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As demonstrated in my last article, you can use KubeKey to easily scale in and out your cluster. Because I only had one master node in the example, the cluster did not feature high availability. In this post, I will continue to demonstrate how to scale out your cluster, this time by adding master nodes to achieve high availability.
The steps are listed as follows:
Cloud Computing has emerged as a novel technology today. Every company is a software company today, and it is evident that no company can service without using the power of cloud computing. The cloud is seen as a conceptual layer on the Internet, making all available software and hardware resources transparent, rendering them accessible through a well-defined interface.
As the companies are increasingly relying on these cloud computing services to be always stable and accessible whenever their customers need them, the services and applications' downtime is highly unacceptable. In this article, we will be discussing the concepts related to high availability (HA), what it is, how it works, and how companies can take advantage of this.
When any messaging system sends messages, there will be errors. Even though the name servers do check the health of brokers beforehand, there is always delay. Plus, a network outage can happen during the transmission. But the handling of errors can be different.
The CAP Theorem is a fundamental theorem in distributed systems that states that any distributed system can have, at most, two of the following three properties: Consistency, Availability, and Partition tolerance. The following diagram shows the tradeoffs:
When we talk about HA, normally people think about the failover mechanism. However, making the cluster available to messages is also considered HA. And to a certain extent, I think it is more important than just making the brokers available. After all, users can and will feel the impact of this availability.
Here is the scenario: