[postgis-users] Geoprocessing & BigData

[Rémi Cura]

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.
> >
> >
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> >
>
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