Back to Basics Webinar 3: Schema Design Thinking in Documents
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This is the third webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will explain the architecture of document databases.
![6
Example Document
{
first_name: ‘Paul’,
surname: ‘Miller’,
cell: 447557505611,
city: ‘London’,
location: [45.123,47.232],
Profession: [‘banking’, ‘finance’, ‘trader’],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
]
}
Fields can contain an array
of sub-documents
Fields
Typed field values
Fields can
contain arrays](https://image.slidesharecdn.com/b2bwebinar3-thinkingindocuments-160614121401/85/Back-to-Basics-Webinar-3-Schema-Design-Thinking-in-Documents-6-320.jpg)
![14
One to Many
{
“Title” : “This is a blog post”,
“Body” : “This is the body text”,
“Comments” : [ { “name” : “Joe Drumgoole”,
“email” : “Joe.Drumgoole@mongodb.com”,
“comment” : “I love your writing style” },
{ “name” : “John Smith”,
“email” : “John.Smith@example.com”,
“comment” : “I hate your writing style” }]
}
Where we expect a small number of comments we can embed them
in the main document](https://image.slidesharecdn.com/b2bwebinar3-thinkingindocuments-160614121401/85/Back-to-Basics-Webinar-3-Schema-Design-Thinking-in-Documents-14-320.jpg)
![16
Approach 2 – Separate Collection
• Keep all comments in a separate comments collection
• Add references to comments as an array of comment IDs
• Requires two queries to display blog post and associated comments
• Requires two writes to create a comments
{
_id : ObjectID( “AAAA” ),
name : “Joe Drumgoole”,
email : “Joe.Drumgoole@mongodb.com”,
comment :“I love your writing style”,
}
{
_id : ObjectID( “AAAB” ),
name : “John Smith”,
email : “Joe.Drumgoole@mongodb.com”,
comment :“I hate your writing style”,
}
{
“_id” : ObjectID( “ZZZZ” ),
“Title” : “A Blog Title”,
“Body” : “A blog post”,
“comments” : [ ObjectID( “AAAA” ),
ObjectID( “AAAB” )]
}
{
“_id” : ObjectID( “ZZZZ” ),
“Title” : “A Blog Title”,
“Body” : “A blog post”,
“comments” : []
}](https://image.slidesharecdn.com/b2bwebinar3-thinkingindocuments-160614121401/85/Back-to-Basics-Webinar-3-Schema-Design-Thinking-in-Documents-16-320.jpg)
![17
Approach 3 – A Hybrid Approach
{
“_id” : ObjectID( “ZZZZ” ),
“Title” : “A Blog Title”,
“Body” : “A blog post”,
“comments” : [{
“_id” : ObjectID( “AAAA” )
“name” : “Joe Drumgoole”,
“email” : “Joe.D@mongodb.com”,
comment :“I love your writing style”,
}
{
_id : ObjectID( “AAAB” ),
name : “John Smith”,
email : “Joe.Drumgoole@mongodb.com”,
comment :“I hate your writing style”,
}]
}
{
“_post_jd” : ObjectID( “ZZZZ” ),
“comments” : [{
“_id” : ObjectID( “AAAA” )
“name” : “Joe Drumgoole”,
“email” : “Joe.D@mongodb.com”,
“comment” :“I love your writing
style”,
}
{...},{...},{...},{...},{...},{...}
,{..},{...},{...},{...} ]](https://image.slidesharecdn.com/b2bwebinar3-thinkingindocuments-160614121401/85/Back-to-Basics-Webinar-3-Schema-Design-Thinking-in-Documents-17-320.jpg)
![18
What About One to A Million
• What is we were tracking mouse position for heat tracking?
– Each user will generate hundreds of data points per visit
– Thousands of data points per post
– Millions of data points per blog site
• Reverse the model
– Store a blog ID per event
{
“post_id” : ObjectID(“ZZZZ”),
“timestamp” : ISODate("2005-01-02T00:00:00Z”),
“location” : [24, 34]
“click” : False,
}](https://image.slidesharecdn.com/b2bwebinar3-thinkingindocuments-160614121401/85/Back-to-Basics-Webinar-3-Schema-Design-Thinking-in-Documents-18-320.jpg)
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Back to Basics Webinar 3: Schema Design Thinking in Documents
- 2. Code JoeD gets you a 25% discount off the list price Early Bird Registration Ends May 13, 2016
- 3. Back to Basics 2016 : Webinar 3 Thinking in Documents Joe Drumgoole Director of Developer Advocacy, EMEA @jdrumgoole V1.2
- 4. 4 Review • Webinar 1 : Introduction to NoSQL – Types of NoSQL database – MongoDB is a document database – Replica Sets and Shards • Webinar 2 – Building a basic application – Adding indexes – Using Explain to measure database operators
- 5. 5 Thinking in Documents • Documents in MongoDB are Javascript Objects (JSON) • Actually they are encoded as BSON • BSON is “Binary JSON” • BSON allows efficient encoding and decoding of JSON • Required for efficient transmission and storage on disk • Eliminates the need to “text parse” all the sub objects • Full spec is online at http://bsonspec.org/
- 6. 6 Example Document { first_name: ‘Paul’, surname: ‘Miller’, cell: 447557505611, city: ‘London’, location: [45.123,47.232], Profession: [‘banking’, ‘finance’, ‘trader’], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, { model: ‘Rolls Royce’, year: 1965, value: 330000, … } ] } Fields can contain an array of sub-documents Fields Typed field values Fields can contain arrays
- 7. 7 Data Stores – Key Value Key 1 Value Key 1 Value Key 1 Value
- 8. 8 Data Stores - Relational Key 1 Value 1 Value 1 Value 1 Value 1 Key 2 Value 1 Value 1 Value 1 Value 1 Key 3 Value 1 Value 1 Value 1 Value 1 Key 4 Value 1 Value 1 Value 1 Value 1
- 9. 9 Data Stores - Document Key3 Key4 Key5 Value 3 Value 5 Value 4Key6 Value 5Key7 Value 2 Value 1Key1 Key1 Key1 Key2
- 10. 10 In Document Form { “key1” : “value 1” } { “key1” : { “key2” : “value 1”, “key3” : { “key4” : “value 3”, “key5” : “value 4” } } { “key1” : { “key6” : “value 5”, “key7” : “value 6” } }
- 11. 11 Some Example Queries # Will find the first two documents db.demo.find( { “key1” : “value” } ) # find the second document by nested value db.demo.find( { "key1.key3.key4" : "value 3" } ) # will find the third document db.demo.find( { "key1.key6" : "value 4" } )
- 12. 12 Modelling and Cardinality • One to One –Title to blog post • One to Many –Blog post to comments • One to Millions –Blog post to site views (e.g. Huffington Post)
- 13. 13 One To One { “Title” : “This is a blog post”, “Body” : “This is the body text of a very short blog post”, … } We can index on “Title” and “Body”.
- 14. 14 One to Many { “Title” : “This is a blog post”, “Body” : “This is the body text”, “Comments” : [ { “name” : “Joe Drumgoole”, “email” : “[email protected]”, “comment” : “I love your writing style” }, { “name” : “John Smith”, “email” : “[email protected]”, “comment” : “I hate your writing style” }] } Where we expect a small number of comments we can embed them in the main document
- 15. 15 Key Concerns • What are the write patterns? –Comments are added more frequently than posts –Comments may have images, tags, large bodies of text • What are the read patterns? –Comments may not be displayed –May be shown in their own window –People rarely look at all the comments
- 16. 16 Approach 2 – Separate Collection • Keep all comments in a separate comments collection • Add references to comments as an array of comment IDs • Requires two queries to display blog post and associated comments • Requires two writes to create a comments { _id : ObjectID( “AAAA” ), name : “Joe Drumgoole”, email : “[email protected]”, comment :“I love your writing style”, } { _id : ObjectID( “AAAB” ), name : “John Smith”, email : “[email protected]”, comment :“I hate your writing style”, } { “_id” : ObjectID( “ZZZZ” ), “Title” : “A Blog Title”, “Body” : “A blog post”, “comments” : [ ObjectID( “AAAA” ), ObjectID( “AAAB” )] } { “_id” : ObjectID( “ZZZZ” ), “Title” : “A Blog Title”, “Body” : “A blog post”, “comments” : [] }
- 17. 17 Approach 3 – A Hybrid Approach { “_id” : ObjectID( “ZZZZ” ), “Title” : “A Blog Title”, “Body” : “A blog post”, “comments” : [{ “_id” : ObjectID( “AAAA” ) “name” : “Joe Drumgoole”, “email” : “[email protected]”, comment :“I love your writing style”, } { _id : ObjectID( “AAAB” ), name : “John Smith”, email : “[email protected]”, comment :“I hate your writing style”, }] } { “_post_jd” : ObjectID( “ZZZZ” ), “comments” : [{ “_id” : ObjectID( “AAAA” ) “name” : “Joe Drumgoole”, “email” : “[email protected]”, “comment” :“I love your writing style”, } {...},{...},{...},{...},{...},{...} ,{..},{...},{...},{...} ]
- 18. 18 What About One to A Million • What is we were tracking mouse position for heat tracking? – Each user will generate hundreds of data points per visit – Thousands of data points per post – Millions of data points per blog site • Reverse the model – Store a blog ID per event { “post_id” : ObjectID(“ZZZZ”), “timestamp” : ISODate("2005-01-02T00:00:00Z”), “location” : [24, 34] “click” : False, }
- 19. 19 But – Finite number of events per second { post_id : ObjectID ( “ZZZZ” ), timeStamp: ISODate("2005-01-02T00:00:00Z”), events : { 0 : { 0 : { <Info> }, 1 : { <Info> }, … 99: { <Info> }}, 1 : { 0 : { <Info> }, 1 : { <Info> }, … 99: { <Info> }}, 2 : { 0 : { <Info> }, 1 : { <Info> }, … 99: { <Info> }}, 3 : { 0 : { <Info> }, 1 : { <Info> }, … 99: { <Info> }}, ... 59 :{ 0 : { <Info> }, 1 : { <Info> }, … 99: { <Info> }} }
- 20. 20 Guidelines • Embed objects for one to one capabilities • Look at read and write patterns to determine when to break out data • Don’t get stuck in “one record” per item thinking • Embrace the hierarchy • Think about cardinality • Grow your data by adding documents not be increasing document size • Think about your indexes • Document updates are transactions