What is Kubernetes Event-Driven Autoscaling (KEDA)?
Kubernetes Event-Driven Autoscaling (KEDA) is an open-source component that automatically scales Kubernetes workloads. We can autoscale the workload depending on the number of incoming events. KEDA is designed to work with any event source that emits events in response to an action or state change.
Let’s consider an example of a message queue—an event source. It will emit events when a new message arrives. Suppose the workload associated with the message queue is currently under-scaled to handle the incoming messages. KEDA will automatically scale up the number of replicas to ensure the workload can handle the increased traffic.
Why choose KEDA
There are a variety of reasons for opting for KEDA. The most critical is autoscaling, which ultimately helps free up resources and reduce cloud costs. Once KEDA is installed, we can create a scaling trigger that defines the event source and sets the criteria for scaling the workload. KEDA will start monitoring the event source and scale the workload based on the criteria defined in the scaling trigger.
Additionally, KEDA allows for seamless integration with other Kubernetes-native tools and platforms. Moreover, it supports various event sources, including Apache Kafka, Azure Event Hubs, and Amazon CloudWatch. These event sources are commonly used for streaming data, logging, and monitoring and can generate a large volume of events that require scalable processing.
How KEDA works
KEDA automatically scales Kubernetes workloads based on events generated by external sources. For example, in the illustration below, a Kubernetes cluster running several workloads and an external event source, let’s say, Apache Kafka, generate events such as messages. KEDA is installed in the cluster. A scaling trigger specifies the event source, Kafka, and sets the criteria for scaling the Kubernetes workloads based on the incoming events. KEDA then monitors the event source and scales the relevant Kubernetes workloads based on the criteria defined in the scaling trigger. The illustration also depicts the scaled object that KEDA can use to adjust the number of replicas in a deployment automatically.
In conclusion, KEDA is critical in automating scaling workloads based on events generated by external sources.
Unlock your potential: Kubernetes Essentials series, all in one place!
To deepen your understanding of Kubernetes, explore our series of Answers below:
What is Kubernetes?
Get an introduction to Kubernetes, the powerful container orchestration platform that automates deployment, scaling, and management of containerized applications.What is Kubernetes Event-Driven Autoscaling (KEDA)?
Learn how KEDA enables event-driven scaling, allowing Kubernetes workloads to automatically scale based on external metrics such as message queues, databases, and cloud events.Why do we use Kubernetes?
Understand the core benefits of Kubernetes, including automated deployment, scaling, and management of containerized applications across distributed environments.What are Kubernetes namespaces?
Discover how Kubernetes namespaces help organize and isolate workloads within a cluster, enhancing security and resource allocation.What are the different types of services in Kubernetes?
Explore the various Kubernetes service types—ClusterIP, NodePort, LoadBalancer, and ExternalName—and their roles in facilitating communication between applications.ReplicationController in Kubernetes
Learn about the ReplicationController, its role in maintaining pod availability, and how it ensures that a specified number of pod replicas are always running.ExternalDNS in Kubernetes
Understand how ExternalDNS simplifies service discovery by dynamically managing DNS records for Kubernetes services, making external access seamless.What are taints and tolerations in Kubernetes?
Gain insights into taints and tolerations and how they control pod scheduling by preventing or allowing specific workloads to run on designated nodes.Introduction to Node Affinity in Kubernetes
Discover how Node Affinity works in Kubernetes to influence pod scheduling by specifying node selection preferences and ensuring efficient workload distribution.
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