5 Ways to Implement Email Validation in Mongoose

Ensuring email addresses are valid in Mongoose is fundamental for clean data. You can find three common ways to handle this validation, each with working code snippets. We'll also examine the shortcomings of these traditional methods and show how Abstract API helps overcome their limitations.

How to Implement Email Validation in Mongoose

Here are four common ways to validate email addresses in Mongoose. Each method includes a description of how it works and a corresponding code snippet for implementation.

Built-in Match Validator

A String path in a schema can take a match option. Mongoose then uses a regular expression to test the input each time the document validates. This method executes synchronously, so it does not require any promise or async setup.

The error message comes from Mongoose’s internal validator. This approach offers a straightforward way to enforce a specific format with zero runtime dependencies.

const userSchema = new mongoose.Schema({
  email: {
    type: String,
    required: true,
    lowercase: true,
    trim: true,
    match: /^[^\s@]+@[^\s@]+\.[^\s@]+$/
  }
});

Inline Custom Validator Function

The validate property accepts an object with a validator function and a message. This method provides full control and can be asynchronous if needed, which allows for more complex logic like external API calls.

It also offers a single place to normalize, trim, and compare domains before the document reaches the database. The custom message property allows for specific feedback when validation fails.

const EMAIL_RX = /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
const userSchema = new mongoose.Schema({
  email: {
    type: String,
    required: true,
    lowercase: true,
    validate: {
      validator: v => EMAIL_RX.test(v),
      message: p => `${p.value} is not RFC-5322 compliant`
    }
  }
});

Validator.js Integration

This approach integrates the popular third-party library, validator.js. The validator.isEmail() function handles complex validation rules, such as IDNA, UTF-8 local parts, and sub-addressing. This method delegates rule maintenance to the library's team.

It offers more nuance than a simple regular expression and can be easily swapped in or out because it uses the same validate hook.

const validator = require('validator');
const userSchema = new mongoose.Schema({
  email: {
    type: String,
    required: true,
    lowercase: true,
    validate: {
      validator: v => validator.isEmail(v, { allow_utf8_local_part: false }),
      message: p => `${p.value} fails validator.js isEmail()`
    }
  }
});

Dedicated Schema-Type Plugin

The mongoose-type-email plugin registers "mongoose.SchemaTypes.Email" as a new schema type upon require-time. This approach keeps the schema definition clean, as you just select a different type instead of the addition of validators.

It includes built-in options like "allowBlank" and "correctTld" to reject bare domains. The plugin can be reused across many schemas without code duplication.

require('mongoose-type-email');
const userSchema = new mongoose.Schema({
  email: {
    type: mongoose.SchemaTypes.Email,
    required: true,
    correctTld: true,
    allowBlank: false
  }
});

Challenges of Implementing Email Validation in Mongoose

Traditional Mongoose validation methods primarily check for syntactic correctness. They often fail to confirm an email's actual deliverability or protect against common data quality issues that arise in production.

• The built-in match validator and other regex-based methods only confirm syntactic correctness. They accept dead mailboxes, disposable domains, and spam-trap addresses because they never verify the email's existence or deliverability.
• Static regexes in the match validator and custom functions quickly become outdated. They fail to account for new TLDs, Unicode characters, or IDNs, which leads to the rejection of valid emails or the acceptance of invalid ones.
• The unique: true option is not a validator but an index build process. During concurrent writes or before the index completes, duplicate emails can bypass the check and cause late-stage database errors that require manual translation.
• Async validators that perform real checks like MX lookups introduce network latency into the save pipeline. This approach adds complexity with retry logic and new failure modes that can stall or even abort document writes.