Hi Paul,
Are you returning the complete dataset for each zoom level or do you have scale
dependent rules in the SLD? If you are returning all features for all zoom
levels, you are potentially bumping into the extra code for label positioning
etc…
Chris Snider
Senior Software Engineer
Intelligent Software Solutions, Inc.
[Description: Description: Description: cid:image001.png@01CA1F1F.CBC93990]
From: Paul Wittle [mailto:p.wit...@dorsetcc.gov.uk]
Sent: Tuesday, January 19, 2016 7:53 AM
To: jonathan-li...@lightpear.com
Cc: geoserver-users@lists.sourceforge.net
Subject: Re: [Geoserver-users] What is Vector Big Data to GeoServer?
Hi Jonathan,
You are correct in your assumption of AddressBase Premium and yes we do have
all the normal methods including spatial indices and pre-seeded caches.
The issue is when people hit a tile that doesn't exist. This is bound to happen
eventually and definitely happens when you seed the cache.
The issue we have noticed is that that layer takes an age to draw tiles when
they are not in the cache and we wondered how to speed it up. There is no issue
from the Oracle point of view, we can query it and it returns reasonably
quickly but the GeoServer is a different. The style is just a couple of
coloured triangles based on property type so it is not complex.
If you give the GeoServer a materialised view of a smaller area it is fine and
fast, but give it the whole dataset with a spatial index and it is horrifically
slow. We thought perhaps there is a known size by which you ought to split the
data into multiple materialised views / layers and just make it a layer group?
Thanks,
Paul
From: Jonathan [mailto:jonathan-li...@lightpear.com]
Sent: 19 January 2016 10:49
To: Paul Wittle
Cc:
geoserver-users@lists.sourceforge.net<mailto:geoserver-users@lists.sourceforge.net>
Subject: Re: [Geoserver-users] What is Vector Big Data to GeoServer?
Hi Paul,
It depends how large your datasets are. I know there are plenty of
instances of GeoServer being fed by an Oracle DB with millions of features;
Warwickshire's implementation has MasterMap features for a large chunk of the
urban midlands, there are around 30 million vector features between the 6
topography layers and it works quite well, albeit with a little bit of clever
pre-tiling to help things along. There should be no need to split datasets.
I'm guessing your addresses for the entire country is the AddressBase
Premium product. In that case you should only have a few million features
depending on how you loaded it. Have you joined it all together in a single
table, a materialised view, or a regular view? The later will have abysmal
performance no mater what you do.
Your email isn't clear on what your system is having the problems with. Can
you give us more details.
Things to bear in mind:
- Do you have spatial indexes?
- Do you have indexes on other columns that you're using for queries. In
ABP it may be one (or several) of the various address columns for WFS
GetFeature queries.
- Does it work if you use a simple style for rendering rather than
something complicated?
There are a number of other things you can do to optimise your GeoServer
install.
Simon's answer here may be helpful:
http://gis.stackexchange.com/questions/157287/how-to-optimise-rendering-of-layer-groups-in-geoserver
And this part of the docs:
http://docs.geoserver.org/stable/en/user/production/index.html
Cheers,
Jonathan
On 19/01/2016 08:01, Paul Wittle wrote:
Hi,
We have been finding that GeoServer struggles to render large datasets from our
Oracle database but we were wondering if there are known levels at which you
ought to split datasets for GeoServer?
I have seen plenty of information regarding Raster data and when this is
considered 'big data' but I have not seen much documentation on vector datasets
from spatial databases.
To give an idea of the scale, we are talking about addresses for a whole
country at the moment but I'm sure there may be any number of datasets and
factors. To give another example, would the limit on complex polygons differ
from simple points?
Would be great to hear some views or read some linked articles.
Thanks,
Paul Wittle
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