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From "LINZ, Arnaud" <AL...@bouyguestelecom.fr>
Subject RE: Best way to join with inequalities (historical data)
Date Mon, 04 May 2015 11:40:59 GMT
Hi,

Thanks. The use case I have right now does not require too much magic ; my historical data
set is small enough to fit in RAM, I'll spread it over each node and use a simple mapping
with a log(n) look up. It was more a theorical question.
If my dataset becomes too large, I may use some hashing techniques (for instance at day level)
and cut the intervals at hash frontiers by duplicating the row to prevent overlapping.

Arnaud




-----Message d'origine-----
De : Matthias J. Sax [mailto:mjsax@informatik.hu-berlin.de] 
Envoyé : lundi 4 mai 2015 11:52
À : user@flink.apache.org
Objet : Re: Best way to join with inequalities (historical data)

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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