MongoDB World 2019: Get the Best of Geo Data with MongoDB Atlas and MongoDB Charts
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As a script kiddo, I was always fascinated by graphical rendering on computers, and how a billion numbers in a list can turn into a visual story - after a few hours of processing. I ended up doing the job of my 11yo kid dreams: tell computers to work for me, and for my customers, and where better than in MongoDB, the database company? Nowadays, nobody wants to wait for hours to get dashboards and charts, or be forced to use programming or third-party software: MongoDB Charts is now GA, allowing for blazing fast data visualisation directly out of MongoDB databases. Every user now has the power to produce storytelling dashboards in a few clicks from any kind of MongoDB data, even the most complex data structures, without having to learn the query language. Amongst all Charts cool features, there is ONE new feature that is a game changer: Geospatial charts In this session, we will review how MongoDB Charts and ATLAS help you render your data into valuable graphics and maps, and I may even share some secrets on the future of this awesome product!
![{
"_id": ObjectId("59a5bd85a745d2c185711a78"),
"id": "box-kitchen-san-francisco",
"name": "Box Kitchen",
"image_url": "https://s3-
media4.fl.yelpcdn.com/bphoto/oebEFX_KJm5rNzGqeyJobg/o.j
pg",
"is_closed": false,
"url": "https://www.yelp.com/biz/box-kitchen-san-
francisco?adjust_creative=zEWBdj_LHCHcT-
9DmV0Bnw&utm_campaign=yelp_api_v3&utm_medium=api_v3_bus
iness_search&utm_source=zEWBdj_LHCHcT-9DmV0Bnw",
"review_count": 515,
"categories": [
{
"alias": "burgers",
"title": "Burgers"
},
{
"alias": "waffles",
"title": "Waffles"
}
],
"rating": 4.5,
Sample dataset – Yelp businesses
"transactions": [ ],
"price": "$",
"location": {
"address1": "431 Natoma St",
"address2": null,
"address3": "",
"city": "San Francisco",
"zip_code": "94103",
"country": "US",
"state": "CA",
"display_address": [
"431 Natoma St",
"San Francisco, CA 94103"
],
"type": "Point",
"coordinates": [
-122.40636505138,
37.7811510761365
]
},
"phone": "+14155807170",
"display_phone": "(415) 580-7170",
"distance": 1340.331737904
}](https://image.slidesharecdn.com/suttonnday1315pmgetthebestofgeodatawithmongodbatlasandmongodbchartsvmacos-190701172336/85/MongoDB-World-2019-Get-the-Best-of-Geo-Data-with-MongoDB-Atlas-and-MongoDB-Charts-9-320.jpg)
![{
"_id": ObjectId("56fda36a0a162d0f051f2c6d"),
"Built": 2015,
"Name": "MSC Zoe",
"Length overall (m)": 395.4,
"Beam (m)": 59,
"Maximum TEU": 19224,
"GT": 193000,
"Owner": "MSC",
"Country": "Switzerland",
"route": {
"origin": { "Name": "Tianjin", "Country": "China"},
"destination": {"Name": "Shanghai", "Country":
"China“}
},
"location": {
"type": "Point",
"coordinates": [
129.15693498213182,
18.108558232731916
]
},
"EAT": ISODate("2016-05-16T10:00:00Z")
}
Sample dataset – Ships and containers
{
"_id": ObjectId("5719290546728347c6fbdc4c"),
"container_id": "00000001",
"type": "40",
"cargo": "Whales",
"Tons": 38,
"location": {
"type": "Point",
"coordinates": [
129.15297142372992,
18.108451503053704
]
},
"shipName": "MSC Zoe"
}](https://image.slidesharecdn.com/suttonnday1315pmgetthebestofgeodatawithmongodbatlasandmongodbchartsvmacos-190701172336/85/MongoDB-World-2019-Get-the-Best-of-Geo-Data-with-MongoDB-Atlas-and-MongoDB-Charts-10-320.jpg)
![{
"coordinates": {
"latitude": 37.77696388,
"longitude": -122.4238
}
}
or
{
"type" : "Point",
"coordinates" : [37.77696388, -122.4238]
}
Geolocation coordinates
{
"offices": [
{
"description": "Headquarters",
"address1": "4900 Hopyard Rd.",
"zip_code": "94588",
"city": "Pleasanton",
"state_code": "CA",
"country_code": "USA",
"latitude": 37.692934,
"longitude": -121.904945
}
],…
}
GeoJSON encoding or simple lat/lon values
array
City,
state,
country](https://image.slidesharecdn.com/suttonnday1315pmgetthebestofgeodatawithmongodbatlasandmongodbchartsvmacos-190701172336/85/MongoDB-World-2019-Get-the-Best-of-Geo-Data-with-MongoDB-Atlas-and-MongoDB-Charts-12-320.jpg)
“
Charts will substitute the `{x} {y} {z}`
parameters with the coordinates and zoom
levels to load the tiles
See https://wiki.openstreetmap.org/wiki/Tile_servers](https://image.slidesharecdn.com/suttonnday1315pmgetthebestofgeodatawithmongodbatlasandmongodbchartsvmacos-190701172336/85/MongoDB-World-2019-Get-the-Best-of-Geo-Data-with-MongoDB-Atlas-and-MongoDB-Charts-16-320.jpg)
![Waleed cv gis[17]](https://cdn.slidesharecdn.com/ss_thumbnails/waleedcvgis17-160330082346-thumbnail.jpg?width=560&fit=bounds)
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MongoDB World 2019: Get the Best of Geo Data with MongoDB Atlas and MongoDB Charts
- 1. Guillaume Meister, Principal Solutions Architect Southern EMEA Get the Best of Geo Data with MongoDB Atlas and MongoDB Charts @mongodb
- 3. Agenda If you missed the previous session Demos! Charting Capabilities Geospatial charts External Embedding How to get Charts Feature Roadmap
- 4. If you missed this morning’s session
- 5. Aggregations + Custom Code 3rd Party ETL and Visualization Tools MongoDB BI Connector + 3rd Party Visualization Tools MongoDB Charts Developers want fine- grained control Need to combine data from multiple sources Already using existing relational BI tools Need fast and simple visualizations directly from MongoDB Options for Data Analytics with MongoDB Consider When...
- 6. Which Charts is for you? Charts on MongoDB Atlas Visualise data only from MongoDB Atlas Don’t want to manage servers or software Immediately get access to the latest Charts features Charts On-Prem Visualise data from MongoDB Community Server, Enterprise Server or Atlas Keep all data within your private network Control the infrastructure hosting Charts
- 7. Charts features Common chart types Aggregation functions Filtering Binning Sorting Change chart colours Change axis and data labels Change axis scales Change number and date formats Type handling Polymorphic collections Nested documents Array reductions Capabilities Customization Document support
- 8. Demo Setup Charts in Atlas
- 9. { "_id": ObjectId("59a5bd85a745d2c185711a78"), "id": "box-kitchen-san-francisco", "name": "Box Kitchen", "image_url": "https://s3- media4.fl.yelpcdn.com/bphoto/oebEFX_KJm5rNzGqeyJobg/o.j pg", "is_closed": false, "url": "https://www.yelp.com/biz/box-kitchen-san- francisco?adjust_creative=zEWBdj_LHCHcT- 9DmV0Bnw&utm_campaign=yelp_api_v3&utm_medium=api_v3_bus iness_search&utm_source=zEWBdj_LHCHcT-9DmV0Bnw", "review_count": 515, "categories": [ { "alias": "burgers", "title": "Burgers" }, { "alias": "waffles", "title": "Waffles" } ], "rating": 4.5, Sample dataset – Yelp businesses "transactions": [ ], "price": "$", "location": { "address1": "431 Natoma St", "address2": null, "address3": "", "city": "San Francisco", "zip_code": "94103", "country": "US", "state": "CA", "display_address": [ "431 Natoma St", "San Francisco, CA 94103" ], "type": "Point", "coordinates": [ -122.40636505138, 37.7811510761365 ] }, "phone": "+14155807170", "display_phone": "(415) 580-7170", "distance": 1340.331737904 }
- 10. { "_id": ObjectId("56fda36a0a162d0f051f2c6d"), "Built": 2015, "Name": "MSC Zoe", "Length overall (m)": 395.4, "Beam (m)": 59, "Maximum TEU": 19224, "GT": 193000, "Owner": "MSC", "Country": "Switzerland", "route": { "origin": { "Name": "Tianjin", "Country": "China"}, "destination": {"Name": "Shanghai", "Country": "China“} }, "location": { "type": "Point", "coordinates": [ 129.15693498213182, 18.108558232731916 ] }, "EAT": ISODate("2016-05-16T10:00:00Z") } Sample dataset – Ships and containers { "_id": ObjectId("5719290546728347c6fbdc4c"), "container_id": "00000001", "type": "40", "cargo": "Whales", "Tons": 38, "location": { "type": "Point", "coordinates": [ 129.15297142372992, 18.108451503053704 ] }, "shipName": "MSC Zoe" }
- 11. Geospatial data and map charts GeoJSON and location data Choropleth map Scatter map Heat map Tile servers
- 12. { "coordinates": { "latitude": 37.77696388, "longitude": -122.4238 } } or { "type" : "Point", "coordinates" : [37.77696388, -122.4238] } Geolocation coordinates { "offices": [ { "description": "Headquarters", "address1": "4900 Hopyard Rd.", "zip_code": "94588", "city": "Pleasanton", "state_code": "CA", "country_code": "USA", "latitude": 37.692934, "longitude": -121.904945 } ],… } GeoJSON encoding or simple lat/lon values array City, state, country
- 13. Scatter maps Scatter maps represent data based on lat/lon coordinates Circles size and colors depend on data fields and aggregations
- 14. Heat maps Heat maps represent data based on lat/lon coordinates Heat spots color and size are based on field aggregated values in the geographical radius
- 15. Choro…what ? Choropleth maps use text info and mapping files for filling geo zones: - Countries - Regions - States - Etc.
- 16. Tile server MongoDB Charts uses a default cloud based tile server When running Charts on-prem, you can select a custom local or a remote tile server by changing the Charts Docker compose file Variables : CHARTS_TILE_SERVER: https://tiles.example.com/{z}/{x}/{y}.png CHARTS_TILE_ATTRIBUTION = "© [Example tile provider](https://tiles.example.com/copyright)“ Charts will substitute the `{x} {y} {z}` parameters with the coordinates and zoom levels to load the tiles See https://wiki.openstreetmap.org/wiki/Tile_servers
- 18. Embedding your Geo charts in a web app Activate embedding Secured vs non secured (digital signature) How to embed using iFrame Refreshing chart
- 19. Demo Embedding geo Charts in your web app
- 20. How to get Charts ?
- 21. Getting MongoDB Charts on Atlas Sign in/up for Atlas: https://cloud.mongodb.com Create a Project (or use an existing one) Create a Cluster (or use an existing one) Add users to your Project Activate Charts in the project Note: each Charts instance can only see Atlas clusters in the same project
- 22. Installing Charts on-prem Deploying Charts is ultra easy: Download the Charts Compose file from MongoDB download center Create a Docker Secret containing the URI for your MongoDB instance Deploy Charts using docker stack deploy Create your first Charts user That’s it, now you can access Charts from a web browser docker stack deploy
- 23. Feature Roadmap Planned features include: Dashboard filtering Calculated fields Interactive dashboards Data Preparation … Tell us what else you want to see! The development, release, and timing of any features or functionality described for our products remains at our sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality.
- 24. Questions, Feedback, Requests, Bugs? Find me and the whole Charts team at MongoDB World Contact me later § [email protected] Send us a message or feature suggestion from any page in the Charts app
- 25. Thank you !