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Schema Design
Software Engineer, MongoDB
Craig Wilson
#MongoDBDays
@craiggwilson
All application development is
Schema Design
Success comes from a
Proper Data Structure
Terminology
RDBMS MongoDB
Database ➜ Database
Table ➜ Collection
Row ➜ Document
Index ➜ Index
Join ➜ Embedding & Linking
Working with Documents
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
authors: [
{ _id: "kchodorow", name: "Kristina Chodorow“ },
{ _id: "mdirold", name: “Mike Dirolf“ }
],
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English",
publisher: {
name: "O’Reilly Media",
founded: "1980",
location: "CA"
}
}
What is a Document?
Traditional Schema Design
Focus on Data Storage
Document Schema Design
Focus on Data Usage
Traditional Schema Design
What answers do I have?
Document Schema Design
What questions do I
have?
Schema Design By Example
Library Management Application
• Patrons/Users
• Books
• Authors
• Publishers
Question:
What is a Patron’s
Address?
> patron = db.patrons.find({ _id : “joe” })
{
_id: "joe“,
name: "Joe Bookreader”
}
> address = db.addresses.find({ _id : “joe” })
{
_id: "joe“,
street: "123 Fake St. ",
city: "Faketon",
state: "MA",
zip: 12345
}
A Patron and their Address
> patron = db.patrons.find({ _id : “joe” })
{
_id: "joe",
name: "Joe Bookreader",
address: {
street: "123 Fake St. ",
city: "Faketon",
state: "MA",
zip: 12345
}
}
A Patron and their Address
One-to-One Relationships
• “Belongs to” relationships are often embedded.
• Holistic representation of entities with their
embedded attributes and relationships.
• Optimized for read performance
Question:
What are a Patron’s
Addresses?
> patron = db.patrons.find({ _id : “bob” })
{
_id: “bob",
name: “Bob Knowitall",
addresses: [
{street: "1 Vernon St.", city: "Newton", …},
{street: "52 Main St.", city: "Boston", …},
]
}
A Patron and their Addresses
> patron = db.patrons.find({ _id : “bob” })
{
_id: “bob",
name: “Bob Knowitall",
addresses: [
{street: "1 Vernon St.", city: "Newton", …},
{street: "52 Main St.", city: "Boston", …},
]
}
> patron = db.patrons.find({ _id : “joe” })
{
_id: "joe",
name: "Joe Bookreader",
address: { street: "123 Fake St. ", city: "Faketon", …}
}
A Patron and their Addresses
Migration Possibilities
• Migrate all documents when the schema changes.
• Migrate On-Demand
– As we pull up a patron’s document, we make the change.
– Any patrons that never come into the library never get
updated.
• Leave it alone
– As long as the application knows about both types…
Question:
Who is the publisher of
this book?
Book
MongoDB: The Definitive Guide,
By Kristina Chodorow and Mike Dirolf
Published: 9/24/2010
Pages: 216
Language: English
Publisher: O’Reilly Media, CA
> book = db.books.find({ _id : “123” })
{
_id: “123”,
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"
}
}
Book with embedded Publisher
Book with embedded Publisher
• Optimized for read performance of Books
• Other queries become difficult
Question:
Who are all the
publishers in the
system?
> publishers = db.publishers.find()
{
_id: “oreilly”,
name: "O’Reilly Media",
founded: "1980",
location: "CA"
}
{
_id: “penguin”,
name: “Penguin”,
founded: “1983”,
location: “CA”
}
All Publishers
> book = db.books.find({ _id: “123” })
{
_id: “123”,
publisher_id: “oreilly”,
title: "MongoDB: The Definitive Guide",
authors: [ "Kristina Chodorow", "Mike Dirolf" ],
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English"
}
> db.publishers.find({ _id : book.publisher_id })
{
_id: “oreilly”,
name: "O’Reilly Media",
founded: "1980",
location: "CA"
}
Book with linked Publisher
Question:
What are all the books a
publisher has
published?
> publisher = db.publishers.find({ _id : “oreilly” })
{
_id: “oreilly”,
name: "O’Reilly Media",
founded: "1980",
location: "CA“,
books: [“123”,…]
}
> books = db.books.find({ _id: { $in : publisher.books } })
Publisher with linked Books
Question:
Who are the authors of a
given book?
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English“,
authors: [“kchodorow”, “mdirolf”]
}
> authors = db.authors.find({ _id : { $in : book.authors } })
{ _id: "kchodorow", name: "Kristina Chodorow”, hometown: … }
{ _id: “mdirolf", name: “Mike Dirolf“, hometown: … }
Books with linked Authors
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
published_date: ISODate("2010-09-24"),
pages: 216,
language: "English“,
authors: [
{ id: "kchodorow", name: "Kristina Chodorow” },
{ id: "mdirolf", name: "Mike Dirolf” }
]
}
Books with linked Authors
Question:
What are all the books
an author has written?
> authors = db.authors.find({ _id : “kchodorow” })
{
_id: "kchodorow",
name: "Kristina Chodorow",
hometown: "Cincinnati",
books: [ {id: “123”, title : "MongoDB: The Definitive Guide“ } ]
}
Authors with linked Books
> authors = db.authors.find({ _id : “kchodorow” })
{
_id: "kchodorow",
name: "Kristina Chodorow",
hometown: "Cincinnati",
books: [ {id: “123”, title : "MongoDB: The Definitive Guide“ } ]
}
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
authors: [
{ id: "kchodorow", name: "Kristina Chodorow” },
{ id: "mdirolf", name: "Mike Dirolf” }
]
}
Links on both Authors and Books
Linking vs. Embedding
• Embedding
– Great for read performance
– Writes can be slow
– Data integrity needs to be managed
• Linking
– Flexible
– Data integrity is built-in
– Work is done during reads
Question:
What are all the books
about databases?
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
category: “MongoDB”
}
> categories = db.categories.find({ _id: “MongoDB” })
{
_id: “MongoDB”,
parent: “Databases”
}
Categories as Documents
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
categories: [“MongoDB”, “Databases”, “Programming”]
}
> db.books.find({ categories: “Databases” })
Categories as an Array
> book = db.books.find({ _id : “123” })
{
_id: “123”,
title: "MongoDB: The Definitive Guide",
category: “Programming/Databases/MongoDB”
}
> db.books.find({ category: ^Programming/Databases/* })
Categories as a Path
Conclusion
• Schema design is different in MongoDB
• Basic data design principals stay the same
• Focus on how an application accesses/manipulates
data
• Evolve the schema to meet requirements as they
change
Schema Design
Software Engineer, 10gen
Craig Wilson
#MongoDBDays
@craiggwilson

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Schema Design

  • 1. Schema Design Software Engineer, MongoDB Craig Wilson #MongoDBDays @craiggwilson
  • 2. All application development is Schema Design
  • 3. Success comes from a Proper Data Structure
  • 4. Terminology RDBMS MongoDB Database ➜ Database Table ➜ Collection Row ➜ Document Index ➜ Index Join ➜ Embedding & Linking
  • 6. { _id: “123”, title: "MongoDB: The Definitive Guide", authors: [ { _id: "kchodorow", name: "Kristina Chodorow“ }, { _id: "mdirold", name: “Mike Dirolf“ } ], published_date: ISODate("2010-09-24"), pages: 216, language: "English", publisher: { name: "O’Reilly Media", founded: "1980", location: "CA" } } What is a Document?
  • 9. Traditional Schema Design What answers do I have?
  • 10. Document Schema Design What questions do I have?
  • 11. Schema Design By Example
  • 12. Library Management Application • Patrons/Users • Books • Authors • Publishers
  • 13. Question: What is a Patron’s Address?
  • 14. > patron = db.patrons.find({ _id : “joe” }) { _id: "joe“, name: "Joe Bookreader” } > address = db.addresses.find({ _id : “joe” }) { _id: "joe“, street: "123 Fake St. ", city: "Faketon", state: "MA", zip: 12345 } A Patron and their Address
  • 15. > patron = db.patrons.find({ _id : “joe” }) { _id: "joe", name: "Joe Bookreader", address: { street: "123 Fake St. ", city: "Faketon", state: "MA", zip: 12345 } } A Patron and their Address
  • 16. One-to-One Relationships • “Belongs to” relationships are often embedded. • Holistic representation of entities with their embedded attributes and relationships. • Optimized for read performance
  • 17. Question: What are a Patron’s Addresses?
  • 18. > patron = db.patrons.find({ _id : “bob” }) { _id: “bob", name: “Bob Knowitall", addresses: [ {street: "1 Vernon St.", city: "Newton", …}, {street: "52 Main St.", city: "Boston", …}, ] } A Patron and their Addresses
  • 19. > patron = db.patrons.find({ _id : “bob” }) { _id: “bob", name: “Bob Knowitall", addresses: [ {street: "1 Vernon St.", city: "Newton", …}, {street: "52 Main St.", city: "Boston", …}, ] } > patron = db.patrons.find({ _id : “joe” }) { _id: "joe", name: "Joe Bookreader", address: { street: "123 Fake St. ", city: "Faketon", …} } A Patron and their Addresses
  • 20. Migration Possibilities • Migrate all documents when the schema changes. • Migrate On-Demand – As we pull up a patron’s document, we make the change. – Any patrons that never come into the library never get updated. • Leave it alone – As long as the application knows about both types…
  • 21. Question: Who is the publisher of this book?
  • 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. > book = db.books.find({ _id : “123” }) { _id: “123”, 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" } } Book with embedded Publisher
  • 24. Book with embedded Publisher • Optimized for read performance of Books • Other queries become difficult
  • 25. Question: Who are all the publishers in the system?
  • 26. > publishers = db.publishers.find() { _id: “oreilly”, name: "O’Reilly Media", founded: "1980", location: "CA" } { _id: “penguin”, name: “Penguin”, founded: “1983”, location: “CA” } All Publishers
  • 27. > book = db.books.find({ _id: “123” }) { _id: “123”, publisher_id: “oreilly”, title: "MongoDB: The Definitive Guide", authors: [ "Kristina Chodorow", "Mike Dirolf" ], published_date: ISODate("2010-09-24"), pages: 216, language: "English" } > db.publishers.find({ _id : book.publisher_id }) { _id: “oreilly”, name: "O’Reilly Media", founded: "1980", location: "CA" } Book with linked Publisher
  • 28. Question: What are all the books a publisher has published?
  • 29. > publisher = db.publishers.find({ _id : “oreilly” }) { _id: “oreilly”, name: "O’Reilly Media", founded: "1980", location: "CA“, books: [“123”,…] } > books = db.books.find({ _id: { $in : publisher.books } }) Publisher with linked Books
  • 30. Question: Who are the authors of a given book?
  • 31. > book = db.books.find({ _id : “123” }) { _id: “123”, title: "MongoDB: The Definitive Guide", published_date: ISODate("2010-09-24"), pages: 216, language: "English“, authors: [“kchodorow”, “mdirolf”] } > authors = db.authors.find({ _id : { $in : book.authors } }) { _id: "kchodorow", name: "Kristina Chodorow”, hometown: … } { _id: “mdirolf", name: “Mike Dirolf“, hometown: … } Books with linked Authors
  • 32. > book = db.books.find({ _id : “123” }) { _id: “123”, title: "MongoDB: The Definitive Guide", published_date: ISODate("2010-09-24"), pages: 216, language: "English“, authors: [ { id: "kchodorow", name: "Kristina Chodorow” }, { id: "mdirolf", name: "Mike Dirolf” } ] } Books with linked Authors
  • 33. Question: What are all the books an author has written?
  • 34. > authors = db.authors.find({ _id : “kchodorow” }) { _id: "kchodorow", name: "Kristina Chodorow", hometown: "Cincinnati", books: [ {id: “123”, title : "MongoDB: The Definitive Guide“ } ] } Authors with linked Books
  • 35. > authors = db.authors.find({ _id : “kchodorow” }) { _id: "kchodorow", name: "Kristina Chodorow", hometown: "Cincinnati", books: [ {id: “123”, title : "MongoDB: The Definitive Guide“ } ] } > book = db.books.find({ _id : “123” }) { _id: “123”, title: "MongoDB: The Definitive Guide", authors: [ { id: "kchodorow", name: "Kristina Chodorow” }, { id: "mdirolf", name: "Mike Dirolf” } ] } Links on both Authors and Books
  • 36. Linking vs. Embedding • Embedding – Great for read performance – Writes can be slow – Data integrity needs to be managed • Linking – Flexible – Data integrity is built-in – Work is done during reads
  • 37. Question: What are all the books about databases?
  • 38. > book = db.books.find({ _id : “123” }) { _id: “123”, title: "MongoDB: The Definitive Guide", category: “MongoDB” } > categories = db.categories.find({ _id: “MongoDB” }) { _id: “MongoDB”, parent: “Databases” } Categories as Documents
  • 39. > book = db.books.find({ _id : “123” }) { _id: “123”, title: "MongoDB: The Definitive Guide", categories: [“MongoDB”, “Databases”, “Programming”] } > db.books.find({ categories: “Databases” }) Categories as an Array
  • 40. > book = db.books.find({ _id : “123” }) { _id: “123”, title: "MongoDB: The Definitive Guide", category: “Programming/Databases/MongoDB” } > db.books.find({ category: ^Programming/Databases/* }) Categories as a Path
  • 41. Conclusion • Schema design is different in MongoDB • Basic data design principals stay the same • Focus on how an application accesses/manipulates data • Evolve the schema to meet requirements as they change
  • 42. Schema Design Software Engineer, 10gen Craig Wilson #MongoDBDays @craiggwilson

Hinweis der Redaktion

  1. Schema Design is very important; its impact on your application is pervasive. We call the “dynamic” nature of a schema in MongoDB an “Application Defined Schema”.
  2. Wrong data structure will hurt you. Proper data structure can make all the pieces fall into place.
  3. A document is JSON. A value can be an integer, string, document, array, array of documents, etc…
  4. Focus on the way we store our data, neglecting the way we use it.
  5. Focus on how we use our data, neglecting (sort-of) how we store it.
  6. Has all the answers, but none can be given in an optimal way. Has zero knowledge of your application’s known queries, use cases, or client-side data structures.
  7. Has all the answers, but also knows what questions are going to be asked. Takes advantage of known queries, use cases, and client-side data structures.
  8. Imagine a patron walks up to the counter and presents his/her library card to check out some books. The first thing a librarian might want to do is confirm the patron’s address so as to have a place to send the library police when the book isn’t returned in a timely manner.
  9. This is entirely doable, and might be advantageous in a number of other use cases. But since we want to lookup the patron and their address at the same time, this is inefficient as it requires 2 queries.
  10. Embedded directly into the patron document. Only 1 query is necessary. Holistic view of a patron.
  11. Read performance is optimized because we only need a single query and a single disk/memory hit. Write performance change is negligible.
  12. Business Requirements Change! A librarian want’s all the places his/her book might be hiding out, and having more addresses for a patron is more places to look.
  13. Now, just store addresses as an array. Embedded directly into the patron document. Only 1 query is necessary. Holistic view of a patron.
  14. Schema isn’t rigid, but dynamic. An application defines the schema, and having two ways to represent addresses is entirely possible.
  15. Duplicate publisher in every book that the publisher has published. Data duplication is OK because the publisher is immutable.
  16. Best way to figure out how something is going to perform is to measure.
  17. Still have the previous question, who is the publisher of this book? Takes 2 queries. Same problems that exist in traditional systems. Foreign keys, while keeping data integrity, tend to erase history.
  18. Unbounded arrays are BAD!
  19. Take advantage of data that’s immutable. Duplicate data is OK.
  20. Recursive search to find all books about databases.
  21. When a category hierarchy gets changed, all documents will need to be re-categorized. If one category name exists in multiple hierarchies, then further refinement would need to happen. Uses a multi-key index.
  22. When a category hierarchy gets changed, all documents will need to be re-categorized. Uses an index because of the anchored regular expression.