Breaking Through AI Workload Bottlenecks How OceanBase’s AI Native Capabilities Empower Enterprise
summary
Five industry trends drive the design: scale, storage, writes, multi-model data, and open compute, solved by compute/storage separation, serverless elasticity, smart sharding and a unified query layer that federates specialized engines. OceanBase claims 707M TPC‑W and 610M QPS production at Alipay, stores 6 PB per cluster and 690 billion rows, and reports a $275 billion storage cost reduction; Haidilao sees 50% TCO savings and 45% analytical speedup; Ctrip runs a 1,113‑node cluster with 384‑dim embeddings and 3,000 partitions for 4× faster hybrid vector+scalar queries. Integrations include LangChain, LlamaIndex and MCP.
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chapters (15)
- 0:00AI workload bottlenecks
- 0:28Scale with agent traffic demands
- 1:36Storage explosion from embeddings
- 3:02Machine writes require LSM trees
- 4:05Multimodal data unified engines
- 5:47Shared compute across databases
- 6:29Distributed architecture solves trends
- 7:55OceanBase reliability proof
- 9:20Alipay storage cost savings
- 10:23OLTP plus OLAP consolidation
- 11:28Vector and scalar search unification
- 13:13OceanBase AI integrations
- 14:19AI database evolution paradigms
- 15:46AI lake engine future
- 16:29Summary and trust proof
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