In version 2.4, there have been significant enhancements to the geospatial indexing capabilities in MongoDB, such as polygon intersections, a more accurate spherical model, and better integration with MongoDB's aggregation framework. In this presentation, you'll learn about the new enhancements and how they are enabling developers to more quickly and easily develop spatially-aware applications.

1. @mongodb @idbentley @10gen
MongoDB 2.4 Geo
Features
Ian Bentley
Web Engineer, 10gen

2. Planar Geometry
2.4 Geospatial features – Ian Bentley

3. xkcd.com/977
Spherical Geometry
2.4 Geospatial features – Ian Bentley

4. MongoDB has had geo for a
while
• `2d` index
– Store points on 2d plane
– Search for points within a:
• Rectangle ($box)
• Polygon ($polygon)
• Circle ($center)
• Circle on a sphere ($centerSphere)
– Search for nearest points ($near, $nearSphere)
2.4 Geospatial features – Ian Bentley

5. Some desirable things!
• Storing non-point geometries
• Within searches on a sphere
• Searching for intersecting geometries on a
sphere
• Better support for compound indexes
2.4 Geospatial features – Ian Bentley

6. Storing non-point geometries
• GeoJSON – A collaborative community project
that produced a specification for encoding
geometric entities in JSON
• Gaining wide support
– OpenLayers
– PostGIS
– Libraries in several languages
2.4 Geospatial features – Ian Bentley

7. GeoJSON allows us to
encode
Points:
{
geo: {
type: "Point",
coordinates: [100.0, 0.0]
}
}
2.4 Geospatial features – Ian Bentley

8. GeoJSON allows us to
encode
LineStrings:
{
geo: {
type: "LineString",
coordinates: [ [100.0, 0.0], [101.0, 1.0] ]
}
}
2.4 Geospatial features – Ian Bentley

9. GeoJSON allows us to
encode
Polygons:
{ geo: {
type: "Polygon",
coordinates: [
[ [100.0, 0.0], [101.0, 0.0],
[101.0, 1.0], [100.0, 1.0],
[100.0, 0.0] ]
]
} }
2.4 Geospatial features – Ian Bentley

10. Within searches on a sphere
• $geoWithin operator
• Takes a GeoJSON polygon geometry as a
specifier
• Returns any geometries of any type that are fully
contained within the polygon
• Works without any index.
2.4 Geospatial features – Ian Bentley

11. Intersecting geometries on a
sphere
• $geoIntersects operator
• Takes any GeoJSON geometry as a specifier
• Returns any geometries that have a non-empty
intersection
• Lots of edge cases – intersecting edges, points
on lines.
• Works without any index.
2.4 Geospatial features – Ian Bentley

12. Better support for compound
indexes
• Unlike 2d indexes, 2dsphere indexes aren’t
required to be the first field of a compound index
– Filtering potential documents before doing geo query can
drastically improve the performance of some queries
• “Find me Hot Dog Stands within New York state”
• “Find me geometries in New York state that are
Hot Dog Stands”
• Multiple geo fields can be in the same index
– “Find routes with start location 50miles from JFK and end
location 100miles from YYC”
2.4 Geospatial features – Ian Bentley

13. Demo Example

14. • You can find all the code, and data powering the
demo on github, and read about it on my blog
• Let’s take a close look at the python that does
the actual work.
2.4 Geospatial features – Ian Bentley

15. It’s this simple - within
def find_within(points):
# When defining a polygon, the first point should
# also appear as the last point.
points.append(points[0])
poly = {
"type": "Polygon",
"coordinates": [points]
}
places = collection.find(
{"geo": { "$within": { "$geometry": poly } } } )
places.limit(500)
return places
2.4 Geospatial features – Ian Bentley

16. It’s this simple - intersects
def find_intersects(points):
line = {
"type": "LineString",
"coordinates": points
}
places = collection.find(
{"geo":{ "$geoIntersects":
{ "$geometry": line } } } )
places.limit(50)
return places
2.4 Geospatial features – Ian Bentley

17. It’s this simple - near
def find_nearest(point):
point = {
"type": "Point",
"coordinates": point
}
places = collection.find(
{"geo": { "$near": { "$geometry": point } } })
places.limit(10)
return places
2.4 Geospatial features – Ian Bentley

18. How 2dsphere works

19. How do you index a spherical
coordinate?
• Divide the geometry that you are indexing into a
grid.
• For each cell in the grid, calculate a key, based
upon its position on the sphere.
• Insert each cell into a standard B-tree
• MongoDB uses google’s S2 C++ library for the
heavy lifting.
2.4 Geospatial features – Ian Bentley

20. Coarse Grid overlayed on the
UK
2.4 Geospatial features – Ian Bentley

21. Coverings
• A covering of a geometry is a minimal set of cells
that completely cover’s a geometry
• S2 can efficiently generate coverings for arbitrary
geometries.
2.4 Geospatial features – Ian Bentley

22. Covering of Grid of the UK
2.4 Geospatial features – Ian Bentley

23. Covering of A4 surrounding
Trafalgar Square
2.4 Geospatial features – Ian Bentley

24. Cells
• S2 defines cell sizes from level 1 to level 31
• The higher the level, the smaller the cell
• Different levels are optimized for different queries
– If you have densely packed geometries, and you are
doing a $near search, a higher level will be efficient
– If you are doing a $within search with a large polygon, a
lower level will be more efficient
• By default we use all levels between 500m and
100km on a side
2.4 Geospatial features – Ian Bentley

25. Near search
2.4 Geospatial features – Ian Bentley

26. Near search
2.4 Geospatial features – Ian Bentley

27. Near search
2.4 Geospatial features – Ian Bentley

28. Near search
2.4 Geospatial features – Ian Bentley

29. Near search
2.4 Geospatial features – Ian Bentley

30. Near search
2.4 Geospatial features – Ian Bentley

31. Q&A