[SERVER-691] n-dimensional geospatial search Created: 02/Mar/10  Updated: 28/Dec/23

Status: Backlog
Project: Core Server
Component/s: Geo, Index Maintenance
Affects Version/s: None
Fix Version/s: None

Type: New Feature Priority: Major - P3
Reporter: Eliot Horowitz (Inactive) Assignee: Backlog - Query Integration
Resolution: Unresolved Votes: 50
Labels: qi-geo
Remaining Estimate: Not Specified
Time Spent: Not Specified
Original Estimate: Not Specified

Issue Links:
Related
is related to SERVER-91 2d spatial indexing Closed
Assigned Teams:
Query Integration
Participants:

 Description   

for 2d you cna use http://www.mongodb.org/display/DOCS/Geospatial+Indexing



 Comments   
Comment by Vlad Radu [ 29/Aug/19 ]

X-tree would be a good candidate for this.

Comment by cristian lorenzetto [ 03/May/18 ]

I m interessed in this  feature . In my case for 1d vectors

Comment by Stav Yagev [ 26/May/16 ]

This could be super useful for machine learning / classification problems!

Comment by Hans Kristian [ 17/Nov/13 ]

Also related to this one: https://jira.mongodb.org/browse/SERVER-9220

Comment by Nick Shaw [ 15/Oct/13 ]

What gets me is that Thermopylae Sciences have largely solved this problem for 10-gen: http://www.slideshare.net/nknize/mongo-sv-knizefinal

Mongos licensing states that "The goal of the server license is to require that enhancements to MongoDB be released to the community."

http://www.mongodb.org/about/licensing/

Am I missing something here?

Comment by Alex [ 11/Oct/13 ]

Very interested in such a feature to build a backend for a 3d world

Comment by Michaël Pico [ 11/Mar/13 ]

I would also be very interested by this feature especially for 3D rendering (where you need 4 dimensional storage with the normalisation parameter !)

Comment by Jamil Bou Kheir [ 05/May/12 ]

Thanks Noeska.

I could implement nearest neighbor in non-realtime MapReduce but it would really help to have native DB implementation of this to be fast.

Even simple n-dimension Euclidean distance would suffice for now. http://en.wikipedia.org/wiki/Euclidean_distance.

Maybe I can hack it in on my own. Let the Source be with me...

Comment by Noeska Smit [ 05/May/12 ]

Hi Jamil, I pasted my kd-tree python code for you here: http://pastebin.com/5e9AAQ7F
I hope that helps!

Comment by Jamil Bou Kheir [ 05/May/12 ]

+1. N-Dimensional distance queries would be highly useful for similarity and other clustering applications!

Noeska – how are you implementing your KDtree in a collection?

Comment by Arun Vijayan [ 07/Mar/12 ]

N-dim spatial index will be awesome for clustering algorithms. Please prioritize this.

Comment by Noeska Smit [ 13/Sep/11 ]

This would be a great feature indeed. For now I implemented this by building a k-d Tree and storing that in a collection for fast graph traversal.

Comment by Yuriy Bogdanov [ 13/Sep/11 ]

+1 for n-dim.
it enables to use mongodb for collaborative filtration purposes!

Comment by Edmunds Kalnins [ 13/Sep/11 ]

+1 for n-dimentional. We use geospatial index with bit interleaving to turn 5 numbers into 2 and then sort by those. n-dimensional index would make this much more precise and simpler.

Comment by Ian Mercer [ 08/Sep/11 ]

+1 but don't stop at 3. I want to be able to search multi-dimensional spaces with an arbitrary number of dimensions.

Comment by John Hurliman [ 03/Mar/10 ]

I would be interested in 3D geospatial searches for our virtual world backend (http://openmetaverse.googlecode.com/). We're currently using 2D MySQL and additional filtering in PHP, and considering PostgreSQL or other alternatives such as MongoDB.

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