Verified75% confidenceFactExact time
Storage-compute separation architecture achieves second-level elastic scaling of compute resources by decoupling the storage layer from the compute layer, allowing storage to scale independently without data migration.
3
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75%
Confidence
Long-term
Relevance
9/15/2026
First Seen
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Unverified存储与计算分离是云原生架构的核心设计原则,工程实现通常依赖CSI(Container Storage Interface)驱动,只要存储和计算分离,停机重启就不会丢失数据79% similarUnverifiedDisaggregated inference decouples the compute-intensive prefill stage from the memory-bandwidth-intensive decode stage, allocating them to separate hardware resource pools best suited to each for independent scaling.78% similarUnverifiedDecoupling storage from compute allows teams to independently select a cheaper storage tier for cold data without being forced to upgrade expensive compute instances when expanding storage.77% similarUnverifiedCompute-storage separation completely decouples data persistence from compute failures, thereby enhancing system reliability.76% similarUnverifiedThe classic dual-store pattern of feature stores: the offline store uses a columnar warehouse to compute features in batches, the online store uses a low-latency key-value store for millisecond reads, and the two are connected by a materialization pipeline.75% similar
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