Overview

Oracle

Stacks2.6K

Followers1.8K

Votes113

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

MongoDB vs Oracle: What are the differences?

Introduction

MongoDB and Oracle are two popular database management systems that are widely used in the industry. While both systems serve the purpose of storing and managing data, there are significant differences between them. In this article, we will explore the key differences between MongoDB and Oracle.

Data Model: MongoDB is a document-oriented database, while Oracle is a relational database. In MongoDB, data is stored in flexible, JSON-like documents, making it easy to handle and store data in a variety of formats. On the other hand, Oracle follows a tabular structure with predefined schemas and rigid relationships between tables.
Scalability: MongoDB is designed to scale horizontally, which means it can distribute data across multiple servers, making it easier to handle large amounts of data and high traffic loads. On the other hand, Oracle is traditionally designed to scale vertically, where you need to upgrade the hardware or move to a more powerful server to handle increased data or traffic.
Query Language: MongoDB uses a querying language called the MongoDB Query Language (MQL), which is designed to work with JSON documents. It allows for complex queries, including the use of aggregation pipelines, to perform data analysis and manipulation. Oracle, on the other hand, uses Structured Query Language (SQL), a standardized language for managing relational databases.
ACID Compliance: ACID stands for Atomicity, Consistency, Isolation, and Durability, which are properties that ensure reliability and integrity in database transactions. Oracle is known for its strong ACID compliance, providing features like transactions, locks, and isolation levels. While MongoDB supports ACID properties at the document level, it does not have the same level of transactional support as Oracle.
Data Replication: MongoDB offers built-in replication, which allows the data to be replicated across multiple servers for fault-tolerance and high availability. It uses a replica set mechanism to maintain multiple copies of data across different nodes. On the other hand, Oracle requires additional configuration and setup for replication, such as Oracle Data Guard, to achieve data redundancy and failover capabilities.
Cost: MongoDB offers a free and open-source community edition, making it a cost-effective choice for small to mid-sized businesses. However, it also provides enterprise editions with additional features and support, which come at a cost. On the other hand, Oracle is a commercial database with licensing costs, which can make it more expensive for certain use cases.

In summary, MongoDB and Oracle differ in their data models, scalability, query languages, ACID compliance, data replication mechanisms, and cost. Understanding these differences is crucial in choosing the right database management system for your specific needs.

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Advice on Oracle, MongoDB

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

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!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

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.

575k views575k

Comments

Mike

Mar 20, 2020

Needs advice

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.

241k views241k

Comments

Detailed Comparison

Oracle	MongoDB
Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.	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.
-	Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
Statistics
GitHub Stars -	GitHub Stars 27.7K
GitHub Forks -	GitHub Forks 5.7K
Stacks 2.6K	Stacks 96.6K
Followers 1.8K	Followers 82.0K
Votes 113	Votes 4.1K
Pros & Cons
Pros 44 Reliable 33 Enterprise 15 High Availability 5 Hard to maintain 5 Expensive Cons 14 Expensive	Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language

What are some alternatives to Oracle, MongoDB?

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.