[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-10443":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":24,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":35,"readmeContent":36,"aiSummary":37,"trendingCount":16,"starSnapshotCount":16,"syncStatus":19,"lastSyncTime":38,"discoverSource":39},10443,"db-tutorial","dunwu\u002Fdb-tutorial","dunwu","📚 后端程序员应该掌握的主流数据库知识","https:\u002F\u002Fdunwu.github.io\u002Fdb-tutorial\u002F",null,"Java",5342,649,45,3,0,6,17,2,39.44,"Creative Commons Attribution Share Alike 4.0 International",false,"master",true,[26,27,28,29,30,31,32,33,34],"database","db","elasticsearch","hbase","mongodb","mysql","nosql","redis","sql","2026-06-12 02:02:21","\u003Cp align=\"center\">\n    \u003Ca href=\"https:\u002F\u002Fdunwu.github.io\u002Fdb-tutorial\u002F\" target=\"_blank\" rel=\"noopener noreferrer\">\n        \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fdunwu\u002Fimages\u002Fmaster\u002Fcommon\u002Fdunwu-logo.png\" alt=\"logo\" width=\"150px\"\u002F>\n    \u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fdunwu\u002Fdb-tutorial\">\n      \u003Cimg alt=\"star\" class=\"no-zoom\" src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fdunwu\u002Fdb-tutorial?style=for-the-badge\">\n  \u003C\u002Fa>\n\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fdunwu\u002Fdb-tutorial\">\n      \u003Cimg alt=\"fork\" class=\"no-zoom\" src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fforks\u002Fdunwu\u002Fdb-tutorial?style=for-the-badge\">\n  \u003C\u002Fa>\n\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fdunwu\u002Fdb-tutorial\u002Fcommits\u002Fmaster\">\n      \u003Cimg alt=\"build\" class=\"no-zoom\" src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Fdunwu\u002Fdb-tutorial\u002Fdeploy.yml?style=for-the-badge\">\n  \u003C\u002Fa>\n\n  \u003Ca href=\"https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby-nc-sa\u002F4.0\u002Fdeed.zh\">\n      \u003Cimg alt=\"code style\" class=\"no-zoom\" src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fdunwu\u002Fdb-tutorial?style=for-the-badge\">\n  \u003C\u002Fa>\n\n\u003C\u002Fp>\n\n\u003Ch1 align=\"center\">DB-TUTORIAL\u003C\u002Fh1>\n\n> 💾 **db-tutorial** 是一个数据库教程。\n>\n> - 🔁 项目同步维护：[Github](https:\u002F\u002Fgithub.com\u002Fdunwu\u002Fdb-tutorial\u002F) | [Gitee](https:\u002F\u002Fgitee.com\u002Fturnon\u002Fdb-tutorial\u002F)\n> - 📖 电子书阅读：[Github Pages](https:\u002F\u002Fdunwu.github.io\u002Fdb-tutorial\u002F) | [Gitee Pages](https:\u002F\u002Fturnon.gitee.io\u002Fdb-tutorial\u002F)\n\n## 数据库综合\n\n### 分布式存储原理\n\n#### 分布式理论\n\n- [分布式一致性](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002Fdac0e2\u002F)\n- [深入剖析共识性算法 Paxos](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002F874539\u002F)\n- [深入剖析共识性算法 Raft](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002Fe40812\u002F)\n- [分布式算法 Gossip](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002Fd15993\u002F)\n\n#### 分布式关键技术\n\n##### 流量调度\n\n- [流量控制](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002F282676\u002F)\n- [负载均衡](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002F98a1c1\u002F)\n- [服务路由](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002Fd04ece\u002F)\n- [分布式会话基本原理](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002F3e66c2\u002F)\n\n##### 数据调度\n\n- [缓存基本原理](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002F471208\u002F)\n- [读写分离基本原理](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002F7da6ca\u002F)\n- [分库分表基本原理](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002F103382\u002F)\n- [分布式 ID 基本原理](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002F0b2e59\u002F)\n- [分布式事务基本原理](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002F910bad\u002F)\n- [分布式锁基本原理](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002Fpages\u002F69360c\u002F)\n\n### 其他\n\n- [Nosql 技术选型](docs\u002F12.数据库\u002F01.数据库综合\u002F01.Nosql技术选型.md)\n- [数据结构与数据库索引](docs\u002F12.数据库\u002F01.数据库综合\u002F02.数据结构与数据库索引.md)\n\n## 数据库中间件\n\n- [ShardingSphere 简介](docs\u002F12.数据库\u002F02.数据库中间件\u002F01.Shardingsphere\u002F01.ShardingSphere简介.md)\n- [ShardingSphere Jdbc](docs\u002F12.数据库\u002F02.数据库中间件\u002F01.Shardingsphere\u002F02.ShardingSphereJdbc.md)\n- [版本管理中间件 Flyway](docs\u002F12.数据库\u002F02.数据库中间件\u002F02.Flyway.md)\n\n## 关系型数据库\n\n> [关系型数据库](docs\u002F12.数据库\u002F03.关系型数据库) 整理主流关系型数据库知识点。\n\n### 关系型数据库综合\n\n- [关系型数据库面试总结](docs\u002F12.数据库\u002F03.关系型数据库\u002F01.综合\u002F01.关系型数据库面试.md) 💯\n- [SQL 语法基础特性](docs\u002F12.数据库\u002F03.关系型数据库\u002F01.综合\u002F02.SQL语法基础特性.md)\n- [SQL 语法高级特性](docs\u002F12.数据库\u002F03.关系型数据库\u002F01.综合\u002F03.SQL语法高级特性.md)\n- [扩展 SQL](docs\u002F12.数据库\u002F03.关系型数据库\u002F01.综合\u002F03.扩展SQL.md)\n- [SQL Cheat Sheet](docs\u002F12.数据库\u002F03.关系型数据库\u002F01.综合\u002F99.SqlCheatSheet.md)\n\n### Mysql\n\n![img](https:\u002F\u002Fraw.githubusercontent.com\u002Fdunwu\u002Fimages\u002Fmaster\u002Fsnap\u002F20200716103611.png)\n\n- [Mysql 应用指南](docs\u002F12.数据库\u002F03.关系型数据库\u002F02.Mysql\u002F01.Mysql应用指南.md) ⚡\n- [Mysql 工作流](docs\u002F12.数据库\u002F03.关系型数据库\u002F02.Mysql\u002F02.MySQL工作流.md) - 关键词：`连接`、`缓存`、`语法分析`、`优化`、`执行引擎`、`redo log`、`bin log`、`两阶段提交`\n- [Mysql 事务](docs\u002F12.数据库\u002F03.关系型数据库\u002F02.Mysql\u002F03.Mysql事务.md) - 关键词：`ACID`、`AUTOCOMMIT`、`事务隔离级别`、`死锁`、`分布式事务`\n- [Mysql 锁](docs\u002F12.数据库\u002F03.关系型数据库\u002F02.Mysql\u002F04.Mysql锁.md) - 关键词：`乐观锁`、`表级锁`、`行级锁`、`意向锁`、`MVCC`、`Next-key 锁`\n- [Mysql 索引](docs\u002F12.数据库\u002F03.关系型数据库\u002F02.Mysql\u002F05.Mysql索引.md) - 关键词：`Hash`、`B 树`、`聚簇索引`、`回表`\n- [Mysql 性能优化](docs\u002F12.数据库\u002F03.关系型数据库\u002F02.Mysql\u002F06.Mysql性能优化.md)\n- [Mysql 运维](docs\u002F12.数据库\u002F03.关系型数据库\u002F02.Mysql\u002F20.Mysql运维.md) 🔨\n- [Mysql 配置](docs\u002F12.数据库\u002F03.关系型数据库\u002F02.Mysql\u002F21.Mysql配置.md) 🔨\n- [Mysql 问题](docs\u002F12.数据库\u002F03.关系型数据库\u002F02.Mysql\u002F99.Mysql常见问题.md)\n\n### 其他\n\n- [PostgreSQL 应用指南](docs\u002F12.数据库\u002F03.关系型数据库\u002F99.其他\u002F01.PostgreSQL.md)\n- [H2 应用指南](docs\u002F12.数据库\u002F03.关系型数据库\u002F99.其他\u002F02.H2.md)\n- [SqLite 应用指南](docs\u002F12.数据库\u002F03.关系型数据库\u002F99.其他\u002F03.Sqlite.md)\n\n## 文档数据库\n\n### MongoDB\n\n> MongoDB 是一个基于文档的分布式数据库，由 C++ 语言编写。旨在为 WEB 应用提供可扩展的高性能数据存储解决方案。\n>\n> MongoDB 是一个介于关系型数据库和非关系型数据库之间的产品。它是非关系数据库当中功能最丰富，最像关系数据库的。它支持的数据结构非常松散，是类似 json 的 bson 格式，因此可以存储比较复杂的数据类型。\n>\n> MongoDB 最大的特点是它支持的查询语言非常强大，其语法有点类似于面向对象的查询语言，几乎可以实现类似关系数据库单表查询的绝大部分功能，而且还支持对数据建立索引。\n\n- [MongoDB 应用指南](docs\u002F12.数据库\u002F04.文档数据库\u002F01.MongoDB\u002F01.MongoDB应用指南.md)\n- [MongoDB 的 CRUD 操作](docs\u002F12.数据库\u002F04.文档数据库\u002F01.MongoDB\u002F02.MongoDB的CRUD操作.md)\n- [MongoDB 聚合操作](docs\u002F12.数据库\u002F04.文档数据库\u002F01.MongoDB\u002F03.MongoDB的聚合操作.md)\n- [MongoDB 事务](docs\u002F12.数据库\u002F04.文档数据库\u002F01.MongoDB\u002F04.MongoDB事务.md)\n- [MongoDB 建模](docs\u002F12.数据库\u002F04.文档数据库\u002F01.MongoDB\u002F05.MongoDB建模.md)\n- [MongoDB 建模示例](docs\u002F12.数据库\u002F04.文档数据库\u002F01.MongoDB\u002F06.MongoDB建模示例.md)\n- [MongoDB 索引](docs\u002F12.数据库\u002F04.文档数据库\u002F01.MongoDB\u002F07.MongoDB索引.md)\n- [MongoDB 复制](docs\u002F12.数据库\u002F04.文档数据库\u002F01.MongoDB\u002F08.MongoDB复制.md)\n- [MongoDB 分片](docs\u002F12.数据库\u002F04.文档数据库\u002F01.MongoDB\u002F09.MongoDB分片.md)\n- [MongoDB 运维](docs\u002F12.数据库\u002F04.文档数据库\u002F01.MongoDB\u002F20.MongoDB运维.md)\n\n## KV 数据库\n\n### Redis\n\n![img](https:\u002F\u002Fraw.githubusercontent.com\u002Fdunwu\u002Fimages\u002Fmaster\u002Fsnap\u002F20200713105627.png)\n\n- [Redis 面试总结](docs\u002F12.数据库\u002F05.KV数据库\u002F01.Redis\u002F01.Redis面试总结.md) 💯\n- [Redis 应用指南](docs\u002F12.数据库\u002F05.KV数据库\u002F01.Redis\u002F02.Redis应用指南.md) ⚡ - 关键词：`内存淘汰`、`事件`、`事务`、`管道`、`发布与订阅`\n- [Redis 数据类型和应用](docs\u002F12.数据库\u002F05.KV数据库\u002F01.Redis\u002F03.Redis数据类型和应用.md) - 关键词：`STRING`、`HASH`、`LIST`、`SET`、`ZSET`、`BitMap`、`HyperLogLog`、`Geo`\n- [Redis 持久化](docs\u002F12.数据库\u002F05.KV数据库\u002F01.Redis\u002F04.Redis持久化.md) - 关键词：`RDB`、`AOF`、`SAVE`、`BGSAVE`、`appendfsync`\n- [Redis 复制](docs\u002F12.数据库\u002F05.KV数据库\u002F01.Redis\u002F05.Redis复制.md) - 关键词：`SLAVEOF`、`SYNC`、`PSYNC`、`REPLCONF ACK`\n- [Redis 哨兵](docs\u002F12.数据库\u002F05.KV数据库\u002F01.Redis\u002F06.Redis哨兵.md) - 关键词：`Sentinel`、`PING`、`INFO`、`Raft`\n- [Redis 集群](docs\u002F12.数据库\u002F05.KV数据库\u002F01.Redis\u002F07.Redis集群.md) - 关键词：`CLUSTER MEET`、`Hash slot`、`MOVED`、`ASK`、`SLAVEOF no one`、`redis-trib`\n- [Redis 实战](docs\u002F12.数据库\u002F05.KV数据库\u002F01.Redis\u002F08.Redis实战.md) - 关键词：`缓存`、`分布式锁`、`布隆过滤器`\n- [Redis 运维](docs\u002F12.数据库\u002F05.KV数据库\u002F01.Redis\u002F20.Redis运维.md) 🔨 - 关键词：`安装`、`命令`、`集群`、`客户端`\n\n## 列式数据库\n\n### HBase\n\n- [HBase 快速入门](docs\u002F12.数据库\u002F06.列式数据库\u002F01.HBase\u002F01.HBase快速入门.md)\n- [HBase 数据模型](docs\u002F12.数据库\u002F06.列式数据库\u002F01.HBase\u002F02.HBase数据模型.md)\n- [HBase Schema 设计](docs\u002F12.数据库\u002F06.列式数据库\u002F01.HBase\u002F03.HBaseSchema设计.md)\n- [HBase 架构](docs\u002F12.数据库\u002F06.列式数据库\u002F01.HBase\u002F04.HBase架构.md)\n- [HBase Java API 基础特性](docs\u002F12.数据库\u002F06.列式数据库\u002F01.HBase\u002F10.HBaseJavaApi基础特性.md)\n- [HBase Java API 高级特性之过滤器](docs\u002F12.数据库\u002F06.列式数据库\u002F01.HBase\u002F11.HBaseJavaApi高级特性之过滤器.md)\n- [HBase Java API 高级特性之协处理器](docs\u002F12.数据库\u002F06.列式数据库\u002F01.HBase\u002F12.HBaseJavaApi高级特性之协处理器.md)\n- [HBase Java API 其他高级特性](docs\u002F12.数据库\u002F06.列式数据库\u002F01.HBase\u002F13.HBaseJavaApi其他高级特性.md)\n- [HBase 运维](docs\u002F12.数据库\u002F06.列式数据库\u002F01.HBase\u002F21.HBase运维.md)\n- [HBase 命令](docs\u002F12.数据库\u002F06.列式数据库\u002F01.HBase\u002F22.HBase命令.md)\n\n## 搜索引擎数据库\n\n### Elasticsearch\n\n> Elasticsearch 是一个基于 Lucene 的搜索和数据分析工具，它提供了一个分布式服务。Elasticsearch 是遵从 Apache 开源条款的一款开源产品，是当前主流的企业级搜索引擎。\n\n- [Elasticsearch 面试总结](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F01.Elasticsearch面试总结.md) 💯\n- [Elasticsearch 快速入门](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F02.Elasticsearch快速入门.md)\n- [Elasticsearch 简介](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F03.Elasticsearch简介.md)\n- [Elasticsearch 索引](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F04.Elasticsearch索引.md)\n- [Elasticsearch 查询](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F05.Elasticsearch查询.md)\n- [Elasticsearch 高亮](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F06.Elasticsearch高亮.md)\n- [Elasticsearch 排序](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F07.Elasticsearch排序.md)\n- [Elasticsearch 聚合](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F08.Elasticsearch聚合.md)\n- [Elasticsearch 分析器](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F09.Elasticsearch分析器.md)\n- [Elasticsearch 性能优化](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F10.Elasticsearch性能优化.md)\n- [Elasticsearch Rest API](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F11.ElasticsearchRestApi.md)\n- [ElasticSearch Java API 之 High Level REST Client](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F12.ElasticsearchHighLevelRestJavaApi.md)\n- [Elasticsearch 集群和分片](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F13.Elasticsearch集群和分片.md)\n- [Elasticsearch 运维](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F01.Elasticsearch\u002F20.Elasticsearch运维.md)\n\n### Elastic\n\n- [Elastic 快速入门](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F02.Elastic\u002F01.Elastic快速入门.md)\n- [Elastic 技术栈之 Filebeat](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F02.Elastic\u002F02.Elastic技术栈之Filebeat.md)\n- [Filebeat 运维](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F02.Elastic\u002F03.Filebeat运维.md)\n- [Elastic 技术栈之 Kibana](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F02.Elastic\u002F04.Elastic技术栈之Kibana.md)\n- [Kibana 运维](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F02.Elastic\u002F05.Kibana运维.md)\n- [Elastic 技术栈之 Logstash](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F02.Elastic\u002F06.Elastic技术栈之Logstash.md)\n- [Logstash 运维](docs\u002F12.数据库\u002F07.搜索引擎数据库\u002F02.Elastic\u002F07.Logstash运维.md)\n\n## 资料 📚\n\n### 数据库综合资料\n\n- [DB-Engines](https:\u002F\u002Fdb-engines.com\u002Fen\u002Franking) - 数据库流行度排名\n- **书籍**\n  - [《数据密集型应用系统设计》](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F30329536\u002F) - 这可能是目前最好的分布式存储书籍，强力推荐【进阶】\n- **教程**\n  - [CMU 15445 数据库基础课程](https:\u002F\u002F15445.courses.cs.cmu.edu\u002Ffall2019\u002Fschedule.html)\n  - [CMU 15721 数据库高级课程](https:\u002F\u002F15721.courses.cs.cmu.edu\u002Fspring2020\u002Fschedule.html)\n  - [检索技术核心 20 讲](https:\u002F\u002Ftime.geekbang.org\u002Fcolumn\u002Fintro\u002F100048401) - 极客教程【进阶】\n  - [后端存储实战课](https:\u002F\u002Ftime.geekbang.org\u002Fcolumn\u002Fintro\u002F100046801) - 极客教程【入门】：讲解存储在电商领域的种种应用和一些基本特性\n- **论文**\n  - [Efficiency in the Columbia Database Query Optimizer](https:\u002F\u002F15721.courses.cs.cmu.edu\u002Fspring2018\u002Fpapers\u002F15-optimizer1\u002Fxu-columbia-thesis1998.pdf)\n  - [How Good Are Query Optimizers, Really?](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol9\u002Fp204-leis.pdf)\n  - [Architecture of a Database System](https:\u002F\u002Fdsf.berkeley.edu\u002Fpapers\u002Ffntdb07-architecture.pdf)\n  - [Data Structures for Databases](https:\u002F\u002Fwww.cise.ufl.edu\u002F~mschneid\u002FResearch\u002Fpapers\u002FHS05BoCh.pdf)\n- **文章**\n  - [Data Structures and Algorithms for Big Databases](https:\u002F\u002Fpeople.csail.mit.edu\u002Fbradley\u002FBenderKuszmaul-tutorial-xldb12.pdf)\n\n### 关系型数据库资料\n\n- **综合资料**\n  - [《数据库的索引设计与优化》](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F26419771\u002F)\n  - [《SQL 必知必会》](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F35167240\u002F) - SQL 的基本概念和语法【入门】\n- **Oracle 资料**\n  - [《Oracle Database 9i\u002F10g\u002F11g 编程艺术》](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F5402711\u002F)\n\n#### Mysql 资料\n\n- **官方**\n  - [Mysql 官网](https:\u002F\u002Fwww.mysql.com\u002F)\n  - [Mysql 官方文档](https:\u002F\u002Fdev.mysql.com\u002Fdoc\u002F)\n  - **官方 PPT**\n    - [How to Analyze and Tune MySQL Queries for Better Performance](https:\u002F\u002Fwww.mysql.com\u002Fcn\u002Fwhy-mysql\u002Fpresentations\u002Ftune-mysql-queries-performance\u002F)\n    - [MySQL Performance Tuning 101](https:\u002F\u002Fwww.mysql.com\u002Fcn\u002Fwhy-mysql\u002Fpresentations\u002Fmysql-performance-tuning101\u002F)\n    - [MySQL Performance Schema & Sys Schema](https:\u002F\u002Fwww.mysql.com\u002Fcn\u002Fwhy-mysql\u002Fpresentations\u002Fmysql-performance-sys-schema\u002F)\n    - [MySQL Performance: Demystified Tuning & Best Practices](https:\u002F\u002Fwww.mysql.com\u002Fcn\u002Fwhy-mysql\u002Fpresentations\u002Fmysql-performance-tuning-best-practices\u002F)\n    - [MySQL Security Best Practices](https:\u002F\u002Fwww.mysql.com\u002Fcn\u002Fwhy-mysql\u002Fpresentations\u002Fmysql-security-best-practices\u002F)\n    - [MySQL Cluster Deployment Best Practices](https:\u002F\u002Fwww.mysql.com\u002Fcn\u002Fwhy-mysql\u002Fpresentations\u002Fmysql-cluster-deployment-best-practices\u002F)\n    - [MySQL High Availability with InnoDB Cluster](https:\u002F\u002Fwww.mysql.com\u002Fcn\u002Fwhy-mysql\u002Fpresentations\u002Fmysql-high-availability-innodb-cluster\u002F)\n- **书籍**\n  - [《高性能 MySQL》](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F23008813\u002F) - 经典，适合 DBA 或作为开发者的参考手册【进阶】\n  - [《MySQL 技术内幕：InnoDB 存储引擎》](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F24708143\u002F)\n  - [《MySQL 必知必会》](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F3354490\u002F) - Mysql 的基本概念和语法【入门】\n- **教程**\n  - [runoob.com MySQL 教程](http:\u002F\u002Fwww.runoob.com\u002Fmysql\u002Fmysql-tutorial.html) - 入门级 SQL 教程\n  - [mysql-tutorial](https:\u002F\u002Fgithub.com\u002Fjaywcjlove\u002Fmysql-tutorial)\n- **文章**\n  - [MySQL 索引背后的数据结构及算法原理](http:\u002F\u002Fblog.codinglabs.org\u002Farticles\u002Ftheory-of-mysql-index.html)\n  - [Some study on database storage internals](https:\u002F\u002Fmedium.com\u002F@kousiknath\u002Fdata-structures-database-storage-internals-1f5ed3619d43)\n  - [Sharding Pinterest: How we scaled our MySQL fleet](https:\u002F\u002Fmedium.com\u002F@Pinterest_Engineering\u002Fsharding-pinterest-how-we-scaled-our-mysql-fleet-3f341e96ca6f)\n  - [Guide to MySQL High Availability](https:\u002F\u002Fwww.mysql.com\u002Fcn\u002Fwhy-mysql\u002Fwhite-papers\u002Fmysql-guide-to-high-availability-solutions\u002F)\n  - [Choosing MySQL High Availability Solutions](https:\u002F\u002Fdzone.com\u002Farticles\u002Fchoosing-mysql-high-availability-solutions)\n  - [High availability with MariaDB TX: The definitive guide](https:\u002F\u002Fmariadb.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fcontent\u002FWhitepaper_High_availability_with_MariaDB-TX.pdf)\n  - Mysql 相关经验\n    - [Booking.com: Evolution of MySQL System Design](https:\u002F\u002Fwww.percona.com\u002Flive\u002Fmysql-conference-2015\u002Fsessions\u002Fbookingcom-evolution-mysql-system-design) ，Booking.com 的 MySQL 数据库使用的演化，其中有很多不错的经验分享，我相信也是很多公司会遇到的的问题。\n    - [Tracking the Money - Scaling Financial Reporting at Airbnb](https:\u002F\u002Fmedium.com\u002Fairbnb-engineering\u002Ftracking-the-money-scaling-financial-reporting-at-airbnb-6d742b80f040) ，Airbnb 的数据库扩展的经验分享。\n    - [Why Uber Engineering Switched from Postgres to MySQL](https:\u002F\u002Feng.uber.com\u002Fmysql-migration\u002F) ，无意比较两个数据库谁好谁不好，推荐这篇 Uber 的长文，主要是想让你从中学习到一些经验和技术细节，这是一篇很不错的文章。\n  - Mysql 集群复制\n    - [Monitoring Delayed Replication, With A Focus On MySQL](https:\u002F\u002Fengineering.imvu.com\u002F2013\u002F01\u002F09\u002Fmonitoring-delayed-replication-with-a-focus-on-mysql\u002F)\n    - [Mitigating replication lag and reducing read load with freno](https:\u002F\u002Fgithubengineering.com\u002Fmitigating-replication-lag-and-reducing-read-load-with-freno\u002F)\n    - [Better Parallel Replication for MySQL](https:\u002F\u002Fmedium.com\u002Fbooking-com-infrastructure\u002Fbetter-parallel-replication-for-mysql-14e2d7857813)\n    - [Evaluating MySQL Parallel Replication Part 2: Slave Group Commit](https:\u002F\u002Fmedium.com\u002Fbooking-com-infrastructure\u002Fevaluating-mysql-parallel-replication-part-2-slave-group-commit-459026a141d2)\n    - [Evaluating MySQL Parallel Replication Part 3: Benchmarks in Production](https:\u002F\u002Fmedium.com\u002Fbooking-com-infrastructure\u002Fevaluating-mysql-parallel-replication-part-3-benchmarks-in-production-db5811058d74)\n    - [Evaluating MySQL Parallel Replication Part 4: More Benchmarks in Production](https:\u002F\u002Fmedium.com\u002Fbooking-com-infrastructure\u002Fevaluating-mysql-parallel-replication-part-4-more-benchmarks-in-production-49ee255043ab)\n    - [Evaluating MySQL Parallel Replication Part 4, Annex: Under the Hood](https:\u002F\u002Fmedium.com\u002Fbooking-com-infrastructure\u002Fevaluating-mysql-parallel-replication-part-4-annex-under-the-hood-eb456cf8b2fb)\n  - Mysql 数据分区\n    - [StackOverflow: MySQL sharding approaches?](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F5541421\u002Fmysql-sharding-approaches)\n    - [Why you don’t want to shard](https:\u002F\u002Fwww.percona.com\u002Fblog\u002F2009\u002F08\u002F06\u002Fwhy-you-dont-want-to-shard\u002F)\n    - [How to Scale Big Data Applications](https:\u002F\u002Fwww.percona.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fpresentations\u002FHow to Scale Big Data Applications.pdf)\n    - [MySQL Sharding with ProxySQL](https:\u002F\u002Fwww.percona.com\u002Fblog\u002F2016\u002F08\u002F30\u002Fmysql-sharding-with-proxysql\u002F)\n  - 各公司的 Mysql 数据分区经验分享\n    - [MailChimp: Using Shards to Accommodate Millions of Users](https:\u002F\u002Fdevs.mailchimp.com\u002Fblog\u002Fusing-shards-to-accommodate-millions-of-users\u002F)\n    - [Uber: Code Migration in Production: Rewriting the Sharding Layer of Uber’s Schemaless Datastore](https:\u002F\u002Feng.uber.com\u002Fschemaless-rewrite\u002F)\n    - [Sharding & IDs at Instagram](https:\u002F\u002Finstagram-engineering.com\u002Fsharding-ids-at-instagram-1cf5a71e5a5c)\n    - [Airbnb: How We Partitioned Airbnb’s Main Database in Two Weeks](https:\u002F\u002Fmedium.com\u002Fairbnb-engineering\u002Fhow-we-partitioned-airbnb-s-main-database-in-two-weeks-55f7e006ff21)\n- **更多资源**\n  - [awesome-mysql](https:\u002F\u002Fgithub.com\u002Fjobbole\u002Fawesome-mysql-cn) - MySQL 的资源列表\n\n### Nosql 数据库综合\n\n- Martin Fowler 在 YouTube 上分享的 NoSQL 介绍 [Introduction To NoSQL](https:\u002F\u002Fyoutu.be\u002FqI_g07C_Q5I)， 以及他参与编写的 [NoSQL Distilled - NoSQL 精粹](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F25662138\u002F)，这本书才 100 多页，是本难得的关于 NoSQL 的书，很不错，非常易读。\n- [NoSQL Databases: a Survey and Decision Guidance](https:\u002F\u002Fmedium.com\u002Fbaqend-blog\u002Fnosql-databases-a-survey-and-decision-guidance-ea7823a822d#.nhzop4d23)，这篇文章可以带你自上而下地从 CAP 原理到开始了解 NoSQL 的种种技术，是一篇非常不错的文章。\n- [Distribution, Data, Deployment: Software Architecture Convergence in Big Data Systems](https:\u002F\u002Fresources.sei.cmu.edu\u002Fasset_files\u002FWhitePaper\u002F2014_019_001_90915.pdf)，这是卡内基·梅隆大学的一篇讲分布式大数据系统的论文。其中主要讨论了在大数据时代下的软件工程中的一些关键点，也说到了 NoSQL 数据库。\n- [No Relation: The Mixed Blessings of Non-Relational Databases](http:\u002F\u002Fianvarley.com\u002FUT\u002FMR\u002FVarley_MastersReport_Full_2009-08-07.pdf)，这篇论文虽然有点年代久远。但这篇论文是 HBase 的基础，你花上一点时间来读读，就可以了解到，对各种非关系型数据存储优缺点的一个很好的比较。\n- [NoSQL Data Modeling Techniques](https:\u002F\u002Fhighlyscalable.wordpress.com\u002F2012\u002F03\u002F01\u002Fnosql-data-modeling-techniques\u002F) ，NoSQL 建模技术。这篇文章我曾经翻译在了 CoolShell 上，标题为 [NoSQL 数据建模技术](https:\u002F\u002Fcoolshell.cn\u002Farticles\u002F7270.htm)，供你参考。\n  - [MongoDB - Data Modeling Introduction](https:\u002F\u002Fdocs.mongodb.com\u002Fmanual\u002Fcore\u002Fdata-modeling-introduction\u002F) ，虽然这是 MongoDB 的数据建模介绍，但是其很多观点可以用于其它的 NoSQL 数据库。\n  - [Firebase - Structure Your Database](https:\u002F\u002Ffirebase.google.com\u002Fdocs\u002Fdatabase\u002Fandroid\u002Fstructure-data) ，Google 的 Firebase 数据库使用 JSON 建模的一些最佳实践。\n- 因为 CAP 原理，所以当你需要选择一个 NoSQL 数据库的时候，你应该看看这篇文档 [Visual Guide to NoSQL Systems](http:\u002F\u002Fblog.nahurst.com\u002Fvisual-guide-to-nosql-systems)。\n\n选 SQL 还是 NoSQL，这里有两篇文章，值得你看看。\n\n- [SQL vs. NoSQL Databases: What’s the Difference?](https:\u002F\u002Fwww.upwork.com\u002Fhiring\u002Fdata\u002Fsql-vs-nosql-databases-whats-the-difference\u002F)\n- [Salesforce: SQL or NoSQL](https:\u002F\u002Fengineering.salesforce.com\u002Fsql-or-nosql-9eaf1d92545b)\n\n### 列式数据库资料\n\n#### Cassandra 资料\n\n- 沃尔玛实验室有两篇文章值得一读。\n  - [Avoid Pitfalls in Scaling Cassandra Cluster at Walmart](https:\u002F\u002Fmedium.com\u002Fwalmartlabs\u002Favoid-pitfalls-in-scaling-your-cassandra-cluster-lessons-and-remedies-a71ca01f8c04)\n  - [Storing Images in Cassandra at Walmart](https:\u002F\u002Fmedium.com\u002Fwalmartlabs\u002Fbuilding-object-store-storing-images-in-cassandra-walmart-scale-a6b9c02af593)\n- [Yelp: How We Scaled Our Ad Analytics with Apache Cassandra](https:\u002F\u002Fengineeringblog.yelp.com\u002F2016\u002F08\u002Fhow-we-scaled-our-ad-analytics-with-cassandra.html) ，Yelp 的这篇博客也有一些相关的经验和教训。\n- [Discord: How Discord Stores Billions of Messages](https:\u002F\u002Fblog.discordapp.com\u002Fhow-discord-stores-billions-of-messages-7fa6ec7ee4c7) ，Discord 公司分享的一个如何存储十亿级消息的技术文章。\n- [Cassandra at Instagram](https:\u002F\u002Fwww.slideshare.net\u002FDataStax\u002Fcassandra-at-instagram-2016) ，Instagram 的一个 PPT，其中介绍了 Instagram 中是怎么使用 Cassandra 的。\n- [Netflix: Benchmarking Cassandra Scalability on AWS - Over a million writes per second](https:\u002F\u002Fmedium.com\u002Fnetflix-techblog\u002Fbenchmarking-cassandra-scalability-on-aws-over-a-million-writes-per-second-39f45f066c9e) ，Netflix 公司在 AWS 上给 Cassandra 做的一个 Benchmark。\n\n#### HBase 资料\n\n- [Imgur Notification: From MySQL to HBASE](https:\u002F\u002Fmedium.com\u002Fimgur-engineering\u002Fimgur-notifications-from-mysql-to-hbase-9dba6fc44183)\n- [Pinterest: Improving HBase Backup Efficiency](https:\u002F\u002Fmedium.com\u002F@Pinterest_Engineering\u002Fimproving-hbase-backup-efficiency-at-pinterest-86159da4b954)\n- [IBM : Tuning HBase performance](https:\u002F\u002Fwww.ibm.com\u002Fsupport\u002Fknowledgecenter\u002Fen\u002FSSPT3X_2.1.2\u002Fcom.ibm.swg.im.infosphere.biginsights.analyze.doc\u002Fdoc\u002Fbigsql_TuneHbase.html)\n- [HBase File Locality in HDFS](http:\u002F\u002Fwww.larsgeorge.com\u002F2010\u002F05\u002Fhbase-file-locality-in-hdfs.html)\n- [Apache Hadoop Goes Realtime at Facebook](http:\u002F\u002Fborthakur.com\u002Fftp\u002FRealtimeHadoopSigmod2011.pdf)\n- [Storage Infrastructure Behind Facebook Messages: Using HBase at Scale](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.294.8459&rep=rep1&type=pdf)\n- [GitHub: Awesome HBase](https:\u002F\u002Fgithub.com\u002Frayokota\u002Fawesome-hbase)\n\n针对于 HBase 有两本书你可以考虑一下。\n\n- 首先，先推荐两本书，一本是偏实践的《[HBase 实战](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F25706541\u002F)》，另一本是偏大而全的手册型的《[HBase 权威指南](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F10748460\u002F)》。\n- 当然，你也可以看看官方的 [The Apache HBase™ Reference Guide](http:\u002F\u002Fhbase.apache.org\u002F0.94\u002Fbook\u002Fbook.html)\n- 另外两个列数据库：\n  - [ClickHouse - Open Source Distributed Column Database at Yandex](https:\u002F\u002Fclickhouse.yandex\u002F)\n  - [Scaling Redshift without Scaling Costs at GIPHY](https:\u002F\u002Fengineering.giphy.com\u002Fscaling-redshift-without-scaling-costs\u002F)\n\n### KV 数据库资料\n\n#### Redis 资料\n\n- **官网**\n  - [Redis 官网](https:\u002F\u002Fredis.io\u002F)\n  - [Redis github](https:\u002F\u002Fgithub.com\u002Fantirez\u002Fredis)\n  - [Redis 官方文档中文版](http:\u002F\u002Fredis.cn\u002F)\n  - [Redis 命令参考](http:\u002F\u002Fredisdoc.com\u002F)\n- **书籍**\n  - [《Redis 实战》](https:\u002F\u002Fitem.jd.com\u002F11791607.html)\n  - [《Redis 设计与实现》](https:\u002F\u002Fitem.jd.com\u002F11486101.html)\n- **源码**\n  - [《Redis 实战》配套 Python 源码](https:\u002F\u002Fgithub.com\u002Fjosiahcarlson\u002Fredis-in-action)\n- **资源汇总**\n  - [awesome-redis](https:\u002F\u002Fgithub.com\u002FJamzyWang\u002Fawesome-redis)\n- **Redis Client**\n  - [spring-data-redis 官方文档](https:\u002F\u002Fdocs.spring.io\u002Fspring-data\u002Fredis\u002Fdocs\u002F1.8.13.RELEASE\u002Freference\u002Fhtml\u002F)\n  - [redisson 官方文档(中文,略有滞后)](https:\u002F\u002Fgithub.com\u002Fredisson\u002Fredisson\u002Fwiki\u002F%E7%9B%AE%E5%BD%95)\n  - [redisson 官方文档(英文)](https:\u002F\u002Fgithub.com\u002Fredisson\u002Fredisson\u002Fwiki\u002FTable-of-Content)\n  - [CRUG | Redisson PRO vs. Jedis: Which Is Faster? 翻译](https:\u002F\u002Fwww.jianshu.com\u002Fp\u002F82f0d5abb002)\n  - [redis 分布锁 Redisson 性能测试](https:\u002F\u002Fblog.csdn.net\u002Feverlasting_188\u002Farticle\u002Fdetails\u002F51073505)\n- **文章**\n  - [Learn Redis the hard way (in production) at Trivago](http:\u002F\u002Ftech.trivago.com\u002F2017\u002F01\u002F25\u002Flearn-redis-the-hard-way-in-production\u002F)\n  - [Twitter: How Twitter Uses Redis To Scale - 105TB RAM, 39MM QPS, 10,000+ Instances](http:\u002F\u002Fhighscalability.com\u002Fblog\u002F2014\u002F9\u002F8\u002Fhow-twitter-uses-redis-to-scale-105tb-ram-39mm-qps-10000-ins.html)\n  - [Slack: Scaling Slack’s Job Queue - Robustly Handling Billions of Tasks in Milliseconds Using Kafka and Redis](https:\u002F\u002Fslack.engineering\u002Fscaling-slacks-job-queue-687222e9d100)\n  - [GitHub: Moving persistent data out of Redis at GitHub](https:\u002F\u002Fgithubengineering.com\u002Fmoving-persistent-data-out-of-redis\u002F)\n  - [Instagram: Storing Hundreds of Millions of Simple Key-Value Pairs in Redis](https:\u002F\u002Fengineering.instagram.com\u002Fstoring-hundreds-of-millions-of-simple-key-value-pairs-in-redis-1091ae80f74c)\n  - [Redis in Chat Architecture of Twitch (from 27:22)](https:\u002F\u002Fwww.infoq.com\u002Fpresentations\u002Ftwitch-pokemon)\n  - [Deliveroo: Optimizing Session Key Storage in Redis](https:\u002F\u002Fdeliveroo.engineering\u002F2016\u002F10\u002F07\u002Foptimising-session-key-storage.html)\n  - [Deliveroo: Optimizing Redis Storage](https:\u002F\u002Fdeliveroo.engineering\u002F2017\u002F01\u002F19\u002Foptimising-membership-queries.html)\n  - [GitHub: Awesome Redis](https:\u002F\u002Fgithub.com\u002FJamzyWang\u002Fawesome-redis)\n\n### 文档数据库资料\n\n- [Couchbase Ecosystem at LinkedIn](https:\u002F\u002Fengineering.linkedin.com\u002Fblog\u002F2017\u002F12\u002Fcouchbase-ecosystem-at-linkedin)\n- [SimpleDB at Zendesk](https:\u002F\u002Fmedium.com\u002Fzendesk-engineering\u002Fresurrecting-amazon-simpledb-9404034ec506)\n- [Data Points - What the Heck Are Document Databases?](https:\u002F\u002Fmsdn.microsoft.com\u002Fen-us\u002Fmagazine\u002Fhh547103.aspx)\n\n#### MongoDB 资料\n\n- **官方**\n  - [MongoDB 官网](https:\u002F\u002Fwww.mongodb.com\u002F)\n  - [MongoDB Github](https:\u002F\u002Fgithub.com\u002Fmongodb\u002Fmongo)\n  - [MongoDB 官方免费教程](https:\u002F\u002Funiversity.mongodb.com\u002F)\n- **教程**\n  - [MongoDB 教程](https:\u002F\u002Fwww.runoob.com\u002Fmongodb\u002Fmongodb-tutorial.html)\n  - [MongoDB 高手课](https:\u002F\u002Ftime.geekbang.org\u002Fcourse\u002Fintro\u002F100040001)\n- **数据**\n  - [mongodb-json-files](https:\u002F\u002Fgithub.com\u002Fozlerhakan\u002Fmongodb-json-files)\n- **文章**\n  - [Introduction to MongoDB](https:\u002F\u002Fwww.slideshare.net\u002Fmdirolf\u002Fintroduction-to-mongodb)\n  - [eBay: Building Mission-Critical Multi-Data Center Applications with MongoDB](https:\u002F\u002Fwww.mongodb.com\u002Fblog\u002Fpost\u002Febay-building-mission-critical-multi-data-center-applications-with-mongodb)\n  - [The AWS and MongoDB Infrastructure of Parse: Lessons Learned](https:\u002F\u002Fmedium.baqend.com\u002Fparse-is-gone-a-few-secrets-about-their-infrastructure-91b3ab2fcf71)\n  - [Migrating Mountains of Mongo Data](https:\u002F\u002Fmedium.com\u002Fbuild-addepar\u002Fmigrating-mountains-of-mongo-data-63e530539952)\n- **更多资源**\n  - [Github: Awesome MongoDB](https:\u002F\u002Fgithub.com\u002Framnes\u002Fawesome-mongodb)\n\n### 搜索引擎数据库资料\n\n#### ElasticSearch\n\n- **官方**\n  - [Elasticsearch 官网](https:\u002F\u002Fwww.elastic.co\u002Fcn\u002Fproducts\u002Felasticsearch)\n  - [Elasticsearch Github](https:\u002F\u002Fgithub.com\u002Felastic\u002Felasticsearch)\n  - [Elasticsearch 官方文档](https:\u002F\u002Fwww.elastic.co\u002Fguide\u002Fen\u002Felasticsearch\u002Freference\u002Fcurrent\u002Findex.html)\n  - [Elasticsearch: The Definitive Guide](https:\u002F\u002Fwww.elastic.co\u002Fguide\u002Fen\u002Felasticsearch\u002Fguide\u002Fmaster\u002Findex.html) - ElasticSearch 官方学习资料\n- **书籍**\n  - [《Elasticsearch 实战》](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F30380439\u002F)\n- **教程**\n  - [ELK Stack 权威指南](https:\u002F\u002Fgithub.com\u002Fchenryn\u002Flogstash-best-practice-cn)\n  - [Elasticsearch 教程](https:\u002F\u002Fwww.knowledgedict.com\u002Ftutorial\u002Felasticsearch-intro.html)\n- **文章**\n  - [Elasticsearch+Logstash+Kibana 教程](https:\u002F\u002Fwww.cnblogs.com\u002Fxing901022\u002Fp\u002F4704319.html)\n  - [ELK（Elasticsearch、Logstash、Kibana）安装和配置](https:\u002F\u002Fgithub.com\u002Fjudasn\u002FLinux-Tutorial\u002Fblob\u002Fmaster\u002FELK-Install-And-Settings.md)\n  - **性能调优相关**的工程实践\n    - [Elasticsearch Performance Tuning Practice at eBay](https:\u002F\u002Fwww.ebayinc.com\u002Fstories\u002Fblogs\u002Ftech\u002Felasticsearch-performance-tuning-practice-at-ebay\u002F)\n    - [Elasticsearch at Kickstarter](https:\u002F\u002Fkickstarter.engineering\u002Felasticsearch-at-kickstarter-db3c487887fc)\n    - [9 tips on ElasticSearch configuration for high performance](https:\u002F\u002Fwww.loggly.com\u002Fblog\u002Fnine-tips-configuring-elasticsearch-for-high-performance\u002F)\n    - [Elasticsearch In Production - Deployment Best Practices](https:\u002F\u002Fmedium.com\u002F@abhidrona\u002Felasticsearch-deployment-best-practices-d6c1323b25d7)\n- **更多资源**\n  - [GitHub: Awesome ElasticSearch](https:\u002F\u002Fgithub.com\u002Fdzharii\u002Fawesome-elasticsearch)\n\n### 图数据库\n\n- 首先是 IBM Devloperworks 上的两个简介性的 PPT。\n  - [Intro to graph databases, Part 1, Graph databases and the CRUD operations](https:\u002F\u002Fwww.ibm.com\u002Fdeveloperworks\u002Flibrary\u002Fcl-graph-database-1\u002Fcl-graph-database-1-pdf.pdf)\n  - [Intro to graph databases, Part 2, Building a recommendation engine with a graph database](https:\u002F\u002Fwww.ibm.com\u002Fdeveloperworks\u002Flibrary\u002Fcl-graph-database-2\u002Fcl-graph-database-2-pdf.pdf)\n- 然后是一本免费的电子书《[Graph Database](http:\u002F\u002Fgraphdatabases.com)》。\n- 接下来是一些图数据库的介绍文章。\n  - [Handling Billions of Edges in a Graph Database](https:\u002F\u002Fwww.infoq.com\u002Fpresentations\u002Fgraph-database-scalability)\n  - [Neo4j case studies with Walmart, eBay, AirBnB, NASA, etc](https:\u002F\u002Fneo4j.com\u002Fcustomers\u002F)\n  - [FlockDB: Distributed Graph Database for Storing Adjacency Lists at Twitter](https:\u002F\u002Fblog.twitter.com\u002Fengineering\u002Fen_us\u002Fa\u002F2010\u002Fintroducing-flockdb.html)\n  - [JanusGraph: Scalable Graph Database backed by Google, IBM and Hortonworks](https:\u002F\u002Farchitecht.io\u002Fgoogle-ibm-back-new-open-source-graph-database-project-janusgraph-1d74fb78db6b)\n  - [Amazon Neptune](https:\u002F\u002Faws.amazon.com\u002Fneptune\u002F)\n\n### 时序数据库\n\n- [What is Time-Series Data & Why We Need a Time-Series Database](https:\u002F\u002Fblog.timescale.com\u002Fwhat-the-heck-is-time-series-data-and-why-do-i-need-a-time-series-database-dcf3b1b18563)\n- [Time Series Data: Why and How to Use a Relational Database instead of NoSQL](https:\u002F\u002Fblog.timescale.com\u002Ftime-series-data-why-and-how-to-use-a-relational-database-instead-of-nosql-d0cd6975e87c)\n- [Beringei: High-performance Time Series Storage Engine @Facebook](https:\u002F\u002Fcode.facebook.com\u002Fposts\u002F952820474848503\u002Fberingei-a-high-performance-time-series-storage-engine\u002F)\n- [Introducing Atlas: Netflix’s Primary Telemetry Platform @Netflix](https:\u002F\u002Fmedium.com\u002Fnetflix-techblog\u002Fintroducing-atlas-netflixs-primary-telemetry-platform-bd31f4d8ed9a)\n- [Building a Scalable Time Series Database on PostgreSQL](https:\u002F\u002Fblog.timescale.com\u002Fwhen-boring-is-awesome-building-a-scalable-time-series-database-on-postgresql-2900ea453ee2)\n- [Scaling Time Series Data Storage - Part I @Netflix](https:\u002F\u002Fmedium.com\u002Fnetflix-techblog\u002Fscaling-time-series-data-storage-part-i-ec2b6d44ba39)\n- [Design of a Cost Efficient Time Series Store for Big Data](https:\u002F\u002Fmedium.com\u002F@leventov\u002Fdesign-of-a-cost-efficient-time-series-store-for-big-data-88c5dc41af8e)\n- [GitHub: Awesome Time-Series Database](https:\u002F\u002Fgithub.com\u002Fxephonhq\u002Fawesome-time-series-database)\n\n## 传送 🚪\n\n◾ 💧 [钝悟的 IT 知识图谱](https:\u002F\u002Fdunwu.github.io\u002Fwaterdrop\u002F) ◾ 🎯 [钝悟的博客](https:\u002F\u002Fdunwu.github.io\u002Fblog\u002F) ◾\n","db-tutorial 是一个面向后端程序员的主流数据库知识教程。该项目涵盖了从关系型数据库（如 MySQL）到 NoSQL 数据库（如 Elasticsearch、HBase、MongoDB 和 Redis）的全面讲解，包括分布式存储原理、关键技术以及数据库中间件等内容。它不仅提供了理论知识，还深入探讨了实际应用场景中的技术选型和最佳实践。适合需要系统学习数据库相关知识或希望提升数据库操作技能的开发者使用。","2026-06-11 03:28:25","top_topic"]