MLSQL Stack如何让流调试更加简单详解

前言

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 newkafkatable1as table21;-- print in webConsole instead of terminal console.save append table21 as webConsole.`` options mode="Append"and duration="15"and checkpointLocation="/tmp/s-cpl4";

运行结果如下：

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 tableload jsonStr.`data` as datasource;-- convert table as stream sourceload mockStream.`datasource` options stepSizeRange="0-3"as newkafkatable1;-- aggregation select cast(value as string) as k from newkafkatable1as 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，谢谢大家对的支持。