Why use MongoDB instead of MySQL?
Organizations of all sizes are adopting MongoDB because it enables
them to build applications faster, handle highly diverse data types, and
manage applications more efficiently at scale.
Development is simplified as MongoDB documents map naturally to
modern, object-oriented programming languages. Using MongoDB removes the
complex object-relational mapping (ORM) layer that translates objects
in code to relational tables.
MongoDB’s flexible data model also means that your database schema
can evolve with business requirements. For example, the ALTER TABLE
command required to add a single, new field to
Craiglist’s MySQL database
would take months to execute. The Craigslist team migrated to MongoDB
because it can accommodate changes to the data model without such costly
schema migrations.
MongoDB can also be scaled within and across multiple distributed
data centers, providing new levels of availability and scalability
previously unachievable with relational databases like MySQL. As your
deployments grow in terms of data volume and throughput, MongoDB scales
easily with no downtime, and without changing your application. In
contrast, to achieve scale with MySQL often requires significant, custom
engineering work.
What are common use cases for MongoDB?
MongoDB is a general purpose database that is used for a variety of use cases. The most common use cases for MongoDB include
Single View,
Internet of Things,
Mobile,
Real-Time Analytics,
Personalization,
Catalog, and
Content Management.
When would MySQL be a better fit?
While most modern applications require a flexible, scalable system
like MongoDB, there are use cases for which a relational database like
MySQL would be better suited. Applications that remquire complex,
multi-row transactions (e.g., a double-entry bookkeeping system) would
be good examples. MongoDB is not a drop-in replacement for legacy
applications built around the relational data model and SQL.
A concrete example would be the booking engine behind a travel
reservation system, which also typically involves complex transactions.
While the core booking engine might run on MySQL, those parts of the app
that engage with users – serving up content, integrating with social
networks, managing sessions – would be better placed in MongoDB
Are MongoDB and MySQL used together?
There are many examples of hybrid deployments of MongoDB and MySQL.
In some cases, it’s a matter of using the right tool for the job. For
example, many e-commerce applications use a combination of MongoDB and
MySQL. The product catalog, which includes multiple products with
different attributes, is a good fit for MongoDB’s flexible data model.
On the other hand, the checkout system, which requires complex
transactions, would likely be built on MySQL or another relational
technology.
In other cases, new business requirements push organizations to adopt
MongoDB for the next-generation components of their applications. For
example,
Sage Group,
one of the world’s leading suppliers of business management software
and services, integrated MongoDB into its popular Enterprise Resource
Planning (ERP) solution for midsize companies. Sage customers now enjoy a
higher degree of functionality and personalization as a result of the
integration. While many Sage products were originally built on and
continue to run on MySQL, the latest user experience functionality
centers around MongoDB.
These few exceptions aside, we think MongoDB is almost always a
better option than MySQL because of its flexible data model and scalable
architecture.
Want to Learn More? Get the RDBMS to MongoDB Migration Guide
Relational databases are being pushed beyond their limits because of
the way we build and run applications today, coupled with growth in data
sources and user loads. To address these challenges, companies like
Salesforce.com, MTV and Cisco have migrated successfully from relational
databases to MongoDB. In this white paper, you'll learn:
- Step by step how to migrate from a relational database to MongoDB.
- The relevant technical considerations, such as differences between
the relational and document data models and the implications for schema
design.
- Indexing, queries, application integration and data migration.
Migration rdbms nosql mongodb
