MLSQL Stack如何让流调试更加简单详解
发布时间：2022-03-02 14:23:31 所属栏目：MySql教程 来源：互联网
导读：有一位同学正在调研MLSQL Stack对流的支持。然后说了流调试其实挺困难的。经过实践，希望实现如下三点： 能随时查看最新固定条数的Kafka数据 调试结果（sink）能打印在web控制台 流程序能自动推测json schema(现在spark是不行的) 实现这三个点之后，我发现调
|
有一位同学正在调研MLSQL Stack对流的支持。然后说了流调试其实挺困难的。经过实践，希望实现如下三点： 能随时查看最新固定条数的Kafka数据 调试结果（sink）能打印在web控制台 流程序能自动推测json schema(现在spark是不行的) 实现这三个点之后，我发现调试确实就变得简单很多了。 流程 首先我新建了一个kaf_write.mlsql,里面方便我往Kafka里写数据： set abc=''' { "x": 100, "y": 200, "z": 200 ,"dataType":"A group"} { "x": 120, "y": 100, "z": 260 ,"dataType":"B group"} { "x": 120, "y": 100, "z": 260 ,"dataType":"B group"} { "x": 120, "y": 100, "z": 260 ,"dataType":"B group"} { "x": 120, "y": 100, "z": 260 ,"dataType":"B group"} { "x": 120, "y": 100, "z": 260 ,"dataType":"B group"} { "x": 120, "y": 100, "z": 260 ,"dataType":"B group"} { "x": 120, "y": 100, "z": 260 ,"dataType":"B group"} { "x": 120, "y": 100, "z": 260 ,"dataType":"B group"} { "x": 120, "y": 100, "z": 260 ,"dataType":"B group"} { "x": 120, "y": 100, "z": 260 ,"dataType":"B group"} '''; load jsonStr.`abc` as table1; select to_json(struct(*)) as value from table1 as table2; save append table2 as kafka.`wow` where kafka.bootstrap.servers="127.0.0.1:9092"; 这样我每次运行，数据就能写入到Kafka. 接着，我写完后，需要看看数据是不是真的都写进去了，写成了什么样子： !kafkaTool sampleData 10 records from "127.0.0.1:9092" wow; 这句话表示，我要采样Kafka 10条Kafka数据，该Kafka的地址为127.0.0.1:9092,主题为wow.运行结果如下 没有什么问题。接着我写一个非常简单的流式程序： -- the stream name, should be uniq. set streamName="streamExample"; -- use kafkaTool to infer schema from kafka !kafkaTool registerSchema 2 records from "127.0.0.1:9092" wow; load kafka.`wow` options kafka.bootstrap.servers="127.0.0.1:9092" as newkafkatable1; select * from newkafkatable1 as table21; -- print in webConsole instead of terminal console. save append table21 as webConsole.`` options mode="Append" and duration="15" and checkpointLocation="/tmp/s-cpl4"; 在终端我们也可以看到实时效果了。 补充 当然，MLSQL Stack 还有对流还有两个特别好地方，第一个是你可以对流的事件设置http协议的callback，以及对流的处理结果再使用批SQL进行处理，最后入库。参看如下脚本： -- the stream name, should be uniq. set streamName="streamExample"; -- mock some data. set data=''' {"key":"yes","value":"no","topic":"test","partition":0,"offset":0,"timestamp":"2008-01-24 18:01:01.001","timestampType":0} {"key":"yes","value":"no","topic":"test","partition":0,"offset":1,"timestamp":"2008-01-24 18:01:01.002","timestampType":0} {"key":"yes","value":"no","topic":"test","partition":0,"offset":2,"timestamp":"2008-01-24 18:01:01.003","timestampType":0} {"key":"yes","value":"no","topic":"test","partition":0,"offset":3,"timestamp":"2008-01-24 18:01:01.003","timestampType":0} {"key":"yes","value":"no","topic":"test","partition":0,"offset":4,"timestamp":"2008-01-24 18:01:01.003","timestampType":0} {"key":"yes","value":"no","topic":"test","partition":0,"offset":5,"timestamp":"2008-01-24 18:01:01.003","timestampType":0} '''; -- load data as table load jsonStr.`data` as datasource; -- convert table as stream source load mockStream.`datasource` options stepSizeRange="0-3" as newkafkatable1; -- aggregation select cast(value as string) as k from newkafkatable1 as table21; !callback post "http://127.0.0.1:9002/api_v1/test" when "started,progress,terminated"; -- output the the result to console. save append table21 as custom.`` options mode="append" and duration="15" and sourceTable="jack" and code=''' select count(*) as c from jack as newjack; save append newjack as parquet.`/tmp/jack`; ''' and checkpointLocation="/tmp/cpl15"; 总结 以上就是这篇文章的全部内容了，希望本文的内容对大家的学习或者工作具有一定的参考学习价值。 （编辑：92站长网） 【声明】本站内容均来自网络，其相关言论仅代表作者个人观点，不代表本站立场。若无意侵犯到您的权利，请及时与联系站长删除相关内容! |
