Amazon RDS vs MongoDB

Overview

Amazon RDS

Stacks16.2K

Followers10.8K

Votes761

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Amazon RDS vs MongoDB: What are the differences?

Amazon RDS (Relational Database Service) and MongoDB are both popular database options. Let's explore the key differences between them.

Data Modeling: Amazon RDS uses a structured schema-based approach where data is organized in tables with predefined columns and relationships. In contrast, MongoDB follows a flexible schema-less model where documents are stored in collections without any predefined structure, allowing for easier handling of unstructured data.
Scalability: With Amazon RDS, scalability is achieved through vertical scaling, which involves increasing the resources of a single instance. Meanwhile, MongoDB utilizes horizontal scaling, also known as sharding, where data is distributed across multiple servers, allowing for better performance and higher scalability in large-scale environments.
Deployment: Amazon RDS is a fully managed service provided by Amazon Web Services (AWS) and is designed to simplify database administration tasks. MongoDB, on the other hand, can be self-managed or accessed through MongoDB Atlas, a managed database service, providing more flexibility in terms of deployment options.
Flexibility: While Amazon RDS is mainly used for traditional relational databases, MongoDB is a NoSQL database that offers more flexibility in terms of data storage and retrieval. MongoDB allows for dynamic updates, nested data structures, and geospatial data support, making it suitable for handling modern application requirements.
Querying Capabilities: Amazon RDS supports SQL queries following the familiar relational database query language. In contrast, MongoDB utilizes the powerful and expressive MongoDB Query Language (MQL), which includes advanced features like aggregation pipelines, map-reduce, and geospatial queries, providing more versatility in data retrieval and analysis.
Transaction Support: Amazon RDS provides transactional consistency for ACID-compliant databases, ensuring data integrity in multi-step operations. MongoDB, on the other hand, offers multi-document transactions but does not support full ACID properties, making it suitable for scenarios where immediate consistency is not a strict requirement.

In summary, Amazon RDS is a managed relational database service supporting various database engines like MySQL, PostgreSQL, and SQL Server, offering ease of setup and scalability. MongoDB, a NoSQL database, provides flexibility in handling unstructured data and is favored for applications with dynamic schema requirements.

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Advice on Amazon RDS, 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

Amazon RDS	MongoDB
Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.	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.
Pre-configured Parameters;Monitoring and Metrics;Automatic Software Patching;Automated Backups;DB Snapshots;DB Event Notifications;Multi-Availability Zone (Multi-AZ) Deployments;Provisioned IOPS;Push-Button Scaling;Automatic Host Replacement;Replication;Isolation and Security	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 16.2K	Stacks 96.6K
Followers 10.8K	Followers 82.0K
Votes 761	Votes 4.1K
Pros & Cons
Pros 165 Reliable failovers 156 Automated backups 130 Backed by amazon 92 Db snapshots 87 Multi-availability	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 Amazon RDS, 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.

Related Comparisons

Amazon RDS vs MongoDB: What are the differences?

Amazon RDS (Relational Database Service) and MongoDB are both popular database options. Let's explore the key differences between them.

Data Modeling: Amazon RDS uses a structured schema-based approach where data is organized in tables with predefined columns and relationships. In contrast, MongoDB follows a flexible schema-less model where documents are stored in collections without any predefined structure, allowing for easier handling of unstructured data.
Scalability: With Amazon RDS, scalability is achieved through vertical scaling, which involves increasing the resources of a single instance. Meanwhile, MongoDB utilizes horizontal scaling, also known as sharding, where data is distributed across multiple servers, allowing for better performance and higher scalability in large-scale environments.
Deployment: Amazon RDS is a fully managed service provided by Amazon Web Services (AWS) and is designed to simplify database administration tasks. MongoDB, on the other hand, can be self-managed or accessed through MongoDB Atlas, a managed database service, providing more flexibility in terms of deployment options.
Flexibility: While Amazon RDS is mainly used for traditional relational databases, MongoDB is a NoSQL database that offers more flexibility in terms of data storage and retrieval. MongoDB allows for dynamic updates, nested data structures, and geospatial data support, making it suitable for handling modern application requirements.
Querying Capabilities: Amazon RDS supports SQL queries following the familiar relational database query language. In contrast, MongoDB utilizes the powerful and expressive MongoDB Query Language (MQL), which includes advanced features like aggregation pipelines, map-reduce, and geospatial queries, providing more versatility in data retrieval and analysis.
Transaction Support: Amazon RDS provides transactional consistency for ACID-compliant databases, ensuring data integrity in multi-step operations. MongoDB, on the other hand, offers multi-document transactions but does not support full ACID properties, making it suitable for scenarios where immediate consistency is not a strict requirement.

Amazon RDS vs MongoDB

Overview