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Apache Dubbo
Apache Dubbo

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KubeAdvisor
KubeAdvisor

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Apache Dubbo vs KubeAdvisor: What are the differences?

Apache Dubbo: A high performance Java RPC framework. It is a high-performance, light weight, java based RPC framework. Dubbo offers three key functionalities, which include interface based remote call, fault tolerance & load balancing, and automatic service registration & discovery; KubeAdvisor: It helps DevOps adopt best practices for Kubernetes. It helps teams adopt best practices to accelerate the adoption of Kubernetes, and optimize their existing stack, with machine learning. It scans K8s to make infrastructure and cloud-native applications reliable, resilient, and observable.

Apache Dubbo belongs to "Remote Procedure Call (RPC)" category of the tech stack, while KubeAdvisor can be primarily classified under "Container Tools".

Some of the features offered by Apache Dubbo are:

  • Transparent interface based RPC
  • Intelligent load balancing
  • Automatic service registration and discovery

On the other hand, KubeAdvisor provides the following key features:

  • Performance by continuously watching throttled containers/apps and recommending improvements
  • Utilization by comparing used resources with the available capacity to reallocate them based on variable workloads
  • Cost Optimization by suggesting changes at the VM level to save money in case of cloud infrastructure or identify the best configurations if you are running Kubernetes on-prem

Apache Dubbo is an open source tool with 31.1K GitHub stars and 20.4K GitHub forks. Here's a link to Apache Dubbo's open source repository on GitHub.

- No public GitHub repository available -

What is Apache Dubbo?

It is a high-performance, light weight, java based RPC framework. Dubbo offers three key functionalities, which include interface based remote call, fault tolerance & load balancing, and automatic service registration & discovery.

What is KubeAdvisor?

It helps teams adopt best practices to accelerate the adoption of Kubernetes, and optimize their existing stack, with machine learning. It scans K8s to make infrastructure and cloud-native applications reliable, resilient, and observable.
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