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Today, I will talk to you about how to use the K8s automatic expansion tool KEDA, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following content for you. I hope you can get something according to this article.

Red Hat and Microsoft have jointly released the Kubernetes automatic scaling tool KEDA 1.0. now the official enhanced KEDA expansion container has released the second major update KEDA 2.0, which can support more kinds of triggers and make it easier to automatically scale Kubernetes deployment. In March this year, more and more vendors joined in contributing to the KEDA project, so the sponsors decided to contribute KEDA to the Cloud Native Foundation (Cloud Native Computing Foundation,CNCF), which is now the KEDA sandboxie project.

The emergence of KEDA is to solve the need of automatic expansion and reduction of Kubernetes. Although Kubernetes provides a container scheduling platform, under preset circumstances, Kubernetes can only scale up and down according to system metrics such as CPU, ignoring a large number of external indicators such as Azure, AWS, GCP, Redis and Kafka, which represents the time for the system to respond to events, and there may be a lot of delay, making the expansion not sensitive enough to keep up with changes in traffic.

KEDA can solve this problem. KEDA is an event-driven automatic expansion container based on Kubernetes. Users can drive the expansion of containers in Kubernetes according to the number of events they need to handle. KEDA provides users with automatic expansion deployment by using a simple and consistent API.

KEDA is a single-purpose lightweight component that can be added to the Kubernetes cluster and used together with standard Kubernetes components such as Horizontal Pod Autoscaler (HPA). The extension functions will not cover or duplicate each other. Officially, it is mentioned that users can specify to use event-driven applications without affecting other applications, which enables KEDA to be used flexibly and safely with other Kubernetes applications and frameworks.

A year after the release of KEDA 1.0, the official version of the major update 2.0 has been released, focusing on KEDA's support for more triggers and the addition of many new patterns and features. KEDA 2.0 now automatically extends deployment load (Deployment) and job (Jobs) workloads. In the past, in KEDA 1.x, users needed ScaledObject resources to specify the type of workload to be extended, and only one of the extended Kubernetes deployments or resources could be specified.

In KEDA 2.0, the two options are separated and separate resources are introduced. In addition to the previous ScaledObject, ScaledJob custom resources are now added to Kubernetes to meet different needs.

In addition, users can now set multiple triggers on ScaledObject and ScaledJob, and automatically scale the workload according to different triggers such as Kafka and Prometheus. KEDA will select the maximum value such as the number of target copies from the extender to define the extension decision.

KEDA 2.0 also adds a number of new extenders. In addition to Azure Log Analytics and IBM MQ extenders, users can also apply new CPU extenders, eliminating the need for a mixture of HPA and ScaledObjects,KEDA to fully handle HPA for users. And the new external push extender allows users to use the push model (Push-Model) to build their own extender and trigger extension behavior instead of using the existing pull model (Pull-Model).

KEDA 2.0 also adds a new Metrics API extender that automatically scales metrics provided through REST API so that users do not need to build their own extenders, which can automate scaling decisions based on standard sources of metrics available in the environment, such as internal API or Microsoft Dynamics CRM API.

After reading the above, do you have any further understanding of how to use the K8s automatic expansion tool KEDA? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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