Hi all, @Gwen From the database's point of view, the only way to avoid Cartesian product in join is to use index, which exhibits as key grouping in Flink. However, it only supports many-to-one mapping now, i.e., a shape or a point can only be distributed to a single group. Only points and shapes belonging to the same group can be joined and that could reduce the inherent pair comparisons (compared with a Cartesian product). It's perfectly suitable for equi-join.
@Fabian I saw this thread when I was just considering about theta-join (which will eventually be supported) in Flink. Since it's impossible to group (index) a dataset for an arbitrary theta-join, I think we may need some duplication mechanism here. For example, split a dataset into n parts and send the other dataset to all of these parts. This could be more useful in stream join. BTW, it seems that I've seen another thread discussing about this, but can not find it now. What do you think? Best, Xingcan On Thu, Feb 23, 2017 at 6:41 AM, Fabian Hueske <fhue...@gmail.com> wrote: > Hi Gwen, > > Flink usually performs a block nested loop join to cross two data sets. > This algorithm spills one input to disk and streams the other input. For > each input it fills a memory buffer and to perform the cross. Then the > buffer of the spilled input is refilled with spilled records and records > are again crossed. This is done until one iteration over the spill records > is done. Then the other buffer of the streamed input is filled with the > next records. > > You should be aware that cross is a super expensive operation, especially > if you evaluate a complex condition for each pair of records. So cross can > be easily too expensive to compute. > For such use cases it is usually better to apply a coarse-grained spatial > partitioning and do a key-based join on the partitions. Within each > partition you'd perform a cross. > > Best, Fabian > > > 2017-02-21 18:34 GMT+01:00 Gwenhael Pasquiers < > gwenhael.pasqui...@ericsson.com>: > >> Hi, >> >> >> >> I need (or at least I think I do) to do a cross operation between two >> huge datasets. One dataset is a list of points. The other one is a list of >> shapes (areas). >> >> >> >> I want to know, for each point, the areas (they might overlap so a point >> can be in multiple areas) it belongs to so I thought I’d “cross” my points >> and areas since I need to test each point against each area. >> >> >> >> I tried it and my job stucks seems to work for some seconds then, at some >> point, it stucks. >> >> >> >> I’m wondering if Flink, for cross operations, tries to load one of the >> two datasets into RAM or if it’s able to split the job in multiple >> iterations (even if it means reading one of the two datasets multiple >> times). >> >> >> >> Or maybe I’m going at it the wrong way, or missing some parameters, feel >> free to correct me J >> >> >> >> I’m using flink 1.0.1. >> >> >> >> Thanks in advance >> >> >> >> Gwen’ >> > >
