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| ### TQ ### | ||
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| # Rocketmq Streams (Dipper) SQL 文档 | ||
| RocketMQ-Streams聚焦大数据量->高过滤->轻窗口计算的场景，核心打造轻资源，高性能优势，在资源敏感场景有很大优势，最低1core，1g可部署，做了大量的过滤优化，性能比其他大数据能有2-5倍性能提升。建议的应用场景（安全，风控，边缘计算，消息队列流计算） RocketMQ-Streams兼容Flink/Blink的sql，udf/udtf/udaf，将来我们会和flink生态做深度融合，即可以独立运行，也可以发步成flink任务，跑在flink集群，对于有flink集群的场景，即能享有轻资源优势，可以做到统一部署和运维 | ||
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| # 适配场景 | ||
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|  | ||
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| - 计算场景，适合大数据量->高过滤->轻窗口计算的场景。它不同于主流的计算引擎，需要先部署集群，写任务，发布，调优，运行这么复杂的过程，它本身就是一个lib包，基于sdk写完流任务，可以直接运行。支持大数据开发需要的计算特性：Exactly-ONCE，灵活窗口（滚动，滑动，会话），双流Join，高吞吐、低延迟、高性能。最低1core，1g可以运行。 | ||
| - SQL引擎，它也是一个SQL引擎，兼容blink sql语法，支持blink udf/udtf/udaf的扩展。支持sql热升级，写完sql，通过sdk提交sql，就可以完成sql的热发布。 | ||
| - ETL引擎，它也是一个etl引擎，在很多大数据场景，需要完成数据从一个源经过etl，汇聚到统一存储，里面内置了grok，正则解析等函数，可以结合sql一块完成数据etl | ||
| - 开发SDK，它也是一个数据开发sdk包，里面的大多数组件都可以单独使用，如source/sink，它屏蔽了数据源，数据存储的细节，提供统一的编程接口，一套代码，切换输入输出，不需要改变代码，这块这次不展开讲 | ||
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| # 特点和创新 | ||
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| - **轻资源** | ||
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| 无过多依赖，1core1g可以部署，按需扩展资源，增加实例即可增加并发，同样规则部署，内存资源是flink的1/24（云盾专有云规则集）。 | ||
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| - **高性能（高过滤场景）** | ||
| - 专门针对过滤做了优化，包括前置过滤指纹，同源规则自动归并，hyperscan加速，表达式指纹等 | ||
| - 计算资源比flink降低（65%-95%），专有云部分规则的测试结果。 | ||
| - 最好效果，一个超大规则，flink消耗3000 CU，消耗264.25 CU | ||
| - **维表 JOIN（千万数据量维表支持）** | ||
| 我们为维表 JOIN 做了高压缩的内存设计，无java头部和对齐的开销，内存存储接近原始数据大小，千万维表加载到内存，不超过2G内存，纯内存操作，性能最大化，同时对于Mysql提供了多线程并发加载，提高加载维表的速度。 | ||
| - **双流 JOIN** | ||
| 双流 JOIN 功能是将两条流进行关联，用来补齐流上的字段。Dipper双流 JOIN 都是基于窗口实现的，如果不指定窗口，就用默认的窗口时间，默认时间可以通过配置文件修改。 | ||
| - **TopN** | ||
| TopN 是统计报表和大屏非常常见的功能，主要用来实时计算排行榜。我们提供了分组 TopN 的功能。流上的 TopN 有非常多的挑战。 | ||
| - **Window** | ||
| 支持滚动窗口（Tumble）、滑动窗口（Hop）、会话窗口（Session）以及传统数据库中的OVER窗口，支持Exactly-ONCE的能力（需要配置db）。 | ||
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| # 消息数据结构 | ||
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| - 基于SQL的流计算，更像是在操作一个表，表是二维的，一行数据，更像是一个Map<String,Object>，在Dipper中，用JSONObject表示一个流数据，代表表的一行。 | 列名 | 列值 | | --- | --- | | JSON的key | JSON的value | | ||
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| - 在数据源中，Dipper默认根据encoding这个属性，把字节数组转化成字符串，如果能转化成json，可以根据下面的设置，自动转化成JSONObject: | ||
| - 如果可以转化成JSONObject，可以设置数据源的with属性，isJsonData=true，这个值默认是true，如果是json可以不设置。 | ||
| - 如果可以转化成JSONArray，可以设置数据源的with属性，msgIsJsonArray=true | ||
| - create table中的字段名，应该和json的key名字相同。 | ||
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| ```sql | ||
| create table metaq_stream | ||
| ( | ||
| x varchar, | ||
| y varchar, | ||
| z varchar | ||
| ) with ( | ||
| type = 'metaq', | ||
| topic = 'metaq_topic', | ||
| consumerGroup = 'metaq_group', | ||
| isJsonData = 'true', | ||
| encoding = 'UTF-8' | ||
| ); | ||
| ``` | ||
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| - 如果不能转化成json，系统会根据createtable的字段名，创建一个 <fieldName,字符串>只有一列的json，如下面的SQL，最终系统会生成{"x":"utf8编码的字符串数据"} | ||
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| ```sql | ||
| create table metaq_stream | ||
| ( | ||
| x varchar | ||
| ) with ( | ||
| type = 'metaq', | ||
| topic = 'metaq_topic', | ||
| consumerGroup = 'metaq_group', | ||
| isJsonData = 'false', | ||
| encoding = 'UTF-8' | ||
| ); | ||
| ``` | ||
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| # 数据类型 | ||
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| ## 基本类型 | ||
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| | 数据类型 | 可以兼容的类型 | | ||
| | --- |---| | ||
| | int | Integer | | ||
| | short | Short | | ||
| | byte | Byte | | ||
| | float | Float | | ||
| | double | Double | | ||
| | boolean | Boolean | | ||
| | long | Long，BigInteger | | ||
| | String | | | ||
| | Date | Timestamp | | ||
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| ## 集合类型 | ||
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| | 数据类型 | 要求 | | ||
| | --- | --- | | ||
| | List<> | 必须指定范型，且范型类型是上面支持的类型| | ||
| | Set<> | | | ||
| | Map<key,value> | | | ||
| | Array | | | ||
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| ## 类型使用说明 | ||
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| 数据源产生的数据，有可能是基于JSONObject.parse完成解析的，里面可能包含各种java类型，可能超出上面支持的类型，不用担心，dipper是可以支持的。原因看下面的序列化部分 | ||
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| # 序列化 | ||
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| Dipper的数据只有两种情况会被序列化和反序列化： | ||
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| - 用到了统计，join等窗口计算，数据需要shuffle，此时需要把数据发送到指定的shuffle消息队列进行转发，此时用到的序列化和反序列化能力是消息队列提供的，Dipper并不关注。 | ||
| - 在窗口计算时，需要保存中间结果，此时需要完成数据的序列化和反序列化，Dipper会采用两种方式： | ||
| - 如果是上面支持的类型，Dipper自己完成序列化和反序列化 | ||
| - 如果有不支持的类型，会用java自带的序列化和反序列化 | ||
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| # 语法介绍 | ||
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| - SQL 输入由一系列语句组成。每个语句都由一系列标记组成，并以分号 “;”结尾。应用的标记取决于被调用的语句。 | ||
| - 多个语句构成一个拓扑结构，一个SQL中既可以有多个数据源，也可以有多个结果表 | ||
| - SQL中用‘常量’，表示常量，true/false表示boolean，数字/浮点数字表示数字，其他的表示字段名 | ||
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| ```sql | ||
| create Function time_function as 'org.apache.rocketmq.streams.sql.local.runner.UDTFTest' | ||
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| -- 数据源 | ||
| CREATE TABLE `data_source` | ||
| ( | ||
| `_index` VARCHAR, | ||
| `_type` VARCHAR, | ||
| `_id` VARCHAR, | ||
| `_score` VARCHAR, | ||
| `_source` VARCHAR | ||
| ) with ( | ||
| type = 'file', | ||
| filePath = '/tmp/input-log.txt' | ||
| ); | ||
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| CREATE TABLE `data_model` | ||
| ( | ||
| `_dm_timestamp` varchar, | ||
| `_dm_source` VARCHAR, | ||
| `_dm_type` VARCHAR, | ||
| `_dm_lable` VARCHAR | ||
| ) | ||
| with ( | ||
| type = 'print' | ||
| ); | ||
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| CREATE TABLE `output_file` | ||
| ( | ||
| `_dm_timestamp` varchar, | ||
| `_dm_source` VARCHAR, | ||
| `_dm_type` VARCHAR, | ||
| `_dm_lable` VARCHAR | ||
| ) | ||
| with ( | ||
| type = 'file' | ||
| filePath = '/tmp/output' | ||
| ); | ||
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| insert into data_model | ||
| select time_function() AS _dm_timestamp | ||
| ,_source AS _dm_source | ||
| ,_type AS _dm_type | ||
| , pk_label as _dm_lable | ||
| from | ||
| data_source, | ||
| LATERAL TABLE (STRING_SPLIT(_ source, ',')) AS T(pk_label) | ||
| ; | ||
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| insert into output_file | ||
| select time_function() AS _dm_timestamp | ||
| ,_source AS _dm_source | ||
| ,_type AS _dm_type | ||
| , pk_label as _dm_lable | ||
| from | ||
| data_source, | ||
| LATERAL TABLE (STRING_SPLIT(_ source, ',')) AS T(pk_label) | ||
| ``` | ||
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