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Why and when should we use Mongo DB instade of MySQL?

Introduction:

MongoDB is a document-oriented, open-source database management system that uses a flexible, JSON-like schema. It is designed for scalability and high performance and is often used for big data and real-time web applications. MongoDB uses a document-based data model, which allows for more flexible and scalable schema design than traditional relational databases. It is also known for its horizontal scalability and its ability to handle large amounts of data and handle high write loads.

MongoDB and MySQL are both popular database management systems, but they have some key differences.

Data Model: MongoDB is a document-based database, while MySQL is a relational database. This means that MongoDB stores data in semi-structured BSON (binary JSON) format, while MySQL stores data in a structured format using tables and rows.
Scalability: MongoDB is designed for horizontal scalability, meaning it can handle large amounts of data and high write loads by adding more machines to a cluster. MySQL, on the other hand, is designed for vertical scalability, meaning it can handle more load by adding more resources to a single machine.
Query Language: MongoDB uses MongoDB Query Language (MQL) for querying the data, which is a JSON-like syntax. MySQL, on the other hand, uses SQL (Structured Query Language) for querying the data, which is a standard language used by most relational databases.
Indexing: MongoDB supports secondary indexes and text search, while MySQL supports only primary and secondary indexes.
ACID Compliance: MongoDB is not fully ACID compliant, while MySQL is fully ACID compliant. MongoDB provides some ACID properties through its atomic operations on a single document level.

In summary MongoDB is a best fit for high-performance, high-scalability, and high-availability use cases where data is unstructured and/or semi-structured, while MySQL is best fit for transactional, high-concurrency, and high-compliance use cases where data is structured and requires full ACID compliance.

More Reason:

Data Model: MongoDB stores data in a flexible, JSON-like format known as BSON (binary JSON). This allows for more dynamic and flexible schema design compared to the rigid table structure of MySQL. MongoDB also supports nested data structures and arrays, which are difficult to model in a relational database.
Scalability: MongoDB’s horizontal scalability allows it to easily add more machines to a cluster to handle large amounts of data and high write loads. This is done through a technique called sharding, which distributes data across multiple machines. MySQL, on the other hand, is limited in its ability to scale horizontally, and instead relies on vertical scaling by adding more resources to a single machine.
Query Language: MongoDB uses a JSON-like syntax for querying data, known as MongoDB Query Language (MQL). MQL is designed to be intuitive and easy to use for developers, and supports complex queries and aggregations. MySQL, on the other hand, uses SQL, which is a standard language used by most relational databases. SQL is more powerful and flexible than MQL, but can also be more complex and harder to learn for developers.
Indexing: MongoDB supports secondary indexes and text search, which allows for faster and more efficient querying of data. MySQL supports only primary and secondary indexes. MongoDB also supports geospatial indexes, which can be used to efficiently query data based on location.
ACID Compliance: MongoDB is not fully ACID compliant, which means that it does not guarantee consistency, isolation, and durability across all operations. MongoDB provides some ACID properties through its atomic operations on a single document level. MySQL is fully ACID compliant, which means that it guarantees consistency, isolation, and durability across all operations.
Concurrency: MongoDB supports multi-document, multi-operation transactions while MySQL supports multi-table, multi-operation transactions.

In summary, MongoDB is a best fit for high-performance, high-scalability, and high-availability use cases where data is unstructured and/or semi-structured, while MySQL is best fit for transactional, high-concurrency, and high-compliance use cases where data is structured and requires full ACID compliance.

Disadvantages:

MongoDB is a popular and powerful document-oriented database management system, but like any technology, it has some disadvantages. Here are a few:

Limited support for complex transactions: MongoDB does not support complex multi-document transactions, which can make it difficult to handle certain types of data that need to be updated atomically.
Limited support for joins: MongoDB does not support joins in the way that relational databases do. This can make it more difficult to combine data from multiple collections in a single query.
Limited support for ad-hoc queries: MongoDB is designed for efficient data retrieval based on a known set of queries. It may not perform as well on ad-hoc queries that involve complex calculations or sorting.
Limited support for secondary indexes: MongoDB only supports a limited number of secondary indexes, which may affect performance on large collections.
Memory usage: MongoDB can consume a lot of memory, especially when working with large datasets. This can lead to performance issues if the server does not have enough memory.
Limited support for ACID: MongoDB does not provide full ACID compliance, which means that it does not guarantee consistency, isolation, and durability across all operations.
Limited Support for SQL: As MongoDB uses its own query language, developers who are familiar with SQL may need to learn a new query language.

In summary, MongoDB is a powerful tool, but it is not well-suited for all types of data and use cases. It is particularly well-suited for handling unstructured and semi-structured data, but may not be the best choice for highly structured data or use cases that require complex transactions and joins.

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