[postgis-users] Geoprocessing & BigData

[Ravi Pavuluri]

Vincent and Remi,

Thank you both for your inputs. I have combined two things in one thread. Parallelization is a secondary need and I will look into "Postgresql-Xc, Greenplum or custom code approach". 

Regarding the PostGIS  performance on intersecting geometries, I am not able to see any improvement. I am looking at intersection because of my use case. (Ex: What % of census blocks fall in Zone A, Zone B, Zone C etc. flood zones from Flood Zones Layer). If intersect is to avoided, can this be achieved through another way?


@Vincent : For ArcGIS Union, please see here. 
http://resources.esri.com/help/9.3/arcgisengine/java/gp_toolref/analysis_tools/union_analysis_.htm


Any inputs are appreciated. 

Thanks again,
Ravi.

--------------------------------------------
On Mon, 1/18/16, Rémi Cura <remi.cura at gmail.com> wrote:

 Subject: Re: [postgis-users] Geoprocessing & BigData
 To: vincent.ml at oslandia.com, "PostGIS Users Discussion" <postgis-users at lists.osgeo.org>
 Cc: "Ravi Pavuluri" <ravitheja at ymail.com>
 Date: Monday, January 18, 2016, 2:51 PM
 
 Hey,
 if you have one
 beefy server you can parallelize throwing several queries
 working on sub set of your data.
 (aka parallel
 processing trough data partition).
 One conceptual
 example : you want to process the world, you create 20
 workers, a list of countries, and then make the worker
 process the list country by country.
 
 If you think one
 postgres server will not be sufficient,
 you
 could of course shard your data across several servers, 
 with options ranging from writting from scratch
 (you rewrite everything),
 to using existing
 open source code, to dedicated solution like
  Postgresql-Xc, greenplum, ...
 
 However, sorry to
 say this but in your case it looks like your first
 improvement step will not come from massive paralleling but
 from first better understanding the world of geospatial data
 and postgis.
 
 Cheers,
 Rémi-C
 
 2016-01-18 19:30 GMT+01:00
 Vincent Picavet (ml) <vincent.ml at oslandia.com>:
 Hi Ravi,
 
 
 
 
 
 
 
 
 
 On 18/01/2016 19:14, Ravi Pavuluri wrote:
 
 > Hi All,
 
 >
 
 > I am checking if there is a way to process quickly
 large datasets such
 
 > as census blocks in PostGIS and also by leveraging big
 data platform. I
 
 > have few questions in this regard.
 
 >
 
 > 1) When I try intersect for sample census blocks with
 another polygon
 
 > layer, PostGIS 2.2(on Postgres 9.4) takes ~60 minutes
 (after optimizing
 
 > from http://postgis.net/2014/03/14/tip_intersection_faster/
 ) while on
 
 > ESRI ArcMap takes ~10 minutes. PostGIS layers already
 have geospatial
 
 > indices. Is there anyway to optimize this further?
 
 
 
 Following the links on your page, here is a good answer from
 Paul (TL;DR
 
 : st_intersection is slow, avoid it) :
 
 http://gis.stackexchange.com/questions/31310/acquiring-arcgis-like-speed-in-postgis/31562
 
 
 
 > 2) What is an equivalent of ESRI Union in PostGIS? I
 didn't see any out
 
 > of the box functions and any tips here are
 appreciated.
 
 
 
 If ESRI Union makes a union, maybe st_union ? But I guess
 there are some
 
 semantic issues here.
 
 
 
 > 3) Is there anyway we can expedite these
 geoprocessing
 
 > tasks(union/intersect etc) using big data platform (Ex:
 hadoop)? Most
 
 > examples talk about analysis (contains etc)  but not
 about geoprocessing
 
 > on geospatial data. Any input is appreciated.
 
 
 
 Lots of people do geoprocessing too with PostGIS, including
 long-running
 
 jobs on large volumes of data ( worldwide osm data
 processing namely).
 
 "Big data" is a really subjective word. Are your
 geoprocessing needs
 
 really parallelizable ? What kind of volumes are we talking
 about ? MB,
 
 GB, TB ? What kind of hardware do you have at hand ?
 
 
 
 One way to do some sort of map-reduce with PostGIS is to use
 a bunch of
 
 servers with FDW connections between a source master and
 these slaves,
 
 map the data processing to the slave servers and reduce it
 on the main
 
 server. With a bit of Python as glue code this can be
 automated and
 
 quite efficient, even though this kind of sharding is not
 automated (
 
 yet ?).
 
 
 
 Vincent
 
 
 
 >
 
 > Thanks,
 
 > Ravi.
 
 >
 
 >
 
 > _______________________________________________
 
 > postgis-users mailing list
 
 > postgis-users at lists.osgeo.org
 
 > http://lists.osgeo.org/mailman/listinfo/postgis-users
 
 >
 
 
 
 _______________________________________________
 
 postgis-users mailing list
 
 postgis-users at lists.osgeo.org
 
 http://lists.osgeo.org/mailman/listinfo/postgis-users