This article explains how BaikalDB leverages the Model Context Protocol to let large language models query and analyze its distributed database without writing any code, detailing the protocol’s principles, server components, transport methods, tool capabilities, use cases, configuration, and future challenges.
BaikalDB, Baidu's distributed storage system, evolves from an OLTP‑focused engine to a hybrid HTAP architecture by introducing a vectorized query engine and a massively parallel processing (MPP) layer, addressing compute and resource bottlenecks for large‑scale analytical workloads while preserving transactional guarantees.
This article examines the business storage requirements of large‑scale advertising platforms and details how BaikalDB was architected as a cloud‑native, MySQL‑compatible distributed database that combines OLTP, OLAP, KV and full‑text capabilities while ensuring high reliability, low cost and linear scalability.
This article examines the evolving data‑storage demands of large‑scale commercial advertising systems, traces the development of BaikalDB from early MySQL sharding to a heterogeneous, cloud‑native distributed database, and explains its storage, compute, and scheduling designs that deliver high reliability, low cost, and millisecond‑level performance.
Tongcheng Yilong’s BaikalDB deployment combines high‑availability multi‑Raft HA, HTAP support, and share‑nothing scalability to deliver over 72K TPS OLTP and ten‑fold faster OLAP queries while cutting operational costs up to a hundredfold through dual‑center, columnar storage and cloud‑native elasticity.
This article examines the requirements of commercial advertising systems for data storage, traces the evolution from single‑node MySQL to BaikalDB’s cloud‑native, MySQL‑compatible distributed architecture, and details its storage, compute, and scheduling designs, highlighting key features such as Raft replication, RocksDB storage, and hybrid OLTP/OLAP support.
This article analyzes the evolution of Baidu's advertising data storage, detailing the business-driven requirements, the design and development of the BaikalDB distributed database, its architecture across storage, compute and scheduling layers, key features such as Raft replication and multi‑index support, and the lessons learned for building cloud‑native, high‑performance databases.
This article details how the DTCC2020 guest speaker from Tongcheng‑Elong introduced BaikalDB’s distributed columnar storage, covering internal and external motivations, technology comparison, architecture, implementation tricks, performance gains in production, and future hybrid row‑column research directions.
