Another more subtle, but in my opinion amazing and very powerful, feature in Log Insight 2.0 beta is machine learning. So what does machine learning look like in Log Insight, what does machine learning provide, and why is it powerful? These are the questions I will address in this post.
After covering the new scale-out and Windows agent features available in the Log Insight 2.0 beta, it is important to understand how to load balance traffic across a Log Insight cluster. I will cover why using a load balancer is important, what to consider when load balancing, high-level load balancer configuration, and how to balance load for the three primary ways to get data into Log Insight.
As you probably know, Windows does not natively support syslog. Several third party syslog agents exist for Windows, but each come with a list of pros and cons (for examples see this post). In addition, getting support for a Window agent can be costly. To address these limitations, Log Insight has introduced a Windows agent. I would like to walk you through how to install and configure the agent.
Log Insight 1.x supported a scale-up model. Log Insight 2.0 Beta supports clustering of Log Insight nodes. I would like to walk through how to configure clustering, what it looks like when complete, and what the benefits of using clustering are.
Less than three months since the announcement of Log Insight 1.5 GA and VMware has announced Log Insight 2.0 Beta. With the beta comes a lot of new features. Over the new few weeks, I will be diving into the new features, but today I want to highlight the important parts of the release notes.