link titleWith the continuous efforts from the community, the Flink system has been continuously improved, which has attracted more and more users. Flink SQL is a canonical, widely used relational query language. However, there are still some scenarios where Flink SQL failed to meet user needs in terms of functionality and ease of use, such as:
- In terms of functionality
Iteration, user-defined window, user-defined join, user-defined GroupReduce, etc. Users cannot express them with SQL;
- In terms of ease of use
- Map - e.g. “dataStream.map(mapFun)”. Although “table.select(udf1(), udf2(), udf3()....)” can be used to accomplish the same function., with a map() function returning 100 columns, one has to define or call 100 UDFs when using SQL, which is quite involved.
- FlatMap - e.g. “dataStrem.flatmap(flatMapFun)”. Similarly, it can be implemented with “table.join(udtf).select()”. However, it is obvious that datastream is easier to use than SQL.
Due to the above two reasons, In this JIRAs group, we will enhance the TableAPI in stages.
The first stage we seek to support (will describe the details in the sub issue) :
The FLIP can be find here: FLIP-29
The second part is about column operator/operations:
1) Table(schema) operators
- Add columns
- Replace columns
- Drop columns
- Rename columns
2）Fine-grained column/row operations
- Column selection
- Row package and flatten
See google doc
- is related to
FLINK-13470 Enhancements to Flink Table API for blink planner
- mentioned in