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Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities
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Webinar: MongoDB 2.4 Feature Demo and Q&A on Geo Capabilities

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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 …

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.

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  • Hit Record and make sure it recordsOpen your demo.Move your mouse.Make announcement about QA five minutes before and as you start
  • This is 6th grade geometry on the cartesian plane. Often called (inexactly) Euclidean geometryAn plane is infinite in all directions. This is a convenient way of reasoning about geometry because math on the plane is easy. As a simplification of a sphere, however, it has pretty big problems as soon as you start to worry about large polygons, long lines, or any degree of accuracy.
  • As is excellently highlighted by Randall Munroe of xkcd, projecting a sphere onto a plane is non-obvious. It’s similarly not easy the other direction.Managing the math for sphere’s is much more difficult than on a plane, and definitely not something most of us want to implement.
  • The 2d index was introduced in Mongodb 2.2End this slide by saying: “All this is great, but there are some additional features that we might like.”
  • Points are great, but we want to store arbitrary polygons, lines, etc.
  • Notice that the first point is the same as the last point.This is the simplest polygon form. The coordinate specification is a list of list of point specs. The first list of point specifications describes the exterior shell of the polygon, and each subsequent list of points describes a hole in the polygon.MongoDB will reject any polygons that self intersect with a parse error.
  • Within searches on the plane with large polygons can be significantly different than on the sphere because they follow the curvature of the sphere.
  • Re: edge cases: Some are documented on mongodb.org, but there are far too many to detail, so make sure to play around with your particular edge cases.
  • If you have a collection of documents that are all the businesses in America, filtering for type Hot Dog Stand will reduce the set of results significantly, and searching for an exact match string compare on a normal mongo index is a very quick operation, compared to a geo index search. Because of that stating the question in the first order will be much faster than stating it in the second way.Indexing multiple geo fields was not possible between 2.4, and make possible a whole suite of queries that weren’t possible before.
  • 1st point and 2nd point define the first line.2nd point and 3rd point define the second line.So on.
  • $maxDistance operator is an optional operator that allows us to specify a maximum distance away from a point, which to go looking.
  • Tricky bitsHow do you use that index efficiently?How do you decide the size of the cells? How do you calculate thebtree key
  • Works by looking at concentric donuts starting from the center point.Here we are searching for pubs near a point on Leicester SquareNothing in donut 1
  • The porcupine is within the second donut, but although the Brewmaster is within the covering for the second donut, it isn’t actually within the donut
  • This continues until we have found enough points to fill a batch
  • Transcript

    • 1. @mongodb @idbentley @10genMongoDB 2.4 GeoFeaturesIan BentleyWeb Engineer, 10gen
    • 2. Planar Geometry 2.4 Geospatial features – Ian Bentley
    • 3. xkcd.com/977Spherical Geometry 2.4 Geospatial features – Ian Bentley
    • 4. MongoDB has had geo for awhile• `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 toencodePoints:{ geo: { type: "Point", coordinates: [100.0, 0.0] }} 2.4 Geospatial features – Ian Bentley
    • 8. GeoJSON allows us toencodeLineStrings:{ geo: { type: "LineString", coordinates: [ [100.0, 0.0], [101.0, 1.0] ] }} 2.4 Geospatial features – Ian Bentley
    • 9. GeoJSON allows us toencodePolygons:{ 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 asphere• $geoIntersects operator• Takes any GeoJSON geometry as a specifier• Returns any geometries that have a non-empty intersection• Lots of edge cases – intersecting edges, points on lines.• Works without any index. 2.4 Geospatial features – Ian Bentley
    • 12. Better support for compoundindexes• Unlike 2d indexes, 2dsphere indexes aren’t required to be the first field of a compound index – Filtering potential documents before doing geo query can drastically improve the performance of some queries • “Find me Hot Dog Stands within New York state” • “Find me geometries in New York state that are Hot Dog Stands”• Multiple geo fields can be in the same index – “Find routes with start location 50miles from JFK and end location 100miles from YYC” 2.4 Geospatial features – Ian Bentley
    • 13. Demo Example
    • 14. • You can find all the code, and data powering the demo on github, and read about it on my blog• Let’s take a close look at the python that does the actual work. 2.4 Geospatial features – Ian Bentley
    • 15. It’s this simple - withindef find_within(points): # When defining a polygon, the first point should # also appear as the last point. points.append(points[0]) poly = { "type": "Polygon", "coordinates": [points] } places = collection.find( {"geo": { "$within": { "$geometry": poly } } } ) places.limit(500) return places 2.4 Geospatial features – Ian Bentley
    • 16. It’s this simple - intersectsdef find_intersects(points): line = { "type": "LineString", "coordinates": points } places = collection.find( {"geo":{ "$geoIntersects": { "$geometry": line } } } ) places.limit(50) return places 2.4 Geospatial features – Ian Bentley
    • 17. It’s this simple - neardef find_nearest(point): point = { "type": "Point", "coordinates": point } places = collection.find( {"geo": { "$near": { "$geometry": point } } }) places.limit(10) return places 2.4 Geospatial features – Ian Bentley
    • 18. How 2dsphere works
    • 19. How do you index a sphericalcoordinate?• 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 theUK 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 surroundingTrafalgar 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

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