通过自动化提高Hugo的稳定性并解决值班挑战

Hugo plays a pivotal role in enabling data ingestion for Grab’s data lake, managing over 4,000 pipelines onboarded by users. The stability of Hugo pipelines is contingent upon the health of both the data sources and various Hugo components. Given the complexity of this system, pipeline failures occasionally occur, necessitating user intervention when retry mechanisms prove insufficient. These incidents present challenges such as:

Hugo 在支持 Grab 的数据湖的数据摄取中发挥了关键作用，管理着用户接入的超过 4,000 条管道。Hugo 管道的稳定性取决于数据源和各种 Hugo 组件的健康状况。鉴于该系统的复杂性，管道故障偶尔会发生，当重试机制不足时，需要用户干预。这些事件带来了以下挑战：

• Limited user visibility into pipeline issues.
• 用户对管道问题的可见性有限。
• Uncertainty about resolution steps due to extensive documentation.
• 由于文档繁多，对解决步骤存在不确定性。
• An overwhelmed Hugo on-call team dealing with ad-hoc requests and growing infrastructure dependencies.
• 一个不堪重负的Hugo值班团队正在处理临时请求和日益增长的基础设施依赖。
• Raised Data Production Issues (DPIs) lacking clear Root Cause Analysis (RCA), hindering effective management.
• 提出的数据生产问题（DPI）缺乏明确的根本原因分析（RCA），妨碍了有效管理。

Such challenges ultimately increase data downtime due to prolonged issue triage and resolution times.

这些挑战最终导致数据停机时间增加，因为问题的分类和解决时间延长。

To address these problems, we conducted a thorough analysis of failure modes and the efforts required to resolve them. Based on our findings, we propose a comprehensive automation solution.

为了解决这些问题，我们对故障模式及其解决所需的努力进行了全面分析。根据我们的发现，我们提出了一项全面的自动化解决方案。

This blog outlines the architecture and implementation of our proposed solution, consisting of modules like Signal, Diagnosis, RCA Table, Auto-resolution, Data Health API, and Data Health WorkBench, each with a specific function to enhance Hugo’s monitoring, diagnosis, and resolution capabilities.

本博客概述了我们提议解决方案的架构和实施，该方案由信号、诊断、根本原因分析表、自动修复、数据健康API和数据健康工作台等模块组成，每个模块都有特定功能，以增强Hugo的监控、诊断和解决能力。

The blog further details the impact of these automated features, such as enhanced data visibility, reduced on-call workload, and concludes with our next steps, which focus on advancing auto-resolution strategies, enriching the Data Health Workbench, and broadenin...