled according to the number of records in the queue.
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Cost Optimization for Bursty Workloads: Workloads that experience bursty traffic, such as social media platforms or gaming apps, can scale up when demand is high and scale down when traffic reduces, saving costs in the process. KEDA ensures that resources are used only when necessary.
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Serverless Workloads: For serverless-like behavior, KEDA can be used to scale workloads up and down quickly in response to real-time events. By integrating with event sources like HTTP requests or cloud storage, KEDA enables lightweight serverless workloads within a Kubernetes environment.
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Background Job Processing: Jobs that are queued for asynchronous processing, such as email processing or video transcoding, can scale dynamically based on the queue length, ensuring that the application always has enough resources to meet demand.
Setting Up KEDA on Kubernetes
Setting up KEDA involves several steps:
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Install KEDA Operator: The first step is installing the KEDA operator in the Kubernetes cluster. This can be done using Helm or kubectl.
helm repo add kedacore https://kedacore.github.io/charts helm repo update helm install keda kedacore/keda --namespace keda --create-namespace -
Create a ScaledObject or ScaledJob: Next, define a ScaledObject (or ScaledJob) that links your deployment to an event source. For example, a ScaledObject might be defined for a deployment that consumes messages from a Kafka topic.
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Configure Scalers: Choose and configure the scaler based on the event source. For instance, if you’re using Kafka, you would configure the Kafka scaler with the necessary connection details and thresholds.
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Monitor Scaling Behavior: After setting up KEDA, you can monitor the scaling behavior of your workloads to ensure the autoscaling is working as expected. You can view scaling events and adjust the scaling policies as needed.
Conclusion
KEDA provides a robust and scalable solution for event-driven workloads in Kubernetes, enhancing the platform’s ability to dynamically scale based on real-time events. By integrating KEDA, organizations can build more efficient, cost-effective, and responsive applications that react to external events and workloads. Whether you’re building microservices, processing real-time data, or handling background tasks, KEDA provides a powerful mechanism for autoscaling, ensuring that your Kubernetes applications always have the right amount of resources at the right time.
By utilizing KEDA, Kubernetes users can achieve event-driven autoscaling that is deeply integrated with modern cloud-native technologies and external event sources, making it an essential tool for building scalable, resilient applications in the cloud.