Automatic replication of MapR-DB data to Elasticsearch is useful for many environments, and I want to share information about a specific customer deployment I worked on recently. Their use case is related to log security analytics and is centered around using Drill for running interactive queries on aggregated data.
In this blog post, I would like to share another, much less talked about advantage that emerges from this strategy. This is because a MapR cluster can naturally take advantage of the very well regarded Elasticsearch and Kibana stack to give cluster admins a near real-time view of their cluster’s health and performance.
There are many options for monitoring the performance and health of a MapR cluster. In this post, I will present the lesser-known method for monitoring the CLDB using the Java Management Extensions (JMX).
We have experimented with on a 5 node MapR 5.1 cluster running Spark 1.5.2 and will share our experience, difficulties, and solutions on this blog post.
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