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https://issues.apache.org/jira/browse/FLINK-6442?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16099824#comment-16099824
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ASF GitHub Bot commented on FLINK-6442:
---------------------------------------
Github user fhueske commented on the issue:
https://github.com/apache/flink/pull/3829
Hi @lincoln-lil, I think it is a very good idea to strive for consistency
with JDBC. Here is what I think about the proposed methods:
- `sqlQuery(sql: String): Table`: Looks good to me
- `sqlUpdate(sql: String): Option[Long]`: This method requires an optional
`QueryConfig` object (either as optional argument or in a second method). We
also need a Java version that does not use `Option` (maybe encode `None` as
`-1L`). We should also think about whether we want the Scala and Java APIs to
be consistent.
- `sql(sql: String): Option[Table]`: I'm not sure about this method. I
think it would rather confuses than helps. IMO, there is a clear separation
between `sqlQuery` and `sqlUpdate` that users have to be aware of. This is even
true, if they would use `sql()` because they would need to handle the returned
`Option`. Moreover, we would need to change the signature of a method which is
a more significant change than deprecating the method.
I think the decision about this API change should not be done in this PR
but discussed on the [email protected] mailing list. Would you like to start a
[DISCUSS] thread and propose your ideas there?
Regarding the issue of distinguishing `SELECT` queries from `INSERT` or
`UPDATE` queries, we can change `FlinkPlannerImpl.parse()` to call
`SqlParser.parseQuery()` instead of `SqlParser.parseStmt()`. `parseQuery()`
accepts `SELECT` queries + `UNION`, `INTERSECT` and `EXCEPT`.
Thanks, Fabian
> Extend TableAPI Support Sink Table Registration and ‘insert into’ Clause in
> SQL
> -------------------------------------------------------------------------------
>
> Key: FLINK-6442
> URL: https://issues.apache.org/jira/browse/FLINK-6442
> Project: Flink
> Issue Type: New Feature
> Components: Table API & SQL
> Reporter: lincoln.lee
> Assignee: lincoln.lee
> Priority: Minor
>
> Currently in TableAPI there’s only registration method for source table,
> when we use SQL writing a streaming job, we should add additional part for
> the sink, like TableAPI does:
> {code}
> val sqlQuery = "SELECT * FROM MyTable WHERE _1 = 3"
> val t = StreamTestData.getSmall3TupleDataStream(env)
> tEnv.registerDataStream("MyTable", t)
> // one way: invoke tableAPI’s writeToSink method directly
> val result = tEnv.sql(sqlQuery)
> result.writeToSink(new YourStreamSink)
> // another way: convert to datastream first and then invoke addSink
> val result = tEnv.sql(sqlQuery).toDataStream[Row]
> result.addSink(new StreamITCase.StringSink)
> {code}
> From the api we can see the sink table always be a derived table because its
> 'schema' is inferred from the result type of upstream query.
> Compare to traditional RDBMS which support DML syntax, a query with a target
> output could be written like this:
> {code}
> insert into table target_table_name
> [(column_name [ ,...n ])]
> query
> {code}
> The equivalent form of the example above is as follows:
> {code}
> tEnv.registerTableSink("targetTable", new YourSink)
> val sql = "INSERT INTO targetTable SELECT a, b, c FROM sourceTable"
> val result = tEnv.sql(sql)
> {code}
> It is supported by Calcite’s grammar:
> {code}
> insert:( INSERT | UPSERT ) INTO tablePrimary
> [ '(' column [, column ]* ')' ]
> query
> {code}
> I'd like to extend Flink TableAPI to support such feature. see design doc:
> https://goo.gl/n3phK5
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