Hi Everyone, Thank you for your suggestions, based on that I was able to move forward.
I am now generating a Geohash for all the lats and lons in our reference data and then creating a trie of all the Geohash's I am then broadcasting that trie and then using it to search the nearest Geohash for the data I want to join with. It works well on small data set but with big data sets I am getting OOM. I have changed the "spark.storage.memoryFraction=0.4" but I still see many partitions are getting recalculated. What are the different settings that I should look at when broadcasting big objects of 3-4 gb? Thanks Ankur On Wed, Mar 11, 2015 at 8:58 AM, Ankur Srivastava < ankur.srivast...@gmail.com> wrote: > Thank you everyone!! I have started implementing the join using the > geohash and using the first 4 alphabets of the HASH as the key. > > Can I assign a Confidence factor in terms of distance based on number of > characters matching in the HASH code? > > I will also look at the other options listed here. > > Thanks > Ankur > > On Wed, Mar 11, 2015, 6:18 AM Manas Kar <manasdebashis...@gmail.com> > wrote: > >> There are few techniques currently available. >> Geomesa which uses GeoHash also can be proved useful.(https://github.com/ >> locationtech/geomesa) >> >> Other potential candidate is >> https://github.com/Esri/gis-tools-for-hadoop especially >> https://github.com/Esri/geometry-api-java for inner customization. >> >> If you want to ask questions like nearby me then these are the basic >> steps. >> 1) Index your geometry data which uses R-Tree. >> 2) Write your joiner logic that takes advantage of the index tree to get >> you faster access. >> >> Thanks >> Manas >> >> >> On Wed, Mar 11, 2015 at 5:55 AM, Andrew Musselman < >> andrew.mussel...@gmail.com> wrote: >> >>> Ted Dunning and Ellen Friedman's "Time Series Databases" has a section >>> on this with some approaches to geo-encoding: >>> >>> https://www.mapr.com/time-series-databases-new-ways- >>> store-and-access-data >>> http://info.mapr.com/rs/mapr/images/Time_Series_Databases.pdf >>> >>> On Tue, Mar 10, 2015 at 3:53 PM, John Meehan <jnmee...@gmail.com> wrote: >>> >>>> There are some techniques you can use If you geohash >>>> <http://en.wikipedia.org/wiki/Geohash> the lat-lngs. They will >>>> naturally be sorted by proximity (with some edge cases so watch out). If >>>> you go the join route, either by trimming the lat-lngs or geohashing them, >>>> you’re essentially grouping nearby locations into buckets — but you have to >>>> consider the borders of the buckets since the nearest location may actually >>>> be in an adjacent bucket. Here’s a paper that discusses an implementation: >>>> http://www.gdeepak.com/thesisme/Finding%20Nearest%20Location%20with% >>>> 20open%20box%20query.pdf >>>> >>>> On Mar 9, 2015, at 11:42 PM, Akhil Das <ak...@sigmoidanalytics.com> >>>> wrote: >>>> >>>> Are you using SparkSQL for the join? In that case I'm not quiet sure >>>> you have a lot of options to join on the nearest co-ordinate. If you are >>>> using the normal Spark code (by creating key-pair on lat,lon) you can apply >>>> certain logic like trimming the lat,lon etc. If you want more specific >>>> computing then you are better off using haversine formula. >>>> <http://www.movable-type.co.uk/scripts/latlong.html> >>>> >>>> >>>> >>> >>
