Meituan Large‑Scale KV Storage: Challenges and Architectural Practices

The article details Meituan’s evolution of KV storage, analyzes scalability and availability challenges of both in‑memory (Squirrel) and persistent (Cellar) systems, and presents concrete architectural solutions such as gossip optimization, fork‑less RDB, multi‑threading, bulkload, and cross‑region disaster recovery, while outlining future directions like Zookeeper removal and vector engine support.

CellarKV storageScalability

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Meituan Large‑Scale KV Storage: Challenges and Architectural Practices

Meituan Technology Team

Jul 2, 2020 · Databases

Meituan's Million‑Level KV Storage Architecture and Practices: Squirrel (In‑Memory) and Cellar (Persistent)

Meituan’s KV platform evolved from Memcached and Redis to its own high‑throughput in‑memory Squirrel and durable Cellar systems, each partitioning keys into 16 384 slots, using ZooKeeper routing, Kubernetes scaling, intelligent migration, cross‑region Raft replication, multi‑queue latency isolation, and hotspot management, while planning further optimizations such as Redis‑Gossip tuning, kernel‑bypass I/O, and FPGA acceleration.

CellarKV storageMeituan

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Meituan's Million‑Level KV Storage Architecture and Practices: Squirrel (In‑Memory) and Cellar (Persistent)

58 Tech

Nov 1, 2018 · Databases

Insights from the 58 Group Technical Salon: Distributed KV Storage Systems Cellar and WTable

The article summarizes the 58 Group technical salon where experts compared Meituan‑Dianping's Cellar and 58's WTable distributed KV storage systems, detailing their architectures, improvements, scalability, high‑availability mechanisms, and operational considerations, and concludes with a comparative analysis and preview of the next session.

BackendCellarKV Store

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Insights from the 58 Group Technical Salon: Distributed KV Storage Systems Cellar and WTable