Hi, there is no other system support to express this join.
However, you could perform some "hand wired" optimization by
partitioning your input data into distinct intervals. It might be tricky
though. Especially, if the time-ranges in your "range-key" dataset are
overlapping everywhere (-> data replication necessary for overlapping
parts).
But it might be worth the effort if you can't get the job done using
cross-product. How large are your data sets? What hardware are you using?
-Matthias
On 05/04/2015 10:47 AM, LINZ, Arnaud wrote:
> Hello,
>
>
>
> I was wondering how to join large data sets on inequalities.
>
>
>
> Let say I have a data set whose “keys” are two timestamps (start time &
> end time of validity) and value is a label :
>
> *final*DataSet<Tuple3<Long, Long, String>> historical= …;
>
>
>
> I also have events, with an event name and a timestamp :
>
> *final*DataSet<Tuple2<String, Long>> events= …;
>
>
>
> I want to join my events with my historical data to get the “active”
> label for the time of the event.
>
> The simple way is to use a cross product + a filter :
>
>
>
> events.cross(historical).filter((crossedRow) -> {
>
> *return*(crossedRow.f0.f1>= crossedRow.f1.f0) &&
> (crossedRow.f0.f1<= crossedRow.f1.f1);
>
> })
>
>
>
> But that’s not efficient with 2 big data sets…
>
>
>
> How would you code that ?
>
>
>
> Greetings,
>
> Arnaud
>
>
>
>
>
>
>
>
>
>
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