Webinar: Schema Design
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One of the challenges that comes with moving to MongoDB is figuring how to best model your data. While most developers have internalized the rules of thumb for designing schemas for relational databases, these rules don't always apply to MongoDB. The simple fact that documents can represent rich, schema-free data structures means that we have a lot of viable alternatives to the standard, normalized, relational model. Not only that, MongoDB has several unique features, such as atomic updates and indexed array keys, that greatly influence the kinds of schemas that make sense.
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Webinar: Schema Design
- 1. Schema Design Solutions Architect, MongoDB Jay Runkel #MongoDB
- 2. First a story: Once upon a time there was a medical records company…
- 8. • Schema Design Challenge • Modeling Relationships in MongoDB • An Example • General Recommendations Agenda
- 10. • Flexibility – Easily adapt to new requirements • Agility – Rapid application development • Scalability – Support large data and query volumes Schema Design Challenge
- 11. • How do we model data and relationships to ensure: –Flexibility –Agility –Scalability Schema Design Challenge
- 12. Schema Design: MongoDB vs. Relational
- 13. MongoDB Relational Collections Tables Documents Rows Data Use Data Storage What questions do I have? What answers do I have? MongoDB versus Relational
- 14. Attribute MongoDB Relational Storage N-dimensional Two-dimensional Field Values 0, 1, many, or embed Single value Query Any field or level Any field Schema Flexible Very structured Updates In line In place
- 15. With relational, this is hard Long development times Inflexible Doesn’t scale
- 16. Document model is much easier Shorter development times Flexible Scalable { "patient_id": "1177099", "first_name": "John", "last_name": "Doe", "middle_initial": "A", "dob": "2000-01-25", "gender": "Male", "blood_type": "B+", "address": "123 Elm St., Chicago, IL 59923", "height": "66", "weight": "110", "allergies": ["Nuts", "Penicillin", "Pet Dander"], "current_medications": [{"name": "Zoloft", "dosage": "2mg", "frequency": "daily", "route": "orally"}], "complaint" : [{"entered": "2000-11-03", "onset": "2000-11-03", "prob_desc": "", "icd" : 250.00, "status" : "Active"}, {"entered": "2000-02-04", "onset": "2000-02-04", "prob_desc": "in spite of regular exercise, ...", "icd" : 401.9, "status" : "Active"}], "diagnosis" : [{"visit" : "2005-07-22" , "narrative" : "Fractured femur", "icd" : "9999", "priority" : "Primary"}, {"visit" : "2005-07-22" , "narrative" : "Type II Diabetes", "icd" : "250.00", "priority" : "Secondary"}] }
- 18. Let’s model something together How about a business card?
- 19. Business Card
- 20. Address Book Entity-Relationship Contacts • name • company • title Addresses • type • street • city • state • zip_code Phones • type • number Emails • type • address Thumbnail s • mime_type • data Portraits • mime_type • data Groups • name N 1 N 1 N N N 1 1 1 11 Twitters • name • location • web • bio 1 1
- 22. Referencing Contact • name • company • title • phone Address • street • city • state • zip_code Use two collections with a reference Similar to relational
- 23. Contact • name • company • adress • Street • City • State • Zip • title • phone • address • street • city • State • zip_code Embedding Document Schema
- 24. Referencing Contacts { “_id”: 2, “name”: “Steven Jobs”, “title”:“VP, New Product Development”, “company”: “Apple Computer”, “phone”: “408-996-1010”, “address_id”: 1 } Addresses { “_id”: 1, “street”:“10260 Bandley Dr”, “city”: “Cupertino”, “state”:“CA”, “zip_code”:”95014”, “country”: “USA” }
- 25. Embedding Contacts { “_id”:2, “name”:“Steven Jobs”, “title”:“VP,New Product Development”, “company”: “AppleComputer”, “address”:{“street”: “10260 BandleyDr”, “city”: “Cupertino”, “state”: “CA”, “zip_code”:”95014”, “country”: “USA”}, “phone”: “408-996-1010” }
- 26. How are they different? Why? Contact • name • company • title • phone Address • street • city • state • zip_code Contact • name • company • adress • Street • City • State • Zip • title • phone • address • street • city • state • zip_code
- 27. Schema Flexibility { “name”: “StevenJobs”, “title”:“VP, NewProductDevelopment”, “company”: “AppleComputer”, “address”:{ “street”: 10260BandleyDr”, “city”: “Cupertino”, “state”: “CA”, “zip_code”:“95014” }, “phone”:“408-996-1010” } { “name”: “Larry Page, “url”:“http://google.com”, “title”:“CEO”, “company”: “Google!”, “address”:{ “street”: 555 Bryant, #106”, “city”: “PaloAlto”, “state”: “CA”, “zip_code”:“94301” }, “phone”: “650-330-0100” “fax”: ”650-330-1499” }
- 28. One to One Schema Design Choices contact twitter_id twitter1 1 contact twitter contact_id1 1 Redundant to track relationship on both sides May save a fetch? Contact twitter twitter 1
- 29. One to One: General Recommendations • Embed – Full contact info all at once – Parent-child relationship “contains” – No additional data duplication – Can query or index on embedded field • e.g., “twitter.name” • Exceptional cases… • Embedding results in large documents Contact twitter twitter 1
- 31. One to Many Schema Design Choices contact phone_ids: [ ] phone1 N contact phone contact_id1 N Redundant to track relationship on both sides Not possible in relational DBs Contact phones phoneN
- 32. One-to-many embedding vs. referencing { “name”: “Larry Page”, “url”: “http://google.com/”, “title”: “CEO”, “company”: “Google!”, “email”: “larry@google.com”, “address”: [{ “street”: “555 Bryant, #106”, “city”: “Palo Alto”, “state”: “CA”, “zip_code”: “94301” }] “phones”: [{“type”: “Office”, “number”: “650-618-1499”}, {“type”: “fax”, “number”: “650-330-0100”}] } { “name”: “Larry Page”, “url”: “http://google.com/”, “title”: “CEO”, “company”: “Google!”, “email”: “larry@google.com”, “address”: [“addr99”], “phones”: [“ph23”, “ph49”]} { “_id”: “addr99”, “street”: “555 Bryant, #106”, “city”: “Palo Alto”, “state”: “CA”, “zip_code”: “94301”} { “_id”: “ph23”, “type”: “Office”, “number”: “650-618-1499”}, { “_id”: “ph49”, “type”: “fax”, “number”: “650-330-0100”}
- 33. One to Many General Recommendation • Embed when possible – Full contact info all at once – Parent-children relationship “contains” – No additional data duplication – Can query or index on any field • e.g., { “phones.type”: “mobile” } • Exceptional cases… • Scaling: maximum document size is 16MB Contact phones phone N
- 35. Many to Many Traditional Relational Association Join table Contacts name company title phone Groups name GroupContacts group_id contact_id X Use arrays instead
- 36. Many to Many Schema Design Choices group contact_ids: [ ] contactN N group contact group_ids: [ ]N N Redundant to track relationship on both sides • Both references must be updated for consistency Redundant to track relationship on both sides • Duplicated data must be updated for consistency group contacts contact N contact groups group N
- 37. Many to Many General Recommendation • Use case determines whether to reference or embed: 1. Simple address book • Contact references groups 2. Corporate email groups • Group embeds contacts for performance • Exceptional cases – Scaling: maximum document size is 16MB – Scaling may affect performance and working set group contact group_ids: [ ]N N
- 38. Address Book Entity-Relationship Contacts • name • company • title Addresses • type • street • city • state • zip_code Phones • type • number Emails • type • address Thumbnail s • mime_type • data Portraits • mime_type • data Groups • name N 1 N 1 N N N 1 1 1 11 Twitters • name • location • web • bio 1 1
- 39. Contacts • name • company • title addresses • type • street • city • state • zip_code phones • type • number emails • type • address thumbnail • mime_type • data Portraits • mime_type • data Groups • name N 1 N 1 twitter • name • location • web • bio N N N 1 1 Document model - holistic and efficient representation
- 42. Legacy Migration 1. Copy existing schema & some data to MongoDB 2. Iterative schema design development – Measure performance, find bottlenecks, and embed 1. one to one associations first 2. one to many associations next 3. many to many associations – eliminate join table using array of references or embedded documents – Measure and analyze, review concerns, scaling
- 43. • Embed by default New Software Application
- 44. Embedding over Referencing • Embedding is a bit like pre-joined data – BSON (Binary JSON) document ops are easy for the server • Embed (90/10 following rule of thumb) – When the “one” or “many” objects are viewed in the context of their parent – For performance – For atomicity • Reference – When you need more scaling – For easy consistency with “many to many” associations without duplicated data
- 45. It’s All About Your Application • Programs+Databases = (Big) Data Applications • Your schema is the impedance matcher – Design choices: normalize/denormalize, reference/embed – Melds programming with MongoDB for best of both – Flexible for development and change • Programs MongoDB = Great Big Data Applications
- 46. Questions?
- 47. Thank You Solutions Architect, MongoDB Jay Runkel jay.runkel@mongodb.com @jayrunkel #MongoDB
Notes de l'éditeur
- Schema Design is very important; its impact on your application is pervasive.
- Wrong data structure will hurt you.Proper data structure can make all the pieces fall into place.
- Two-dimensional storage of ordered tuples or traditional records.The winning technology is that every field/value is first class,In essence, every field can be addressed in queries and can be indexed for faster processing.Normalization process requires many tables, joins to rehydrate, indexes to make joins faster, and results in poor data locality.
- The essential capability of the winningtechnology frompersists and gets even better.The document structure can match your data structures – your schema.
- One-dimensional storage can be very fast but is relatively limited with respect to other DBMS.
- Not “schema-less” but rather “flexible schema”Common structure can be enforced by applicationWhile MongoDB does not enforce common structure, neither does it restrict your applicationDocuments may have a common structure that is optionally extended at the document-levelExample problems for traditionalMany empty columns instead of subclassing via yet another tableThree days for schema migrationKeywords: flexible, choice, evolve, change, modify
- Concept of arrays incorporates multiple values, associations involving many entities.The lack of multivalued fields is usually the first complaint of programmers that don’t wish to pay the cost for normalization.Keywords: array, multiple, many
- Documents may have a common structure that is optionally extended at the document-level.The application mapping can enforce the required and optional fields.
- A common example will help us understand the joy of flexible document structure.
- Left: One to one We're going to assume users only have on Twitter account. A thumbnail is a small profile image while portrait is a very large profile image.Right: One to manyMiddle: Many to many
- $project allows you to select top level fields and can be used to reduce data for a fetch. Note that some ODMs may not allow you to specify $project.
- BSON (Binary JSON) is the “magic” or core technology in MongoDB for data structures and performance.BSON does not have to be parsed like JSON, but is rather a format that can be traversed easily.Can choose a language to fit your application, or multiple languages to fit multiple components of your application as appropriate.