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The `explain()` method in MongoDB provides insights into how a query is executed, helping developers optimize performance. It returns details about query execution plans, index usage, and performance metrics. For example, using `db.users.find({age: 25}).explain()` reveals if an index was used, helping to identify potential performance bottlenecks.

To restore a MySQL database, use the command line with the `mysql` tool. For example, running `mysql -u username -p database_name < backup.sql` will restore the database from the specified backup file, recreating the original structure and data.

User roles and permissions in MongoDB are managed using role-based access control (RBAC). You can define custom roles with specific privileges. For example, `db.createRole({role: 'readWrite', privileges: [{resource: {db: 'myDB', collection: ''}, actions: ['find', 'insert', 'update']}]})` creates a role that allows reading and writing.

The MongoDB configuration file defines server settings and options for running the database instance. It specifies parameters such as port number, data directory, log file path, and replica set configurations. For example, a config file may include `storage: { dbPath: '/var/lib/mongo' }` to set the data storage location.

To query documents in MongoDB, use the `find()` method. The basic syntax is `db.collection.find({query})`. For example, `db.users.find({age: 30})` retrieves all users aged 30. You can also use conditions like `$gt`, `$lt`, and `$in` for advanced filtering.

To filter documents with multiple conditions in MongoDB, combine conditions using logical operators like `$and` and `$or`. For example, `db.users.find({$and: [{age: {$gt: 20}}, {city: 'New York'}]})` retrieves users over 20 years old living in New York.

The AUTO_INCREMENT attribute allows MySQL to automatically generate a unique integer for a column, typically used for primary keys. For example, defining a column as `user_id INT AUTO_INCREMENT PRIMARY KEY` ensures each new user record gets a unique ID without manual input.

User-defined functions (UDFs) allow users to create custom functions to encapsulate reusable logic in SQL. UDFs can take parameters and return values. For example, a UDF to calculate tax could be defined as `CREATE FUNCTION CalculateTax(amount DECIMAL) RETURNS DECIMAL BEGIN RETURN amount * 0.1; END;`.

To check the performance of a MySQL query, use the `EXPLAIN` statement before your SELECT query. This provides insights into how MySQL executes the query, revealing details such as which indexes are used and the estimated number of rows processed. For example, `EXPLAIN SELECT * FROM users WHERE age > 30;` gives performance metrics.

Wildcard indexes in MongoDB enable indexing of fields within documents that may have unpredictable structures. They allow querying on any field without explicitly defining all possible fields. For instance, `db.collection.createIndex({'$**': 1})` creates a wildcard index, which is useful for collections with varying schema attributes.

Change streams in MongoDB provide a way to listen for real-time changes to documents in a collection. They allow applications to react to updates, insertions, and deletions without polling. For example, using `db.collection.watch()` lets you respond instantly to changes, enabling real-time applications like chat systems.

The CASE statement allows conditional logic in SQL queries, returning values based on specified conditions. It works like an IF statement. For example, `SELECT name, CASE WHEN score >= 60 THEN 'Pass' ELSE 'Fail' END AS result FROM exams;` assigns 'Pass' or 'Fail' based on the score.

Stored procedures offer several advantages: they enhance performance by reducing the amount of data sent over the network, promote code reusability, encapsulate business logic, and improve security by limiting direct access to tables. For example, a stored procedure for processing orders can manage all related SQL operations.

TRUNCATE is used to remove all rows from a table quickly without logging individual row deletions. It resets any AUTO_INCREMENT values to zero. For example, executing `TRUNCATE TABLE orders;` removes all orders in a fraction of the time compared to DELETE, but cannot be rolled back.

Database migration involves transferring data between different database systems or versions. This can be achieved using tools like `mysqldump` for exporting and importing data or third-party migration tools. For instance, exporting a database with `mysqldump` and importing it to a new server using `mysql` command facilitates migration.

MongoDB Atlas is a fully managed cloud database service provided by MongoDB. It simplifies database deployment, scaling, and management, offering automated backups, monitoring, and security features. By using Atlas, developers can focus on building applications without worrying about infrastructure management, as it handles scaling and redundancy automatically.

Key differences between SQL databases and MongoDB include data structure, schema, and query language. SQL uses tables with fixed schemas, while MongoDB uses collections of documents with flexible schemas. SQL queries are structured in SQL language, whereas MongoDB uses a JSON-like query syntax, making it more intuitive for developers familiar with JSON.

A database index is a data structure that improves the speed of data retrieval operations. It works by creating a sorted representation of the indexed column(s). For instance, indexing the 'email' column allows for quick lookups of user accounts based on their email addresses.

You can adjust salt rounds based on your server's capabilities. If performance is critical, start with a lower number, like 8, and gradually increase it as your system's performance improves. Monitor the hash time and user experience to find an optimal balance.

Yes, BcryptJS is suitable for modern applications due to its strong security features and adaptability. Its resistance to common attacks makes it a reliable choice for password hashing. Additionally, it integrates easily with Node.js applications, ensuring secure user authentication practices.

Common pitfalls include using too low salt rounds, which makes passwords vulnerable, or failing to handle errors properly in asynchronous operations. Additionally, avoid hardcoding sensitive data like passwords or salts, and ensure that you always store the hash securely after hashing.

BcryptJS produces a string output that includes the algorithm identifier, cost factor, salt, and hash, formatted as `$2a$<cost>$<salt>$<hash>`. This format allows the library to extract the parameters during verification, ensuring consistent hash comparisons for security.

While BcryptJS is designed for password hashing, it can technically hash any data. However, it's optimized for passwords, and other hashing algorithms like SHA-256 may be more appropriate for data integrity checks or non-sensitive information due to performance considerations.

BcryptJS prevents brute force attacks by using adaptive hashing with configurable salt rounds, which increases the time it takes to compute a hash. This makes it more computationally expensive for attackers to try multiple passwords, enhancing overall security against such attacks.

A rainbow table attack involves using precomputed tables of hash values to quickly find plaintext passwords. BcryptJS mitigates this risk through its unique salting process, ensuring that even identical passwords produce different hashes, making rainbow tables ineffective against BcryptJS hashes.