I (almost) made it work the following way: 1rst job : Read all the shapes, repartition() them equally on my N nodes, then on each node fill a static RTree (thanks for the tip).
2nd job : Read all the points, use a flatmap + custom partitioner to “clone” the dataset to all nodes, then apply a simple flatmap that will use the previously initialized static RTree, adding the Shape information to the point. Then do a groupBy to merge the points that were inside of multiple shapes. This works very well in a local runtime but fails on yarn because it seems that the taskmanager reloads the jar file between two jobs, making me lose my static RTree (I guess that newly loaded class overwrites the older one). I have two questions : - Is there a way to avoid that jar reload // can I store my RTree somewhere in jdk or flink, locally to the taskmanager, in a way that it wouldn’t be affected by the jar reload (since it would not be stored in any class from MY jar)? o I could also try to do it in a single job, but I don’t know how to ensure that some operations are done (parsing of shape) BEFORE starting others handling the points. - Is there a way to do that in a clean way using flink operators ? I’d need to be able to use the result of the iteration of a dataset inside of my map. Something like : datasetA.flatmap(new MyMapOperator(datasetB))… And In my implementation I would be able to iterate the whole datasetB BEFORE doing any operation in datasetA. That way I could parse all my shapes in an RTree before handling my points, without relying on static Or any other way that would allow me to do something similar. Thanks in advance for your insight. Gwen’ From: Jain, Ankit [mailto:ankit.j...@here.com] Sent: jeudi 23 février 2017 19:21 To: user@flink.apache.org Cc: Fabian Hueske <fhue...@gmail.com> Subject: Re: Cross operation on two huge datasets Hi Gwen, I would recommend looking into a data structure called RTree that is designed specifically for this use case, i.e matching point to a region. Thanks Ankit From: Fabian Hueske <fhue...@gmail.com<mailto:fhue...@gmail.com>> Date: Wednesday, February 22, 2017 at 2:41 PM To: <user@flink.apache.org<mailto:user@flink.apache.org>> Subject: Re: Cross operation on two huge datasets 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<mailto: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 ☺ I’m using flink 1.0.1. Thanks in advance Gwen’
