Geospatial Enhancements in MongoDB 2.4

Map the Globe, simply

Polygon indexing and GeoJSON support in MongoDB

Greg [email protected]

mongoNYC 2013

● 2d vs 2dsphere

● (Quick) overview of previous geospatial features in MongoDB

● New line/polygon indexing on spherical globeGeoJSON - lines and polygons

● Geo in aggregation

● Future Roadmap

Why use (2dsphere) geospatial indexing?

1. Have earth-like coordinates and regions tagged to other data over large areas

2. Want to query this databased on location

3. Don't necessarily want tothink a lot about the fact that the earth is round

[ from Jpatokal at wikimedia.org ]https://secure.wikimedia.org/wikipedia/commons/wiki/File:World-airline-routemap-2009.png

[ from Jpatokal at wikimedia.org ]https://secure.wikimedia.org/wikipedia/commons/wiki/File:World-airline-routemap-2009.png

https://www.e-education.psu.edu/natureofgeoinfo

Why use (2d) geospatial indexing?

1. Have regional or virtual world coordinates embedded in other data

2. (For now) Only indexinglocations

3. Don't want sphericaloverhead

Overview of geo features

● 2.2 - "2d"Index only (multi-)pointsQuery near points or within simple polygons

// Make a flat 2d index [-180 -> 180)> db.coll.ensureIndex( { location : "2d", type : 1 } )

// Insert some point data> db.coll.insert({ name : "Shake Shack", type : "rest", location : [ -73.9881353, 40.7415064 ] })> db.coll.insert({ name : "Park Avenue Tavern", type : "bar", location : [ -73.9786924, 40.7502231 ] })

// Find in Manhattan Mall> db.coll.find({ location : { $geoWithin : { $polygon : [[ -73.9884761, 40.7491204 ], [ -73.988829, 40.7486387 ], [ -73.9901297, 40.7491856 ], [ -73.9897768, 40.7496673 ], [ -73.9884761, 40.7491204 ]] }}}, type : "rest" })

// Find near Washington Square (center)> db.coll.find({ location : { $geoNear : [ -73.7695467, 42.6622728 ] }, type : "rest" });

Overview of geo features

● 2.2 - "2d"

DEMO - NYC OpenStreetMaps datagithub.com/mongodb/mongo-snippets/tree/master/2dsphere

Overview of geo features

● Current 2.4 - "2dsphere"Index points, lines, and polygons (on sphere)Understands simple GeoJSONQuery with points, lines, and polygonsWithin-region, Intersect-region, Near-point queries

GeoJSON

● Widely supported open JSON standardhttp://www.geojson.org/

● Simple language:

{ type : 'Point', coordinates : [<lon>, <lat>] }{ type : 'LineString', coordinates : [[<lon>, <lat>],...] }{ type : 'Polygon', coordinates : [[[<lon>,<lat>],...],...]}

+ other MultiXXX types (future) ...

// Make a spherical 2d compound index> db.coll.ensureIndex( { type : 1, location : "2dsphere" } )

// Insert Park Avenue> db.coll.insert({ name : "Park Avenue", type : "road", location : { type : 'LineString', coordinates : [ [ -73.9359712600708, 40.80942429324451], [ -73.93676519393921, 40.80820620367037], ... ]} });

// Insert Great Hill> db.coll.insert({ name : "Great Hill", type : "park", location : { type : 'Polygon', coordinates : [[ [ -73.95841598510742, 40.79724239132546], [ -73.95817995071411, 40.79691751000055], ... ]]} });

// Find docs with geometry that intersects// a route> db.coll.find({ location : { $geoIntersects : { $geometry : { type : 'LineString', // Run coordinates coordinates : [ [ -73.95586252212524, 40.77964777966238 ], [ -73.95886659622192, 40.78091513739611 ], ... ]} }}})

Overview of geo features

● Current 2.4 - "2dsphere"

DEMO - NYC OpenStreetMaps datagithub.com/mongodb/mongo-snippets/tree/master/2dsphere

Overview of geo features

● Current 2.4 - "2dsphere"Reduced shape distortion

DEMO - NYC OpenStreetMaps datagithub.com/mongodb/mongo-snippets/tree/master/2dsphere

Technology

● GeoJSONhttp://www.geojson.org

● Google S2 Geometry LibraryApache License 2.0http://code.google.com/p/s2-geometry-library

image by user geek3 @ http://en.wikipedia.org/wiki/File:Sphere_wireframe_10deg_6r.svg

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● 2d vs 2dsphere

● (Quick) overview of previous geospatial features in MongoDB

● New line/polygon indexing on spherical globeGeoJSON - lines and polygons

● Geo in aggregation

● Future Roadmap

Overview of geo features

● Current 2.4 - aggregation supportGeospatial predicates in aggregationNear sorting in aggregation, custom output fields

> db.ny.aggregate([ // Find everything in MidTown/Hell's Kitchen { $match : { geo: { $geoWithin: { $geometry:

{ type: "Polygon", coordinates: [[ // Midtown/Hell's Kitchen [ -73.9984130859375, 40.78028146155187 ], [ -73.95240783691406, 40.76182096906601 ], [ -73.96888732910156, 40.73945350425846 ], [ -74.01420593261719, 40.75531957477602 ], [ -73.9984130859375, 40.78028146155187 ] ]] } } } } },

...

... // Organize stuff we want { $project : { tags : 1, info : { name : "$name", lonLat : "$geo.coordinates" }} }, { $unwind : "$tags" }, // Count by tag types { $group : { _id : "$tags", total : { $sum : 1 }, locs : { $push : "$info" } }}]); // end

{ "result" : [ { "_id" : "park", "total" : 18, "locs" : [{ "name" : "Hells Kitchen Park", "lonLat" : [ -73.9925, 40.7630556 ] }, ... { "_id" : "bar", "total" : 28, "locs" : [ { "name" : "Landmark Tavern", "lonLat" : [ -73.9963261, 40.7631922 ] }, { "name" : "Pony Bar", "lonLat" : [ -73.994236, 40.761723 ] },

...

...

> db.ny.aggregate([ // Find near Times Square { $geoNear : { near : { type: "Point", // Times Square coordinates: [ -73.98508787155151, 40.75905795418586 ] }, distanceField : "distance", spherical : true, // 2dsphere num : 1000 } },...

... // Organize stuff we want { $project : { tags : 1, info : { name : "$name", distance : "$distance", lonLat : "$geo.coordinates" }} }, { $unwind : "$tags" },

// Count by tag types { $group : { _id : "$tags", total : { $sum : 1 }, locs : { $push : "$info" } }}]); // end

...

{ "result" : [ { "_id" : "park", "total" : 11, "locs" : [{ "name" : "Ramone Aponte Park", "distance" : 449.7417003854033, // m "lonLat" : [ -73.9894444, 40.7613889 ]}, ... { "_id" : "bar", "total" : 24, "locs" : [{ "name" : "The Lambs Club", "distance" : 275.0270424641914, // m "lonLat" : [ -73.9963261, 40.7631922 ]}, ...

...

[ from Jpatokal at wikimedia.org ]https://secure.wikimedia.org/wikipedia/commons/wiki/File:World-airline-routemap-2009.png

Future

● Further support for other predicatesOGC Simple Features

● GeoJSON composite shapes

● Indexing API to allow similar enhancements for "flat plane" work

● Space open for non-default CRSes

Thanks!

[email protected]