Hi James,
the easiest solution for this bahavior is to use a user-defined
LAST_VALUE aggregate function as discussed here [1].
I hope this helps.
Regards,
Timo
[1]
http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/Using-SQL-with-dynamic-tables-where-rows-are-updated-td20519.html
Am 18.07.18 um 12:54 schrieb Andrey Zagrebin:
Hi James,
There are over windows in Flink Table API:
https://ci.apache.org/projects/flink/flink-docs-release-1.5/dev/table/tableApi.html#over-windows
It should be possible to implement this behaviour using them.
Cheers,
Andrey
On 17 Jul 2018, at 18:27, Porritt, James <[email protected]
<mailto:[email protected]>> wrote:
In Spark if I want to be able to get a set of unique rows by id,
using the criteria of keeping the row with the latest timestamp, I
would do the following:
.withColumn("rn",
F.row_number().over(
Window.partitionBy(‘id’) \
.orderBy(F.col('timestamp').desc())
)
) \
.where(F.col("rn") == 1)
I see Flink has windowing functionality, but I don’t see it has row
enumeration? How best in that case would I achieve the above?
Thanks,
James.
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