Downloaded 25 times
![Documents?
doc1 = {!
_id: ObjectId('4b97e62bf1d8c7152c9ccb74'),
key1: value1,!
key2: value2,
key3: {..., ..., ...},
key4: [..., ..., ]
}](https://image.slidesharecdn.com/firstmongodbapplication-120609113153-phpapp01/85/Building-Your-First-MongoDB-Application-14-320.jpg)
![Places v2
place2 = {
name: "Blake Hotel",!
address: "555 South McDowell Street",
city: "Charlotte",
zip: "28204",
tags: ["hotel", "recommended"]}
db.places.find({
zip: "28204", tags: "hotel"}).limit(10)](https://image.slidesharecdn.com/firstmongodbapplication-120609113153-phpapp01/85/Building-Your-First-MongoDB-Application-17-320.jpg)
![Places v3
place3 = {
name: "Blake Hotel",!
address: "555 South McDowell Street",
city: "Charlotte",
zip: "28204",
tags: ["hotel", "recommended"],
latlon: [ 35.21, 80.83 ] }
db.places.ensureIndex({latlong:"2d"})
db.places.find({latlong:{$near:[35,80]}})](https://image.slidesharecdn.com/firstmongodbapplication-120609113153-phpapp01/85/Building-Your-First-MongoDB-Application-18-320.jpg)
![Places v4
place4 = {
name: "Blake Hotel",!
address: "555 South McDowell Street",
city: "Charlotte",
zip: "28204",
tags: ["hotel", "recommended"],
latlon: [ 35.21, 80.83 ],
tips: [
{ user: "rick",
time: ISODateTime(...),
tip: "Come learn about #self2012"},
{ ... },
{ ... } ] }](https://image.slidesharecdn.com/firstmongodbapplication-120609113153-phpapp01/85/Building-Your-First-MongoDB-Application-19-320.jpg)
![Some Queries
/* First, some indexes */
db.places.ensureIndex({tags:1})
db.places.ensureIndex({name:1})
db.places.ensureIndex({latlong:"2d"})
/* Find places */
db.places.find({latlong:{$near:[40,70]}})
/* Regex searching */
db.places.find({name: /^typeaheadstring/)
/* Searching arrays */
db.places.find({tags: "business"})](https://image.slidesharecdn.com/firstmongodbapplication-120609113153-phpapp01/85/Building-Your-First-MongoDB-Application-20-320.jpg)
![Inserting and updating
/* Initial data load */
db.places.insert([place1, place2, ...])
/* Update-in-place */
db.places.update(
{ name:"Blake Hotel" },
{ $push : {
tips: {
user: "rick",
time: ISODateTime(...),
tip: "Come learn about #self2012" }
} } )](https://image.slidesharecdn.com/firstmongodbapplication-120609113153-phpapp01/85/Building-Your-First-MongoDB-Application-21-320.jpg)
![Users
user1 = {
name: "rick",
email: "rick@arborian.com",
...
checkins: [
ObjectId('4b97e62bf1d8c7152c9ccb74'),
... ] }
/* checkins [] = references checkin
collection _id field */](https://image.slidesharecdn.com/firstmongodbapplication-120609113153-phpapp01/85/Building-Your-First-MongoDB-Application-23-320.jpg)
![MapReduce
mapFunc = function() {
emit(this.place.name, 1); }
reduceFunc = function(key, values) {
return Array.sum(values); }
res = db.checkins.mapReduce(
mapFunc, reduceFunc, !
{ query: { ts: { $gt: nowminus3hrs } }
out: {inline: 1} })
res.results = [
{_id:"Blake Hotel", value: 17}, ...]](https://image.slidesharecdn.com/firstmongodbapplication-120609113153-phpapp01/85/Building-Your-First-MongoDB-Application-28-320.jpg)
Uploaded byRick Copeland
Building Your First MongoDB Application
The document provides a comprehensive guide on building a MongoDB application, focusing on data modeling, queries, geospatial indexing, updates, and deployment concerns. It introduces an application called 'threetriangles' where users can check in at different locations, and details MongoDB operations including querying, indexing, and atomic updates. The document also discusses MongoDB's advantages and limitations compared to traditional relational databases.
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Building Your First MongoDB Application
- 1. Building your First MongoDBApplication Rick Copeland @rick446 http://arborian.com
- 2. Who am I? •Now a consultant, but formerly... • Software engineer at SourceForge • Author of Essential SQLAlchemy • Primarily code Python
- 3. What will youlearn here?
- 4. What will youlearn here? • Data Modeling
- 5. What will youlearn here? • Data Modeling • Queries
- 6. What will youlearn here? • Data Modeling • Queries • Geospatial indexing
- 7. What will youlearn here? • Data Modeling • Queries • Geospatial indexing • Updates
- 8. What will youlearn here? • Data Modeling • Queries • Geospatial indexing • Updates • Map/Reduce
- 9. What will youlearn here? • Data Modeling • Queries • Geospatial indexing • Updates • Map/Reduce • Deployment and Scaling Concerns
- 10. What will youlearn here? • Data Modeling • Queries • Geospatial indexing • Updates • Map/Reduce • Deployment and Scaling Concerns • Why MongoDB?
- 11. Our Application • Userscan create different locations • Users can “check in” to these locations • Users can see who else is checked in • Let’s call it ThreeTriangles (3tri)
- 12. 3tri Operations Record Find nearby checkins places Places Checkins User-generated Checkin content Stats
- 13. MongoDB Terminology Relational MongoDB Database Database Table Collection Index Index Row Document Column Field
- 14. Documents? doc1 = {! _id: ObjectId('4b97e62bf1d8c7152c9ccb74'), key1: value1,! key2: value2, key3: {..., ..., ...}, key4: [..., ..., ] }
- 15. Collections • No schema enforcement doc1,doc2,... doc5,doc6,... • Per-collection... Places Users • Querying • Indexing doc8,doc9,... • Updating Checkins • Sharding
- 16. Places v1 place1 ={! name: "Blake Hotel",! address: "555 South McDowell Street", city: "Charlotte", zip: "28204"} db.places.find({zip:"28204"}).limit(10)
- 17. Places v2 place2 ={ name: "Blake Hotel",! address: "555 South McDowell Street", city: "Charlotte", zip: "28204", tags: ["hotel", "recommended"]} db.places.find({ zip: "28204", tags: "hotel"}).limit(10)
- 18. Places v3 place3 ={ name: "Blake Hotel",! address: "555 South McDowell Street", city: "Charlotte", zip: "28204", tags: ["hotel", "recommended"], latlon: [ 35.21, 80.83 ] } db.places.ensureIndex({latlong:"2d"}) db.places.find({latlong:{$near:[35,80]}})
- 19. Places v4 place4 ={ name: "Blake Hotel",! address: "555 South McDowell Street", city: "Charlotte", zip: "28204", tags: ["hotel", "recommended"], latlon: [ 35.21, 80.83 ], tips: [ { user: "rick", time: ISODateTime(...), tip: "Come learn about #self2012"}, { ... }, { ... } ] }
- 20. Some Queries /* First,some indexes */ db.places.ensureIndex({tags:1}) db.places.ensureIndex({name:1}) db.places.ensureIndex({latlong:"2d"}) /* Find places */ db.places.find({latlong:{$near:[40,70]}}) /* Regex searching */ db.places.find({name: /^typeaheadstring/) /* Searching arrays */ db.places.find({tags: "business"})
- 21. Inserting and updating /*Initial data load */ db.places.insert([place1, place2, ...]) /* Update-in-place */ db.places.update( { name:"Blake Hotel" }, { $push : { tips: { user: "rick", time: ISODateTime(...), tip: "Come learn about #self2012" } } } )
- 22. 3tri Operations Record Find nearby checkins places Places Checkins User-generated Checkin content Stats
- 23. Users user1 = { name: "rick", email: "rick@arborian.com", ... checkins: [ ObjectId('4b97e62bf1d8c7152c9ccb74'), ... ] } /* checkins [] = references checkin collection _id field */
- 24. Checkins db.checkins.ensureIndex({place:1, ts:1}) db.checkins.ensureIndex({ts:1}) checkin1 ={ place: { id: ObjectId(...), name: "Blake Hotel" }, ts: ISODateTime(...), user: { id: ObjectId(...), name: "rick" } }
- 25. Atomic Updates • $set • $pull • $unset • $pullAll • $rename • $addToSet • $push • $inc • $pushAll • $bit • $pop
- 26. 3tri Operations Record Find nearby checkins places Places Checkins User-generated Checkin content Stats
- 27. Simple Statistics /* Allcheckins */ db.checkins.find({"place.name": "Blake Hotel"}) /* Last 10 checkins */ db.checkins.find({"place.name": "Blake Hotel"}) .sort({ts:-1}).limit(10) /* Number of checkins today */ db.checkins.find( { "place.name": "Blake Hotel", ts: { $gt: ISODateTime(...)} }) .count()
- 28. MapReduce mapFunc = function(){ emit(this.place.name, 1); } reduceFunc = function(key, values) { return Array.sum(values); } res = db.checkins.mapReduce( mapFunc, reduceFunc, ! { query: { ts: { $gt: nowminus3hrs } } out: {inline: 1} }) res.results = [ {_id:"Blake Hotel", value: 17}, ...]
- 29. Deployment and Scaling Options
- 30. Single Master Deployment Read / Write Primary Read Secondary Read Secondary
- 31. Auto-Sharding Shard1 (0..10) Shard 2 (10..20) Shard 3 (20..30) Primary Primary Primary Secondary Secondary Secondary Config Secondary Secondary Secondary
- 32. Use Cases • ReplaceRDBMS for high-traffic web applications • CMS-style applications • Social and mobile applications • Real-time analytics, high-speed logging • Maybe not double-entry bookkeeping
- 33. What do yougive up? • No multi-document atomic operations (i.e. transactions) • No server-side JOINs • No referential integrity constraints between documents • Data model is typically tied to query patterns (less flexible than relational DBs)
- 34. Questions? Please rate this talk at http://svy.mk/L3jM7f Interested in training? http://Arborian.com/training MongoDB Info & Downloads: http://mongodb.org Rick Copeland @rick446 http://arborian.com
Editor's Notes
- #2 \n
- #3 SF - early adopters of MongoDB (0.8), so we went through some of the growing pains\nSQLAlchemy - I don’t hate SQL!\nKnow Python best, but also C, C++, etc.\n
- #4 \n
- #5 \n
- #6 \n
- #7 \n
- #8 \n
- #9 \n
- #10 \n
- #11 \n
- #12 \n
- #13 \n
- #14 Tend to not have auto-increment keys\nStandard JSON values + a few new primitives\nEmbedded docs & arrays OK (and encouraged!)\n
- #15 \n
- #16 Lots of places, hard to find the one you need\n
- #17 Better, now at least we’re just looking for hotels, still too many\n
- #18 Results sorted by distance\n
- #19 Embedded tips for data locality\n
- #20 Regex has same benefits and drawbacks as LIKE in sql -- prefix queries are your friend!\n
- #21 Lots of in-place operators so you can update your document atomically (makes transactionlessness less important)\n
- #22 So we’ve mostly covered the “Places” side, now let’s look at “Checkins”\n
- #23 \n
- #24 Checking in inserts checkin and then $pushes user object\n
- #25 You can also “reach inside” your objects for updating\n
- #26 \n
- #27 \n
- #28 Flexible output options for dumping results in a collection\n
- #29 \n
- #30 \n
- #31 Data automatically balanced between replica sets\nConfig server stores ‘chunk’ locations\nTransparent to applications\n
- #32 \n
- #33 No transactions, but doc model is rich enough to do without in many apps\nNo server-side JOIN, but can be emulated in client (use $in)\nNo referential integrity, but 1:N joins become embedded documents\nData model *can* be relational, but you give up performance (and introduce integrity issues, etc) - many will just denormalize data to get both flexibility and performance at the expense of application complexity.\n
- #34 \n