MongoDB vs Oracle PL/SQL

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Oracle PL/SQL

Stacks749

Followers598

Votes8

MongoDB vs Oracle PL/SQL: What are the differences?

Introduction

This article will outline the key differences between MongoDB and Oracle PL/SQL, comparing their functionalities and features. MongoDB is a NoSQL database management system, while Oracle PL/SQL is a procedural language specifically designed for Oracle's Relational Database Management System (RDBMS).

Data Model: MongoDB follows a flexible and dynamic schema, allowing for a more adaptable approach to data structure. On the other hand, Oracle PL/SQL uses a strict and predefined schema, adhering to the relational model.
Data Relationships: MongoDB is a document-oriented database, where data is stored and accessed within documents. It allows for easy embedding and nesting of related data, providing more natural relationships between entities. In contrast, Oracle PL/SQL follows a relational model and uses tables with explicit relationships defined through primary and foreign keys.
Query Language: MongoDB uses a JSON-like query language called MongoDB Query Language (MQL), which supports a wide range of querying capabilities, including dynamic queries, flexible joins, and full-text search. Oracle PL/SQL, however, uses Structured Query Language (SQL) for querying and manipulating data, which provides powerful relational database functionality but may require more complex syntax for certain queries.
Scalability: MongoDB is designed to be scalable horizontally, allowing for distributed database systems across multiple servers. It can handle large amounts of data and high read/write workloads efficiently. On the other hand, Oracle PL/SQL is primarily scaled vertically, by adding more resources to a single server, making it more suitable for smaller to medium-sized applications with moderate workloads.
Data Integrity and ACID Transactions: Oracle PL/SQL provides full ACID (Atomicity, Consistency, Isolation, Durability) compliance, ensuring data integrity and supporting transactions that can span multiple statements. MongoDB, while supporting multi-document transactions in recent versions, has traditionally been designed for high scalability and performance, trading off some ACID properties in favor of eventual consistency.
Community and Ecosystem: MongoDB has a vibrant and growing open-source community, with a wide range of libraries, frameworks, and tools built around it. It has extensive documentation and resources available online. Oracle PL/SQL, being a proprietary language, has a more focused community around Oracle technologies, with a narrower range of available resources and community support.

In summary, MongoDB offers a flexible and dynamic data model, supports natural relationships between data, uses a flexible query language, scales horizontally, provides eventual consistency, and has a vibrant open-source community. Oracle PL/SQL, on the other hand, follows a strict relational model, uses SQL for querying, scales vertically, provides full ACID compliance, and has a more focused proprietary ecosystem.

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Advice on MongoDB, Oracle PL/SQL

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

MongoDB	Oracle PL/SQL
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 is a powerful, yet straightforward database programming language. It is easy to both write and read, and comes packed with lots of out-of-the-box optimizations and security features.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	-
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 749
Followers 82.0K	Followers 598
Votes 4.1K	Votes 8
Pros & Cons
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	Pros 2 Powerful 2 Multiple ways to accomplish the same end 1 Pl/sql 1 Not mysql 1 Massive, continuous investment by Oracle Corp Cons 2 High commercial license cost
Integrations
No integrations available	Python PHP .NET Node.js Oracle Hadoop Java

What are some alternatives to MongoDB, Oracle PL/SQL?

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.

GraphQL

GraphQL is a data query language and runtime designed and used at Facebook to request and deliver data to mobile and web apps since 2012.

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.

Related Comparisons

MongoDB vs Oracle PL/SQL: What are the differences?

Introduction

Data Model: MongoDB follows a flexible and dynamic schema, allowing for a more adaptable approach to data structure. On the other hand, Oracle PL/SQL uses a strict and predefined schema, adhering to the relational model.
Data Relationships: MongoDB is a document-oriented database, where data is stored and accessed within documents. It allows for easy embedding and nesting of related data, providing more natural relationships between entities. In contrast, Oracle PL/SQL follows a relational model and uses tables with explicit relationships defined through primary and foreign keys.
Query Language: MongoDB uses a JSON-like query language called MongoDB Query Language (MQL), which supports a wide range of querying capabilities, including dynamic queries, flexible joins, and full-text search. Oracle PL/SQL, however, uses Structured Query Language (SQL) for querying and manipulating data, which provides powerful relational database functionality but may require more complex syntax for certain queries.
Scalability: MongoDB is designed to be scalable horizontally, allowing for distributed database systems across multiple servers. It can handle large amounts of data and high read/write workloads efficiently. On the other hand, Oracle PL/SQL is primarily scaled vertically, by adding more resources to a single server, making it more suitable for smaller to medium-sized applications with moderate workloads.
Data Integrity and ACID Transactions: Oracle PL/SQL provides full ACID (Atomicity, Consistency, Isolation, Durability) compliance, ensuring data integrity and supporting transactions that can span multiple statements. MongoDB, while supporting multi-document transactions in recent versions, has traditionally been designed for high scalability and performance, trading off some ACID properties in favor of eventual consistency.
Community and Ecosystem: MongoDB has a vibrant and growing open-source community, with a wide range of libraries, frameworks, and tools built around it. It has extensive documentation and resources available online. Oracle PL/SQL, being a proprietary language, has a more focused community around Oracle technologies, with a narrower range of available resources and community support.

MongoDB vs Oracle PL/SQL

Overview

MongoDB vs Oracle PL/SQL: What are the differences?