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The data format of VOS is designed for generic requirements so it depends on scalable data structure like B+Tree and EVTree. However, for ML workload, because most of read calls are small so keeping scalable index for data does not help too much on performance, it however even brings a lot of metadata overhead, which consumes DRAM after removing PMEM from the stack.
In order to reduce the metadata overhead of indexing user data of small objects/files, a technology called object flattening is proposed in this document.
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