MongoDB Schema Validation : A Beginner’s Guide

 

MongoDB Schema Validation Explained with Practical Example

 Introduction

MongoDB is a NoSQL database that allows flexible document structures. However, to ensure data consistency and quality, we can apply schema validation. In this blog, I’ll explain how I implemented schema validation on a student performance dataset using MongoDB Compass, step by step.

 What is Schema Validation?

MongoDB allows inserting documents without enforcing a strict schema, but in real-world applications, we often need rules to control the data structure.

Schema validation helps ensure:

 Only allowed data types are stored (e.g., string, int)

 Certain fields are required

 Values stay within expected ranges

 Invalid documents are rejected before insertion

 Dataset Overview

The dataset used was about student performance, including fields like:

 Hours\_Studied

Attendance

 Sleep\_Hours

 Exam\_Score

 Internet\_Access

 Gender

 And more (total of 20 fields)

This data helps understand what factors impact exam scores.

 Steps to Apply Schema Validation in MongoDB Compass

 Step 1: Open MongoDB Compass

Connect to your database and select the collection. In this case, the collection is named `student_performance`.

 Step 2: Navigate to the “Validation” Tab

Click the “Validation” tab at the top.

 Step 3: Add the Validation Schema

Paste your JSON schema into the textbox. Here's the schema I used:

json

{

  "$jsonSchema": {

    "bsonType": "object",

    "required": [

      "Hours_Studied", "Attendance", "Parental_Involvement",

      "Access_to_Resources", "Sleep_Hours", "Previous_Scores",

      "Motivation_Level", "Internet_Access", "Tutoring_Sessions",

      "Family_Income", "Teacher_Quality", "School_Type",

      "Peer_Influence", "Physical_Activity", "Learning_Disabilities",

      "Parental_Education_Level", "Distance_from_Home", "Gender",

      "Exam_Score"

    ],

    "properties": {

      "Hours_Studied": { "bsonType": "int", "minimum": 0, "maximum": 30 },

      "Attendance": { "bsonType": "int", "minimum": 0, "maximum": 100 },

      "Parental_Involvement": { "bsonType": "string", "enum": ["Low", "Medium", "High"] },

      "Access_to_Resources": { "bsonType": "string", "enum": ["Low", "Medium", "High"] },

      "Sleep_Hours": { "bsonType": "int", "minimum": 4, "maximum": 12 },

      "Previous_Scores": { "bsonType": "int", "minimum": 0, "maximum": 100 },

      "Motivation_Level": { "bsonType": "string", "enum": ["Low", "Medium", "High"] },

      "Internet_Access": { "bsonType": "string", "enum": ["Yes", "No"] },

      "Tutoring_Sessions": { "bsonType": "int", "minimum": 0 },

      "Family_Income": { "bsonType": "string", "enum": ["Low", "Medium", "High"] },

      "Teacher_Quality": { "bsonType": "string", "enum": ["Low", "Medium", "High"] },

      "School_Type": { "bsonType": "string", "enum": ["Public", "Private"] },

      "Peer_Influence": { "bsonType": "string", "enum": ["Negative", "Neutral", "Positive"] },

      "Physical_Activity": { "bsonType": "int", "minimum": 0, "maximum": 7 },

      "Learning_Disabilities": { "bsonType": "string", "enum": ["Yes", "No"] },

      "Parental_Education_Level": { "bsonType": "string", "enum": ["High School", "College", "Postgraduate"] },

      "Distance_from_Home": { "bsonType": "string", "enum": ["Near", "Moderate", "Far"] },

      "Gender": { "bsonType": "string", "enum": ["Male", "Female"] },

      "Exam_Score": { "bsonType": "int", "minimum": 35, "maximum": 100 }

    }

  }

}

Click **Confirm** after pasting the schema.

 Testing the Schema

 Valid Document Example

json

{

  "Hours_Studied": 22,

  "Attendance": 92,

  "Parental_Involvement": "Medium",

  "Access_to_Resources": "High",

  "Extracurricular_Activities": "Yes",

  "Sleep_Hours": 7,

  "Previous_Scores": 88,

  "Motivation_Level": "High",

  "Internet_Access": "Yes",

  "Tutoring_Sessions": 2,

  "Family_Income": "Medium",

  "Teacher_Quality": "High",

  "School_Type": "Private",

  "Peer_Influence": "Positive",

  "Physical_Activity": 3,

  "Learning_Disabilities": "No",

  "Parental_Education_Level": "College",

  "Distance_from_Home": "Moderate",

  "Gender": "Female",

  "Exam_Score": 81

}

This document inserts successfully because it follows all validation rules.

 Invalid Document Example

json

{

  "Hours_Studied": -5,

  "Attendance": 105,

  "Parental_Involvement": 2,

  "Access_to_Resources": "Extreme",

  "Extracurricular_Activities": "Sometimes",

  "Sleep_Hours": "Seven",

  "Previous_Scores": 89,

  "Motivation_Level": "Medium",

  "Internet_Access": "Maybe",

  "Tutoring_Sessions": -1,

  "Family_Income": "High",

  "Teacher_Quality": "Medium",

  "School_Type": "Govt",

  "Peer_Influence": "Negative",

  "Physical_Activity": 3,

  "Learning_Disabilities": "Unknown",

  "Parental_Education_Level": "Illiterate",

  "Distance_from_Home": "Very Far",

  "Gender": "Other",

  "Exam_Score": 110

}

MongoDB Compass will reject this document with an error message because several fields violate the schema rules.

 Conclusion

MongoDB Schema Validation is essential when working with structured data in dynamic applications. It prevents invalid data from being saved, ensures quality, and supports future data analysis and reporting.

If you're working with MongoDB in your projects, adding validation rules will help you maintain consistency and reliability from day one.

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TEJAS MUSMADE

University: Shree Balaji University, Pune

School: School of Computer Studies

Course: BCA (Bachelor of Computer Applications)

Interests: NoSQL, MongoDB, and related technologies

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