├── .gitignore ├── README.md ├── bin ├── __pycache__ │ └── preproc_config.cpython-37.pyc ├── preproc └── preproc_config.py ├── docs ├── Makefile ├── bg-css.html ├── book.toml ├── book │ ├── .nojekyll │ ├── 404.html │ ├── FontAwesome │ │ ├── css │ │ │ └── font-awesome.css │ │ └── fonts │ │ │ ├── FontAwesome.ttf │ │ │ ├── fontawesome-webfont.eot │ │ │ ├── fontawesome-webfont.svg │ │ │ ├── fontawesome-webfont.ttf │ │ │ ├── fontawesome-webfont.woff │ │ │ └── fontawesome-webfont.woff2 │ ├── ayu-highlight.css │ ├── book.js │ ├── chapter01-00-00-第一章，有状态的流式处理简介.html │ ├── chapter01-01-00-传统数据处理架构.html │ ├── chapter01-01-01-事务处理.html │ ├── chapter01-01-02-分析处理.html │ ├── chapter01-02-00-有状态的流式处理.html │ ├── chapter01-02-01-事件驱动应用程序.html │ ├── chapter01-02-02-数据管道.html │ ├── chapter01-02-03-流分析.html │ ├── chapter01-03-00-开源流处理的演进.html │ ├── chapter01-03-01-流处理的历史.html │ ├── chapter01-04-00-Flink简介.html │ ├── chapter02-00-00-第二章，流处理基础.html │ ├── chapter02-01-00-数据流编程简介.html │ ├── chapter02-01-01-数据流图.html │ ├── chapter02-01-02-数据并行和任务并行.html │ ├── chapter02-01-03-数据交换策略.html │ ├── chapter02-02-00-并行处理流数据.html │ ├── chapter02-02-01-延迟和吞吐量.html │ ├── chapter02-02-02-延迟.html │ ├── chapter02-02-03-吞吐量.html │ ├── chapter02-02-04-延迟与吞吐量的对比.html │ ├── chapter02-03-00-数据流上的操作.html │ ├── chapter02-03-01-数据摄入和数据吞吐量.html │ ├── chapter02-03-02-转换算子.html │ ├── chapter02-03-03-滚动聚合.html │ ├── chapter02-03-04-窗口操作符.html │ ├── chapter02-04-00-时间语义.html │ ├── chapter02-04-01-在流处理中一分钟代表什么？.html │ ├── chapter02-04-02-处理时间.html │ ├── chapter02-04-03-事件时间.html │ ├── chapter02-04-04-水位线.html │ ├── chapter02-04-05-处理时间和事件时间.html │ ├── chapter02-05-00-状态和持久化模型.html │ ├── chapter02-05-01-任务失败.html │ ├── chapter03-00-00-第三章，Flink运行架构.html │ ├── chapter03-01-00-系统架构.html │ ├── chapter03-01-01-Flink运行时组件.html │ ├── chapter03-01-02-应用部署.html │ ├── chapter03-01-03-任务执行.html │ ├── chapter03-01-04-高可用配置.html │ ├── chapter03-02-01-基于信任度的流控制.html │ ├── chapter03-02-02-任务链.html │ ├── chapter03-02-Flink中的数据传输.html │ ├── chapter03-03-00-事件时间处理.html │ ├── chapter03-03-01-时间戳.html │ ├── chapter03-03-02-水位线.html │ ├── chapter03-03-03-watermark的传递和事件时间.html │ ├── chapter03-03-04-时间戳的分配和水位线的产生.html │ ├── chapter03-04-00-状态管理.html │ ├── chapter03-04-01-算子状态.html │ ├── chapter03-04-02-键控状态.html │ ├── chapter03-04-03-状态后端.html │ ├── chapter03-04-04-调整有状态算子的并行度.html │ ├── chapter03-05-00-检查点，保存点和状态恢复.html │ ├── chapter03-05-01-一致的检查点.html │ ├── chapter03-05-02-从一致检查点中恢复状态.html │ ├── chapter03-05-03-Flink的检查点算法.html │ ├── chapter03-05-04-检查点的性能影响.html │ ├── chapter03-05-05-保存点.html │ ├── chapter04-00-00-第四章，编写第一个Flink程序.html │ ├── chapter04-01-00-在IDEA中编写Flink程序.html │ ├── chapter04-02-00-下载Flink运行时环境，提交Jar包的运行方式.html │ ├── chapter05-00-00-第五章，Flink-DataStream-API.html │ ├── chapter05-01-00-你好，Flink！.html │ ├── chapter05-02-00-搭建执行环境.html │ ├── chapter05-03-00-读取输入流.html │ ├── chapter05-04-00-转换算子的使用.html │ ├── chapter05-05-00-输出结果.html │ ├── chapter05-06-00-执行.html │ ├── chapter05-07-00-产生传感器读数代码编写.html │ ├── chapter05-07-01-从批读取数据.html │ ├── chapter05-07-02-从文件读取数据.html │ ├── chapter05-07-03-以Kafka消息队列的数据为数据来源.html │ ├── chapter05-07-04-自定义数据源.html │ ├── chapter05-08-00-转换算子.html │ ├── chapter05-08-01-基本转换算子.html │ ├── chapter05-08-02-键控流转换算子.html │ ├── chapter05-08-03-多流转换算子.html │ ├── chapter05-08-04-分布式转换算子.html │ ├── chapter05-09-00-设置并行度.html │ ├── chapter05-10-00-类型.html │ ├── chapter05-10-01-支持的数据类型.html │ ├── chapter05-10-02-为数据类型创建类型信息.html │ ├── chapter05-11-00-定义Key以及引用字段.html │ ├── chapter05-11-01-使用字段位置进行keyBy.html │ ├── chapter05-11-02-使用字段表达式来进行keyBy.html │ ├── chapter05-11-03-Key选择器.html │ ├── chapter05-12-00-实现UDF函数，更细粒度的控制流.html │ ├── chapter05-12-01-函数类.html │ ├── chapter05-12-02-匿名函数.html │ ├── chapter05-12-03-富函数.html │ ├── chapter05-13-00-Sink.html │ ├── chapter05-13-01-Kafka.html │ ├── chapter05-13-02-Redis.html │ ├── chapter05-13-03-ElasticSearch.html │ ├── chapter05-13-04-JDBC自定义sink.html │ ├── chapter06-00-00-第六章，基于时间和窗口的操作符.html │ ├── chapter06-01-00-设置时间属性.html │ ├── chapter06-01-01-指定时间戳和产生水位线.html │ ├── chapter06-01-02-周期性的生成水位线.html │ ├── chapter06-01-03-如何产生不规则的水位线.html │ ├── chapter06-02-00-处理函数.html │ ├── chapter06-02-01-时间服务和定时器.html │ ├── chapter06-02-02-将事件发送到侧输出.html │ ├── chapter06-02-03-CoProcessFunction.html │ ├── chapter06-03-00-窗口操作符.html │ ├── chapter06-03-01-定义窗口操作符.html │ ├── chapter06-03-02-内置的窗口分配器.html │ ├── chapter06-03-03-调用窗口计算函数.html │ ├── chapter06-03-04-自定义窗口操作符.html │ ├── chapter06-04-00-基于时间的双流Join.html │ ├── chapter06-04-01-基于间隔的Join.html │ ├── chapter06-04-02-基于窗口的Join.html │ ├── chapter06-05-00-处理迟到的元素.html │ ├── chapter06-05-01-抛弃迟到元素.html │ ├── chapter06-05-02-重定向迟到元素.html │ ├── chapter06-05-03-使用迟到元素更新窗口计算结果.html │ ├── chapter07-00-00-第七章，有状态算子和应用.html │ ├── chapter07-01-00-实现有状态的用户自定义函数.html │ ├── chapter07-01-01-在RuntimeContext中定义键控状态.html │ ├── chapter07-01-02-使用ListCheckpointed接口来实现操作符的列表状态.html │ ├── chapter07-01-03-使用连接的广播状态.html │ ├── chapter07-02-00-配置检查点.html │ ├── chapter07-02-01-将hdfs配置为状态后端.html │ ├── chapter07-03-01-指定唯一的操作符标识符.html │ ├── chapter07-03-02-指定操作符的最大并行度.html │ ├── chapter07-03-保证有状态应用的可维护性.html │ ├── chapter07-04-00-有状态应用的性能和健壮性.html │ ├── chapter07-04-01-选择一个状态后端.html │ ├── chapter07-04-02-防止状态泄露.html │ ├── chapter08-00-00-第八章，读写外部系统.html │ ├── chapter08-01-00-应用的一致性保证.html │ ├── chapter08-01-01-幂等性写入.html │ ├── chapter08-01-02-事务性写入.html │ ├── chapter08-02-00-Flink提供的连接器.html │ ├── chapter08-02-01-Apache-Kafka-Source连接器.html │ ├── chapter08-02-02-Apache-Kafka-Sink连接器.html │ ├── chapter08-02-03-Kakfa-Sink的at-least-once保证.html │ ├── chapter08-02-04-Kafka-Sink的恰好处理一次语义保证.html │ ├── chapter08-02-05-文件系统source连接器.html │ ├── chapter08-02-06-文件系统sink连接器.html │ ├── chapter08-03-00-实现自定义源函数.html │ ├── chapter08-03-01-可重置的源函数.html │ ├── chapter08-04-00-实现自定义sink函数.html │ ├── chapter08-04-01-幂等性sink连接器.html │ ├── chapter08-04-02-事务性sink连接器.html │ ├── chapter09-00-00-第九章，搭建Flink运行流式应用.html │ ├── chapter09-01-00-部署方式.html │ ├── chapter09-01-01-独立集群.html │ ├── chapter09-01-02-Apache-Hadoop-Yarn.html │ ├── chapter09-02-00-高可用配置.html │ ├── chapter09-02-01-独立集群高可用配置.html │ ├── chapter09-02-02-yarn集群高可用配置.html │ ├── chapter09-03-00-与Hadoop集成.html │ ├── chapter09-04-00-保存点操作.html │ ├── chapter09-05-00-取消一个应用.html │ ├── chapter09-06-00-从保存点启动应用程序.html │ ├── chapter09-07-00-扩容，改变并行度操作.html │ ├── chapter10-00-00-第十章，Flink和流式应用运维.html │ ├── chapter11-00-00-第十一章，Flink-CEP简介.html │ ├── chapter12-00-00-第十二章，Table-API和Flink-SQL.html │ ├── chapter12-01-00-整体介绍.html │ ├── chapter12-01-01-什么是Table-API和Flink-SQL.html │ ├── chapter12-01-02-需要引入的依赖.html │ ├── chapter12-01-03-两种planner（old-&-blink）的区别.html │ ├── chapter12-02-00-API调用.html │ ├── chapter12-02-01-基本程序结构.html │ ├── chapter12-02-02-创建表环境.html │ ├── chapter12-02-03-在Catalog中注册表.html │ ├── chapter12-02-04-表的查询.html │ ├── chapter12-02-05-将DataStream转换成表.html │ ├── chapter12-02-06-创建临时视图.html │ ├── chapter12-02-07-输出表.html │ ├── chapter12-02-08-将表转换成DataStream.html │ ├── chapter12-02-09-Query的解释和执行.html │ ├── chapter12-03-00-流处理中的特殊概念.html │ ├── chapter12-03-01-流处理和关系代数（表，及SQL）的区别.html │ ├── chapter12-03-02-动态表.html │ ├── chapter12-03-03-流式持续查询的过程.html │ ├── chapter12-03-04-时间特性.html │ ├── chapter12-04-00-窗口.html │ ├── chapter12-04-01-分组窗口.html │ ├── chapter12-04-02-Over-Windows.html │ ├── chapter12-04-03-SQL中窗口的定义.html │ ├── chapter12-04-04-代码练习（以分组滚动窗口为例）.html │ ├── chapter12-05-00-函数.html │ ├── chapter12-05-01-系统内置函数.html │ ├── chapter12-05-02-UDF.html │ ├── chapter12-06-00-Flink和Hive集成.html │ ├── chapter12-06-01-Maven依赖.html │ ├── chapter12-06-02-示例程序.html │ ├── chapter12-06-03-一个复杂一点的程序.html │ ├── chapter12-06-04-彻底重置hadoop和hive的方法.html │ ├── chapter13-00-00-第十三章，尚硅谷大数据技术之电商用户行为分析.html │ ├── chapter13-01-00-数据集解析.html │ ├── chapter13-01-01-淘宝数据集解析.html │ ├── chapter13-01-02-Apache服务器日志数据集解析.html │ ├── chapter13-02-00-实时热门商品统计.html │ ├── chapter13-03-00-实时流量统计.html │ ├── chapter13-04-00-Uv统计的布隆过滤器实现.html │ ├── chapter13-05-00-APP分渠道数据统计.html │ ├── chapter13-06-00-APP不分渠道数据统计.html │ ├── chapter13-07-00-恶意登陆实现.html │ ├── chapter13-08-00-订单支付实时监控.html │ ├── chapter13-08-01-使用Flink-CEP来实现.html │ ├── chapter13-08-02-使用Process-Function实现订单超时需求.html │ ├── chapter13-09-00-实时对帐：实现两条流的Join.html │ ├── chapter13-10-00-使用Flink-SQL实现实时热门商品统计.html │ ├── chapter13-11-00-使用Flink-SQL实现端到端的流式应用.html │ ├── chapter14-00-第十四章，常见面试题解答.html │ ├── chapter14-01-面试题一.html │ ├── chapter14-02-面试题二.html │ ├── chapter14-03-面试题三.html │ ├── chapter14-04-面试题四.html │ ├── chapter14-05-面试题五.html │ ├── chapter14-06-面试题六.html │ ├── chapter14-07-面试题七.html │ ├── chapter14-08-面试题八.html │ ├── chapter14-09-面试题九.html │ ├── chapter14-10-面试题十.html │ ├── chapter14-11-面试题十一.html │ ├── chapter14-12-面试题十二.html │ ├── chapter14-13-面试题十三.html │ ├── chapter14-14-面试题十四.html │ ├── chapter14-15-面试题十五.html │ ├── chapter14-16-面试题十六.html │ ├── chapter14-17-面试题十七.html │ ├── chapter14-18-面试题十八.html │ ├── chapter14-19-面试题十九.html │ ├── chapter14-20-面试题二十.html │ ├── clipboard.min.js │ ├── css │ │ ├── chrome.css │ │ ├── general.css │ │ ├── print.css │ │ └── variables.css │ ├── elasticlunr.min.js │ ├── favicon.png │ ├── favicon.svg │ ├── fonts │ │ ├── OPEN-SANS-LICENSE.txt │ │ ├── SOURCE-CODE-PRO-LICENSE.txt │ │ ├── fonts.css │ │ ├── open-sans-v17-all-charsets-300.woff2 │ │ ├── open-sans-v17-all-charsets-300italic.woff2 │ │ ├── open-sans-v17-all-charsets-600.woff2 │ │ ├── open-sans-v17-all-charsets-600italic.woff2 │ │ ├── open-sans-v17-all-charsets-700.woff2 │ │ ├── open-sans-v17-all-charsets-700italic.woff2 │ │ ├── open-sans-v17-all-charsets-800.woff2 │ │ ├── open-sans-v17-all-charsets-800italic.woff2 │ │ ├── open-sans-v17-all-charsets-italic.woff2 │ │ ├── open-sans-v17-all-charsets-regular.woff2 │ │ └── source-code-pro-v11-all-charsets-500.woff2 │ ├── highlight.css │ ├── highlight.js │ ├── images │ │ ├── append-mode.png │ │ ├── cep1.jpg │ │ ├── cep6.png │ │ ├── query-groupBy-cnt.png │ │ ├── query-groupBy-window-cnt.png │ │ ├── redo-mode.png │ │ ├── spaf_0101.png │ │ ├── spaf_0102.png │ │ ├── spaf_0103.png │ │ ├── spaf_0104.png │ │ ├── spaf_0105.png │ │ ├── spaf_0106.png │ │ ├── spaf_0107.png │ │ ├── spaf_0201.png │ │ ├── spaf_0202.png │ │ ├── spaf_0203.png │ │ ├── spaf_0204.png │ │ ├── spaf_0205.png │ │ ├── spaf_0206.png │ │ ├── spaf_0207.png │ │ ├── spaf_0208.png │ │ ├── spaf_0209.png │ │ ├── spaf_0210.png │ │ ├── spaf_0211.png │ │ ├── spaf_0212.png │ │ ├── spaf_0213.png │ │ ├── spaf_0301.png │ │ ├── spaf_0302.png │ │ ├── spaf_0303.png │ │ ├── spaf_0304.png │ │ ├── spaf_0305.png │ │ ├── spaf_0306.png │ │ ├── spaf_0307.png │ │ ├── spaf_0308.png │ │ ├── spaf_0309.png │ │ ├── spaf_0310.png │ │ ├── spaf_0311.png │ │ ├── spaf_0312.png │ │ ├── spaf_0313.png │ │ ├── spaf_0314.png │ │ ├── spaf_0315.png │ │ ├── spaf_0316.png │ │ ├── spaf_0317.png │ │ ├── spaf_0318.png │ │ ├── spaf_0319.png │ │ ├── spaf_0320.png │ │ ├── spaf_0321.png │ │ ├── spaf_0322.png │ │ ├── spaf_0323.png │ │ ├── spaf_0324.png │ │ ├── spaf_0325.png │ │ ├── spaf_0326.png │ │ ├── spaf_0501.png │ │ ├── spaf_0502.png │ │ ├── spaf_0503.png │ │ ├── spaf_0504.png │ │ ├── spaf_0505.png │ │ ├── spaf_0506.png │ │ ├── spaf_0507.png │ │ ├── spaf_0601.png │ │ ├── spaf_0602.png │ │ ├── spaf_0603.png │ │ ├── spaf_0604.png │ │ ├── spaf_0605.png │ │ ├── spaf_0606.png │ │ ├── spaf_0607.png │ │ ├── spaf_0608.png │ │ ├── spaf_0701.png │ │ ├── spaf_0801.png │ │ ├── spaf_0901.png │ │ ├── spaf_0902.png │ │ ├── spaf_0903.png │ │ ├── spaf_0904.png │ │ ├── spaf_0905.png │ │ ├── spaf_1001.png │ │ ├── spaf_1002.png │ │ ├── spaf_1003.png │ │ ├── spaf_1004.png │ │ ├── spaf_1005.png │ │ ├── stream-query-stream.png │ │ ├── streamtable.png │ │ ├── streamtable1.png │ │ ├── streamtable2.png │ │ ├── streamtable3.png │ │ ├── streamtable4.png │ │ ├── streamtable6.png │ │ ├── tablestream5.png │ │ ├── udagg-mechanism.png │ │ ├── udtagg-mechanism.png │ │ └── undo-redo-mode.png │ ├── index.html │ ├── mark.min.js │ ├── print.html │ ├── searcher.js │ ├── searchindex.js │ ├── searchindex.json │ └── tomorrow-night.css ├── flinktutorial.html ├── flinktutorial.md ├── index.css ├── index.html ├── split-md.py └── src │ ├── SUMMARY.md │ ├── chapter01-00-00-第一章，有状态的流式处理简介.md │ ├── chapter01-01-00-传统数据处理架构.md │ ├── chapter01-01-01-事务处理.md │ ├── chapter01-01-02-分析处理.md │ ├── chapter01-02-00-有状态的流式处理.md │ ├── chapter01-02-01-事件驱动应用程序.md │ ├── chapter01-02-02-数据管道.md │ ├── chapter01-02-03-流分析.md │ ├── chapter01-03-00-开源流处理的演进.md │ ├── chapter01-03-01-流处理的历史.md │ ├── chapter01-04-00-Flink简介.md │ ├── chapter02-00-00-第二章，流处理基础.md │ ├── chapter02-01-00-数据流编程简介.md │ ├── chapter02-01-01-数据流图.md │ ├── chapter02-01-02-数据并行和任务并行.md │ ├── chapter02-01-03-数据交换策略.md │ ├── chapter02-02-00-并行处理流数据.md │ ├── chapter02-02-01-延迟和吞吐量.md │ ├── chapter02-02-02-延迟.md │ ├── chapter02-02-03-吞吐量.md │ ├── chapter02-02-04-延迟与吞吐量的对比.md │ ├── chapter02-03-00-数据流上的操作.md │ ├── chapter02-03-01-数据摄入和数据吞吐量.md │ ├── chapter02-03-02-转换算子.md │ ├── chapter02-03-03-滚动聚合.md │ ├── chapter02-03-04-窗口操作符.md │ ├── chapter02-04-00-时间语义.md │ ├── chapter02-04-01-在流处理中一分钟代表什么？.md │ ├── chapter02-04-02-处理时间.md │ ├── chapter02-04-03-事件时间.md │ ├── chapter02-04-04-水位线.md │ ├── chapter02-04-05-处理时间和事件时间.md │ ├── chapter02-05-00-状态和持久化模型.md │ ├── chapter02-05-01-任务失败.md │ ├── chapter03-00-00-第三章，Flink运行架构.md │ ├── chapter03-01-00-系统架构.md │ ├── chapter03-01-01-Flink运行时组件.md │ ├── chapter03-01-02-应用部署.md │ ├── chapter03-01-03-任务执行.md │ ├── chapter03-01-04-高可用配置.md │ ├── chapter03-02-00-Flink中的数据传输.md │ ├── chapter03-02-01-基于信任度的流控制.md │ ├── chapter03-02-02-任务链.md │ ├── chapter03-02-Flink中的数据传输.md │ ├── chapter03-03-00-事件时间处理.md │ ├── chapter03-03-01-时间戳.md │ ├── chapter03-03-02-水位线.md │ ├── chapter03-03-03-watermark的传递和事件时间.md │ ├── chapter03-03-04-时间戳的分配和水位线的产生.md │ ├── chapter03-04-00-状态管理.md │ ├── chapter03-04-01-算子状态.md │ ├── chapter03-04-02-键控状态.md │ ├── chapter03-04-03-状态后端.md │ ├── chapter03-04-04-调整有状态算子的并行度.md │ ├── chapter03-05-00-检查点，保存点和状态恢复.md │ ├── chapter03-05-01-一致的检查点.md │ ├── chapter03-05-02-从一致检查点中恢复状态.md │ ├── chapter03-05-03-Flink的检查点算法.md │ ├── chapter03-05-04-检查点的性能影响.md │ ├── chapter03-05-05-保存点.md │ ├── chapter04-00-00-第四章，编写第一个Flink程序.md │ ├── chapter04-01-00-在IDEA中编写Flink程序.md │ ├── chapter04-02-00-下载Flink运行时环境，提交Jar包的运行方式.md │ ├── chapter05-00-00-第五章，Flink-DataStream-API.md │ ├── chapter05-01-00-你好，Flink！.md │ ├── chapter05-02-00-搭建执行环境.md │ ├── chapter05-03-00-读取输入流.md │ ├── chapter05-04-00-转换算子的使用.md │ ├── chapter05-05-00-输出结果.md │ ├── chapter05-06-00-执行.md │ ├── chapter05-07-00-产生传感器读数代码编写.md │ ├── chapter05-07-01-从批读取数据.md │ ├── chapter05-07-02-从文件读取数据.md │ ├── chapter05-07-03-以Kafka消息队列的数据为数据来源.md │ ├── chapter05-07-04-自定义数据源.md │ ├── chapter05-08-00-转换算子.md │ ├── chapter05-08-01-基本转换算子.md │ ├── chapter05-08-02-键控流转换算子.md │ ├── chapter05-08-03-多流转换算子.md │ ├── chapter05-08-04-分布式转换算子.md │ ├── chapter05-09-00-设置并行度.md │ ├── chapter05-10-00-类型.md │ ├── chapter05-10-01-支持的数据类型.md │ ├── chapter05-10-02-为数据类型创建类型信息.md │ ├── chapter05-11-00-定义Key以及引用字段.md │ ├── chapter05-11-01-使用字段位置进行keyBy.md │ ├── chapter05-11-02-使用字段表达式来进行keyBy.md │ ├── chapter05-11-03-Key选择器.md │ ├── chapter05-12-00-实现UDF函数，更细粒度的控制流.md │ ├── chapter05-12-01-函数类.md │ ├── chapter05-12-02-匿名函数.md │ ├── chapter05-12-03-富函数.md │ ├── chapter05-13-00-Sink.md │ ├── chapter05-13-01-Kafka.md │ ├── chapter05-13-02-Redis.md │ ├── chapter05-13-03-ElasticSearch.md │ ├── chapter05-13-04-JDBC自定义sink.md │ ├── chapter06-00-00-第六章，基于时间和窗口的操作符.md │ ├── chapter06-01-00-设置时间属性.md │ ├── chapter06-01-01-指定时间戳和产生水位线.md │ ├── chapter06-01-02-周期性的生成水位线.md │ ├── chapter06-01-03-如何产生不规则的水位线.md │ ├── chapter06-02-00-处理函数.md │ ├── chapter06-02-01-时间服务和定时器.md │ ├── chapter06-02-02-将事件发送到侧输出.md │ ├── chapter06-02-03-CoProcessFunction.md │ ├── chapter06-03-00-窗口操作符.md │ ├── chapter06-03-01-定义窗口操作符.md │ ├── chapter06-03-02-内置的窗口分配器.md │ ├── chapter06-03-03-调用窗口计算函数.md │ ├── chapter06-03-04-自定义窗口操作符.md │ ├── chapter06-04-00-基于时间的双流Join.md │ ├── chapter06-04-01-基于间隔的Join.md │ ├── chapter06-04-02-基于窗口的Join.md │ ├── chapter06-05-00-处理迟到的元素.md │ ├── chapter06-05-01-抛弃迟到元素.md │ ├── chapter06-05-02-重定向迟到元素.md │ ├── chapter06-05-03-使用迟到元素更新窗口计算结果.md │ ├── chapter07-00-00-第七章，有状态算子和应用.md │ ├── chapter07-01-00-实现有状态的用户自定义函数.md │ ├── chapter07-01-01-在RuntimeContext中定义键控状态.md │ ├── chapter07-01-02-使用ListCheckpointed接口来实现操作符的列表状态.md │ ├── chapter07-01-03-使用连接的广播状态.md │ ├── chapter07-02-00-配置检查点.md │ ├── chapter07-02-01-将hdfs配置为状态后端.md │ ├── chapter07-03-00-保证有状态应用的可维护性.md │ ├── chapter07-03-01-指定唯一的操作符标识符.md │ ├── chapter07-03-02-指定操作符的最大并行度.md │ ├── chapter07-03-保证有状态应用的可维护性.md │ ├── chapter07-04-00-有状态应用的性能和健壮性.md │ ├── chapter07-04-01-选择一个状态后端.md │ ├── chapter07-04-02-防止状态泄露.md │ ├── chapter08-00-00-第八章，读写外部系统.md │ ├── chapter08-01-00-应用的一致性保证.md │ ├── chapter08-01-01-幂等性写入.md │ ├── chapter08-01-02-事务性写入.md │ ├── chapter08-02-00-Flink提供的连接器.md │ ├── chapter08-02-01-Apache-Kafka-Source连接器.md │ ├── chapter08-02-02-Apache-Kafka-Sink连接器.md │ ├── chapter08-02-03-Kakfa-Sink的at-least-once保证.md │ ├── chapter08-02-04-Kafka-Sink的恰好处理一次语义保证.md │ ├── chapter08-02-05-文件系统source连接器.md │ ├── chapter08-02-06-文件系统sink连接器.md │ ├── chapter08-03-00-实现自定义源函数.md │ ├── chapter08-03-01-可重置的源函数.md │ ├── chapter08-04-00-实现自定义sink函数.md │ ├── chapter08-04-01-幂等sink连接器.md │ ├── chapter08-04-01-幂等性sink连接器.md │ ├── chapter08-04-02-事务性sink连接器.md │ ├── chapter09-00-00-第九章，搭建Flink运行流式应用.md │ ├── chapter09-01-00-部署方式.md │ ├── chapter09-01-01-独立集群.md │ ├── chapter09-01-02-Apache-Hadoop-Yarn.md │ ├── chapter09-02-00-高可用配置.md │ ├── chapter09-02-01-独立集群高可用配置.md │ ├── chapter09-02-02-yarn集群高可用配置.md │ ├── chapter09-03-00-与Hadoop集成.md │ ├── chapter09-04-00-保存点操作.md │ ├── chapter09-05-00-取消一个应用.md │ ├── chapter09-06-00-从保存点启动应用程序.md │ ├── chapter09-07-00-扩容，改变并行度操作.md │ ├── chapter10-00-00-第十章，Flink和流式应用运维.md │ ├── chapter11-00-00-第十一章，Flink-CEP简介.md │ ├── chapter12-00-00-第十二章，Table-API和Flink-SQL.md │ ├── chapter12-01-00-整体介绍.md │ ├── chapter12-01-01-什么是Table-API和Flink-SQL.md │ ├── chapter12-01-02-需要引入的依赖.md │ ├── chapter12-01-03-两种planner（old-&-blink）的区别.md │ ├── chapter12-02-00-API调用.md │ ├── chapter12-02-01-基本程序结构.md │ ├── chapter12-02-02-创建表环境.md │ ├── chapter12-02-03-在Catalog中注册表.md │ ├── chapter12-02-04-表的查询.md │ ├── chapter12-02-05-将DataStream转换成表.md │ ├── chapter12-02-06-创建临时视图.md │ ├── chapter12-02-07-输出表.md │ ├── chapter12-02-08-将表转换成DataStream.md │ ├── chapter12-02-09-Query的解释和执行.md │ ├── chapter12-03-00-流处理中的特殊概念.md │ ├── chapter12-03-01-流处理和关系代数（表，及SQL）的区别.md │ ├── chapter12-03-02-动态表.md │ ├── chapter12-03-03-流式持续查询的过程.md │ ├── chapter12-03-04-时间特性.md │ ├── chapter12-04-00-窗口.md │ ├── chapter12-04-01-分组窗口.md │ ├── chapter12-04-02-Over-Windows.md │ ├── chapter12-04-03-SQL中窗口的定义.md │ ├── chapter12-04-04-代码练习（以分组滚动窗口为例）.md │ ├── chapter12-05-00-函数.md │ ├── chapter12-05-01-系统内置函数.md │ ├── chapter12-05-02-UDF.md │ ├── chapter12-06-00-Flink和Hive集成.md │ ├── chapter12-06-01-Maven依赖.md │ ├── chapter12-06-02-示例程序.md │ ├── chapter12-06-03-一个复杂一点的程序.md │ ├── chapter12-06-04-彻底重置hadoop和hive的方法.md │ ├── chapter12-06-05-将代码部署到flink运行时环境.md │ ├── chapter13-00-00-第十三章，尚硅谷大数据技术之电商用户行为分析.md │ ├── chapter13-01-00-数据集解析.md │ ├── chapter13-01-01-淘宝数据集解析.md │ ├── chapter13-01-02-Apache服务器日志数据集解析.md │ ├── chapter13-02-00-实时热门商品统计.md │ ├── chapter13-03-00-实时流量统计.md │ ├── chapter13-04-00-Uv统计的布隆过滤器实现.md │ ├── chapter13-05-00-APP分渠道数据统计.md │ ├── chapter13-06-00-APP不分渠道数据统计.md │ ├── chapter13-07-00-恶意登陆实现.md │ ├── chapter13-08-00-订单支付实时监控.md │ ├── chapter13-08-01-使用Flink-CEP来实现.md │ ├── chapter13-08-02-使用Process-Function实现订单超时需求.md │ ├── chapter13-09-00-实时对帐：实现两条流的Join.md │ ├── chapter13-10-00-使用Flink-SQL实现实时热门商品统计.md │ ├── chapter13-11-00-使用Flink-SQL实现端到端的流式应用.md │ ├── chapter14-00-第十四章，常见面试题解答.md │ ├── chapter14-01-面试题一.md │ ├── chapter14-02-面试题二.md │ ├── chapter14-03-面试题三.md │ ├── chapter14-04-面试题四.md │ ├── chapter14-05-面试题五.md │ ├── chapter14-06-面试题六.md │ ├── chapter14-07-面试题七.md │ ├── chapter14-08-面试题八.md │ ├── chapter14-09-面试题九.md │ ├── chapter14-10-面试题十.md │ ├── chapter14-11-面试题十一.md │ ├── chapter14-12-面试题十二.md │ ├── chapter14-13-面试题十三.md │ ├── chapter14-14-面试题十四.md │ ├── chapter14-15-面试题十五.md │ ├── chapter14-16-面试题十六.md │ ├── chapter14-17-面试题十七.md │ ├── chapter14-18-面试题十八.md │ ├── chapter14-19-面试题十九.md │ ├── chapter14-20-面试题二十.md │ └── images │ ├── append-mode.png │ ├── cep1.jpg │ ├── cep6.png │ ├── query-groupBy-cnt.png │ ├── query-groupBy-window-cnt.png │ ├── redo-mode.png │ ├── spaf_0101.png │ ├── spaf_0102.png │ ├── spaf_0103.png │ ├── spaf_0104.png │ ├── spaf_0105.png │ ├── spaf_0106.png │ ├── spaf_0107.png │ ├── spaf_0201.png │ ├── spaf_0202.png │ ├── spaf_0203.png │ ├── spaf_0204.png │ ├── spaf_0205.png │ ├── spaf_0206.png │ ├── spaf_0207.png │ ├── spaf_0208.png │ ├── spaf_0209.png │ ├── spaf_0210.png │ ├── spaf_0211.png │ ├── spaf_0212.png │ ├── spaf_0213.png │ ├── spaf_0301.png │ ├── spaf_0302.png │ ├── spaf_0303.png │ ├── spaf_0304.png │ ├── spaf_0305.png │ ├── spaf_0306.png │ ├── spaf_0307.png │ ├── spaf_0308.png │ ├── spaf_0309.png │ ├── spaf_0310.png │ ├── spaf_0311.png │ ├── spaf_0312.png │ ├── spaf_0313.png │ ├── spaf_0314.png │ ├── spaf_0315.png │ ├── spaf_0316.png │ ├── spaf_0317.png │ ├── spaf_0318.png │ ├── spaf_0319.png │ ├── spaf_0320.png │ ├── spaf_0321.png │ ├── spaf_0322.png │ ├── spaf_0323.png │ ├── spaf_0324.png │ ├── spaf_0325.png │ ├── spaf_0326.png │ ├── spaf_0501.png │ ├── spaf_0502.png │ ├── spaf_0503.png │ ├── spaf_0504.png │ ├── spaf_0505.png │ ├── spaf_0506.png │ ├── spaf_0507.png │ ├── spaf_0601.png │ ├── spaf_0602.png │ ├── spaf_0603.png │ ├── spaf_0604.png │ ├── spaf_0605.png │ ├── spaf_0606.png │ ├── spaf_0607.png │ ├── spaf_0608.png │ ├── spaf_0701.png │ ├── spaf_0801.png │ ├── spaf_0901.png │ ├── spaf_0902.png │ ├── spaf_0903.png │ ├── spaf_0904.png │ ├── spaf_0905.png │ ├── spaf_1001.png │ ├── spaf_1002.png │ ├── spaf_1003.png │ ├── spaf_1004.png │ ├── spaf_1005.png │ ├── stream-query-stream.png │ ├── streamtable.png │ ├── streamtable1.png │ ├── streamtable2.png │ ├── streamtable3.png │ ├── streamtable4.png │ ├── streamtable6.png │ ├── tablestream5.png │ ├── udagg-mechanism.png │ ├── udtagg-mechanism.png │ └── undo-redo-mode.png ├── flink-code-java-0421 ├── pom.xml └── src │ └── main │ ├── java │ └── com │ │ └── atguigu │ │ ├── day01 │ │ ├── WordCountFromBatch.java │ │ └── WordCountFromSocket.java │ │ ├── day02 │ │ ├── FilterExample.java │ │ ├── FlatMapExample.java │ │ ├── MapExample.java │ │ ├── MultiStreamTransformations.java │ │ ├── ReduceExample.java │ │ ├── ReduceWordCount.java │ │ ├── RichFunctionExample.java │ │ ├── SensorStream.java │ │ ├── UnionExample.java │ │ └── util │ │ │ ├── Alert.java │ │ │ ├── SensorReading.java │ │ │ ├── SensorSource.java │ │ │ ├── SmokeLevel.java │ │ │ └── SmokeLevelSource.java │ │ ├── day03 │ │ ├── AggregateFunctionExample.java │ │ ├── AvgTempPerWindow.java │ │ ├── AvgTempWithWindow.java │ │ ├── HighLowTempPerWindow.java │ │ ├── KeyedProcessFunctionExample.java │ │ ├── MinTempPerWindow.java │ │ ├── TempIncreaseAlert.java │ │ └── util │ │ │ └── HighLowTemp.java │ │ ├── day04 │ │ ├── AscendingTs.java │ │ ├── EventTimeTimer.java │ │ ├── PeriodicWatermarkGenerator.java │ │ ├── RedirectLateEventToSideOutputExample1.java │ │ ├── RedirectLateEventToSideOutputExample2.java │ │ ├── SideOutputExample.java │ │ ├── SwitchSensorStream.java │ │ ├── WatermarkBroadcast.java │ │ ├── WatermarkTest.java │ │ └── WindowWatermark.java │ │ ├── day05 │ │ ├── IntervalJoinExample.java │ │ ├── ListStateExample.java │ │ ├── TriggerExample.java │ │ ├── TwoWIndowJoin.java │ │ └── UpdateWindowResultWithLateEvent.java │ │ ├── day06 │ │ ├── ItemViewCount.java │ │ ├── UserBehavior.java │ │ ├── UserBehaviorAnalysis.java │ │ ├── UserBehaviorFromKafka.java │ │ ├── UserBehaviorProduceToKafka.java │ │ ├── WriteToES.java │ │ └── WriteToRedis.java │ │ ├── day07 │ │ ├── CheckPointExample.java │ │ ├── LoginEvent.java │ │ ├── LoginFailDetect.java │ │ ├── LoginFailDetectWithoutCEP.java │ │ ├── OrderEvent.java │ │ ├── OrderTimeoutDetect.java │ │ ├── OrderTimeoutDetectWithoutCEP.java │ │ └── WriteToMySQL.java │ │ ├── day08 │ │ ├── AggregateFunctionExample.java │ │ ├── AppendModeExample.java │ │ ├── EventtimeWindowConcept.java │ │ ├── FlinkTableExample.java │ │ ├── ProctimeWindowConcept.java │ │ ├── RetractModeExample.java │ │ ├── ScalarFunctionExample.java │ │ ├── TableAggregateFunctionExample.java │ │ ├── TableEventTimeExample.java │ │ └── TableFunctionExample.java │ │ ├── day09 │ │ ├── OrderEvent.java │ │ ├── PayEvent.java │ │ ├── PvStatistics.java │ │ ├── TestHiveStreaming.java │ │ ├── TwoStreamJoin.java │ │ ├── UserBehaviorAnalysisWithSQL.java │ │ ├── UvStatistics.java │ │ └── UvStatisticsWithBloomFilter.java │ │ └── day10 │ │ ├── ApacheLogAnalysis.java │ │ ├── ApacheLogEvent.java │ │ ├── AppMarketingByChannel.java │ │ ├── AppMarketingStatistics.java │ │ ├── FlinkMySQLjoin.java │ │ ├── MarketingUserBehavior.java │ │ ├── SimulatedEventSource.java │ │ └── UrlViewCount.java │ └── resources │ ├── UserBehavior.csv │ ├── apachelog.txt │ ├── checkpoints │ └── 53dd8ada5a03199e0ae2316343536fb0 │ │ └── chk-7 │ │ └── _metadata │ └── log4j2.properties ├── flink-code-scala-0421 ├── pom.xml └── src │ └── main │ ├── resources │ └── log4j2.properties │ └── scala │ └── com │ └── atguigu │ ├── day01 │ ├── WordCountFromBatch.scala │ └── WordCountFromSocket.scala │ ├── day02 │ ├── FilterExample.scala │ ├── FlatMapExample.scala │ ├── MapExample.scala │ ├── ReduceExample.scala │ ├── SensorStream.scala │ └── util │ │ ├── SensorReading.scala │ │ └── SensorSource.scala │ └── day06 │ └── UserBehaviorAnalysis.scala ├── flink-java-code ├── pom.xml └── src │ └── main │ ├── java │ └── com │ │ └── atguigu │ │ ├── BatchJob.java │ │ ├── StreamingJob.java │ │ ├── day1 │ │ ├── WordCountFromBatch.java │ │ └── WordCountFromSocket.java │ │ ├── day10 │ │ ├── HotItemsSQL.java │ │ ├── TwoStreamsJoin.java │ │ └── util │ │ │ ├── OrderEvent.java │ │ │ └── PayEvent.java │ │ ├── day11 │ │ └── ScalarFunctionExample.java │ │ ├── day2 │ │ ├── CoFlatMapFunctionExample.java │ │ ├── FilterFunctionExample.java │ │ ├── FlatMapFunctionExample.java │ │ ├── KeyedStreamExample.java │ │ ├── MapFunctionExample.java │ │ ├── SensorReading.java │ │ ├── SensorSource.java │ │ └── SensorStream.java │ │ ├── day3 │ │ ├── Alert.java │ │ ├── AvgTempPerWindow.java │ │ ├── MinMaxTemp.java │ │ ├── MinMaxTempPerWindow.java │ │ ├── MinMaxTempWithAggAndWin.java │ │ ├── MinTempPerWindow.java │ │ ├── MultiStreamTransformations.java │ │ ├── RichFunctionExample.java │ │ ├── SmokeLevel.java │ │ └── SmokeLevelSource.java │ │ ├── day4 │ │ ├── KeyedProcessExample.java │ │ ├── KeyedProcessExampleProcessingTime.java │ │ ├── TempIncreaseAlert.java │ │ └── WatermarkTest.java │ │ ├── day5 │ │ ├── RedirectLateEvent.java │ │ ├── RedirectLateEvent1.java │ │ ├── SensorSwitch.java │ │ ├── SideOutputExample.java │ │ ├── TwoStreamsWatermarkTest.java │ │ └── UpdateWindowResultWithLateEvent.java │ │ ├── day6 │ │ ├── GenPeriodicWatermarks.java │ │ ├── IntervalJoinExample.java │ │ ├── ListStateExample.java │ │ └── TwoWindowJoinExample.java │ │ ├── day7 │ │ ├── CepExample.java │ │ └── LoginEvent.java │ │ ├── day8 │ │ ├── FlinkKafkaProducerExample.java │ │ ├── KafkaConsumerExample.java │ │ ├── OrderEvent.java │ │ ├── OrderTimeoutDetect.java │ │ ├── OrderTimeoutDetectWithoutCEP.java │ │ ├── WriteToESExample.java │ │ ├── WriteToMySQLExample.java │ │ └── WriteToRedisExample.java │ │ └── day9 │ │ ├── ItemViewCount.java │ │ ├── UserBehavior.java │ │ └── UserBehaviorAnalysis.java │ └── resources │ └── log4j2.properties ├── flink-scala-code ├── pom.xml └── src │ └── main │ ├── resources │ ├── UserBehavior.csv │ ├── checkpoints │ │ └── 6723ab126fe99d67898f041161998148 │ │ │ └── chk-4 │ │ │ └── _metadata │ └── log4j2.properties │ └── scala │ └── com │ └── atguigu │ ├── BatchJob.scala │ ├── StreamingJob.scala │ ├── day1 │ ├── TestSimpleParser.scala │ ├── WordCountFromBatch.scala │ └── WordCountFromSocket.scala │ ├── day10 │ ├── HotItemsSQL.scala │ ├── PVPerWindow.scala │ ├── TwoStreamJoin.scala │ ├── UVPerWindowNaive.scala │ ├── UVPerWindowWithAggAndWindowProcess.scala │ ├── UVPerWindowWithBloomFilter.scala │ └── UserBehaviorAnalysis.scala │ ├── day11 │ ├── AggregateFunctionExample.scala │ ├── TableAggregateFunctionExample.scala │ └── TableFunctionExample.scala │ ├── day2 │ ├── Alert.scala │ ├── CoFlatMapFunctionExample.scala │ ├── FilterFunctionExample.scala │ ├── FlatMapFunctionExample.scala │ ├── KeyedStreamExample.scala │ ├── KeyedStreamReduceExample.scala │ ├── MapFunctionExample.scala │ ├── MultiStreamTransformations.scala │ ├── SensorReading.scala │ ├── SensorSource.scala │ └── SensorStream.scala │ ├── day3 │ ├── AvgTempPerWindow.scala │ ├── MinMaxTempPerWindow.scala │ ├── MinMaxTempWithAggAndWindow.scala │ ├── MinTempPerWindow.scala │ └── RichFunctionExample.scala │ ├── day4 │ ├── KeyedProcessExample.scala │ ├── KeyedProcessExampleProcessingTime.scala │ ├── TempIncreaseAlert.scala │ └── WatermarkTest.scala │ ├── day5 │ ├── RedirectLateEvent.scala │ ├── RedirectLateEvent1.scala │ ├── SensorSwitch.scala │ ├── SideOutputExample.scala │ ├── TwoStreamsWatermarkTest.scala │ └── UpdateWindowResultWithLateEvent.scala │ ├── day6 │ ├── GenPeriodicWatermark.scala │ ├── IntervalJoinExample.scala │ ├── ListStateExample.scala │ ├── StateBackendExample.scala │ └── TwoWindowJoinExample.scala │ ├── day7 │ ├── CepExample.scala │ ├── OrderTimeoutDetect.scala │ └── OrderTimeoutDetectWithoutCEP.scala │ ├── day8 │ ├── FlinkKafkaProducerExample.scala │ ├── KafkaConsumerExample.scala │ ├── KafkaProducerExample.scala │ ├── SinkToEsExample.scala │ ├── SinkToRedisExample.scala │ └── WriteToMySQLExample.scala │ └── day9 │ ├── ProcTimeExample.scala │ ├── RetractStreamExample.scala │ ├── TableSQLExample.scala │ └── TumblingWindowExample.scala ├── settings.xml └── slides ├── 1_Flink简介.pptx 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