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Choice of redundancy may be driven by data size, and whether data can be retrieved from some other backing store or Geode is the only store. Other considerations might go into that decision as well. For instance, Geode can be deployed in an active/active configuration in two data centers such that each can take on the entire load, but only will do so only if necessitated by a failure. In such deployments like that typically there are 4 live copies of the data at any time, 2 in each datacenter. A failure of 2 nodes in a single datacenter would cause data loss in that datacenter, but the other datacenter would take over the entire workload until those 2 nodes can be restored. Another possibility might be to set redundancy to 2 (for a total of 3 copies of data) in order to have high availability even in case of a single node failure, and avoid paying the price of rebalancing when a single node fails. Instead of rebalancing, a failed node is restarted, and in the meantime there are still 2 copies of data.

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Geode Queues

If any Geode queueing is capabilities are used, such as for WAN distribution, client subscription, or asynchronous event listeners, it is important to consider the queues’ capacity in the context of the desired SLA. For example, for how long should gateway or client subscription queues be able to keep queueing events when the connection is lost? Given that, how large should the queues have be able to grow? The best way to find out is by watching the queues’ growth during sizing experiments, using Geode statistics (more on this in Vertical Sizing section of The Sizing Process, below) .

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Total memory and system requirements can be approximated using the attached sizing spreadsheet, System_Sizing_Worksheet.xlsx, which calculates in takes into account all the Geode region related per-entry overhead, and takes into account the desired memory headroom. The spreadsheet formulas are rough approximations that serve to inform a very high level estimate, as they do not account for any other overhead (buffers, threads, queues, application workload, etc). In addition, the results obtained from the spreadsheet do not have any performance context. For this reason, the next step is to take the results for memory allocation per server obtained from the spreadsheet and use them as the starting point for the vertical sizing.

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