高容错！银行如何搞定PB级非结构化数据的存储与快速搜索

{"type":"doc","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"本文由 dbaplus 社群授权转载。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"引言"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"本文针对银行内非结构化数据增长迅速、存储困难、搜索缓慢、识别采集空缺等问题，提出了非结构化数据服务平台的设计和部署方案，并对平台中的非结构化数据生命周期管理策略与平台容灾容错架构设计思路的进行了分析与总结，最后，做出了商业银行非结构化数据服务平台的业务对接计划，提出了未来业务的发展方向。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"一、背景：非结构化数据服务平台现状与行内要解决的实际问题"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"银行作为非结构化数据密集的企业，基本上涵盖所有类型的非结构化数据，如视频数据、音频数据、图片数据，既包括系统自己产生的近些年越来越多的行内业务系统，也包括与客户交换产生的，还有购买获取的，这些数据按照格式分为电子文档、图像、音频、视频、XML\/HTML等类型。非结构化数据格式、标准多样，而且这些数据每年以几何级数在增长，在技术上比结构化数据更难存储和分析。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}}]}