Create a data model that uses an array of references to group related documents of different types into a single collection. The Single Collection Pattern can be especially useful for modeling many-to-many relationships with only one copy of the data. This pattern can reduce data duplication in use cases where the cost of duplication is a concern.
The following characteristics define the Single Collection Pattern:
Stores all frequently accessed documents together in a single collection.
Stores relationships between documents as pointers or structures within each document.
Maps the relationships between documents via indexes on the field or array. This index supports the retrieval of related documents in a single query without the use of database join operations.
Consider using the Single Collection Pattern when modeling systems with high-velocity operations, low-latency requirements, and frequent write operations. By breaking the document into smaller parts, rather than large embedded documents, the database can perform smaller write operations and improve performance.
이 작업에 대하여
In the following example, an application allows students to see the status of the classes they enroll in for the semester. The system may need to frequently update several fields for each class, such as the current_topic, upcoming_session_summary, or next_class_time. To avoid duplicating information for multiple students taking the same class, we want to keep students and classes as separate document entities in the same collection.
예시
Consider the following example schemas for a class and student. To model their relationship, the class and student documents contain the links array. This array includes the doc_type and target fields to reference the relationship between the student and class entities.
Class document:
{ _id : "CS101-001", doc_type : "class", class_name : "Introduction to Programming", course_id : "CS101", instructor : { name : "Dr. Emily Smith", email : "emily.smith@example.com", office_hours : "Tuesdays and Thursdays 2:00 PM - 4:00 PM" }, semester : "Spring 2025", schedule : [ { day_time : "Monday 10:00 AM - 11:30 AM", location : "Room 101, Science Building" }, { day_time : "Wednesday 10:00 AM - 11:30 AM", location : "Room 101, Science Building" }, { day_time : "Friday 10:00 AM - 11:30 AM", location : "Room 101, Science Building" } ], current_topic : "Loops and Iterations", next_class_time : "2025-01-09T10:00:00Z", upcoming_session_summary : "We will explore different types of loops (for, while) and how to use them effectively in Python.", links : [ { target : "CS101-001", doc_type : "class" }, { target : "S10023", doc_type : "student" }, { target : "S12345", doc_type : "student" }, ... { target : "S12355", doc_type : "student" } ] }
Student document:
{ _id : "S12345", doc_type : "student", name : "Jane Doe", major : "Computer Science", semester : "Spring 2025", registered_classes : [ { course_id : "CS101", class_instance_id : "CS101-001", class_name : "Introduction to Programming" }, { course_id : "MATH201", class_instance_id : "MATH201-002", class_name : "Calculus II" } ], links : [ { target : "CS101-001", doc_type : "class" }, { target : "MATH201-002", doc_type : "class" }, { target : "S12345", doc_type : "student" } ] }
To optimize queries, index the links field:
db.students_classes.createIndex({ "links.target": 1, "links.doc_type": 1 })
You can query for a student's information and all the classes they take without having to perform any join operations:
db.students_classes.find({ "links.target": "S12345" })
Similarly, you can query for all registered students in a given class:
db.students_classes.find({ "doc_type": "student", "links.target": "CS101-001" })