MongoDB and Oracle Compared

What is Oracle?

Oracle is a global technology company specializing in database management systems. It’s core database offering is Oracle Database 12c Enterprise Edition (and forthcoming 18c). This is sold via a per-processing licensing model with required licensed add-ons for specific functionality. In Oracle, you pre-define your database schema based on your requirements. You then set up rules to govern the relationships between fields in your tables. Related information may be stored in separate tables. This information is then associated through the use of foreign keys and joins. Any changes in schema necessitates a migration procedure. This procedure can take the database offline or significantly reduce application performance.

What is MongoDB?

MongoDB is a non-relational database developed by MongoDB, Inc. MongoDB stores data as documents in a binary representation called BSON (Binary JSON). Related information is stored together for fast query access through the MongoDB query language. Fields can vary from document to document; there is no need to declare the structure of documents to the system – documents are self-describing. If a new field needs to be added to a document, then the field can be created without affecting all other documents in the collection, without updating a central system catalog, and without taking the system offline. Optionally, schema validation can be used to enforce data governance controls over each collection.

MongoDB’s document data model maps naturally to objects in application code. This makes it simple for developers to learn and use. Documents give you the ability to represent hierarchical relationships to store arrays and other more complex structures easily.

Native, idiomatic drivers are provided for 12+ languages – and the community has built dozens more – enabling ad-hoc queries, real-time aggregation and rich indexing to provide powerful programmatic ways to access and analyze data of any structure.

Documents can bring together related data that would otherwise be modeled across separate parent-child tables in a relational schema. As a result, MongoDB’s atomic single-document operations already provide transaction semantics that meet the data integrity needs of the majority of applications. One or more fields may be written in a single operation. This includes updates to multiple sub-documents and elements of an array. The guarantees provided by MongoDB ensure complete isolation as a document is updated; any errors cause the operation to roll back so that clients receive a consistent view of the document.

Multi-document transactions make MongoDB the only database to combine the ACID guarantees of traditional relational databases, the speed, flexibility, and power of the document model, with the intelligent distributed systems design to scale-out and place data where you need it. Through snapshot isolation, transactions provide a consistent view of data, and enforce all-or-nothing execution to maintain data integrity. Transactions in MongoDB feel just like transactions developers are familiar with in Oracle. They are multi-statement, with similar syntax (e.g. start_transaction and commit_transaction), and therefore easy for anyone with prior transaction experience to add to any application.

Unlike Oracle and other relational databases, MongoDB is built on a distributed systems architecture, rather than a monolithic, single node design. As a result, MongoDB offers out-of-the-box scale-out and data localization with automatic sharding, and replica sets to maintain always-on availability.

Increasing Developer Productivity with MongoDB Atlas for the Edge and Serverless Platforms

Bring the power of the document model to your mobile and IoT devices with Atlas for the Edge. With local access to your data and the full MongoDB query language, your apps run faster, and keep on running – even when disconnected from the network. Atlas Device Sync keeps the data in MongoDB Atlas and all your devices in sync. There is no native mobile Oracle Enterprise database. When developers need to sync with the backend Oracle database, they are forced to use another database technology (such as SQLite or BerkeleyDB) and write bespoke, complex solutions, or to license expense database options, to sync with the backend Oracle database.

The Atlas App Services serverless platform is the best way to work with MongoDB. It cuts development time in half by taking care of mundane backend jobs such as service integrations, and getting data safely to your application frontend. Atlas App Services Data Access Controls let you execute any MongoDB query, right from inside your frontend app. Atlas App Services Triggers let your app respond in real time to data changes, wherever the changes originated. The trigger code is written and executed within Atlas, giving those triggers far more flexibility and making them easier to maintain than stored procedures and triggers in Oracle. Another benefit is that Atlas App Services Triggers do not consume valuable database resources. Oracle offers no equivalent way of working with data or services. This forces you to waste months writing thousands of lines of undifferentiated, boilerplate code, and then provisioning application servers to run it on.

Why use MongoDB instead of Oracle?

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 cells in relational tables.

When evaluating databases, it is critical to consider the relative costs of each solution -- not just the cost of the software, but also the hardware, development and deployment costs. Organizations save $ millions in cost by switching from Oracle to MongoDB as a result of gains in developer productivity, reduced licensing, and lower hardware requirements.

MongoDB can also be scaled within and across multiple distributed data centers. This provides new levels of availability and scalability previously unachievable with relational databases like Oracle. 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 Oracle often requires significant, custom engineering work or investment in expensive, custom hardware.

If MongoDB is the right solution for you and you’re currently using Oracle, check out our migration guide and MongoDB's Relational Migrator - a tool that simplifies migrating and transforming data from a relational database to MongoDB, de-risking your transition to MongoDB while helping you take full advantage of the document model.

Users Selecting MongoDB over Oracle

As the following examples illustrate, MongoDB’s selection over Oracle is driven by radical improvements to developer productivity, application performance, and scale, while significantly reducing cost and lock-in:

To keep pace with demands from the business, Travelers Insurance modernized its development processes with a microservices architecture supported by agile and DevOps methodologies. But the rigidity of its existing Oracle and SQL Server databases imposed blockers to moving at the speed they needed. The solution was MongoDB and its flexible data model. They eliminated the 3-day wait to make any database changes, creating a software development pipeline supporting continuous delivery of new business functionality.

Financial giant RBS has modernized its investment banking systems with a new data fabric powered by MongoDB . As a result of decommissioning hundreds of Oracle servers, it has accelerated developer productivity to build new applications faster, created a simplified cloud-ready infrastructure for data scale, and avoided millions of dollars of cost.

Telefonica migrated its customer personalization service from Oracle to MongoDB. Using Oracle, it took 7 developers, multiple iterations and 14 months to build a system that just didn't perform. Using MongoDB, a team of 3 developers built its new personalization service in 3 months, which now powers both legacy and new products across the globe. MongoDB helps Telefonica be more agile, save money and drive new revenues streams.

China Eastern moved its from Oracle to MongoDB. Using MongoDB enabled the company’s project and engineering teams to build an application that was not possible with Oracle, transforming customer experience, and driving more business online. The simplicity of the document data model, dynamic schema, idiomatic drivers and indexing flexibility means the development teams can now launch new applications faster, while also unlocking significant cost savings.

When would Oracle be a better fit?

While many organizations have successfully migrated from an RDBMS to MongoDB, you cannot drop-in MongoDB as a replacement for legacy applications built around the relational data model and SQL. However, organizations are benefiting from modernizing mission-critical, revenue generating applications to MongoDB. For example, from a legacy relational database to MongoDB. As a result, it has improved customer experience by reducing latency 8x and eliminated downtime during system upgrades. Cisco's development teams can build and release new applications faster, while the company’s e-commerce platform can tap into the business agility enabled by cloud computing.