Lecture 4: Data modelling in MongoDB

Learning objectives

By the end of this lecture, students should understand:

• Why data modelling is important

• The key principle of data modelling in MongoDB

• Techniques to develop a data model: workload estimate, identify relationships, schema patterns

• The difference between embedding and referencing and when to use each type

Slides

Note

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Supplemental materials

Embed vs reference guideline

Guideline name	Question	Embed	Reference
Simplicity	Would keeping the pieces of information together lead to a simpler data model and code?	Yes	No
Go Together	Do the pieces of information have a “has-a,” “contains,” or similar relationship?	Yes	No
Query Atomicity	Does the application query the pieces of information together?	Yes	No
Update Complexity	Are the pieces of information updated together?	Yes	No
Archival	Should the pieces of information be archived at the same time?	Yes	No
Cardinality	Is there a high cardinality (current or growing) in the child side of the relationship?	No	Yes
Data Duplication	Would data duplication be too complicated to manage and undesired?	No	Yes
Document Size	Would the combined size of the pieces of information take too much memory or transfer bandwidth for the application?	No	Yes
Document Growth	Would the embedded piece grow without bound?	No	Yes
Workload	Are the pieces of information written at different times in a write-heavy workload?	No	Yes
Individuality	For the children side of the relationship, can the pieces exist by themselves without a parent?	No	Yes