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Let me rephrase rather. a scale out architecture with commodity machines (using Hadoop, kube, mesos etc.) versus an engineered system in hardware. A client purc
by ismail 9y ago
Let me rephrase rather. a scale out architecture with commodity machines (using Hadoop, kube, mesos etc.) versus an engineered system in hardware. A client purchased a an HP super dome (another highly engineered system) the cost per CPU core, gb ram, tb of storage was horrendous. More than 10x comparable commodity hardware solutions. 8 months later and they hit performance problems, space issues and adding capacity is a serious chunk of change.
What I am trying to understand in what use cases would this make sense?
- SomeStupidPoint 9y agoI believe the usecase is needing high bandwidth interconnects, eg, problems with a lot of synchronizations across the dataset.
- loeg 9y agoThis is a scale-out solution, it's just higher performance / lower effort than building your own solution from commodity harwdare. Enterprises often want to buy canned, supported solutions rather than expend the R&D to build their own. What workload does this configuration make sense for? I don't know, especially with those ARM cores. My guess would be the cluster performs well on workloads similar to Apache Spark. But I don't know why you would choose one over the other.