Entity Relationships in a Document Database at CouchConf Boston

Unlike relational databases, document databases like CouchDB and Couchbase do not directly support entity relationships. This talk will explore patterns of modeling one-to-many and many-to-many entity relationships in a document database. These patterns include using an embedded JSON array, relating documents using identifiers, using a list of keys, and using relationship documents.

Transcript

1. Entity Relationships in a Document Database MapReduce Views for SQL Users
2. Entity: An object de ned by its identity and a thread of continuity[1] 1. "Entity" Domain-driven Design Community <http://domaindrivendesign.org/node/109>.
3. Entity Relationship Model
4. Join vs. Collation
5. SELECT `publisher`.`id`, `publisher`.`name`, `book`.`title` FROM `publisher` FULL OUTER JOIN `book` ON `publisher`.`id` = `book`.`publisher_id` ORDER BY `publisher`.`id`, `book`.`title`; SQL Query Joining Publishers and Books
6. Joined Result Set publisher.id publisher.name book.title oreilly O'Reilly Media Building iPhone Apps with HTML, CSS, and JavaScript oreilly O'Reilly Media CouchDB: The Definitive Guide oreilly O'Reilly Media DocBook: The Definitive Guide oreilly O'Reilly Media RESTful Web Services
7. Joined Result Set publisher.id publisher.name book.title oreilly O'Reilly Media Building iPhone Apps with HTML, CSS, and JavaScript oreilly O'Reilly Media CouchDB: The Definitive Guide oreilly O'Reilly Media DocBook: The Definitive Guide oreilly O'Reilly Media RESTful Web Services Publisher (“left”)
8. Joined Result Set publisher.id publisher.name book.title oreilly O'Reilly Media Building iPhone Apps with HTML, CSS, and JavaScript oreilly O'Reilly Media CouchDB: The Definitive Guide oreilly O'Reilly Media DocBook: The Definitive Guide oreilly O'Reilly Media RESTful Web Services Publisher (“left”) Book “right”
9. Collated Result Set key id value ["oreilly",0] "oreilly" "O'Reilly Media" ["oreilly",1] "oreilly" "Building iPhone Apps with HTML, CSS, and JavaScript" ["oreilly",1] "oreilly" "CouchDB: The Definitive Guide" ["oreilly",1] "oreilly" "DocBook: The Definitive Guide" ["oreilly",1] "oreilly" "RESTful Web Services"
10. Collated Result Set key id value ["oreilly",0] "oreilly" "O'Reilly Media" ["oreilly",1] "oreilly" "Building iPhone Apps with HTML, CSS, and JavaScript" ["oreilly",1] "oreilly" "CouchDB: The Definitive Guide" ["oreilly",1] "oreilly" "DocBook: The Definitive Guide" ["oreilly",1] "oreilly" "RESTful Web Services" Publisher
11. Collated Result Set key id value ["oreilly",0] "oreilly" "O'Reilly Media" ["oreilly",1] "oreilly" "Building iPhone Apps with HTML, CSS, and JavaScript" ["oreilly",1] "oreilly" "CouchDB: The Definitive Guide" ["oreilly",1] "oreilly" "DocBook: The Definitive Guide" ["oreilly",1] "oreilly" "RESTful Web Services" Publisher Books
12. View Result Sets Made up of columns and rows Every row has the same three columns: • key • id • value Columns can contain a mixture of logical data types
13. One to Many Relationships
14. Embedded Entities: Nest related entities within a document
15. A single document represents the “one” entity Nested entities (JSON Array) represents the “many” entities Simplest way to create a one to many relationship Embedded Entities
16. Example: Publisher with Nested Books { "_id":"oreilly", "collection":"publisher", "name":"O'Reilly Media", "books":[ { "title":"CouchDB: The Definitive Guide" }, { "title":"RESTful Web Services" }, { "title":"DocBook: The Definitive Guide" }, { "title":"Building iPhone Apps with HTML, CSS, and JavaScript" } ] }
17. function(doc) { if ("publisher" == doc.collection) { emit([doc._id, 0], doc.name); for (var i in doc.books) { emit([doc._id, 1], doc.books[i].title); } } } Map Function
18. Result Set key id value ["oreilly",0] "oreilly" "O'Reilly Media" ["oreilly",1] "oreilly" "Building iPhone Apps with HTML, CSS, and JavaScript" ["oreilly",1] "oreilly" "CouchDB: The Definitive Guide" ["oreilly",1] "oreilly" "DocBook: The Definitive Guide" ["oreilly",1] "oreilly" "RESTful Web Services"
19. Only works if there aren’t a large number of related entities: • Too many nested entities can result in very large documents • Slow to transfer between client and server • Unwieldy to modify • Time-consuming to index Limitations
20. Related Documents: Reference an entity by its identi er
21. A document representing the “one” entity Separate documents for each “many” entity Each “many” entity references its related “one” entity by the “one” entity’s document identi er Makes for smaller documents Reduces the probability of document update con icts Related Documents
22. Example: Publisher { "_id":"oreilly", "collection":"publisher", "name":"O'Reilly Media" }
23. Example: Related Book { "_id":"9780596155896", "collection":"book", "title":"CouchDB: The Definitive Guide", "publisher":"oreilly" }
24. Map Function function(doc) { if ("publisher" == doc.collection) { emit([doc._id, 0], doc.name); } if ("book" == doc.collection) { emit([doc.publisher, 1], doc.title); } }
25. Result Set key id value ["oreilly",0] "oreilly" "O'Reilly Media" ["oreilly",1] "9780596155896" "CouchDB: The Definitive Guide" ["oreilly",1] "9780596529260" "RESTful Web Services" ["oreilly",1] "9780596805791" "Building iPhone Apps with HTML, CSS, and JavaScript" ["oreilly",1] "9781565925809" "DocBook: The Definitive Guide"
26. When retrieving the entity on the “right” side of the relationship, one cannot include any data from the entity on the “left” side of the relationship without the use of an additional query Only works for one to many relationships Limitations
27. Many to Many Relationships
28. List of Keys: Reference entities by their identi ers
29. A document representing each “many” entity on the “left” side of the relationship Separate documents for each “many” entity on the “right” side of the relationship Each “many” entity on the “right” side of the relationship maintains a list of document identi ers for its related “many” entities on the “left” side of the relationship List of Keys
30. Books and Related Authors
31. Example: Book { "_id":"9780596805029", "collection":"book", "title":"DocBook 5: The Definitive Guide" }
32. Example: Book { "_id":"9781565920514", "collection":"book", "title":"Making TeX Work" }
33. Example: Book { "_id":"9781565925809", "collection":"book", "title":"DocBook: The Definitive Guide" }
34. Example: Author { "_id":"muellner", "collection":"author", "name":"Leonard Muellner", "books":[ "9781565925809" ] }
35. Example: Author { "_id":"walsh", "collection":"author", "name":"Norman Walsh", "books":[ "9780596805029", "9781565925809", "9781565920514" ] }
36. Map Function function(doc) { if ("book" == doc.collection) { emit([doc._id, 0], doc.title); } if ("author" == doc.collection) { for (var i in doc.books) { emit([doc.books[i], 1], doc.name); } } }
37. Result Set key id value ["9780596805029",0] "9780596805029" "DocBook 5: The Definitive Guide" ["9780596805029",1] "walsh" "Norman Walsh" ["9781565920514",0] "9781565920514" "Making TeX Work" ["9781565920514",1] "walsh" "Norman Walsh" ["9781565925809",0] "9781565925809" "DocBook: The Definitive Guide" ["9781565925809",1] "muellner" "Leonard Muellner" ["9781565925809",1] "walsh" "Norman Walsh"
38. Authors and Related Books
39. function(doc) { if ("author" == doc.collection) { emit([doc._id, 0], doc.name); for (var i in doc.books) { emit([doc._id, 1], {"_id":doc.books[i]}); } } } Map Function
40. Result Set key id value ["muellner",0] "muellner" "Leonard Muellner" ["muellner",1] "muellner" {"_id":"9781565925809"} ["walsh",0] "walsh" "Norman Walsh" ["walsh",1] "walsh" {"_id":"9780596805029"} ["walsh",1] "walsh" {"_id":"9781565920514"} ["walsh",1] "walsh" {"_id":"9781565925809"}
41. Including Docs include_docs=true key id value doc (truncated) ["muellner",0] "muellner" … {"name":"Leonard Muellner"} ["muellner",1] "muellner" … {"title":"DocBook: The Definitive Guide"} ["walsh",0] "walsh" … {"name":"Norman Walsh"} ["walsh",1] "walsh" … {"title":"DocBook 5: The Definitive Guide"} ["walsh",1] "walsh" … {"title":"Making TeX Work"} ["walsh",1] "walsh" … {"title":"DocBook: The Definitive Guide"}
42. Or, we can reverse the references…
43. Example: Author { "_id":"muellner", "collection":"author", "name":"Leonard Muellner" }
44. Example: Author { "_id":"walsh", "collection":"author", "name":"Norman Walsh" }
45. Example: Book { "_id":"9780596805029", "collection":"book", "title":"DocBook 5: The Definitive Guide", "authors":[ "walsh" ] }
46. Example: Book { "_id":"9781565920514", "collection":"book", "title":"Making TeX Work", "authors":[ "walsh" ] }
47. Example: Book { "_id":"9781565925809", "collection":"book", "title":"DocBook: The Definitive Guide", "authors":[ "muellner", "walsh" ] }
48. Map Function function(doc) { if ("author" == doc.collection) { emit([doc._id, 0], doc.name); } if ("book" == doc.collection) { for (var i in doc.authors) { emit([doc.authors[i], 1], doc.title); } } }
49. Result Set key id value ["muellner",0] "muellner" "Leonard Muellner" ["muellner",1] "9781565925809" "DocBook: The Definitive Guide" ["walsh",0] "walsh" "Norman Walsh" ["walsh",1] "9780596805029" "DocBook 5: The Definitive Guide" ["walsh",1] "9781565920514" "Making TeX Work" ["walsh",1] "9781565925809" "DocBook: The Definitive Guide"
50. Queries from the “right” side of the relationship cannot include any data from entities on the “left” side of the relationship (without the use of include_docs) A document representing an entity with lots of relationships could become quite large Limitations
51. Relationship Documents: Create a document to represent each individual relationship
52. A document representing each “many” entity on the “left” side of the relationship Separate documents for each “many” entity on the “right” side of the relationship Neither the “left” nor “right” side of the relationship contain any direct references to each other For each distinct relationship, a separate document includes the document identi ers for both the “left” and “right” sides of the relationship Relationship Documents
53. Example: Book { "_id":"9780596805029", "collection":"book", "title":"DocBook 5: The Definitive Guide" }
54. Example: Book { "_id":"9781565920514", "collection":"book", "title":"Making TeX Work" }
55. Example: Book { "_id":"9781565925809", "collection":"book", "title":"DocBook: The Definitive Guide" }
56. Example: Author { "_id":"muellner", "collection":"author", "name":"Leonard Muellner" }
57. Example: Author { "_id":"walsh", "collection":"author", "name":"Norman Walsh" }
58. Example: Relationship Document { "_id":"44005f2c", "collection":"book-author", "book":"9780596805029", "author":"walsh" }
59. Example: Relationship Document { "_id":"44005f72", "collection":"book-author", "book":"9781565920514", "author":"walsh" }
60. Example: Relationship Document { "_id":"44006720", "collection":"book-author", "book":"9781565925809", "author":"muellner" }
61. Example: Relationship Document { "_id":"44006b0d", "collection":"book-author", "book":"9781565925809", "author":"walsh" }
62. Books and Related Authors
63. function(doc) { if ("book" == doc.collection) { emit([doc._id, 0], doc.title); } if ("book-author" == doc.collection) { emit([doc.book, 1], {"_id":doc.author}); } } Map Function
64. Result Set key id value ["9780596805029",0] "9780596805029" "DocBook 5: The Definitive Guide" ["9780596805029",1] "44005f2c" {"_id":"walsh"} ["9781565920514",0] "9781565920514" "Making TeX Work" ["9781565920514",1] "44005f72" {"_id":"walsh"} ["9781565925809",0] "9781565925809" "DocBook: The Definitive Guide" ["9781565925809",1] "44006720" {"_id":"muellner"} ["9781565925809",1] "44006b0d" {"_id":"walsh"}
65. Including Docs include_docs=true key id value doc (truncated) ["9780596805029",0] … … {"title":"DocBook 5: The Definitive Guide"} ["9780596805029",1] … … {"name":"Norman Walsh"} ["9781565920514",0] … … {"title":"Making TeX Work"} ["9781565920514",1] … … {"author","name":"Norman Walsh"} ["9781565925809",0] … … {"title":"DocBook: The Definitive Guide"} ["9781565925809",1] … … {"name":"Leonard Muellner"} ["9781565925809",1] … … {"name":"Norman Walsh"}
66. Authors and Related Books
67. function(doc) { if ("author" == doc.collection) { emit([doc._id, 0], doc.name); } if ("book-author" == doc.collection) { emit([doc.author, 1], {"_id":doc.book}); } } Map Function
68. Result Set key id value ["muellner",0] "muellner" "Leonard Muellner" ["muellner",1] "44006720" {"_id":"9781565925809"} ["walsh",0] "walsh" "Norman Walsh" ["walsh",1] "44005f2c" {"_id":"9780596805029"} ["walsh",1] "44005f72" {"_id":"9781565920514"} ["walsh",1] "44006b0d" {"_id":"9781565925809"}
69. Including Docs include_docs=true key id value doc (truncated) ["muellner",0] … … {"name":"Leonard Muellner"} ["muellner",1] … … {"title":"DocBook: The Definitive Guide"} ["walsh",0] … … {"name":"Norman Walsh"} ["walsh",1] … … {"title":"DocBook 5: The Definitive Guide"} ["walsh",1] … … {"title":"Making TeX Work"} ["walsh",1] … … {"title":"DocBook: The Definitive Guide"}
70. Queries can only contain data from the “left” or “right” side of the relationship (without the use of include_docs) Maintaining relationship documents may require more work Limitations
71. Final Thoughts
72. Document databases have no tables (and therefore no columns) Indexes (views) are queried directly, instead of being used to optimize more generalized queries Result set columns can contain a mix of logical data types No built-in concept of relationships between documents Related entities can be embedded in a document, referenced from a document, or both Document Databases Compared to Relational Databases
73. Caveats No referential integrity No atomic transactions across document boundaries Some patterns may involve denormalized (i.e. redundant) data Data inconsistencies are inevitable (i.e. eventual consistency) Consider the implications of replication—what may seem consistent with one database may not be consistent across nodes (e.g. referencing entities that don’t yet exist on the node)
74. Additional Techniques Use the startkey and endkey parameters to retrieve one entity and its related entities: startkey=["9781565925809"]&endkey=["9781565925809",{}] De ne a reduce function and use grouping levels Use UUIDs rather than natural keys for better performance Use the bulk document API when writing Relationship Documents When using the List of Keys or Relationship Documents patterns, denormalize data so that you can have data from the “right” and “left” side of the relationship within your query results
75. Cheat Sheet One to Many Many to Many <= N* Relations > N* Relations Embedded Entities Related Documents List of Keys Relationship Documents ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ * where N is a large number for your system
76. http://oreilly.com/catalog/9781449303129/ http://oreilly.com/catalog/9781449303433/
77. Thank You @BradleyHolt http://bradley-holt.com bradley.holt@foundline.com Copyright © 2011-2012 Bradley Holt. All rights reserved.