Mastering vRealize Operations Manager（Second Edition）

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GemFire clustering

At the core of vRealize Operations, 6.6 architecture is the powerful GemFire in-memory clustering and distributed cache. GemFire provides the internal transport bus, as well as the ability to balance CPU and memory consumption across all nodes through compute pooling, memory sharing, and data partitioning. With this change, it is better to then think of the Controller, Analytics, and Persistence layers as components that span nodes, rather than individual components on individual nodes:

During deployment, ensure all your vRealize Operations 6.6 nodes are configured with the same amount of vCPUs and memory. This is because, from a load balancing point of view, vRealize Operations expects all nodes to have the same amount of resources as part of the controller's round-robin load balancing.

The migration to GemFire is probably the single largest underlying architectural change from vCenter Operations Manager 5.x, and the result of moving to a distributed in-memory database has made many of the new vRealize Operations 6.x features possible, including the following:

Elasticity and scale: Nodes can be added on demand, allowing vRealize Operations to scale as required. This allows a single Operations Manager instance to scale to 6 extra large nodes in a cluster, which can support up to 180,000 objects and 45,000,000 metrics.
Reliability: When GemFire HA is enabled, a backup copy of all data is stored in both the Analytics GemFire cache and the Persistence layer.
Availability: Even with the GemFire HA mode disabled, in the event of a failure, other nodes take over the failed services and the load of the failed node (assuming the failure was not the master node).
Data partitioning: vRealize Operations leverages GemFire data partitioning to distribute data across nodes in units called buckets. A partition region will contain multiple buckets that are configured during a startup, or migrated during a rebalance operation. Data partitioning allows the use of the GemFire MapReduce function. This function is a data-aware query, that supports parallel data querying on a subset of the nodes. The result of this is then returned to the coordinator node for final processing.