這篇文章主要給大家介紹了關於MLSQL Stack如何讓流調試更加簡單的相關資料,文中通過示例代碼介紹的非常詳細,對大家學習或者使用MLSQL具有一定的參考學習價值,需要的朋友們下面來一起學習學習吧
前言
有一位同學正在調研MLSQL Stack對流的支持。然後說了流調試其實挺困難的。經過實踐,希望實現如下三點:
- 能隨時查看最新固定條數的Kafka數據
- 調試結果(sink)能打印在web控制檯
- 流程序能自動推測json schema(現在spark是不行的)
實現這三個點之後,我發現調試確實就變得簡單很多了。
流程
首先我新建了一個kaf_write.mlsql,裏面方便我往Kafka裏寫數據:
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";
這樣我每次運行,數據就能寫入到Kafka.
接着,我寫完後,需要看看數據是不是真的都寫進去了,寫成了什麼樣子:
!kafkaTool sampleData 10 records from "127.0.0.1:9092" wow;
這句話表示,我要採樣Kafka 10條Kafka數據,該Kafka的地址爲127.0.0.1:9092,主題爲wow.運行結果如下:
沒有什麼問題。接着我寫一個非常簡單的流式程序:
-- 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";
運行結果如下:
在終端我們也可以看到實時效果了。
補充
當然,MLSQL Stack 還有對流還有兩個特別好地方,第一個是你可以對流的事件設置http協議的callback,以及對流的處理結果再使用批SQL進行處理,最後入庫。參看如下腳本:
-- 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";
總結
以上就是這篇文章的全部內容了,希望本文的內容對大家的學習或者工作具有一定的參考學習價值,謝謝大家對神馬文庫的支持。