vivo is a global consumer electronics company serving users in more than 60 countries and regions. Its database estate had grown to nearly 1,000 servers running 15 different engines across self-built data centers, AWS, and Alibaba Cloud — a fragmented stack assembled workload by workload over years of growth. By migrating 17 production systems to OceanBase, vivo unified that fleet onto a single distributed platform, reducing total database resources on its core production–sales–inventory system by 80%.

The Challenge: Fifteen database engines and stranded RDS capacity

Three forces converged on vivo's database team: heterogeneous engines multiplying operational overhead, a sharded MySQL platform that could no longer serve aggregated queries, and managed-cloud capacity provisioned far beyond actual use. Each was tolerable alone; together they demanded a different architecture.

A heterogeneous database fleet at multi-cloud scale

With 15 engine types spanning self-managed and managed-service environments, the operations team carried the cost of supporting every one — plus the synchronization tooling tying them together. A single business often combined MySQL primary-replica clusters with MySQL Group Replication (MGR), and any read-write split layered a proxy on top. Each permutation added another runbook, another monitoring integration, and another failure mode.

Sharding could not serve aggregated queries

The production–sales–inventory platform — vivo's core line-of-business system spanning every regional sales territory — ran on manually sharded MySQL. Larger regions had dedicated shards (the biggest reaching 2 TB), smaller regions shared, and a separate aggregation pipeline stitched everything together for cross-region reporting. As query complexity grew, that aggregation layer could no longer deliver real-time, accurate results, and large analytical queries degraded under load.

Stranded RDS capacity, rising cost

On AWS RDS, every system was provisioned for its own future peak. CPU utilization told the story: average CPU sat below 5%, and even peak-time CPU averaged under 20%. Storage and IOPS were similarly stranded — because AWS ties IOPS to storage type, each system had to size for its own maximum, even though peak windows rarely overlapped across systems. The result was a fleet where the most expensive resource was the least utilized, and horizontal scaling still required application-level sharding work.

The Solution: One distributed database replacing 15 engines

vivo's adoption of OceanBase is a consolidation story, not a swap-out. All 15 engine types have been re-platformed onto a single OceanBase deployment, with OceanBase Migration Service (OMS) handling online cutover and MySQL wire-protocol compatibility keeping application changes minimal. Three pillars carry the re-platforming: engine consolidation, native distributed scale, and unified operations.

Consolidation: fifteen engines, one engine

The 17 migrated systems run four distinct workload patterns on one engine: cold-data archival (replacing a TokuDB archive that delivered ~4× compression but had hit a hard DDL ceiling on 40-billion-row tables), OLTP, high-concurrency read/write, and HTAP. Multi-tenancy is the consolidation mechanism — workloads whose peak windows do not overlap share infrastructure rather than each provisioning for its own maximum. Where MySQL would have required three dedicated 4C/8G instances, OceanBase allocates three 1C/8G tenants and lets spare CPU flex between them, with read-write split extending that elasticity within each tenant.

Scale: native distributed architecture replaces manual sharding

OceanBase's native partitioning replaces the application-level sharding vivo used to push MySQL beyond a single host's storage and throughput ceiling. The production–sales–inventory platform collapsed from dozens of sharded hosts into a single OceanBase tenant, and the same multi-node architecture lifts the throughput cap on high-concurrency workloads, distributing load across the cluster transparently to the application.

Efficiency: unified operations and a leaner footprint

OceanBase's LSM-tree storage engine delivers significantly higher compression than MySQL's file-system-level approach, shrinking both the storage footprint and the spend attached to it. On the operations side, OCP centralizes deployment, upgrades, monitoring, backup, and recovery; OMS handles ongoing data movement; and OceanBase Developer Center (ODC) lets developers self-serve SQL review, schema changes, and data operations that previously queued with the DBA team. What had been a mix of vendor consoles, custom scripts, and per-engine runbooks is now one stack.

The Results

Seventeen production systems — logging, analytics, and transactional — are now live on OceanBase, with the headline gains concentrated on the production–sales–inventory platform that had carried the most operational debt.

• 15 database engines consolidated to one — TokuDB, manually sharded MySQL, MySQL Group Replication with proxies, AWS RDS, and 11 other engines now unified under a single database with one operations surface.
• 80% total resource savings on the core system — production–sales–inventory consolidated from 20 hosts and ~4.5 TB into a single 12-core, 20 GB OceanBase tenant holding ~600 GB.
• ~60% fleet-wide storage footprint reduction and ~50% storage cost reduction — 5.7–15.3× compression across the 17 migrated systems vs. the prior MySQL baseline, freeing both AWS RDS spend and self-built capacity.
• 80% faster TP+AP queries, 30% lower latency on large-data queries, 10× gain on cross-region aggregation — performance lifts across the workload mix, plus 60% higher high-concurrency update throughput with the MySQL primary-replica lag eliminated.
• 40-billion-row archival DDL completed in 2h18m — a schema change the prior TokuDB-based archive could not perform at all, executed in OceanBase in sync with the source business table.
• DBA workload shifted to developer self-service — SQL review, monitoring, backup, capacity changes, and data movement now run through one web UI with role-based permissions, removing routine tasks from the DBA queue.

Conclusion

With OceanBase, vivo has converted a fragmented multi-cloud database fleet into one unified platform — one engine, one operations surface, one architectural model — that runs leaner and gives the team performance and schema-change headroom the legacy stacks could not provide. That is the foundation for scaling a global business without scaling the operational surface area underneath it.