many questions (kafka table KafkaDeserilizationSchema support, recommended enrichment approach, prevent JDBC temporal dimension table from N +1 queries, etc.

[Marco Villalobos <mv...@kineteque.com>]

1. How can I create a kafka table that can use headers and map them to columns? Currently, I am using KafkaDeserilizationSchema to create a DataStream, and then I convert that DataStream into a Table. I would like to use a more direct approach.

2. What is the recommended way to enrich a kafka table or data-stream with data-from postgres?
	a) kafka table and JDBC temporal dimension table with temporal join and lookup cache setup
	b) data-stream with async io which connects via JDBC.  (note that asycio does not support Keyed State cache)
	c) data-stream rich function or process function that uses Keyed State.

3. When using a kafka told and JDBC temporal dimension table how do I prevent N + 1 queries per join row?

	When I issued a query such as this:

	SELECT k.name, t1.id, t2.metadata, SUM(k.cost)
	FROM kafka_table AS k
	JOIN jdbc_table_one AS t1 ON k.t1_id = t1.ID
	LEFT JOIN jdbc_table_two FOR SYSTEM_TIME AS OF k.proc_time AS t2 ON t1.t2_id = t2.id AND t2.name = k.name
	GROUP BY TUMBLE (k.proc_time, INTERVAL '3' MINUTE), k.name, t1.id, t2.metadata

	My PostgreSQL sql logs show that jdbc_table_two has a query per each distinct t2.name.

	In a real production system, that would be 200,000 queries!

4. When using a JDBC temporal dimension table does Flink retrieve the from the database asynchronously , or is it possible for Flink to multiple join rows at time with a IN (subquery) syntax?