Thanks all for the feedbacks. I will start a VOTE soon.
Best,
Jark
On Mon, 23 Dec 2019 at 15:45, Jark Wu wrote:
> I agree with Jingsong, we are discussing to align the "concepts", not
> align the "implementations".
>
> For the "concepts", the "Time-windowed Join" in SQL and "Interval Join" in
>
I agree with Jingsong, we are discussing to align the "concepts", not align
the "implementations".
For the "concepts", the "Time-windowed Join" in SQL and "Interval Join" in
DataStream are the same thing.
Best,
Jark
On Mon, 23 Dec 2019 at 15:16, Jingsong Li wrote:
> Hi Danny,
>
> > DatasStream
Hi Danny,
> DatasStream interval join and Table/SQL Time-windowed Join are
not equivalent
In my opinion, there is no difference between table and DataStream except
that outer join is not implemented in DataStream.
KeyedStream has defined equivalent conditions.
Other conditions can be completed in
Thanks Jark for bringing up this discussion, just look at the api definitions,
it seems that Flink DatasStream interval join and Table/SQL Time-windowed Join
are
not equivalent for the join conditions:
The Interval Join only supports event time columnbs comparison of the
joined streams[1]; while
Thanks Jark for bringing this.
+1 to use a unify name: "Interval Join" before 1.10 is release.
I think maybe "Interval Join" was come from SQL world too in [1].
Another candidate is to use "Range Join", But considering DataStream, I am
OK with "Interval".
[1] https://issues.apache.org/jira/brows
Hi everyone,
Currently, in the Table API & SQL documentation[1], we call the joins with
time conditions as "Time-windowed Join". However, the same feature is
called "Interval Join" in DataStream[2]. We should align the terminology in
Flink project.
>From my point of view, "Interval Join" is more
