一.背景

阿里工作的时候是使用Blink进行流数据处理和计算，通过编写sql实现Blink的计算job，开发简单高效，产品易用。 目前尝试实现Flink产品化，类似Blink。使用SQL为统一开发规范，SQL语言的好处是：声明式，易理解，稳定可靠，自动优化。 如果采用API开发的话，最大的问题是对于job调优依赖程序员经验，比较困难，同时API开发方式侵入性太强(数据安全，集群安全等)，而sql可以自动调优，避免这种问题的产生。

二.实现思路：

用户输入sql（ddl,query,dml）  -> ddl对应为Flink的source和sink
                       
                       
                            -> query/dml的insert into数据处理和计算
                       
                       
--> 封装为对应Flink的Job:env.sqlQuery/env.sqlUpdate


--> JobGraph和对应job提交，StandaloneClusterClient.submitJob或者YarnClusterClient.runDetached

三.发布版本：

v3.0.0 待开发 使用最新发布版本的flink 1.9版本或者更高:

         [flink-sql-parser] flink自带的sql解析
         流批处理一体化实现
         钉钉/微信告警通知

v2.0.0 2019年4月

    blink-client    接口定义
    blink-sql/calcite   stream和batch table的sql解析
    blink-libraries   自定义source, sink, side开发
    blink-batch   BatchTableSource和BatchTableSink
    blink-stream  StreamTableSource和StreamTableSink
    blink-job   batch/stream job 提交

SQL书写语法参考Flink issues和对应提供的doc: SQL DDL ISSUE, SQL DDL DOC。

新特性

    1. 抽取sql层被流和批使用,SQL参考flink issues和对应提供的doc
    2. 增加批处理开发
    3. 增加维表功能
    4. 升级flink版本为1.7.x

v1.0.0 2018年7月

      blink-client 接口定义
      blink-sqlserver  stream table的sql解析
      blink-job  封装为stream job    

新特性

      1. 实现create function
      2. 实现sql开发流处理程序任务  
      3. 更改源码实现sql CEP     

四.样例

v1.0.0sql开发流任务示例:

CREATE FUNCTION demouf AS 
      'ambition.api.sql.function.DemoUDF' 
USING JAR 'hdfs://flink/udf/jedis.jar',
      JAR 'hdfs://flink/udf/customudf.jar';
      
      
CREATE TABLE kafka_source (
      `date` string,
      amount float, 
      proctime timestamp
      ) 
with (
      type=kafka,
      'flink.parallelism'=1,
      'kafka.topic'=topic,
      'kafka.group.id'=flinks,
      'kafka.enable.auto.commit'=true,
      'kafka.bootstrap.servers'='localhost:9092'
);
CREATE TABLE mysql_sink (
      `date` string, 
      amount float, 
      PRIMARY KEY (`date`,amount)
      ) 
with (
      type=mysql,
      'mysql.connection'='localhost:3306',
      'mysql.db.name'=flink,
      'mysql.batch.size'=0,
      'mysql.table.name'=flink_table,
      'mysql.user'=root,
      'mysql.pass'=root
);
CREATE VIEW view_select AS 
      SELECT `date`, 
              amount 
      FROM kafka_source 
      GROUP BY 
            `date`,
            amount
      ;
INSERT INTO mysql_sink 
       SELECT 
          `date`, 
          sum(amount) 
       FROM view_select 
       GROUP BY 
          `date`
      ;

v2.0.0sql开发流任务示例:

batch sql示例：

CREATE FUNCTION demouf AS 'ambition.api.sql.function.DemoUDF' 
LIBRARY 'hdfs://flink/udf/jedis.jar','hdfs://flink/udf/customudf.jar';

CREATE SOURCE TABLE json_source (
      id int, 
      name varchar, 
      `date` date , 
      age int
) 
with (
      type=json,
      'file.path'='file:///FlinkSQL/blink-job/src/test/resources/demo.json'
);

CREATE SINK TABLE csv_sink (
      `date` date, 
      total_age int
) 
with (
      type=csv,
      'file.path'='file:///FlinkSQL/blink-job/src/test/resources/demo_out.csv'
);

CREATE VIEW view_select as  
      SELECT `date`, age 
      FROM json_source 
      GROUP BY `date`,age;
  
INSERT INTO csv_sink 
      SELECT `date`, sum(age) as total_age
      FROM view_select 
      GROUP BY `date`;

stream sql 示例：

CREATE FUNCTION demouf AS 
      'ambition.api.sql.function.DemoUDF' 
LIBRARY 'hdfs://flink/udf/jedis.jar','hdfs://flink/udf/customudf.jar';
      
CREATE SOURCE TABLE kafka_source (
      `date` varchar,
      amount float, 
      proctime timestamp
      ) 
with (
      type=kafka,
      'flink.parallelism'=1,
      'kafka.topic'=topic,
      'kafka.group.id'=flinks,
      'kafka.enable.auto.commit'=true,
      'kafka.bootstrap.servers'='localhost:9092'
);

CREATE SINK TABLE mysql_sink (
      `date` varchar, 
      total_amount float, 
      PRIMARY KEY (`date`)
      ) 
with (
      type=mysql,
      'mysql.connection'='localhost:3306',
      'mysql.db.name'=flink,
      'mysql.batch.size'=10,
      'mysql.table.name'=flink_table,
      'mysql.user'=root,
      'mysql.pass'=root
);

CREATE VIEW view_select AS 
      SELECT `date`, 
              amount 
      FROM kafka_source 
      GROUP BY 
            `date`,
            amount
      ;


INSERT INTO mysql_sink 
       SELECT 
          `date`, 
          sum(amount) as total_amount
       FROM view_select 
       GROUP BY 
          `date`
      ;

五.代码关注

apache flink

apache calcite

uber AthenaX

DTStack flinkStreamSQL