Meet OceanBase AI Database, the unified database for operational data, real-time analytics, and AI. Explore ->
Meet OceanBase AI Database, the unified database for operational data, real-time analytics, and AI. Explore ->

AI is changing what enterprises need from databases. Learn how OceanBase AI Database combines lakebase architecture, multimodal data, hybrid search, and agent-friendly capabilities for the AI era.


How OceanBase goes beyond high availability with physical and logical verification, LSM-based compaction, and recovery to keep data correct.


MaLT embeds transaction metadata into LSM-tree, making commit, rollback, and recovery constant-time regardless of size. Bulk import of 2.5M rows speeds up 23.9%; recovery stays under 25s.


OceanBase uses background DR tasks (migrate, add, type-transform, rebuild) to keep tenant locality compliant, all online without pausing writes, observable via task views.


How OceanBase uses Multi-Paxos, log streams, replica types, and leader election to achieve zero data loss and fast failover.


RTO < 8s on the cluster isn't the same as RTO < 8s to the application. Trace how OBProxy's Location Cache, route feedback, and bounded retry close the gap.


seekdb, a MySQL-compatible state store for AI agents, delivers hybrid search in one SQL, 1,523 QPS streaming with 1.1× P99 jitter, and kernel-level Fork/Diff/Merge sandboxes for safe agent exploration.


Streaming benchmark shows traditional bulk-load tests miss agent needs. seekdb's fixed dual-index keeps P99 jitter at 1.1×.


Nature designed memory and forgetting together. We want to translate that design into code that can be configured and tuned.


Learns how locality in OceanBase turns a DR topology into something the cluster enforces - replica counts, replica types (F/R/C), and zone placement.

Product insights, engineering deep dives, and real-world use cases from the OceanBase team.

AI is changing what enterprises need from databases. Learn how OceanBase AI Database combines lakebase architecture, multimodal data, hybrid search, and agent-friendly capabilities for the AI era.


AI is changing what enterprises need from databases. Learn how OceanBase AI Database combines lakebase architecture, multimodal data, hybrid search, and agent-friendly capabilities for the AI era.


How OceanBase goes beyond high availability with physical and logical verification, LSM-based compaction, and recovery to keep data correct.


MaLT embeds transaction metadata into LSM-tree, making commit, rollback, and recovery constant-time regardless of size. Bulk import of 2.5M rows speeds up 23.9%; recovery stays under 25s.


OceanBase uses background DR tasks (migrate, add, type-transform, rebuild) to keep tenant locality compliant, all online without pausing writes, observable via task views.


How OceanBase uses Multi-Paxos, log streams, replica types, and leader election to achieve zero data loss and fast failover.


RTO < 8s on the cluster isn't the same as RTO < 8s to the application. Trace how OBProxy's Location Cache, route feedback, and bounded retry close the gap.


seekdb, a MySQL-compatible state store for AI agents, delivers hybrid search in one SQL, 1,523 QPS streaming with 1.1× P99 jitter, and kernel-level Fork/Diff/Merge sandboxes for safe agent exploration.


Streaming benchmark shows traditional bulk-load tests miss agent needs. seekdb's fixed dual-index keeps P99 jitter at 1.1×.


Nature designed memory and forgetting together. We want to translate that design into code that can be configured and tuned.


Learns how locality in OceanBase turns a DR topology into something the cluster enforces - replica counts, replica types (F/R/C), and zone placement.


How to choose the right OceanBase disaster recovery architecture by failure domain - Server, Zone/IDC, Region, or full cluster - with a decision matrix.


Under streaming AI workloads, vector databases see high P99 jitter (1.1×–10.3×) under concurrency. seekdb v1.3.0’s fixed delta+snapshot HNSW avoids this, delivering 22× QPS and 19× P99 gains over prior version.
