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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.

MyISAM is a non-transactional storage engine, ideal for read-heavy applications, while InnoDB supports transactions, foreign keys, and row-level locking, making it suitable for high-concurrency environments. For instance, InnoDB is preferable for e-commerce sites where data integrity is critical.

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.

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.

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.

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.

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.

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.

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.

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.

Data validation in MongoDB can be implemented using schema validation rules defined in collections. By specifying validation criteria using JSON Schema, you can enforce data integrity. For example, a `users` collection can have a validation rule that ensures the 'age' field is an integer between 0 and 120, preventing invalid data entries.

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.

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.

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.

Install BcryptJS using npm with the command `npm install bcryptjs`. After installation, require it in your code: `const bcrypt = require('bcryptjs');`. This enables you to start hashing and comparing passwords in your application.

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.

Hashing is a one-way function that transforms data into a fixed-size string, making it irreversible, while encryption is reversible, allowing the original data to be retrieved with a key. BcryptJS uses hashing to securely store passwords, ensuring they cannot be easily decrypted.