This article describes the design, architecture, and technical implementation of BMR's fault self‑healing platform, which automatically collects data, analyzes failures, defines decision rules, and executes safe recovery workflows to improve reliability and efficiency of massive, heterogeneous big‑data environments.
Bilibili's fault‑self‑healing platform for its massive BMR big‑data cluster—over 10,000 machines and 1 EB storage—adds near‑real‑time fault discovery, intelligent diagnosis, and automated workflow handling, dramatically cutting resolution time, improving stability across services, and scaling to dozens of daily automated repairs.
Bilibili’s one‑stop Big Data Cluster Management Platform (BMR) consolidates HDFS, Spark, Flink, ClickHouse, Kafka and other services into a unified system that evolved through four stages—standardization, metadata‑driven construction, containerization, and observability—addressing node consistency, scaling, fault self‑healing, and resource optimization while delivering elastic scaling, automated start/stop, and future cost‑saving and stability enhancements.
This article explains why low‑disk alerts demand automation, outlines the concept of fault self‑healing versus manual response, and provides practical guidelines—including standards, monitoring dimensions, CMDB integration, script execution tools, and notification channels—to build a reliable self‑healing system for large‑scale environments.
Discover how to transform nightly disk‑space alerts into automated self‑healing workflows, covering prerequisite standards, multi‑dimensional monitoring, CMDB integration, script‑based remediation, and multi‑channel notifications to scale operations across thousands of servers without manual intervention.
This article explains the concept of AIOps, details China Mobile Information Technology's successful comprehensive‑level assessment of its centralized operations management platform's fault‑self‑healing module, shares insights from an interview with the project director, and introduces the national AIOps capability maturity model.
This article explains why nightly disk‑space alerts demand automated fault‑self‑healing, outlines the necessary process standards, describes monitoring platform dimensions, details a multi‑source self‑healing platform with CMDB integration, and provides practical options for script execution and result notification.
This article explains why disk‑space alerts demand automated handling, introduces the concept of fault self‑healing, outlines required process standards, describes monitoring platform dimensions, details a multi‑source self‑healing platform architecture, and offers practical steps for integration, notification, and continuous improvement.
This article explains why traditional manual responses to alerts are insufficient, outlines the concept of fault self‑healing, and provides a step‑by‑step guide on establishing standards, monitoring dimensions, a unified CMDB, automation tools, and notification channels to achieve automated recovery at scale.
