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A key decision for cluster performance in an environment is the type and quantity of nodes deployed. Heterogeneous clusters can be architected with a wide variety of node styles and capacities, in order to meet the needs of a varied dataset and wide spectrum of workloads. These node styles encompass several hardware generations and fall loosely into four main categories or tiers.
The following figure illustrates these tiers, and the associated hardware generations and models:
While heterogeneous clusters can easily include multiple hardware classes and configurations with a minimum of three of each, the best practice of simplicity for building large clusters holds true here too. The smaller the disparity in hardware style across the cluster, the less opportunity there is for overloading, or bullying, the more capacity-oriented nodes. Some points to consider are: