Introduction to MongoDB
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The document is an introduction to MongoDB, focusing on data modeling, queries, and geospatial updates for a location-based application. It outlines application goals, including querying places nearby, adding user-generated content, and recording user check-ins, while providing examples of document structures and MongoDB queries. Additionally, it discusses deploying MongoDB in a high-traffic environment and its use cases in various industries.
![http://mongodb.org http://10gen.com Building applications with MongoDB – An introduction MongoNYC – June 7, 2011 Nosh Petigara [email_address] @noshinosh](https://image.slidesharecdn.com/mongodbwebapplication-mongodc2011-110711151201-phpapp02/85/Introduction-to-MongoDB-1-320.jpg)
![Documents doc1 = { _id: 4b97e62bf1d8c7152c9ccb74 , key1: value1, key2: value2, key3: {..., ..., ...}, key4: [..., ..., ] }](https://image.slidesharecdn.com/mongodbwebapplication-mongodc2011-110711151201-phpapp02/85/Introduction-to-MongoDB-4-320.jpg)
![Places v2 place1 = { name: "10gen HQ”, address: "17 West 18th Street 8th Floor”, city: "New York”, zip: "10011”, tags: [“business”, “recommended”] } db.places.find({zip:”10011”, tags:”business”})](https://image.slidesharecdn.com/mongodbwebapplication-mongodc2011-110711151201-phpapp02/85/Introduction-to-MongoDB-7-320.jpg)
![Places v3 place1 = { name: "10gen HQ”, address: "17 West 18th Street 8th Floor”, city: "New York”, zip: "10011”, tags: [“business”, “cool place”], latlong: [40.0,72.0] } db.places.ensureIndex({latlong:”2d”}) db.places.find({latlong:{$near:[40,70]}})](https://image.slidesharecdn.com/mongodbwebapplication-mongodc2011-110711151201-phpapp02/85/Introduction-to-MongoDB-8-320.jpg)
![Places v4 place1 = { name: "10gen HQ”, address: "17 West 18th Street 8th Floor”, city: "New York”, zip: "10011”, latlong: [40.0,72.0], tags: [“business”, “cool place”], tips: [ {user:"nosh", time:6/26/2010, tip:"stop by for office hours on Wednesdays from 4-6pm"}, {.....}, {.....} ] }](https://image.slidesharecdn.com/mongodbwebapplication-mongodc2011-110711151201-phpapp02/85/Introduction-to-MongoDB-9-320.jpg)
![Querying your Places Creating your indexes db.places.ensureIndex({tags:1}) db.places.ensureIndex({name:1}) db.places.ensureIndex({latlong:”2d”}) Finding places: db.places.find({latlong:{$near:[40,70]}}) With regular expressions: db.places.find({name: /^ typeaheadstring /) By tag: db.places.find({tags: “business”})](https://image.slidesharecdn.com/mongodbwebapplication-mongodc2011-110711151201-phpapp02/85/Introduction-to-MongoDB-10-320.jpg)
![Users user1 = { name: “nosh” email: “nosh@10gen.com”, . . . checkins: [ 4b97e62bf1d8c7152c9ccb74, 5a20e62bf1d8c736ab ] } checkins [] = ObjectId reference to checkin collection](https://image.slidesharecdn.com/mongodbwebapplication-mongodc2011-110711151201-phpapp02/85/Introduction-to-MongoDB-14-320.jpg)
![Checkins checkin1 = { place: “10gen HQ”, ts: 6/7/2011 10:12:00, userId: <objectid of user> } Check-in = 2 ops Insert check in object [checkin collection] Update ($push) user object [user collection] Indexes: db.checkins.ensureIndex({place:1, ts:1}) db.checkins.ensureIndex({ts:1})](https://image.slidesharecdn.com/mongodbwebapplication-mongodc2011-110711151201-phpapp02/85/Introduction-to-MongoDB-15-320.jpg)
![Stats with MapReduce mapFunc = function() { emit(this.place, 1);} reduceFunc = function(key, values) { return Array.sum(values); } db.checkins.mapReduce(mapFunc,reduceFunc, {query: {timestamp: {$gt:nowminus3hrs}}, out: “result”}) result = [{_id:”10gen HQ”, value: 17}, {…..}, {….}] db.result.find({ value: {$gt: 15}})](https://image.slidesharecdn.com/mongodbwebapplication-mongodc2011-110711151201-phpapp02/85/Introduction-to-MongoDB-18-320.jpg)
![Nosh Petigara [email_address] Director of Product Strategy, 10gen http://mongodb.org http://10gen.com We are hiring! @mongodb [email_address] @noshinosh](https://image.slidesharecdn.com/mongodbwebapplication-mongodc2011-110711151201-phpapp02/85/Introduction-to-MongoDB-23-320.jpg)
Introduction to MongoDB
- 1. http://mongodb.org http://10gen.com Building applications with MongoDB – An introduction MongoNYC – June 7, 2011 Nosh Petigara [email_address] @noshinosh
- 2. Today’s Talk MongoDB: Data modeling, queries, geospatial, updates, map reduce Using a location-based app as an example Example Works in MongoDB JS shell
- 3. Application Goals Places Check ins (1) Q: Current location A: Places near location (2) Add user generated content (3) Record user checkins (4) Stats about checkins
- 4. Documents doc1 = { _id: 4b97e62bf1d8c7152c9ccb74 , key1: value1, key2: value2, key3: {..., ..., ...}, key4: [..., ..., ] }
- 5. Collections doc1, doc2, doc3 Places Users Checkins doc3, doc4, doc5 doc6, doc7, doc8
- 6. Places v1 place1 = { name: "10gen HQ”, address: ”134 5 th Avenue 3 rd Floor”, city: "New York”, zip: "10011” } db.places.find({zip:”10011”}).limit(10)
- 7. Places v2 place1 = { name: "10gen HQ”, address: "17 West 18th Street 8th Floor”, city: "New York”, zip: "10011”, tags: [“business”, “recommended”] } db.places.find({zip:”10011”, tags:”business”})
- 8. Places v3 place1 = { name: "10gen HQ”, address: "17 West 18th Street 8th Floor”, city: "New York”, zip: "10011”, tags: [“business”, “cool place”], latlong: [40.0,72.0] } db.places.ensureIndex({latlong:”2d”}) db.places.find({latlong:{$near:[40,70]}})
- 9. Places v4 place1 = { name: "10gen HQ”, address: "17 West 18th Street 8th Floor”, city: "New York”, zip: "10011”, latlong: [40.0,72.0], tags: [“business”, “cool place”], tips: [ {user:"nosh", time:6/26/2010, tip:"stop by for office hours on Wednesdays from 4-6pm"}, {.....}, {.....} ] }
- 10. Querying your Places Creating your indexes db.places.ensureIndex({tags:1}) db.places.ensureIndex({name:1}) db.places.ensureIndex({latlong:”2d”}) Finding places: db.places.find({latlong:{$near:[40,70]}}) With regular expressions: db.places.find({name: /^ typeaheadstring /) By tag: db.places.find({tags: “business”})
- 11. Inserting and updating places Initial data load: db.places.insert(place1) Updating tips: db.places.update({name:"10gen HQ"}, {$push :{tips: {user:"nosh", time:6/26/2010, tip:"stop by for office hours on Wednesdays from 4-6"}}}}
- 12. Atomic Updates $set, $unset, $rename $push, $pop, $pull, $addToSet $inc
- 13. Application Goals Places Check ins (1) Q: Current location A: Places near location (2) Add user generated content (3) Record user checkins (4) Stats about checkins
- 14. Users user1 = { name: “nosh” email: “[email protected]”, . . . checkins: [ 4b97e62bf1d8c7152c9ccb74, 5a20e62bf1d8c736ab ] } checkins [] = ObjectId reference to checkin collection
- 15. Checkins checkin1 = { place: “10gen HQ”, ts: 6/7/2011 10:12:00, userId: <objectid of user> } Check-in = 2 ops Insert check in object [checkin collection] Update ($push) user object [user collection] Indexes: db.checkins.ensureIndex({place:1, ts:1}) db.checkins.ensureIndex({ts:1})
- 16. Application Goals Places Check ins (1) Q: Current location A: Places near location (2) Add user generated content (3) Record user checkins (4) Stats about checkins
- 17. Simple Stats db.checkins.find({place: “10gen HQ”) db.checkins.find({place: “10gen HQ”}) .sort({ts:-1}).limit(10) db.checkins.find({place: “10gen HQ”, ts: {$gt: midnight}}).count() db.checkins.find().sort(ts:-1)}.limit(50)
- 18. Stats with MapReduce mapFunc = function() { emit(this.place, 1);} reduceFunc = function(key, values) { return Array.sum(values); } db.checkins.mapReduce(mapFunc,reduceFunc, {query: {timestamp: {$gt:nowminus3hrs}}, out: “result”}) result = [{_id:”10gen HQ”, value: 17}, {…..}, {….}] db.result.find({ value: {$gt: 15}})
- 19. Application Goals Places Check ins (1) Q: Current location A: Places near location (2) Add user generated content (3) Record user checkins (4) Stats about checkins
- 20. Single Master Deployments Configure as a replica set for automated failover Add more secondaries to scale reads Primary/Master Secondary/Slave
- 21. Auto Sharded Deployment Autosharding distributes data among two or more replica sets Mongo Config Server(s) handles distribution & balancing Transparent to applications Primary/Master Secondary/Slave MongoS Mongo Config
- 22. Use Cases RDBMS replacement for high-traffic web applications Content Management-type applications Real-time analytics High-speed data logging Web 2.0, Media, SaaS, Gaming, Finance, Telecom, Healthcare
- 23. Nosh Petigara [email_address] Director of Product Strategy, 10gen http://mongodb.org http://10gen.com We are hiring! @mongodb [email_address] @noshinosh
Editor's Notes
- #4: Memory mapped files, BSON, indexes, multiple data types, binary files, etc
- #14: Memory mapped files, BSON, indexes, multiple data types, binary files, etc
- #17: Memory mapped files, BSON, indexes, multiple data types, binary files, etc
- #20: Memory mapped files, BSON, indexes, multiple data types, binary files, etc