Best way to join with inequalities (historical data)
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 DataSetTuple3Long, Long, String historical = …;
I also have events, with an event name and a timestamp :
final DataSetTuple2String, 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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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*DataSetTuple3Long, Long, String historical= …;
I also have events, with an event name and a timestamp :
*final*DataSetTuple2String, 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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expéditrice ne peut être tenue responsable de son contenu ni de ses
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détruire et d'avertir l'expéditeur.
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RE: Best way to join with inequalities (historical data)
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:mj...@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*DataSetTuple3Long, Long, String historical= …;
I also have events, with an event name and a timestamp :
*final*DataSetTuple2String, 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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détruire et d'avertir l'expéditeur.
The integrity of this message cannot be guaranteed on the Internet.
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then please delete it and notify the sender.
