When not to use MongoDB? Here’re 7 common scenarios

While MongoDB, a leading NoSQL database, offers many advantages such as scalability, flexibility, and high performance, it’s not a panacea for all data storage needs. Understanding when not to use MongoDB is essential for architects, developers, and database administrators to make informed decisions that align with their application requirements, data consistency needs, and long-term scalability plans. This article dives into the scenarios where MongoDB might not be the ideal choice, providing code examples along the way to illustrate the potential issues and alternative strategies.

Scenario 1: When ACID Transactions are Critical

In cases where atomicity, consistency, isolation, and durability (ACID) properties are paramount, such as financial applications handling transactions, MongoDB’s eventually consistent nature could pose challenges. Although MongoDB has introduced multi-document transactions in v4.0, the simplicity and robustness of ACID compliance in traditional SQL databases might still be preferable for such critical applications.

// MongoDB transaction example
const session = db.startSession();
session.startTransaction();
try {
    db.collection('accounts').updateOne({ accountID: '123' }, { $inc: { balance: -100 } }, { session });
    db.collection('transactions').insertOne({ accountID: '123', amount: -100, time: new Date() }, { session });
    session.commitTransaction();
} catch (e) {
    session.abortTransaction();
    throw e;
}

Output: Transaction committed or aborted based on operation success

Scenario 2: Data is Highly Relational

If your application deals with highly relational data where entities are tightly coupled and complex joins are frequently required, a relational database management system (RDBMS) like MySQL or PostgreSQL might be a better fit. Although MongoDB supports the $lookup operator for joining documents, the performance and simplicity of SQL joins in a RDBMS can be more efficient for complex queries.

// MongoDB's way to handle relationships
const orders = db.collection('orders').aggregate([
    {
        $lookup: {
            from: 'customers',
            localField: 'customerID',
            foreignField: '_id',
            as: 'customer_info'
        }
    }
]);

Example output: [{‘_id’: ‘order1’, ‘customerID’: ‘123’, ‘customer_info’: [{‘_id’: ‘123’, ‘name’: ‘John Doe’}]}]

Scenario 3: Need for Complex Transactions and Locking Mechanisms

Applications requiring complex transactions, sophisticated locking mechanisms, or strict data integrity controls may find MongoDB’s optimistic concurrency control and document-level locking insufficient. In such scenarios, an RDBMS with support for fine-grained locking and sophisticated transaction management may provide a more reliable solution.

// Example of complex transaction needing sophisticated locking
BEGIN TRANSACTION;
UPDATE accounts SET balance = balance - 100 WHERE accountID = '123';
INSERT INTO transactions (accountID, amount, time) VALUES ('123', -100, NOW());
COMMIT;

Output: Successfully executed transaction with strict data integrity

Scenario 4: Full-text Search and Advanced Analytics Needs

Although MongoDB offers full-text search capabilities and some analytical functions, applications with advanced search and analytics needs might benefit from dedicated search and analytics tools or databases. Solutions like Elasticsearch for full-text search or data warehousing options like Amazon Redshift or Google BigQuery offer specialized features that are optimized for these tasks.

// MongoDB full-text search example
const results = db.collection('articles').find({ $text: { $search: ' "NoSQL" -"MongoDB" ' } });

Output: Array of articles related to NoSQL, excluding MongoDB references

Scenario 5: Precise Data Validation and Schema Enforcement

For applications requiring precise data validation, schema enforcement, and structure, the schema-less nature of MongoDB may introduce challenges in maintaining data quality and consistency. While MongoDB does offer schema validation features, the stringent and complex validations that can be applied in RDBMS through constraints and triggers might be more suitable for applications needing stringent data quality controls.

// MongoDB schema validation example
const schema = {
    bsonType: 'object',
    required: ['name', 'age', 'email'],
    properties: {
        name: {
            bsonType: 'string',
            description: 'must be a string and is required'
        },
        age: {
            bsonType: 'int',
            minimum: 18,
            description: 'must be an integer and at least 18'
        },
        email: {
            bsonType: 'string',
            pattern: '.+@.+\..+',
            description: 'must be a valid email address and is required'
        }
    }
};
db.command({ collMod: 'users', validator: schema });

Output: Validation rules applied to the ‘users’ collection

Scenario 6: Legacy Systems and Integration Complexity

When integrating with legacy systems that heavily rely on SQL or have complex integration requirements, the non-relational nature of MongoDB can add complexity and require additional work to map data between systems. An RDBMS might offer more straightforward integration with existing infrastructures.

Scenario 7: Limited Resources and Budget for Scaling

Finally, MongoDB’s scalability and performance come at the cost of hardware and maintenance. For startups or projects with limited resources, the overhead of managing sharding, replication, and high availability might not be justifiable, making simpler database solutions more appealing.

Conclusion

While MongoDB is a powerful tool for many use cases, it’s not universally the best fit. Careful consideration of database requirements, data relationships, transactional needs, search and analytics capabilities, data validation, integration with legacy systems, and cost of ownership should guide the decision-making process. Choosing the right database solution is critical for the success and scalability of your application.