千亿数据扛不住，三思后还是从MySQL迁走了……

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样存在该问题，例如申请的单个容器是16CPU，实际上真实只消耗了几个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，磁盘节省是实实在在的，"},{"type":"text","text":"是相同数据情况下mysql和MongoDB的真实磁盘消耗对比。"}]},{"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":"当前该集群总数据量已经达到近千亿，并以每个月200亿规模增加，单从容器计费层面上换算，1000亿数据按照等比例换算，预计节省成本10倍。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"八、最后：千亿级中等规模MongoDB集群注意事项"}]},{"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无需分库分表，单表可以无限大，但是单表随着数据量的增多会引起以下问题："}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"切记提前建好索引，否则影响查询更新性能(数据越多，无索引查询扫描会越慢)。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"切记提前评估好业务需要那些索引，单节点单个表数百亿数据，加索引执行时间较长。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"服务器异常情况下节点替换时间相比会更长。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"切记数据备份不要采用mongodump\/mongorestore方式，而是采用热备或者文件拷贝方式备份。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"节点替换尽量从备份中拷贝数据加载方式恢复，而不是通过主从全量同步方式，全量同步过程较长。"}]}]}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"九、未来挑战(该集群未来万亿级实时数据规模挑战)"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"随着时间推移，业务数据增长也会越来越多，单月数据量增长曲线预计会直线增加(当前每月数据量增加200亿左右)，预计未来2-3年该集群总数据量会达到万亿级，分片数也会达到20个分片左右，可能会遇到各自各样的问题。"}]},{"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业务数据存在明显的冷数问题，一年前的数据用户基本上不会访问，因此我们考虑做如下优后来满足性能、成本的进一步提升：冷数据归档到低成本SATA盘"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"冷数据提升压缩比，最大化减少磁盘消耗"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如何解决冷数据归档sata盘过程中的性能问题"}]}]}]},{"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":"冷热归档存储可以参考之前在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.《用最少人力玩转万亿级数据，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源码分析、更多实践案例细节"}]},{"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、最后说明(业务场景)"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"本千亿级IOT业务使用场景总结如下："}]},{"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":"查询都是单表查询，不涉及多表联合查询。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"数据库场景非常重要，脱离业务场景谈数据库优劣无任何意义。例如本文的业务场景，业务能确定需要建那些索引，同时所有的更新、查询、排序都可以对应具体的最优索引，因此该场景就非常适合MongoDB。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"每种数据库都有其适合的业务场景，没有万能的数据库。此外，不能因为某种场景不适合而全盘否定没数据库，主流数据库都有其存在的意义，千万不能因为某种场景下的不合适而全盘否定某个数据库。"}]}]}]},{"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":"作者介绍"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"杨亚洲，"},{"type":"text","text":"前滴滴出行专家工程师，现任OPPO文档数据库MongoDB负责人，负责数万亿级数据量文档数据库MongoDB内核研发、性能优化及运维工作，一直专注于分布式缓存、高性能服务端、数据库、中间件等相关研发。后续持续分享《MongoDB内核源码设计、性能优化、最佳运维实践》。"}]},{"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":"本文转载自：dbaplus社群（ID：dbaplus）"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"原文链接："},{"type":"link","attrs":{"href":"https:\/\/mp.weixin.qq.com\/s\/fC4TCkyy6lF2On2Y7DgYcg","title":"xxx","type":null},"content":[{"type":"text","text":"千亿数据扛不住，三思后还是从MySQL迁走了……"}]}]}]}