5 ways to mimic ENUM behavior in MongoDB (with examples)

Introduction

MongoDB, a NoSQL database, is known for its flexible schema, which allows you to store documents without the need to define the structure of the data in advance. This flexibility is often an advantage, but it can also lead to challenges when your application needs to enforce a certain level of data integrity, similar to an ENUM type as found in SQL databases.

ENUM types are used to define a field with a set pre-defined set of values. Unfortunately, MongoDB does not natively support ENUM types, but we can mimic this functionality using various strategies to enforce data integrity within our database. In this tutorial, we are going to explore different approaches to mimic ENUM behavior in MongoDB with examples.

Why Mimic ENUM in MongoDB?

Before we dig into the solutions, let’s discuss why you might want to mimic ENUM behavior in MongoDB. MongoDB is schema-less in nature, it offers a rich set of validations that you can apply to your data. However, for cases where you want to restrict a field to accept only a specific set of values, like order status (e.g., ‘pending’, ‘shipped’, ‘delivered’), mimicking an ENUM becomes desirable to prevent invalid data entries.

Method 1: Schema Validation

The most straightforward approach in mimicking ENUMs is by using MongoDB’s built-in schema validation feature, introduced in MongoDB version 3.2.

db.createCollection("orders", {
  validator: {
    $jsonSchema: {
      bsonType: "object",
      required: ["status"],
      properties: {
        status: {
          enum: ["pending", "shipped", "delivered"],
          description: "can only be one of the enum values"
        }
      }
    }
  }
});

In the code snippet above, we’ve defined a collection named ‘orders’ where the ‘status’ field can only take the values ‘pending’, ‘shipped’, or ‘delivered’. If an attempt is made to insert or update a document with a value outside of this set, MongoDB will reject the operation.

Method 2: Custom Validation Logic

If you are using an application layer (for instance, in Node.js using Mongoose), you can implement custom validation logic in your models. Here’s an example using Mongoose:

const mongoose = require('mongoose');
const orderSchema = new mongoose.Schema({
  status: {
    type: String,
    enum: ['pending', 'shipped', 'delivered'],
    required: true
  }
});

const Order = mongoose.model('Order', orderSchema);

This schema tells Mongoose that the ‘status’ field is required and can only have one of the pre-specified values. If a value outside of these options is used, Mongoose will return a validation error.

Method 3: Mongoose Custom Validators

Mongoose, a popular ODM library for MongoDB, allows you to define comprehensive custom validation functions. Here is an example:

const orderStatuses = ['pending', 'shipped', 'delivered'];

const orderSchema = new mongoose.Schema({
  status: {
    type: String,
    required: true,
    validate: {
      validator: function(v) {
        return orderStatuses.includes(v);
      },
      message: props => `${props.value} is not a valid order status`
    }
  }
});

const Order = mongoose.model('Order', orderSchema);

This custom validator will check if the provided value for the ‘status’ field exists in our ‘orderStatuses’ array. If not, it throws an error with a custom message.

Method 4: Using $jsonSchema with the match Expression

For cases where more complex validation is required and you’re not using an ODM like Mongoose, MongoDB’s $jsonSchema can be combined with other MongoDB operators for more advanced use cases.

db.runCommand({
  collMod: "orders",
  validator: {
    $jsonSchema: {
      bsonType: "object",
      properties: {
        status: {
          bsonType: "string",
          description: "must be a string and match one of the predefined statuses",
          pattern: "^pending$|^shipped$|^delivered$"
        }
      }
    }
  }
});

In this example, we use the ‘pattern’ keyword combined with a regular expression to validate our document’s status field against specific permitted values.

Method 5: Enumeration Documents

An alternative way to enforce enumeration in MongoDB is by creating a separate collection that stores the enum values. You can then perform application-level checks or database-level joins using lookup logic to ensure references are valid:

db.StatusEnums.insertMany([
  { "_id": "pending" },
  { "_id": "shipped" },
  { "_id": "delivered" }
]);

// In your application logic or database-layer code
if (!db.StatusEnums.find({ _id: prospectiveStatus }).count()) {
  throw new Error('Invalid status!');
}

This method offers a dynamic yet slightly more complex solution which is advantageous if the enum values change frequently.

Conclusion

In this tutorial, we’ve explored various methods to enforce ENUM-like behavior within MongoDB, emphasizing the importance of application design and schema considerations in NoSQL databases. Whether you choose to implement schema validations at the database level using MongoDB’s native features, or utilize an ODM like Mongoose with custom validation functions, ensuring data consistency and integrity should be a key aspect of any database implementation.

By following these approaches to mimic ENUMs, you can achieve a balance between MongoDB’s flexibility and the structured predictability that ENUM types can provide, helping you maintain robust data management practices throughout your applications.