Schema Design by Example ~ MongoSF 2012

8,419 views
8,729 views

Published on

This is my presentation about schema design in MongoDB. I gave this talk at MongoSF 2012.

Published in: Technology
0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
8,419
On SlideShare
0
From Embeds
0
Number of Embeds
6,233
Actions
Shares
0
Downloads
88
Comments
0
Likes
2
Embeds 0
No embeds

No notes for slide
  • \n
  • \n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • schema in an RDBMS is a THING. You must create a schema, and it is an object. In mongo, a schema is implied based on similar data structure, but there is NO schema...\n\nThe first rule of schema is that there is no schema.\n
  • normal relational model - only reason to deviate is performance\n
  • \n
  • \n
  • How should the documents look = what should the schema be?\n\nWhat are we going to do with our data?\n\n\n
  • How should the documents look = what should the schema be?\n\nWhat are we going to do with our data?\n\n\n
  • How should the documents look = what should the schema be?\n\nWhat are we going to do with our data?\n\n\n
  • Mention that I have another talk\n
  • Mention that I have another talk\n
  • Mention that I have another talk\n
  • Mention that I have another talk\n
  • Mention that I have another talk\n
  • Mention that I have another talk\n
  • expensive to do the most obvious things... cheap to do the other operations....\n\n
  • expensive to do the most obvious things... cheap to do the other operations....\n\n
  • expensive to do the most obvious things... cheap to do the other operations....\n\n
  • pushing to arrays... doc size growing...\n\ncomments / history...\n\npre-allocate... larger than the current doc. \n\nstrategy 2.5 - chunks of ten comments\n\ndownsides... investigate compaction / avoid\n
  • \n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • key-values... where values are constant, array of values, or document\n
  • \n
  • key-values... where values are constant, array of values, or document\n
  • \n
  • Schema Design by Example ~ MongoSF 2012

    1. 1. Schema Design by Example Kevin Hanson Solutions Architect @hungarianhc kevin@10gen.com 1
    2. 2. Agenda• MongoDB Data Model• Blog Posts & Comments• Geospatial Check-Ins• Food For Thought
    3. 3. MongoDB Data Model: Rich Documents { title: ‘Who Needs Rows?’, reasons: [ { name: ‘scalability’, desc: ‘no more joins!’ }, { name: ‘human readable’, desc: ‘ah this is nice...’ } ], model: { relational: false, awesome: true } }
    4. 4. Embedded Documents { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "roger", date : "Sat Jul 24 2010 19:47:11 GMT-0700 (PDT)", text : "Spirited Away", tags : [ "Tezuka", "Manga" ], comments : [ { author : "Fred", date : "Sat Jul 24 2010 20:51:03 GMT-0700 (PDT)", text : "Best Movie Ever" } ]}
    5. 5. Parallels
    6. 6. ParallelsRDBMS MongoDBTable CollectionRow DocumentColumn FieldIndex IndexJoin Embedding & LinkingSchema Object
    7. 7. Relational Category • Name • Url Article User • Name Tag• Name • Slug • Name• Email Address • Publish date • Url • Text Comment • Comment • Date • Author
    8. 8. MongoDB Article • Name • Slug • Publish date User • Text• Name • Author• Email Address Comment[] • Comment • Date • Author Tag[] • Value Category[] • Value
    9. 9. Blog Posts and Comments
    10. 10. How Should the Documents Look?What Are We Going to Do with the Data? To embed or to link... That is the question!
    11. 11. 1) Fully Embedded{ blog-title: ‘Commuting to Work’, blog-text: [ ‘This section is about airplanes’, ‘this section is about trains’ ], comments: [ { author: ‘Kevin Hanson’, comment: ‘dude, what about driving?’ }, { author: ‘John Smith’, comment: ‘this blog is aWful!!11!!!!’ } ],}
    12. 12. 1) Fully Embedded
    13. 13. 1) Fully EmbeddedPros• Can query the comments or the blog for results• Cleanly encapsulated
    14. 14. 1) Fully EmbeddedPros• Can query the comments or the blog for results• Cleanly encapsulatedCons• What if we get too many comments? (16MBMongoDB doc size)• What if we want our results to be comments, notblog posts?
    15. 15. 2) Separating Blog & Comments{ { _id: _id:ObjectId("4c4ba5c0672c685 ObjectId("4c4ba5c0672c685e5ee5e8aabf3") 8aabf4") comment-ref: blog-ref:ObjectId("4c4ba5c0672c685 ObjectId("4c4ba5c0672c685e5ee5e8aabf4") 8aabf3") blog-title: ‘Commuting to comments: [Work’, { author: ‘Kevin Hanson’, blog-text: [ comment: ‘dude, what about ‘This section is about driving?’ },airplanes’, { author: ‘John Smith’, ‘this section is about comment: ‘this blog istrains’ aWful!!11!!!!’ } ] ],} }
    16. 16. 2) Separating Blog & Comments
    17. 17. 2) Separating Blog & CommentsPros• Blog Post Size Stays Constant• Can Search Sets of Comments
    18. 18. 2) Separating Blog & CommentsPros• Blog Post Size Stays Constant• Can Search Sets of CommentsCons• Too Many Comments? (same problem)• Managing Document Links
    19. 19. 3) Each Comment Gets Own Doc { blog-title: ‘Commuting to Work’, blog-text: [ ‘This section is about airplanes’, ‘this section is about trains’] } { commenter: ‘Kevin Hanson’, comment: ‘dude, what about driving?’ } { commenter: ‘John Smith’, comment: ‘this blog is aWful!!11!!!!’ }
    20. 20. 3) Each Comment Gets Own Doc
    21. 21. 3) Each Comment Gets Own DocPros• Can Query Individual Comments• Never Need to Worry About Doc Size
    22. 22. 3) Each Comment Gets Own DocPros• Can Query Individual Comments• Never Need to Worry About Doc SizeCons• Many Documents• Standard Use Cases Become Complicated
    23. 23. Managing ArraysPushing to an Array Infinitely...• Document Will Grow Larger than AllocatedSpace• Document May Increase Max Doc Size of16MBCan this be avoided??• Yes!• A Hybrid of Linking and Embedding
    24. 24. Geospatial Check-Ins
    25. 25. We Need 3 Things
    26. 26. We Need 3 ThingsPlaces Check-Ins Users
    27. 27. Places Q: Current location A: Places near locationUser Generated Content Places
    28. 28. Inserting a Placevar p = { name: “10gen HQ”, address: “578 Broadway, 7thFloor”, city: “New York”, zip: “10012”}> db.places.save(p)
    29. 29. Tags, Geo Coordinates, and Tips { name: “10gen HQ”, address: “578 Broadway, 7th Floor”, city: “New York”, zip: “10012”, tags: [“MongoDB”, “business”], latlong: [“40.0”, “72.0”], tips: [{user: “kevin”, time:“3/15/2012”,tip: “Make sure to stop byfor office hours!”}],}
    30. 30. Updating Tipsdb.places.update({name:"10gen HQ"}, {$push :{tips: {user:"nosh", time:3/15/2012, tip:"stop by foroffice hours on Wednesdays from 4-6"}}}}
    31. 31. Querying Places• Creating Indexes ★db.places.ensureIndex({tags:1}) ★db.places.ensureIndex({name:1}) ★db.places.ensureIndex({latlong:”2d”} )• Finding Places ★db.places.find({latlong:{$near: [40,70]}})• Regular Expressions ★db.places.find({name: / ^typeaheadstring/)
    32. 32. User Check-InsRecord User Check-Ins Users Stats Stats Check-Ins Users
    33. 33. Usersuser1 = { name: “Kevin Hanson” e-mail: “kevin@10gen.com”, check-ins:[4b97e62bf1d8c7152c9ccb74,5a20e62bf1d8c736ab]}checkins [] = ObjectId reference to Check-Ins Collection
    34. 34. Check-Inscheckin = { place: “10gen HQ”, ts: 9/20/2010 10:12:00, userId: <object id of user>}Every Check-In is Two Operations• Insert a Check-In Object (check-inscollection)• Update ($push) user object with check-in
    35. 35. Simple Statsdb.checkins.find({place: “10gen HQ”)db.checkins.find({place: “10gen HQ”}) .sort({ts:-1}).limit(10)db.checkins.find({place: “10gen HQ”, ts: {$gt: midnight}}).count()
    36. 36. Stats w/ MapReducemapFunc = function() {emit(this.place, 1);}reduceFunc = function(key, values) {returnArray.sum(values);}res =db.checkins.mapReduce(mapFunc,reduceFunc, {query: {timestamp: {$gt:nowminus3hrs}}})res = [{_id:”10gen HQ”, value: 17}, ….., ….] ... or try using the new aggregation framework! Chris Westin @ 1:50PM
    37. 37. Food For Thought
    38. 38. Data How the App Wants ItThink About How the Application Wantsthe Data, Not How it is most “Normalized”Example: Our Business Cards
    39. 39. More info at http://www.mongodb.org/ Kevin Hanson Solutions Architect, 10gen twitter: @hungarianhc kevin@10gen.com Facebook | Twitter |http://bit.ly/ @mongodb http://linkd.in/joinmongo LinkedInmongofb

    ×