how was the data put in the db though? 1 document per node?
On 4/12/2011 1:39 PM, Nolan Darilek wrote:
Oopse, meant for this to go to the whole list.
-------- Original Message --------
Subject: Re: [OSM-dev] OSM and MongoDB
Date: Tue, 12 Apr 2011 15:26:41 -0500
From: Nolan Darilek <[email protected]>
To: Ian Dees <[email protected]>
I had/am having a somewhat bad experience storing OSM data in MongoDB.
Initially I stored all map data in MongoDB, but queries took ages. The
same queries that happen in 100-200 MS now often took nearly a second.
Additionally, some took upwards of 5, and I even found spots on my map
sparsely populated with points, but which reliably performed the
queries I need in 30+ seconds.
I filed a thorough bug in their tracker, including a dataset and
queries that reliably duplicated the issue. It was marked wontfix, I
abandoned MongoDB, and it was apparently re-opened and fixed several
months later. So perhaps it's a non-issue now.
I'm still using MongoDB for part of my current project, user POI
storage. It does indeed use geohashes, and I'm experiencing strange
accuracy issues. My platform is pedestrian navigation with many small
distance queries. Points in the non-MongoDB dataset are reliably
detected in a radius roughly 100 meters around the traveler. Points in
MongoDB queried with the same bounding boxes don't appear until
they're within 30-40 meters. I recently updated from an older version
to a new build of 1.8. The older version widely varied the detection
range. Some points were detected 100 or so meters out, while others
weren't picked up until 30 or so. It was always the same points, too.
The point for my apartment remains reliably visible for ~100 meters or
so, while the corner store and restaurant didn't appear until I was
very close. 1.8 at least appears to be consistent, always detecting at
30 meters or so. I can only assume that this is a geohash oddity that
only appears for very small differences, something that works out to
rounding error for larger values.
I like MongoDB for many things, but not for geospatial data more
complicated than a series of points. I'm working on migrating user/POI
storage to a geospatial store.
On 04/12/2011 01:20 PM, Ian Dees wrote:
Yep, and I think Mongo uses geohashes as their index behind the
scenes. One of the problems with that, though, is they have some
arbitrary length that they compute the geohash to and when you have
lots of points (as OSM data does) the buckets they're searching are
very full.
On Tue, Apr 12, 2011 at 1:00 PM, Steve Coast <[email protected]
<mailto:[email protected]>> wrote:
bbox queries using the built in spatial indexing presumably? OSM
has it's own magical bitmask for that, that may also be as fast
in mongo, who knows.
On 4/11/2011 5:58 PM, Ian Dees wrote:
On Mon, Apr 11, 2011 at 6:36 PM, Sergey Galuzo
<[email protected] <mailto:[email protected]>> wrote:
Hi,
I am working on evaluation of MongoDB for several storage
solutions at hand. Some of them resemble current OSM editing
database. I have heard that OSM dev is/was evaluating
MongoDB also. I was wondering whether it possible to share
the findings?
In my experimentation with MongoDB (seen here:
https://github.com/iandees/mongosm/) I found it to be very slow.
Inserts were speedy, but bounding-box queries took a long time.
The most recent dev version of MongoDB includes "multi-location
documents" support:
http://www.mongodb.org/display/DOCS/Geospatial+Indexing#GeospatialIndexing-MultilocationDocuments
This would allow a single way document to be indexed at multiple
locations and vastly speed up the map query.
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