Webinar Intro to Schema Design

MongoDB has been designed for versatility, but the techniques you might use to build, say, an analytics engine or a hierarchical data store might not be obvious. In this talk, we'll learn about MongoDB in practice by looking at hypothetical application designs (based on real-world designs, of course). Topics to be covered include schema design, indexing, transactions (gasp!), trees, what's fast, and what's not. Sprinkled with tips, tricks, shoots, ladders, and trap doors, you're guaranteed to learn something new in this interdisciplinary talk.

Transcript

1. Schema  Design  by  Example     Kevin  Hanson   Solutions  Architect   @hungarianhc   kevin@10gen.com   Audio  should  start  immediatly  when  you  log  into  the  event  via  Audio  Broadcast.   If  you  are  having  issues  connecting,  please  dial     1-­‐877-­‐668-­‐4493  or  +1-­‐408-­‐600-­‐3600Access  code:  667  326  336   Global  dial-­‐in  numbers  can  be  found  on  the  Event  Info  tab  of  your  WebEx  Event  Center  screen.  There  is   a  Q&A  following  the  talk.  Please  enter  in  all  questions  in  the  WebEx  chat  box.A  recording  of  the  webinar   will  be  available  24  hours  after  the  event  is  complete.   1  
2. •  MongoDB Data Model •  Blog Posts & Comments •  Geospatial Check-Ins •  Food For Thought Agenda
3. { title: bWho Needs Rows?`, reasons: [ { name: bscalability`, desc: bno more joins!` }, { name: bhuman readable`, desc: bah this is nice...` } ], model: { relational: false, awesome: true } } MongoDB Data Model: Rich Documents
4. { _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" } ]} Embedded Documents
5. Parallels RDBMS   MongoDB   Table   Collection   Row   Document   Column   Field   Index   Index   Join   Embedding  &  Linking   Schema  Object  
6. User • Name • Email Address Category • Name • Url Article • Name • Slug • Publish date • Text Tag • Name • Url Comment • Comment • Date • Author Relational
7. User • Name • Email Address Article • Name • Slug • Publish date • Text • Author Tag[] • Value Comment[] • Comment • Date • Author Category[] • Value MongoDB
8. Blog Posts and Comments
9. How Should the Documents Look? What Are We Going to Do with the Data?
10. 1) Fully Embedded { blog-title: bCommuting to Work`, blog-text: [ bThis section is about airplanes`, bthis section is about trains` ], comments: [ { author: bKevin Hanson`, comment: bdude, what about driving?` }, { author: bJohn Smith`, comment: bthis blog is aWful!!11!!!!` } ], }
11. 1) Fully Embedded Pros •  Can query the comments or the blog for results •  Cleanly encapsulated Cons •  What if we get too many comments? (16MB MongoDB doc size) •  What if we want our results to be comments, not blog posts?
12. 2) Separating Blog & Comments { _id: ObjectId("4c4ba5c0672c685e 5e8aabf3") comment-ref: ObjectId("4c4ba5c0672c685e 5e8aabf4") blog-title: bCommuting to Work`, blog-text: [ bThis section is about airplanes`, bthis section is about trains` ] } { _id: ObjectId("4c4ba5c0672c685e5e 8aabf4") blog-ref: ObjectId("4c4ba5c0672c685e5e 8aabf3") comments: [ { author: ‘Kevin Hanson’, comment: ‘dude, what about driving?’ }, { author: ‘John Smith’, comment: ‘this blog is aWful!!11!!!!’ } ], }
13. 2) Separating Blog & Comments Pros •  Blog Post Size Stays Constant •  Can Search Sets of Comments Cons •  Too Many Comments? (same problem) •  Managing Document Links
14. 3) Each Comment Gets Own Doc { blog-title: bCommuting to Work`, blog-text: [ bThis section is about airplanes`, bthis section is about trains`] } { commenter: bKevin Hanson`, comment: bdude, what about driving?` } { commenter: bJohn Smith`, comment: bthis blog is aWful!!11!!!!` }
15. 3) Each Comment Gets Own Doc Pros •  Can Query Individual Comments •  Never Need to Worry About Doc Size Cons •  Many Documents •  Standard Use Cases Become Complicated
16. Managing Arrays Pushing to an Array Infinitely... •  Document Will Grow Larger than Allocated Space •  Document May Increase Max Doc Size of 16MB Can this be avoided?? •  Yes! •  A Hybrid of Linking and Embedding
17. Geospatial Check-Ins
18. We Need 3 Things Places Check-Ins Users
19. Places Q: Current location A: Places near location User Generated Content Places
20. Inserting a Place   var  p  =  {  name:  “10gen  HQ”,                    address:  “578  Broadway,  7th  Floor”,                    city:  “New  York”,                    zip:  “10012”}     >  db.places.save(p)  
21. 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  by  for  office  hours!”}],}  
22. Updating Tips db.places.update({name:"10gen  HQ"},    {$push  :{tips:        {user:"nosh",  time: 3/15/2012,        tip:"stop  by  for  office  hours   on        Wednesdays  from  4-­‐6"}}})    
23. Querying Places ★ Creating   Indexesdb.places.ensureIndex({tags: 1})db.places.ensureIndex({name: 1})db.places.ensureIndex({latlong:”2d”})Findi ng  Placesdb.places.find({latlong:{$near: [40,70]}})Regular   Expressionsdb.places.find({name:  / ^typeaheadstring/)Using   Tagsdb.places.find({tags:  “business”})  
24. User Check-Ins Record User Check-Ins Check-Ins Users Stats Users Stats
25. Users user1  =  {  name:  “Kevin  Hanson”  e-­‐mail:   “kevin@10gen.com”,  check-­‐ins:   [4b97e62bf1d8c7152c9ccb74,    5a20e62bf1d8c736ab]   }   checkins  []  =  ObjectId  reference  to  Check-­‐Ins   Collection  
26. Check-Ins checkin  =  {  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-­‐ins  collection)   •   Update  ($push)  user  object  with  check-­‐in  ID  (users   collection)  
27. Simple Stats db.checkins.find({place: “10gen HQ”)db.checkins.find({place: “10gen HQ”}) .sort({ts:-1}).limit(10)db.checkins.fin d({place: “10gen HQ”, ts: {$gt: midnight}}).count()
28. Stats w/ MapReduce mapFunc  =  function()  {emit(this.place,  1);}reduceFunc  =   function(key,  values)  {return  Array.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! Available in MongoDB 2.2!
29. Food For Thought
30. Data How the App Wants It Think  About  How  the  Application  Wants  the  Data,   Not  How  it  is  most  “Normalized”     Example:  Our  Business  Cards  
31. @mongodb   http://bit.ly/mongox     Facebook                    |                  Twitter                  |                  LinkedIn   http://linkd.in/joinmongo   More info at http://www.mongodb.org/ Kevin  Hanson   Solutions  Architect,  10gen   twitter:  @hungarianhc   kevin@10gen.com