How to implement Window Function in Apache Flink? - scala

Question

everyone,
I have a kafka topic source, I group it by a 1 minute window.
What I want to do in that window is to create new columns with Window Function as in SQL, for example I want to use
SUM(amount) OVER(PARTITION BY
COUNT(user) OVER(PARTITION BY
ROW_NUMBER() OVER(PARTITION BY
Can I use DataStream functions for these operations? or
How can I operate my kafka data to convert it to DataTable and use sqlQuery?
Destination is another kafka topic.
val stream = senv
.addSource(new FlinkKafkaConsumer[String]("flink", new SimpleStringSchema(), properties))
I've tried to do this
val tableA = tableEnv.fromDataStream(stream, 'user, 'product, 'amount)
but I get the following error back
Exception in thread "main" org.apache.flink.table.api.ValidationException: Too many fields referenced from an atomic type.
test data
1,"beer",3
1,"beer",1
2,"beer",3
3,"diaper",4
4,"diaper",1
5,"diaper",5
6,"rubber",2
Query example
SELECT
user, product, amount,
COUNT(user) OVER(PARTITION BY product) AS count_product
FROM table;
expected performance
1,"beer",3,3
1,"beer",1,3
2,"beer",3,3
3,"diaper",4,3
4,"diaper",1,3
5,"diaper",5,3
6,"rubber",2,1

Answer 1

You need to parse the string into fields and then rename them afterwards.
val env = StreamExecutionEnvironment.getExecutionEnvironment
val tEnv = StreamTableEnvironment.create(env)
val stream = env.fromElements("1,beer,3",
"1,beer,1","2,beer,3","3,diaper,4","4,diaper,1","5,diaper,5","6,rubber,2");
val parsed = stream.map(x=> {
val arr = x.split(",")
(arr(0).toInt, arr(1), arr(2).toInt)
})
val tableA = tEnv.fromDataStream(parsed, $"_1" as "user", $"_2" as "product", $"_3" as "amount")
// example query
val result = tEnv.sqlQuery(s"SELECT user, product, amount from $tableA")
val rs = result.toAppendStream[(Int, String, Int)]
rs.print()
I'm not sure how can we implement the desired window function in Flink SQL. Alternatively, it can be implemented in simple Flink as follows:
parsed.keyBy(x => x._2) // key by product id.
.window(TumblingEventTimeWindows.of(Time.milliseconds(2)))
.process(new ProcessWindowFunction[
(Int, String, Int), (Int, String, Int, Int), String, TimeWindow
]() {
override def process(key: String, context: Context,
elements: Iterable[(Int, String, Int)],
out: Collector[(Int, String, Int, Int)]): Unit = {
val lst = elements.toList
lst.foreach(x => out.collect((x._1, x._2, x._3, lst.size)))
}
})
.print()