Meituan MySQL Database Inspection System Architecture and Design
Meituan’s MySQL database inspection system uses a three‑layer architecture—execution agents managed by Crane, a metadata‑rich inspection database, and an integrated application UI—to run 64 automated checks, resolve over 8,000 hazards with sub‑four‑day remediation, and continuously improve automation and analytics.
Database inspection is essential for ensuring stable and effective system operation. This article introduces Meituan's MySQL database inspection system, its framework, inspection contents, and how it helps maintain MySQL service stability.
Background : To guarantee database stability, core functional components such as execution, storage, and application layers are required. Traditional inspection solutions suffered from single‑point failures, scattered results, inconsistent scripts, and manual handling.
Design Principles : (1) Stability – the inspection tool itself must be reliable. (2) Efficiency – simplify usage, reduce learning cost, accelerate deployment. (3) Operability – use data to drive hazard remediation, track trends and effectiveness.
System Architecture : The system consists of three layers.
Execution Layer – multiple inspection agents run scripts managed via Python virtualenv and Git. Tasks are scheduled by Meituan's distributed scheduler Crane, which avoids single‑point failures.
Storage Layer – an inspection database stores hazard records with auto‑filled metadata, idempotent inserts, and support for semi‑structured results. A Git repository holds inspection scripts with shared utility functions.
Application Layer – integrated into the database operation platform, providing hazard detail pages, configuration UI, whitelist management, and an operation backend for reporting, trend analysis, and remediation reminders. External data services expose inspection results to other internal platforms.
Inspection Items – 64 items covering clusters, machines, schema/SQL, high‑availability, backup, middleware, and alerts. Items are divided between DBA‑responsible and RD‑responsible categories.
Results : After nearly a year of operation, the system runs 49 new inspection items, has resolved over 8,000 core hazards, and maintains an average remediation cycle under 4 days. Hazard volume shows a clear downward trend.
Future Plans – improve automation and CI, enhance operation analytics to prioritize hazards, and develop automatic hazard remediation.
Author : Wang Qi, DBA team member in Meituan's infrastructure department.
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