MongoDB Academy · レッスン

コレクションへのバリデーターの追加

createCollection と collMod を使って JSON Schema バリデーターを関連付け、必須フィールドとデータ型を強制します。

「コレクションへのバリデーターの追加」はCoddyKit上の無料MongoDB Academyレッスンです。これはレッスン1/4です。下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。これはMongoDB Academy学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 MongoDB Academyコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

Why Schema Validation Matters

MongoDB is schema-flexible by default—any document can be inserted regardless of its shape. While this is useful in development, production databases need guardrails to prevent malformed data. MongoDB's schema validation feature lets you attach a JSON Schema rule set to a collection so that only well-formed documents can be inserted or updated, catching data quality problems at the database layer before they propagate.

JSON Schema as the Validation Language

MongoDB uses the industry-standard JSON Schema specification (draft 4) to express validation rules. You define a $jsonSchema object that declares which fields are required, what types they must be, and any additional constraints. The same format is used everywhere JSON Schema appears—in OpenAPI specs, form libraries, and now MongoDB validators.

Adding a Validator at Collection Creation

Pass a validator option when calling db.createCollection(). The validator contains a $jsonSchema document. The example below requires every users document to have a name (string) and an email (string), and optionally an age (integer).

db.createCollection('users', {
  validator: {
    $jsonSchema: {
      bsonType: 'object',
      required: ['name', 'email'],
      properties: {
        name:  { bsonType: 'string', description: 'must be a string and is required' },
        email: { bsonType: 'string', description: 'must be a string and is required' },
        age:   { bsonType: 'int',    minimum: 0, description: 'optional, must be a non-negative int' }
      }
    }
  }
});

Adding a Validator to an Existing Collection

Use the collMod (collection modification) command to attach or update a validator on a collection that already exists and may already contain data. This does not validate existing documents by default—it only applies to future writes unless you also change validationLevel.

db.runCommand({
  collMod: 'users',
  validator: {
    $jsonSchema: {
      bsonType: 'object',
      required: ['name', 'email'],
      properties: {
        name:  { bsonType: 'string' },
        email: { bsonType: 'string' }
      }
    }
  }
});

What Happens When Validation Fails

By default, if a document violates the validator, MongoDB rejects the write and throws an error: Document failed validation. The error includes a details field explaining exactly which rule was broken, making it easy to diagnose and fix the offending document. The insert or update is rolled back completely—no partial writes occur.

// This insert violates the validator — email is missing
try {
  db.users.insertOne({ name: 'Bob' });
} catch (err) {
  console.error(err.errInfo.details);
  // Output: required field 'email' is missing
}

Viewing the Current Validator

To inspect the validator attached to a collection, use db.getCollectionInfos() or query the system.js namespace. The returned document includes the full validator specification under options.validator, allowing you to review, copy, or compare validators across environments.

// List all collections and their options, including validators
const info = db.getCollectionInfos({ name: 'users' });
console.log(JSON.stringify(info[0].options.validator, null, 2));

Removing a Validator

To remove all validation from a collection, run collMod with an empty validator object. This returns the collection to its default schema-free state. You might do this temporarily during a bulk data migration or permanently when retiring validation in favour of application-layer checks.

// Remove the validator entirely
db.runCommand({
  collMod: 'users',
  validator: {}
});

Validation in Mongoose vs Native MongoDB

Mongoose has its own schema validation at the ODM layer that runs in JavaScript before sending data to MongoDB. However, Mongoose validation can be bypassed with insertMany or direct driver calls. Adding a JSON Schema validator at the database level creates an additional safety net that no client can bypass, regardless of the driver or language used.

Nested Object Validation

JSON Schema validators can reach into embedded sub-documents. Use the properties key to define rules for nested fields, and mark the nested object itself with bsonType: 'object'. This allows you to validate every level of a hierarchical document.

db.createCollection('orders', {
  validator: {
    $jsonSchema: {
      bsonType: 'object',
      required: ['customerId', 'shippingAddress'],
      properties: {
        customerId: { bsonType: 'objectId' },
        shippingAddress: {
          bsonType: 'object',
          required: ['street', 'city'],
          properties: {
            street: { bsonType: 'string' },
            city:   { bsonType: 'string' }
          }
        }
      }
    }
  }
});

Array Item Validation

To validate that an array field contains only documents of a specific shape, use items inside the property definition. Each element of the array will be validated against the items schema. This is useful for enforcing the shape of embedded line items, tags, or address arrays.

db.createCollection('carts', {
  validator: {
    $jsonSchema: {
      bsonType: 'object',
      properties: {
        items: {
          bsonType: 'array',
          items: {
            bsonType: 'object',
            required: ['productId', 'qty'],
            properties: {
              productId: { bsonType: 'objectId' },
              qty: { bsonType: 'int', minimum: 1 }
            }
          }
        }
      }
    }
  }
});

Schema Validation in Atlas

MongoDB Atlas provides a graphical interface for building and editing collection validators without writing raw JSON. Under the collection's Schema tab you can add properties, set types, and mark required fields through a form. Atlas also shows a validation score—the percentage of existing documents that pass the current schema—helping you measure data quality before enforcing strict validation.

Quick Check

Test your understanding of MongoDB & NoSQL Databases concepts from this lesson.

Lesson Recap

In this lesson you learned: JSON Schema validators are attached via createCollection or collMod, validation failures reject the write and return a detailed error, and nested objects and arrays can also be validated within the same schema document. Next up we explore type, required, and enum constraints in depth.

よくある質問

「コレクションへのバリデーターの追加」レッスンは無料ですか？

はい。「コレクションへのバリデーターの追加」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、MongoDB Academyコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 MongoDB Academyコースには全4レッスンが含まれています。

「コレクションへのバリデーターの追加」で何を学びますか？

createCollection と collMod を使って JSON Schema バリデーターを関連付け、必須フィールドとデータ型を強制します。ブラウザで直接実行するハンズオンコードでMongoDB Academyを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

MongoDB Academyを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのMongoDB Academyは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。これはレッスン1/4です。

「コレクションへのバリデーターの追加」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このMongoDB Academyレッスンでコードを書いて実行できますか？

はい。すべてのMongoDB Academyレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

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