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MongoDB Design patterns based on application usage perspective:
The scheme depends on your application use case
Data Access Patterns
Number of reading vs update
what is the size of expected documents
Performance issues in MongoDB:
Schema Design:
{
Inherently slow operation
Blocked waiting for a slow operation
}
Insufficient or poorly tuned hardware
Design for :
Simplicity
Performance
Scalability
Index makes faster = Effiecenit execution
many indexes extra memory and slow down
do not change index field because has to rebuild indexing again
Aggregation is a pipeline until output comes out stage by stage- 25 stages
$match - get results
$unwind - create separate documents from array
$group - group data
$project -> rename fields
$lookup - join
$indexState - index status
$out - storing result into new collection
$sort
$limit
$skip
Advantages:
Faster because of No join at run time and less code
Flexible schema structure
Scalable
Handle messy Data
Combine different data from a different source
Database Normalization
1. Avoid data redundancy - repeative data
2. Ensure data integrity - Should able to CURD without negative affect on other data
Normalization:
Process of organizing database to avoid data redundancy and ensure data integrity.
1NF
1.each record represent one primary key
2.one columns should have single value ( atomic values)
3. no repeating groups - example class1, class2 ,class3
2NF
no partial dependacies - non primary columns should depeding only on primary column
Problem: lead to data redundancy
student_id class_id teacher
1 1 Smith
1 2 Jackson
2 3 James
2 2 Jackson
teacher is not depending on one primary key
3NF
There are no transitive dependencies.
Problem: lead to data redundancy
A determines B
B does not determine A
B determines C
id class_name teacher office
1 Math Smith A107
2 Programming Jackson B205
3 History James A100
4 Science Jackson B205
teach is not determine id and class so create them into separate table
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