DOC-1153 Add Datasize Estimates - #968
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| The following example demonstrates how workload type and compression ratio affect hardware sizing. |
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May add explanation for "compression ratio": (10% compression ratio: data becomes 90% of original)
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| * At a 10% compression ratio: | ||
| ** Memory: **1.35 TB** | ||
| ** Disk per node: **~2 TB** |
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how many node for this example?
Or it's total memory and disk?
| |CPU Recommendation | ||
| |Memory (Algorithmic) | ||
| |Memory (Non-Algorithmic) | ||
| |Disk per Node |
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same, do you assume single node (this is total disk) or what?
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| The estimates in this section provide general guidance for sizing hardware based on dataset size, workload type, and compression ratio. They are intended to help with initial capacity planning before selecting hardware for a TigerGraph deployment. | ||
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| Algorithmic workloads, such as PageRank, Community Detection, and Shortest Path, generally require more memory than non-algorithmic workloads because they perform additional graph computations. |
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May just add the methods for computation as well.
Memory
| Workload-type | Memory sizing factor |
|---|---|
| Non-algorithmic | 2 (1x for graph size + 1x for compute) |
| Algorithmic | 3 (1x for graph size + 2x for compute) |
Disk
Disk space per node = Roughly same as the memory per node.
If looking to store multiple backups, large data files, or export data locally, 2x the memory size per node is recommended.
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@qingGongTG
I have committed the suggested changes. Please review
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