This Markdown code presents the key differences between Flyway and MongoDB.
Flyway: Migration Tool for Databases
Flyway is a database migration tool that helps developers easily manage and version database schema changes.
It supports SQL-based migrations and tracks each migration script's execution state, allowing for seamless collaboration and synchronization across development environments.
Flyway provides a CLI (Command Line Interface) for running migrations, as well as APIs for integration with build tools and frameworks.
MongoDB: NoSQL Document Database
MongoDB is a NoSQL document database that provides flexibility and scalability for storing and managing unstructured data.
It uses a flexible JSON-like document model, providing a rich set of features for data manipulation and querying.
MongoDB offers horizontal scaling through sharding, allowing for distributed data storage and processing across multiple nodes in a cluster.
Migration vs. Schema-less Storage
Flyway focuses on database migration, ensuring that database schemas are versioned and can be upgraded or rolled back as needed.
MongoDB, on the other hand, emphasizes schema-less storage, allowing for the flexible storage and retrieval of data without predefined schemas.
While Flyway helps maintain a consistent and controlled database schema, MongoDB provides the freedom to evolve the schema as requirements change.
SQL vs. NoSQL
Flyway supports SQL-based migrations, allowing developers to use the powerful SQL language to define and execute database schema changes.
MongoDB uses a flexible query language called MongoDB Query Language (MQL) that supports powerful document-based queries, making it suitable for unstructured data and rapid development.
The choice between Flyway and MongoDB depends on the requirements and the preferred query language for interacting with the data.
Transactions vs. Atomic Operations
Flyway supports transactions, ensuring that database changes are executed as atomic units, allowing for rollbacks in case of failures.
MongoDB offers atomic operations at the document level, ensuring that a single document's update or modification is executed atomically.
Accordingly, Flyway is more suitable for transactional systems with complex dependencies, while MongoDB's atomic operations are beneficial for high-speed data processing.
Relational Data Models vs. Document Data Models
Flyway is designed for relational databases that follow a predefined schema with tables, columns, and relationships.
MongoDB uses a document data model, allowing for the storage of hierarchical and complex structures natively, without the need for joins or complex relationships.
The choice between Flyway and MongoDB depends on the data model requirements and the level of flexibility needed in representing the data.
In summary, Flyway is a migration tool that focuses on versioning and upgrading database schemas using SQL-based migrations and transactions, while MongoDB is a flexible NoSQL document database that allows for schema-less storage and atomic document-level operations. The choice between the two depends on the specific requirements and the preferred data model and query language.
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Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?
*The project is going to use React for the front-end and GraphQL is going to be used for the API.
Thank you all. Any answer or advice would be really helpful!
My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit.
In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.
We Have thousands of .pdf docs generated from the same form but with lots of variability.
We need to extract data from open text and more important - from tables inside the docs.
The output of Couchbase/Mongo will be one row per document for backend processing.
ADOBE renders the tables in an unusable form.
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
It lets you regain control of your database migrations with pleasure and plain sql. Solves only one problem and solves it well. It migrates your database, so you don't have to worry about it anymore.
