千億數據扛不住，三思後還是從MySQL遷走了……

{"type":"doc","content":[{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"前言"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"線上某IOT核心業務集羣之前採用MySQL作爲主存儲數據庫，隨着業務規模的不斷增加，MySQL已無法滿足海量數據存儲需求，業務面臨着容量痛點、成本痛點問題、數據不均衡問題等。"}]},{"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":"400億該業務遷移MongoDB後，同樣的數據節省了極大的內存、CPU、磁盤成本，同時完美解決了容量痛點、數據不均衡痛點，並且實現了一定的性能提升。"}]},{"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":"此外，遷移時候的MySQL數據爲400億，3個月後的現在對應MongoDB集羣數據已增長到1000億，如果以1000億數據規模等比例計算成本，實際成本節省比例會更高。遷移MongoDB後，除了解決業務痛點問題，同時也促進了業務的快速迭代開發，業務不在關心數據庫容量痛點、數據不均衡痛點、成本痛點等問題。"}]},{"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":"當前國內很多mongod文檔資料、性能數據等還停留在早期的MMAP_V1存儲引擎，實際上從MongoDB-3.x版本開始，MongoDB默認存儲引擎已經採用高性能、高壓縮比、更小鎖粒度的wiredtiger存儲引擎，因此其性能、成本等優勢相比之前的MMAP_V1存儲引擎更加明顯。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"一、業務遷移背景"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"該業務在遷移MongoDB前已有約400億數據，申請了64套MySQL集羣，由業務通過shardingjdbc做分庫分表，提前拆分爲64個庫，每個庫100張表。主從高可用選舉通過依賴開源orchestrator組建，MySQL架構圖如下圖所示："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/infoq\/a8\/a8b8bed71237274c29b00e2ea03607a0.png","alt":"圖片","title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"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","marks":[{"type":"strong"}],"text":"說明："},{"type":"text","text":"上圖中紅色代表磁盤告警，磁盤使用水位即將100%。如上圖所示，業務一年多前一次性申請了64套MySQL集羣，單個集羣節點數一主三從，每個節點規格如下："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"cpu：4"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"mem：16G"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"磁盤：500G"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"總節點數：64*4=256"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"SSD服務器"}]}]}]},{"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":"該業務運行一年多時間後，總集羣數據量達到了400億，並以每月200億速度增長，由於數據不均衡等原因，造成部分集羣數據量大，持續性耗光磁盤問題。由於節點衆多，越來越多的集羣節點磁盤突破瓶頸，爲了解決磁盤瓶頸，DBA不停的提升節點磁盤容量。業務和DBA都面臨嚴重痛點，主要如下："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"數據不均衡問題"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"節點容量問題"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"成本持續性增加"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"DBA工作量劇增(部分磁盤提升不了需要遷移數據到新節點)，業務也提心吊膽"}]}]}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"二、爲何選擇MongoDB-附十大核心優勢總結"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"業務遇到瓶頸後，基於MongoDB在公司已有的影響力，業務開始調研MongoDB，通過和業務接觸瞭解到，業務使用場景都是普通的增、刪、改、查、排序等操作，同時查詢條件都比較固定，用MongoDB完全沒任何問題。"}]},{"type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Concern 、Write Concern讀寫相關配置，客戶端可以根據實際情況設置。此外，MongoDB內核設計擁有完善的rollback機制來保證數據安全性和一致性。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"優勢六：高併發、高性能"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"爲了適應大規模高併發業務讀寫，MongoDB在線程模型設計、併發控制、高性能存儲引擎等方面做了很多細緻化優化。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"優勢七：wiredtiger高性能存儲引擎設計"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"網上很多評論還停留在早期MMAPv1存儲引擎，相比MMAPv1，wiredtiger引擎性能更好，壓縮比更高，鎖粒度更小，具體如下："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"WiredTiger提供了低延遲和高吞吐量"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"處理比內存大得多的數據，而不會降低性能或資源"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"系統故障後可快速恢復到最近一個checkpoint"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"支持PB級數據存儲"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"多線程架構，盡力利用樂觀鎖併發控制算法減少鎖操作"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"具有hot-caches能力"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"磁盤IO最大化利用，提升磁盤IO能力"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"其他"}]}]}]},{"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":"更多WT存儲引擎設計細節可以參考："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"http:\/\/source.wiredtiger.com\/3.2.1\/architecture.html"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"優勢八：成本節省-WT引擎高壓縮比支持"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"MongoDB對數據的壓縮支持snappy、zlib算法，在以往線上真實的數據空間大小與真實磁盤空間消耗進行對比，可以得出以下結論："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"MongoDB默認的snappy壓縮算法壓縮比約爲2.2-4.5倍"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"zlib壓縮算法壓縮比約爲4.5-7.5倍(本次遷移採用zlib高壓縮算法)"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/infoq\/8f\/8fc063cd3c7cbac4839fa02a7d0ebb43.png","alt":"圖片","title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"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":"此外，以線上已有的從MySQL、Es遷移到MongoDB的真實業務磁盤消耗統計對比，同樣的數據，存儲在MongoDB、MySQL、Es的磁盤佔比≈1：3.5：6。"}]},{"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":"後續會有數千億hbase數據遷移MongoDB，到時候總結同樣數據MongoDB和Hbase的磁盤消耗比。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem"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Preference實現，支持primary 、primaryPreferred 、secondary 、secondaryPreferred 、nearest 五種客戶端均衡訪問策略。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"分佈式事務支持"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"MongoDB-4.2 版本開始已經支持分佈式事務功能，當前對外文檔版本已經迭代到 version-4.2.11，分佈式事務功能也進一步增強。此外，從 MongoDB-4.4 版本產品規劃路線圖可以看出，MongoDB 官方將會持續投入開發查詢能力和易用性增強功能，例如 union 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C爲同城機房，則沒用該弊端，同城機房時延可以忽略。"}]}]}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"四、業務全量+增量遷移方式"}]},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/infoq\/b6\/b6b24f2f11a71234ab78aba2e1295bc1.png","alt":"圖片","title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"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":"遷移過程由業務自己完成，通過阿里開源的datax工具實現，該遷移工具的更多細節可以參考："},{"type":"text","marks":[{"type":"underline"}],"text":"https:\/\/github.com\/alibaba\/DataX"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"五、性能優化過程"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"該集羣優化過程按照如下兩個步驟優化：數據遷移開始前的提前預優化、遷移過程中瓶頸分析及優化、遷移完成後性能優化。"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"1、數據遷移開始前的提前預操作"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"和業務溝通確定，業務每條數據都攜帶有一個設備標識ssoid，同時業務查詢更新等都是根據ssoid維度查詢該設備下面的單條或者一批數據，因此片建選擇ssoid。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"分片方式"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"爲了充分散列數據到4個分片，因此選擇hash分片方式，這樣數據可以最大化散列，同時可以滿足同一個ssoid數據落到同一個分片，保證查詢效率。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"預分片"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"MongoDB如果分片片建爲hashed分片，則可以提前做預分片，這樣就可以保證數據寫進來的時候比較均衡的寫入多個分片。預分片的好處可以規避非預分片情況下的chunk遷移問題，最大化提升寫入性能。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"codeblock","attrs":{"lang":"text"},"content":[{"type":"text","text":"\nsh.shardCollection(\"xxx.xxx\", 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樣存在該問題，例如申請的單個容器是16CPU，實際上真實只消耗了幾個CPU。"}]},{"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","marks":[{"type":"strong"}],"text":"但是，磁盤節省是實實在在的，"},{"type":"text","text":"是相同數據情況下mysql和MongoDB的真實磁盤消耗對比。"}]},{"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":"當前該集羣總數據量已經達到近千億，並以每個月200億規模增加，單從容器計費層面上換算，1000億數據按照等比例換算，預計節省成本10倍。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"八、最後：千億級中等規模MongoDB集羣注意事項"}]},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"MongoDB無需分庫分表，單表可以無限大，但是單表隨着數據量的增多會引起以下問題："}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"切記提前建好索引，否則影響查詢更新性能(數據越多，無索引查詢掃描會越慢)。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"切記提前評估好業務需要那些索引，單節點單個表數百億數據，加索引執行時間較長。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"服務器異常情況下節點替換時間相比會更長。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"切記數據備份不要採用mongodump\/mongorestore方式，而是採用熱備或者文件拷貝方式備份。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"節點替換儘量從備份中拷貝數據加載方式恢復，而不是通過主從全量同步方式，全量同步過程較長。"}]}]}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"九、未來挑戰(該集羣未來萬億級實時數據規模挑戰)"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"隨着時間推移，業務數據增長也會越來越多，單月數據量增長曲線預計會直線增加(當前每月數據量增加200億左右)，預計未來2-3年該集羣總數據量會達到萬億級，分片數也會達到20個分片左右，可能會遇到各自各樣的問題。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"但是，IOT業務數據存在明顯的冷數問題，一年前的數據用戶基本上不會訪問，因此我們考慮做如下優後來滿足性能、成本的進一步提升：冷數據歸檔到低成本SATA盤"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"冷數據提升壓縮比，最大化減少磁盤消耗"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如何解決冷數據歸檔sata盤過程中的性能問題"}]}]}]},{"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":"冷熱歸檔存儲可以參考之前在dbaplus分享的另一篇文章："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"link","attrs":{"href":"http:\/\/mp.weixin.qq.com\/s?__biz=MzkwOTIxNDQ3OA==&mid=2247534129&idx=1&sn=f3d5e27be2e4ae73c28949f2a1f5bda7&chksm=c13c1050f64b9946a2525685af2dec7572fa3c14fd0d4acae8a29cf6c768b92ff10ece2488c2&scene=21#wechat_redirect","title":null,"type":null},"content":[{"type":"text","text":"1.《用最少人力玩轉萬億級數據，我用的就是MongoDB！"}]},{"type":"link","attrs":{"href":"http:\/\/mp.weixin.qq.com\/s?__biz=MzkwOTIxNDQ3OA==&mid=2247534129&idx=1&sn=f3d5e27be2e4ae73c28949f2a1f5bda7&chksm=c13c1050f64b9946a2525685af2dec7572fa3c14fd0d4acae8a29cf6c768b92ff10ece2488c2&scene=21#wechat_redirect","title":null,"type":null},"content":[{"type":"text","text":"》"}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"2.MongoDB源碼分析、更多實踐案例細節"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"https:\/\/github.com\/y123456yz\/reading-and-annotate-mongodb-3.6"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"九、最後說明(業務場景)"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"本千億級IOT業務使用場景總結如下："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align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  "}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"查詢都是單表查詢，不涉及多表聯合查詢。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"數據庫場景非常重要，脫離業務場景談數據庫優劣無任何意義。例如本文的業務場景，業務能確定需要建那些索引，同時所有的更新、查詢、排序都可以對應具體的最優索引，因此該場景就非常適合MongoDB。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"每種數據庫都有其適合的業務場景，沒有萬能的數據庫。此外，不能因爲某種場景不適合而全盤否定沒數據庫，主流數據庫都有其存在的意義，千萬不能因爲某種場景下的不合適而全盤否定某個數據庫。"}]}]}]},{"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","marks":[{"type":"strong"}],"text":"作者介紹"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"楊亞洲，"},{"type":"text","text":"前滴滴出行專家工程師，現任OPPO文檔數據庫MongoDB負責人，負責數萬億級數據量文檔數據庫MongoDB內核研發、性能優化及運維工作，一直專注於分佈式緩存、高性能服務端、數據庫、中間件等相關研發。後續持續分享《MongoDB內核源碼設計、性能優化、最佳運維實踐》。"}]},{"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":"本文轉載自：dbaplus社羣（ID：dbaplus）"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"原文鏈接："},{"type":"link","attrs":{"href":"https:\/\/mp.weixin.qq.com\/s\/fC4TCkyy6lF2On2Y7DgYcg","title":"xxx","type":null},"content":[{"type":"text","text":"千億數據扛不住，三思後還是從MySQL遷走了……"}]}]}]}