Amazon RDS for Aurora vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Amazon Aurora

Stacks807

Followers745

Votes55

Amazon RDS for Aurora vs MongoDB: What are the differences?

Amazon RDS for Aurora and MongoDB are both popular database management systems. Let's explore the key differences between them.

Data Model: Aurora follows a relational database model, while MongoDB utilizes a document database model. In Aurora, data is organized into tables with predefined schemas, while MongoDB stores data in flexible, JSON-like documents without strict schemas.
Scalability: Aurora allows for horizontal scalability through its database clustering feature. It can automatically scale read and write capacity to meet application demands. On the other hand, MongoDB offers horizontal scaling through data sharding, where data is distributed across multiple servers. This allows for increased scalability and performance in MongoDB.
Replication: Aurora provides automated, synchronous replication across multiple Availability Zones for high availability and durability. It also supports read replicas for scaling read workloads. In MongoDB, replication is achieved through replica sets, which consist of primary and secondary nodes. Replica sets provide high availability and can be used for read scaling as well.
Query Language and Indexing: Aurora supports the SQL language for querying and manipulating data using standard SQL commands. It also supports various index types, including primary keys, secondary indexes, and full-text search indexes. MongoDB uses the MongoDB Query Language (MQL) which includes rich query expressions and aggregation pipelines. It supports various indexing options, including single field indexes, compound indexes, and geospatial indexes.
ACID Compliance: Aurora is designed to support ACID (Atomicity, Consistency, Isolation, Durability) properties, making it suitable for transactional workloads. MongoDB, on the other hand, provides eventual consistency by default, which may be acceptable for some use cases but not for all transactional workloads.
Cost: While the exact cost may vary depending on the specific use case and configuration, generally, Aurora tends to have higher upfront costs due to the additional management overhead of a relational database. MongoDB, being open-source, may have lower upfront costs but could potentially have higher ongoing operational costs.

In summary, RDS for Aurora is a fully managed relational database service designed for high performance, scalability, and compatibility with MySQL and PostgreSQL. MongoDB, on the other hand, is a NoSQL database known for its flexibility in handling unstructured data, scalability, and ease of use with a JSON-like document model.

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Advice on MongoDB, Amazon Aurora

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	Amazon Aurora
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.	Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	High Throughput with Low Jitter;Push-button Compute Scaling;Storage Auto-scaling;Amazon Aurora Replicas;Instance Monitoring and Repair;Fault-tolerant and Self-healing Storage;Automatic, Continuous, Incremental Backups and Point-in-time Restore;Database Snapshots;Resource-level Permissions;Easy Migration;Monitoring and Metrics
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 807
Followers 82.0K	Followers 745
Votes 4.1K	Votes 55
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 14 MySQL compatibility 12 Better performance 10 Easy read scalability 9 Speed 7 Low latency read replica Cons 2 Vendor locking 1 Rigid schema
Integrations
No integrations available	PostgreSQL MySQL

What are some alternatives to MongoDB, Amazon Aurora?

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.

Amazon RDS

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.

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.

Related Comparisons

Amazon RDS for Aurora vs MongoDB: What are the differences?

Amazon RDS for Aurora and MongoDB are both popular database management systems. Let's explore the key differences between them.

Data Model: Aurora follows a relational database model, while MongoDB utilizes a document database model. In Aurora, data is organized into tables with predefined schemas, while MongoDB stores data in flexible, JSON-like documents without strict schemas.
Scalability: Aurora allows for horizontal scalability through its database clustering feature. It can automatically scale read and write capacity to meet application demands. On the other hand, MongoDB offers horizontal scaling through data sharding, where data is distributed across multiple servers. This allows for increased scalability and performance in MongoDB.
Replication: Aurora provides automated, synchronous replication across multiple Availability Zones for high availability and durability. It also supports read replicas for scaling read workloads. In MongoDB, replication is achieved through replica sets, which consist of primary and secondary nodes. Replica sets provide high availability and can be used for read scaling as well.
Query Language and Indexing: Aurora supports the SQL language for querying and manipulating data using standard SQL commands. It also supports various index types, including primary keys, secondary indexes, and full-text search indexes. MongoDB uses the MongoDB Query Language (MQL) which includes rich query expressions and aggregation pipelines. It supports various indexing options, including single field indexes, compound indexes, and geospatial indexes.
ACID Compliance: Aurora is designed to support ACID (Atomicity, Consistency, Isolation, Durability) properties, making it suitable for transactional workloads. MongoDB, on the other hand, provides eventual consistency by default, which may be acceptable for some use cases but not for all transactional workloads.
Cost: While the exact cost may vary depending on the specific use case and configuration, generally, Aurora tends to have higher upfront costs due to the additional management overhead of a relational database. MongoDB, being open-source, may have lower upfront costs but could potentially have higher ongoing operational costs.

Amazon RDS for Aurora vs MongoDB

Overview