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Schema Design
Jared Rosoff
Technical Director
Agenda
• Working with documents
• Evolving a Schema
• Queries and Indexes
• Common Patterns
RDBMS                MongoDB
Database      ➜ Database
Table         ➜ Collection
Row           ➜ Document
Index         ➜ Index
Join          ➜ Embedded Document
Foreign Key   ➜ Reference

Terminology
Working with
Documents
Modeling Data
Documents
Provide flexibility and
performance
Normalized Data
De-Normalized (embedded)
Data
Relational Schema Design
Focus on data storage
Document Schema Design
Focus on data use
Schema Design
Considerations
• How do we manipulate the data?
   – Dynamic Ad-Hoc Queries
   – Atomic Updates
   – Map Reduce

• What are the access patterns of the application?
   – Read/Write Ratio
   – Types of Queries / Updates
   – Data life-cycle and growth rate
Data Manipulation
• Conditional Query Operators
  – Scalar: $ne, $mod, $exists, $type, $lt, $lte, $gt, $gte, $ne
  – Vector: $in, $nin, $all, $size

• Atomic Update Operators
  – Scalar: $inc, $set, $unset
  – Vector: $push, $pop, $pull, $pushAll, $pullAll, $addToSet
Data Access
• Flexible Schemas
• Ability to embed complex data structures
• Secondary Indexes
• Multi-Key Indexes
• Aggregation Framework
   – $project, $match, $limit, $skip, $sort, $group, $unwind

• No Joins
Getting Started
Library Management
Application
• Patrons
• Books
• Authors
• Publishers
An Example
One to One Relations
Modeling Patrons

patron = {                   patron = {
  _id: "joe"                   _id: "joe"
  name: "Joe Bookreader”       name: "Joe Bookreader",
}                              address: {
                                  street: "123 Fake St. ",
address = {                       city: "Faketon",
  patron_id = "joe",
                                  state: "MA",
  street: "123 Fake St. ",
  city: "Faketon",                zip: 12345
  state: "MA",                 }
  zip: 12345                 }
}
One to One Relations
• Mostly the same as the relational approach
• Generally good idea to embed “contains”
 relationships
• Document model provides a holistic
 representation of objects
An Example
One To Many Relations
Modeling Patrons

patron = {
  _id: "joe"
  name: "Joe Bookreader",
  join_date: ISODate("2011-10-15"),
  addresses: [
    {street: "1 Vernon St.", city: "Newton", state: "MA", …},
    {street: "52 Main St.", city: "Boston", state: "MA", …},
  ]
}
Publishers and Books
• Publishers put out many books
• Books have one publisher
Book


MongoDB: The Definitive Guide,
By Kristina Chodorow and Mike Dirolf
Published: 9/24/2010
Pages: 216
Language: English

Publisher: O’Reilly Media, CA
Modeling Books – Embedded
Publisher

book = {
  title: "MongoDB: The Definitive Guide",
  authors: [ "Kristina Chodorow", "Mike Dirolf" ]
  published_date: ISODate("2010-09-24"),
  pages: 216,
  language: "English",
  publisher: {
      name: "O’Reilly Media",
      founded: "1980",
      location: "CA"
  }
}
Modeling Books & Publisher
Relationship
publisher = {
  name: "O’Reilly Media",
  founded: "1980",
  location: "CA"
}

book = {
  title: "MongoDB: The Definitive Guide",
  authors: [ "Kristina Chodorow", "Mike Dirolf" ]
  published_date: ISODate("2010-09-24"),
  pages: 216,
  language: "English"
}
Publisher _id as a Foreign
Key
publisher = {
  _id: "oreilly",
  name: "O’Reilly Media",
  founded: "1980",
  location: "CA"
}

book = {
  title: "MongoDB: The Definitive Guide",
  authors: [ "Kristina Chodorow", "Mike Dirolf" ]
  published_date: ISODate("2010-09-24"),
  pages: 216,
  language: "English",
  publisher_id: "oreilly"
}
Book _id as a Foreign Key
publisher = {
  name: "O’Reilly Media",
  founded: "1980",
  location: "CA"
  books: [ "123456789", ... ]
}

book = {
  _id: "123456789",
  title: "MongoDB: The Definitive Guide",
  authors: [ "Kristina Chodorow", "Mike Dirolf" ]
  published_date: ISODate("2010-09-24"),
  pages: 216,
  language: "English"
}
Where do you put the foreign
Key?
• Array of books inside of publisher
   – Makes sense when many means a handful of items
   – Useful when items have bound on potential growth

• Reference to single publisher on books
   – Useful when items have unbounded growth (unlimited # of
    books)
• SQL doesn’t give you a choice, no arrays
Another Example
One to Many Relations
Books and Patrons
• Book can be checked out by one Patron at a
 time
• Patrons can check out many books (but not
 1000s)
Modeling Checkouts
patron = {
  _id: "joe"
  name: "Joe Bookreader",
  join_date: ISODate("2011-10-15"),
  address: { ... }
}

book = {
  _id: "123456789"
  title: "MongoDB: The Definitive Guide",
  authors: [ "Kristina Chodorow", "Mike Dirolf" ],
  ...
}
Modeling Checkouts
patron = {
    _id: "joe"
    name: "Joe Bookreader",
    join_date: ISODate("2011-10-15"),
    address: { ... },
    checked_out: [
        { _id: "123456789", checked_out: "2012-10-15" },
        { _id: "987654321", checked_out: "2012-09-12" },
        ...
    ]
}
De-normalization
Provides data locality




           De-normalize for speed
Modeling Checkouts -- de-
normalized
patron = {
  _id: "joe"
  name: "Joe Bookreader",
  join_date: ISODate("2011-10-15"),
  address: { ... },
  checked_out: [
     { _id: "123456789",
        title: "MongoDB: The Definitive Guide",
        authors: [ "Kristina Chodorow", "Mike Dirolf" ],
        checked_out: ISODate("2012-10-15")
     },
     { _id: "987654321"
        title: "MongoDB: The Scaling Adventure", ...
     }, ...
  ]
}
Referencing vs. Embedding
• Embedding is a bit like pre-joined data
• Document level ops are easy for server to
 handle
• Embed when the “many” objects always appear
 with (viewed in the context of) their parents.
• Reference when you need more flexibility
An Example
Single Table Inheritance
Single Table Inheritance

book = {
  title: "MongoDB: The Definitive Guide",
  authors: [ "Kristina Chodorow", "Mike Dirolf" ]
  published_date: ISODate("2010-09-24"),
  kind: loanable
  locations: [ ... ]
  pages: 216,
  language: "English",
  publisher: {
      name: "O’Reilly Media",
      founded: "1980",
      location: "CA"
  }
}
An Example
Many to Many Relations
Relational Approach
Books and Authors
book = {
  title: "MongoDB: The Definitive Guide",
  authors = [
      { _id: "kchodorow", name: "K-Awesome” },
      { _id: "mdirolf", name: "Batman Mike” }
  ]
  published_date: ISODate("2010-09-24"),
  pages: 216,
  language: "English"
}

author = {
  _id: "kchodorow",
  name: "Kristina Chodorow",
  hometown: "New York"
}
An Example
Trees
Parent Links

book = {
  title: "MongoDB: The Definitive Guide",
  authors: [ "Kristina Chodorow", "Mike Dirolf" ],
  published_date: ISODate("2010-09-24"),
  pages: 216,
  language: "English",
  category: "MongoDB"
}

category = { _id: MongoDB, parent: Databases }
category = { _id: Databases, parent: Programming }
Child Links

book = {
  _id: 123456789,
  title: "MongoDB: The Definitive Guide",
  authors: [ "Kristina Chodorow", "Mike Dirolf" ],
  published_date: ISODate("2010-09-24"),
  pages: 216,
  language: "English"
}

category = { _id: MongoDB, children: [ 123456789, … ] }
category = { _id: Databases, children: [ MongoDB, Postgres }
category = { _id: Programming, children: [ DB, Languages ] }
Modeling 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)
Array of Ancestors
book = {
  title: "MongoDB: The Definitive Guide",
  authors: [ "Kristina Chodorow", "Mike Dirolf" ],
  published_date: ISODate("2010-09-24"),
  pages: 216,
  language: "English",
  categories: [ Programming, Databases, MongoDB ]
}

book = {
  title: "MySQL: The Definitive Guide",
  authors: [ ”Michael Kofler" ],
  published_date: ISODate("2010-09-24"),
  pages: 216,
  language: "English",
  parent: "MongoDB",
  ancestors: [ Programming, Databases, MongoDB ]
}
An Example
Queues
Book Document

book = {
  _id: 123456789,
  title: "MongoDB: The Definitive Guide",
  authors: [ "Kristina Chodorow", "Mike Dirolf" ],
  published_date: ISODate("2010-09-24"),
  pages: 216,
  language: "English",
  available: 3
}

db.books.findAndModify({
   query: { _id: 123456789, available: { "$gt": 0 } },
   update: { $inc: { available: -1 } }
})
Questions?
Jared Rosoff
Technical Director

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Webinar: Schema Design

  • 2. Agenda • Working with documents • Evolving a Schema • Queries and Indexes • Common Patterns
  • 3. RDBMS MongoDB Database ➜ Database Table ➜ Collection Row ➜ Document Index ➜ Index Join ➜ Embedded Document Foreign Key ➜ Reference Terminology
  • 11. Schema Design Considerations • How do we manipulate the data? – Dynamic Ad-Hoc Queries – Atomic Updates – Map Reduce • What are the access patterns of the application? – Read/Write Ratio – Types of Queries / Updates – Data life-cycle and growth rate
  • 12. Data Manipulation • Conditional Query Operators – Scalar: $ne, $mod, $exists, $type, $lt, $lte, $gt, $gte, $ne – Vector: $in, $nin, $all, $size • Atomic Update Operators – Scalar: $inc, $set, $unset – Vector: $push, $pop, $pull, $pushAll, $pullAll, $addToSet
  • 13. Data Access • Flexible Schemas • Ability to embed complex data structures • Secondary Indexes • Multi-Key Indexes • Aggregation Framework – $project, $match, $limit, $skip, $sort, $group, $unwind • No Joins
  • 15. Library Management Application • Patrons • Books • Authors • Publishers
  • 16. An Example One to One Relations
  • 17. Modeling Patrons patron = { patron = { _id: "joe" _id: "joe" name: "Joe Bookreader” name: "Joe Bookreader", } address: { street: "123 Fake St. ", address = { city: "Faketon", patron_id = "joe", state: "MA", street: "123 Fake St. ", city: "Faketon", zip: 12345 state: "MA", } zip: 12345 } }
  • 18. One to One Relations • Mostly the same as the relational approach • Generally good idea to embed “contains” relationships • Document model provides a holistic representation of objects
  • 19. An Example One To Many Relations
  • 20. Modeling Patrons patron = { _id: "joe" name: "Joe Bookreader", join_date: ISODate("2011-10-15"), addresses: [ {street: "1 Vernon St.", city: "Newton", state: "MA", …}, {street: "52 Main St.", city: "Boston", state: "MA", …}, ] }
  • 21. Publishers and Books • Publishers put out many books • Books have one publisher
  • 22. Book MongoDB: The Definitive Guide, By Kristina Chodorow and Mike Dirolf Published: 9/24/2010 Pages: 216 Language: English Publisher: O’Reilly Media, CA
  • 23. Modeling Books – Embedded Publisher book = { title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ] published_date: ISODate("2010-09-24"), pages: 216, language: "English", publisher: { name: "O’Reilly Media", founded: "1980", location: "CA" } }
  • 24. Modeling Books & Publisher Relationship publisher = { name: "O’Reilly Media", founded: "1980", location: "CA" } book = { title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ] published_date: ISODate("2010-09-24"), pages: 216, language: "English" }
  • 25. Publisher _id as a Foreign Key publisher = { _id: "oreilly", name: "O’Reilly Media", founded: "1980", location: "CA" } book = { title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ] published_date: ISODate("2010-09-24"), pages: 216, language: "English", publisher_id: "oreilly" }
  • 26. Book _id as a Foreign Key publisher = { name: "O’Reilly Media", founded: "1980", location: "CA" books: [ "123456789", ... ] } book = { _id: "123456789", title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ] published_date: ISODate("2010-09-24"), pages: 216, language: "English" }
  • 27. Where do you put the foreign Key? • Array of books inside of publisher – Makes sense when many means a handful of items – Useful when items have bound on potential growth • Reference to single publisher on books – Useful when items have unbounded growth (unlimited # of books) • SQL doesn’t give you a choice, no arrays
  • 28. Another Example One to Many Relations
  • 29. Books and Patrons • Book can be checked out by one Patron at a time • Patrons can check out many books (but not 1000s)
  • 30. Modeling Checkouts patron = { _id: "joe" name: "Joe Bookreader", join_date: ISODate("2011-10-15"), address: { ... } } book = { _id: "123456789" title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ], ... }
  • 31. Modeling Checkouts patron = { _id: "joe" name: "Joe Bookreader", join_date: ISODate("2011-10-15"), address: { ... }, checked_out: [ { _id: "123456789", checked_out: "2012-10-15" }, { _id: "987654321", checked_out: "2012-09-12" }, ... ] }
  • 32. De-normalization Provides data locality De-normalize for speed
  • 33. Modeling Checkouts -- de- normalized patron = { _id: "joe" name: "Joe Bookreader", join_date: ISODate("2011-10-15"), address: { ... }, checked_out: [ { _id: "123456789", title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ], checked_out: ISODate("2012-10-15") }, { _id: "987654321" title: "MongoDB: The Scaling Adventure", ... }, ... ] }
  • 34. Referencing vs. Embedding • Embedding is a bit like pre-joined data • Document level ops are easy for server to handle • Embed when the “many” objects always appear with (viewed in the context of) their parents. • Reference when you need more flexibility
  • 35. An Example Single Table Inheritance
  • 36. Single Table Inheritance book = { title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ] published_date: ISODate("2010-09-24"), kind: loanable locations: [ ... ] pages: 216, language: "English", publisher: { name: "O’Reilly Media", founded: "1980", location: "CA" } }
  • 37. An Example Many to Many Relations
  • 39. Books and Authors book = { title: "MongoDB: The Definitive Guide", authors = [ { _id: "kchodorow", name: "K-Awesome” }, { _id: "mdirolf", name: "Batman Mike” } ] published_date: ISODate("2010-09-24"), pages: 216, language: "English" } author = { _id: "kchodorow", name: "Kristina Chodorow", hometown: "New York" }
  • 41. Parent Links book = { title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ], published_date: ISODate("2010-09-24"), pages: 216, language: "English", category: "MongoDB" } category = { _id: MongoDB, parent: Databases } category = { _id: Databases, parent: Programming }
  • 42. Child Links book = { _id: 123456789, title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ], published_date: ISODate("2010-09-24"), pages: 216, language: "English" } category = { _id: MongoDB, children: [ 123456789, … ] } category = { _id: Databases, children: [ MongoDB, Postgres } category = { _id: Programming, children: [ DB, Languages ] }
  • 43. Modeling 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)
  • 44. Array of Ancestors book = { title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ], published_date: ISODate("2010-09-24"), pages: 216, language: "English", categories: [ Programming, Databases, MongoDB ] } book = { title: "MySQL: The Definitive Guide", authors: [ ”Michael Kofler" ], published_date: ISODate("2010-09-24"), pages: 216, language: "English", parent: "MongoDB", ancestors: [ Programming, Databases, MongoDB ] }
  • 46. Book Document book = { _id: 123456789, title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ], published_date: ISODate("2010-09-24"), pages: 216, language: "English", available: 3 } db.books.findAndModify({ query: { _id: 123456789, available: { "$gt": 0 } }, update: { $inc: { available: -1 } } })

Editor's Notes

  1. In the filing cabinet model, the patient’s x-rays, checkups, and allergies are stored in separate drawers and pulled together (like an RDBMS)In the file folder model, we store all of the patient information in a single folder (like MongoDB)
  2. Flexibility – Ability to represent rich data structuresPerformance – Benefit from data locality
  3. Concrete example of typical blog in typical relational normalized form
  4. Concrete example of typical blog using a document oriented de-normalized approach
  5. Tools for data manipulation
  6. Tools for data access
  7. Slow to get address data every time you query for a user. Requires an extra operation.
  8. Patron may have two addresses, in this case, you would need a separate table in a relation databaseWith MongoDB, you simply start storing the address field as an arrayOnly patrons which have multiple addresses could have this schema!No migration necessary! but Caution: Additional application logic required!
  9. Publisher is repeated for every book, data duplication!
  10. Publisher is better being a separate entity and having its own collection.
  11. Now to create a relation between the two entities, you can choose to reference the publisher from the book document.This is similar to the relational approach for this very same problem.
  12. OR: because we are using MongoDB and documents can have arrays you can choose to model the relation by creating and maintaining an array of books within each publisher entity.Careful with mutable, growing arrays. See next slide.
  13. Costly for a small number of books because to get the publisher
  14. And data locality provides speed
  15. Book’s kind attribute could be local or loanableNote that we have locations for loanable books but not for localNote that these two separate schemas can co-exist (loanable books / local books are both books)
  16. Kristine to update this graphic at some point
  17. Authors often use pseudonyms for a book even though it’s the same individualTo get books by a particular author: - get the author - get books that have that author id in array