1. open-source, high-performance,
document-oriented database

2. Schema Design Basics
roger@10Gen.com

3. This talk

4. This talk
‣Intro
-Terms / Definitions

5. This talk
‣Intro
-Terms / Definitions

6. This talk
‣Intro
-Terms / Definitions
‣Getting a flavor
-Creating a Schema
-Indexes
-Evolving the Schema

7. This talk
‣Intro
-Terms / Definitions
‣Getting a flavor
-Creating a Schema
-Indexes
-Evolving the Schema
‣Data modeling
-DBRef
-Single Table Inheritance
-Many - Many
-Trees
-Lists / Queues / Stacks

8. Document Oriented
Basic unit of data: JSON Documents
Not Relational, Key Value
Not OODB
- Associations implied by Document Structure
- but your database schema != your program schema

9. Terms
Table -> Collection
Row(s) -> JSON Document
Index -> Index
Join -> Embedding and Linking
across documents
Partition -> Shard
Partition Key -> Shard Key

10. Considerations
What are the requirements ?
- Functionality to be supported
- Access Patterns ?
- Data Life Cycle (insert, update, deletes)
- Expected Performance / Workload ?
Capabilities of the database ?

11. DB Considerations
How can we manipulate this data ?
Dynamic Queries
Secondary Indexes
Atomic Updates
Map Reduce
Access Patterns ?
Read / Write Ratio
Types of updates
Types of queries
Considerations
No Joins
Single Document Transactions only

12. Design Session
Use Rich Design Documents
post = {author: “kyle”,
date: new Date(),
text: “my blog post...”,
tags: [“mongodb”, “intro”]}
>db.post.save(post)

13. >db.posts.find()
{ _id : ObjectId("4c4ba5c0672c685e5e8aabf3"),
author : "kyle",
date : "Sat Jul 24 2010 19:47:11 GMT-0700 (PDT)",
text : "My first blog",
tags : [ "mongodb", "intro" ] }
Notes:
- ID is unique, but can be anything you’d like

14. Secondary index for “author”
// 1 means ascending, -1 means descending
>db.posts.ensureIndex({author: 1})
>db.posts.find({author: 'kyle'})
{ _id : ObjectId("4c4ba5c0672c685e5e8aabf3"),
author : "kyle",
... }

15. Verifying indexes exist
>db.system.indexes.find()
// Index on ID
{ name : "_id_",
ns : "test.posts",
key : { "_id" : 1 } }

16. Verifying indexes exist
>db.system.indexes.find()
// Index on ID
{ name : "_id_",
ns : "test.posts",
key : { "_id" : 1 } }
// Index on author
{ _id : ObjectId("4c4ba6c5672c685e5e8aabf4"),
ns : "test.posts",
key : { "author" : 1 },
name : "author_1" }

17. Query operators
Conditional operators:
$ne, $in, $nin, $mod, $all, $size, $exists, $type, ..
$lt, $lte, $gt, $gte, $ne,
// find posts with any tags
>db.posts.find({tags: {$exists: true}})

18. Query operators
Conditional operators:
$ne, $in, $nin, $mod, $all, $size, $exists, $type, ..
$lt, $lte, $gt, $gte, $ne,
// find posts with any tags
>db.posts.find({tags: {$exists: true}})
Regular expressions:
// posts where author starts with k
>db.posts.find({author: /^k*/i })

19. Query operators
Conditional operators:
$ne, $in, $nin, $mod, $all, $size, $exists, $type, ..
$lt, $lte, $gt, $gte, $ne,
// find posts with any tags
>db.posts.find({tags: {$exists: true}})
Regular expressions:
// posts where author starts with k
>db.posts.find({author: /^k*/i })
Counting:
// posts written by mike
>db.posts.find({author: “mike”}).count()

20. Extending the Schema
comment = {author: “fred”,
date: new Date(),
text: “super duper”}
update = { ‘$push’: {comments: comment},
‘$inc’: {comments_count: 1}}
>db.posts.update({_id: “...” }, update)

21. { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"),
author : "kyle",
date : "Sat Jul 24 2010 19:47:11 GMT-0700 (PDT)",
text : "My first blog",
tags : [ "mongodb", "intro" ],
comments_count: 1,
comments : [
{
author : "Fred",
date : "Sat Jul 24 2010 20:51:03 GMT-0700 (PDT)",
text : "Super Duper"
}
]}

22. // create index on nested documents:
>db.posts.ensureIndex({"comments.author": 1})
>db.posts.find({comments.author:”Fred”})

23. // create index on nested documents:
>db.posts.ensureIndex({"comments.author": 1})
>db.posts.find({comments.author:”kyle”})
// find last 5 posts:
>db.posts.find().sort({date:-1}).limit(5)

24. // create index on nested documents:
>db.posts.ensureIndex({"comments.author": 1})
>db.posts.find({comments.author:”kyle”})
// find last 5 posts:
>db.posts.find().sort({date:-1}).limit(5)
// most commented post:
>db.posts.find().sort({comments_count:-1}).limit(1)
When sorting, check if you need an index

25. Map Reduce
Aggregation and batch manipulation
Collection in, Collection out
Parallel in sharded environments

26. Map reduce
mapFunc = function () {
this.tags.forEach(function (z) {emit(z, {count:1});});
}
reduceFunc = function (k, v) {
var total = 0;
for (var i = 0; i < v.length; i++) { total += v[i].count; }
return {count:total}; }
res = db.posts.mapReduce(mapFunc, reduceFunc)
>db[res.result].find()
{ _id : "intro", value : { count : 1 } }
{ _id : "mongodb", value : { count : 1 } }

27. Review
So Far:
- Started out with a simple schema
- Queried Data
- Evolved the schema
- Queried / Updated the data some more

28. Wordnik
9B records, 100M queries / week, 1,2TB
{
entry : {
header: { id: 0,
headword: "m",
sourceDictionary: "GCide",
textProns : [
{text: "(em)",
seq:0}
],
syllables: [
{id: 0,
text: "m"}
],
sourceDictionary: "1913 Webster",
headWord: "m",
id: 1,
definitions: : [
{text: "M, the thirteenth letter..."},
{text: "As a numeral, M stands for 1000"}]
}
}
}

29. Review
So Far:
- Started out with a simple schema
- Queried Data
- Evolved the schema
- Queried / Updated the data some more
Observations:
- Using Rich Documents works well
- Simplify relations by embedding them
- Iterative development is easy with MongoDB

31. Single Table Inheritance
>db.shapes.find()
{ _id: ObjectId("..."), type: "circle", area: 3.14, radius: 1}
{ _id: ObjectId("..."), type: "square", area: 4, d: 2}
{ _id: ObjectId("..."), type: "rect", area: 10, length: 5, width: 2}
// find shapes where radius > 0
>db.shapes.find({radius: {$gt: 0}})
// create index
>db.shapes.ensureIndex({radius: 1})

32. One to Many
- Embedded Array / Array Keys
- slice operator to return subset of array
- hard to find latest comments across all documents

33. One to Many
- Embedded Array / Array Keys
- slice operator to return subset of array
- hard to find latest comments across all documents
- Embedded tree
- Single document
- Natural
- Hard to query

34. One to Many
- Embedded Array / Array Keys
- slice operator to return subset of array
- hard to find latest comments across all documents
- Embedded tree
- Single document
- Natural
- Hard to query
- Normalized (2 collections)
- most flexible
- more queries

35. Many - Many
Example:
- Product can be in many categories
- Category can have many products
Products id | product_id | category_id Category

36. products:
{ _id: ObjectId("4c4ca23933fb5941681b912e"),
name: "Sumatra Dark Roast",
category_ids: [ ObjectId("4c4ca25433fb5941681b912f"),
ObjectId("4c4ca25433fb5941681b92af”]}

37. products:
{ _id: ObjectId("4c4ca23933fb5941681b912e"),
name: "Sumatra Dark Roast",
category_ids: [ ObjectId("4c4ca25433fb5941681b912f"),
ObjectId("4c4ca25433fb5941681b92af”]}
categories:
{ _id: ObjectId("4c4ca25433fb5941681b912f"),
name: "Indonesia",
product_ids: [ ObjectId("4c4ca23933fb5941681b912e"),
ObjectId("4c4ca30433fb5941681b9130"),
ObjectId("4c4ca30433fb5941681b913a"]}

38. products:
{ _id: ObjectId("4c4ca23933fb5941681b912e"),
name: "Sumatra Dark Roast",
category_ids: [ ObjectId("4c4ca25433fb5941681b912f"),
ObjectId("4c4ca25433fb5941681b92af”]}
categories:
{ _id: ObjectId("4c4ca25433fb5941681b912f"),
name: "Indonesia",
product_ids: [ ObjectId("4c4ca23933fb5941681b912e"),
ObjectId("4c4ca30433fb5941681b9130"),
ObjectId("4c4ca30433fb5941681b913a"]}
//All categories for a given product
>db.categories.find({product_ids: ObjectId("4c4ca23933fb5941681b912e")})

39. products:
{ _id: ObjectId("4c4ca23933fb5941681b912e"),
name: "Sumatra Dark Roast",
category_ids: [ ObjectId("4c4ca25433fb5941681b912f"),
ObjectId("4c4ca25433fb5941681b92af”]}
categories:
{ _id: ObjectId("4c4ca25433fb5941681b912f"),
name: "Indonesia",
product_ids: [ ObjectId("4c4ca23933fb5941681b912e"),
ObjectId("4c4ca30433fb5941681b9130"),
ObjectId("4c4ca30433fb5941681b913a"]}
//All categories for a given product
>db.categories.find({product_ids: ObjectId("4c4ca23933fb5941681b912e")})
//All products for a given category
>db.products.find({category_ids: ObjectId("4c4ca25433fb5941681b912f")})

40. Alternative
products:
{ _id: ObjectId("4c4ca23933fb5941681b912e"),
name: "Sumatra Dark Roast",
category_ids: [ ObjectId("4c4ca25433fb5941681b912f"),
ObjectId("4c4ca25433fb5941681b92af”]}
categories:
{ _id: ObjectId("4c4ca25433fb5941681b912f"),
name: "Indonesia"}

41. Alternative
products:
{ _id: ObjectId("4c4ca23933fb5941681b912e"),
name: "Sumatra Dark Roast",
category_ids: [ ObjectId("4c4ca25433fb5941681b912f"),
ObjectId("4c4ca25433fb5941681b92af”]}
categories:
{ _id: ObjectId("4c4ca25433fb5941681b912f"),
name: "Indonesia"}
// All products for a given category
>db.products.find({category_ids: ObjectId("4c4ca25433fb5941681b912f")})

42. Alternative
products:
{ _id: ObjectId("4c4ca23933fb5941681b912e"),
name: "Sumatra Dark Roast",
category_ids: [ ObjectId("4c4ca25433fb5941681b912f"),
ObjectId("4c4ca25433fb5941681b92af”]}
categories:
{ _id: ObjectId("4c4ca25433fb5941681b912f"),
name: "Indonesia"}
// All products for a given category
>db.products.find({category_ids: ObjectId("4c4ca25433fb5941681b912f")})
// All categories for a given product
product = db.products.find(_id : some_id)
>db.categories.find({_id : {$in : product.category_ids}})

43. Trees
Full Tree in Document
{ comments: [
{ author: “rpb”, text: “...”,
replies: [
{author: “Fred”, text: “...”,
replies: []}
]}
]}
Pros: Single Document, Performance, Intuitive
Cons: Hard to search, Partial Results, 4MB limit

44. Trees
Parent Links
- Each node is stored as a document
- Contains the id of the parent
Child Links
- Each node contains the id’s of the children
- Can support graphs (multiple parents / child)

45. Array of Ancestors
- Store Ancestors of a node
{ _id: "a" }
{ _id: "b", ancestors: [ "a" ], parent: "a" }
{ _id: "c", ancestors: [ "a", "b" ], parent: "b" }
{ _id: "d", ancestors: [ "a", "b" ], parent: "b" }
{ _id: "e", ancestors: [ "a" ], parent: "a" }
{ _id: "f", ancestors: [ "a", "e" ], parent: "e" }
{ _id: "g", ancestors: [ "a", "b", "d" ], parent: "d" }

46. Array of Ancestors
- Store Ancestors of a node
{ _id: "a" }
{ _id: "b", ancestors: [ "a" ], parent: "a" }
{ _id: "c", ancestors: [ "a", "b" ], parent: "b" }
{ _id: "d", ancestors: [ "a", "b" ], parent: "b" }
{ _id: "e", ancestors: [ "a" ], parent: "a" }
{ _id: "f", ancestors: [ "a", "e" ], parent: "e" }
{ _id: "g", ancestors: [ "a", "b", "d" ], parent: "d" }
//find all descendants of b:
>db.tree2.find({ancestors: ‘b’})

47. Array of Ancestors
- Store Ancestors of a node
{ _id: "a" }
{ _id: "b", ancestors: [ "a" ], parent: "a" }
{ _id: "c", ancestors: [ "a", "b" ], parent: "b" }
{ _id: "d", ancestors: [ "a", "b" ], parent: "b" }
{ _id: "e", ancestors: [ "a" ], parent: "a" }
{ _id: "f", ancestors: [ "a", "e" ], parent: "e" }
{ _id: "g", ancestors: [ "a", "b", "d" ], parent: "d" }
//find all descendants of b:
>db.tree2.find({ancestors: ‘b’})
//find all ancestors of f:
>ancestors = db.tree2.findOne({_id:’f’}).ancestors
>db.tree2.find({_id: { $in : ancestors})

48. findAndModify
Queue example
//Example: grab highest priority job and mark
job = db.jobs.findAndModify({
query: {inprogress: false},
sort: {priority: -1),
update: {$set: {inprogress: true,
started: new Date()}},
new: true})

49. More Cool Stuff
• Aggregation
• Capped collections
• GridFS
• Geo

50. Learn More
Kyle’s presentation + video:
http://www.slideshare.net/kbanker/mongodb-schema-design
http://www.blip.tv/file/3704083
Dwight’s presentation
http://www.slideshare.net/mongosf/schema-design-with-mongodb-dwight-
merriman
Documentation
Trees: http://www.mongodb.org/display/DOCS/Trees+in+MongoDB
Queues: http://www.mongodb.org/display/DOCS/findandmodify+Command
Aggregration: http://www.mongodb.org/display/DOCS/Aggregation
Capped Col. : http://www.mongodb.org/display/DOCS/Capped+Collections
Geo: http://www.mongodb.org/display/DOCS/Geospatial+Indexing
GridFS: http://www.mongodb.org/display/DOCS/GridFS+Specification

51. Thank You :-)

52. Download
MongoDB
and let us know what you think
@mongodb
http://www.mongodb.org

53. DBRef
DBRef
{$ref: collection, $id: id_value}
- Think URL
- YDSMV: your driver support may vary
Sample Schema:
nr = {note_refs: [{"$ref" : "notes", "$id" : 5}, ... ]}
Dereferencing:
nr.forEach(function(r) {
printjson(db[r.$ref].findOne({_id: r.$id}));
}

54. BSON
Mongodb stores data in BSON internally
Lightweight, Traversable, Efficient encoding
Typed
boolean, integer, float, date, string, binary, array...