Hi all,
I thought about the API of the FLIP again. If we allow the "systemtime"
attribute, we cannot implement a nice method chaining where the user can
define a "allowLateness" only on event time. So even if the user
expressed that "systemtime" is used we have to offer a "allowLateness"
method because we have to assume that this attribute can also be the
batch event time column, which is not very nice.
class TumblingWindow(size: Expression) extends Window {
def on(timeField: Expression): TumblingEventTimeWindow =
new TumblingEventTimeWindow(alias, timeField, size) // has
allowLateness() method
}
What do you think?
Timo
Am 05/09/16 um 10:41 schrieb Fabian Hueske:
Hi Jark,
you had asked for non-windowed aggregates in the Table API a few times.
FLIP-11 proposes row-window aggregates which are a generalization of
running aggregates (SlideRow unboundedPreceding).
Can you have a look at the FLIP and give feedback whether this is what you
are looking for?
Improvement suggestions are very welcome as well.
Thank you,
Fabian
2016-09-01 16:12 GMT+02:00 Timo Walther <twal...@apache.org>:
Hi all!
Fabian and I worked on a FLIP for Stream Aggregations in the Table API.
You can find the FLIP-11 here:
https://cwiki.apache.org/confluence/display/FLINK/FLIP-11%
3A+Table+API+Stream+Aggregations
Motivation for the FLIP:
The Table API is a declarative API to define queries on static and
streaming tables. So far, only projection, selection, and union are
supported operations on streaming tables.
This FLIP proposes to add support for different types of aggregations on
top of streaming tables. In particular, we seek to support:
- Group-window aggregates, i.e., aggregates which are computed for a group
of elements. A (time or row-count) window is required to bound the infinite
input stream into a finite group.
- Row-window aggregates, i.e., aggregates which are computed for each row,
based on a window (range) of preceding and succeeding rows.
Each type of aggregate shall be supported on keyed/grouped or
non-keyed/grouped data streams for streaming tables as well as batch tables.
We are looking forward to your feedback.
Timo
--
Freundliche Grüße / Kind Regards
Timo Walther
Follow me: @twalthr
https://www.linkedin.com/in/twalthr
