高容錯！銀行如何搞定PB級非結構化數據的存儲與快速搜索

{"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 社羣授權轉載。"}]},{"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":"本文針對銀行內非結構化數據增長迅速、存儲困難、搜索緩慢、識別採集空缺等問題，提出了非結構化數據服務平臺的設計和部署方案，並對平臺中的非結構化數據生命週期管理策略與平臺容災容錯架構設計思路的進行了分析與總結，最後，做出了商業銀行非結構化數據服務平臺的業務對接計劃，提出了未來業務的發展方向。"}]},{"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：非結構化數據服務平臺現狀與行內要解決的實際問題"}]},{"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":"銀行作爲非結構化數據密集的企業，基本上涵蓋所有類型的非結構化數據，如視頻數據、音頻數據、圖片數據，既包括系統自己產生的近些年越來越多的行內業務系統，也包括與客戶交換產生的，還有購買獲取的，這些數據按照格式分爲電子文檔、圖像、音頻、視頻、XML\/HTML等類型。非結構化數據格式、標準多樣，而且這些數據每年以幾何級數在增長，在技術上比結構化數據更難存儲和分析。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}}]}