Amazon DocumentDB vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Amazon DocumentDB

Stacks72

Followers64

Votes0

Amazon DocumentDB vs MongoDB: What are the differences?

Introduction

In this Markdown formatted code, we will discuss the key differences between Amazon DocumentDB and MongoDB. Both are NoSQL databases, but they have distinct characteristics and functionalities. Let's explore these differences in more detail below.

Scalability: One key difference between Amazon DocumentDB and MongoDB is their approach to scalability. Amazon DocumentDB is designed to scale horizontally, allowing you to add more instances to handle increasing workloads. MongoDB, on the other hand, scales both horizontally and vertically, offering more flexibility in terms of scaling options.
Data Consistency: Another significant difference between the two databases is their approach to data consistency. Amazon DocumentDB offers "cluster-level" consistency, where data is synchronized across all instances in the cluster in near real-time. MongoDB, on the other hand, offers "document-level" consistency, where updates to a document may take some time to propagate to other instances.
Backup and Restore: When it comes to backup and restore functionality, Amazon DocumentDB provides automated backup and recovery features, allowing you to easily restore data to a specific point-in-time. MongoDB, on the other hand, requires you to implement your own backup and restore procedures, although it provides the necessary tools for these tasks.
Managed Service vs. Self-Managed: Amazon DocumentDB is a fully managed service, meaning that Amazon takes care of the maintenance, upgrades, and scaling of the database infrastructure. MongoDB, on the other hand, can be self-managed, giving you more control over the database deployment and configuration.
Compatibility: While both Amazon DocumentDB and MongoDB are based on the same document model, they have some differences in terms of compatibility. Amazon DocumentDB is compatible with MongoDB 3.6, which means that most MongoDB applications can run on DocumentDB with little to no modification. However, some MongoDB features may not be fully supported in DocumentDB.
Indexing: Indexing is an essential aspect of database performance. Amazon DocumentDB supports a subset of MongoDB's indexing options, including single-field and compound indexes, but it does not support some advanced index types like text indexes and geospatial indexes. MongoDB, on the other hand, offers a wider range of indexing options, including the advanced ones mentioned above.

In summary, key differences between Amazon DocumentDB and MongoDB include their scalability approaches, data consistency model, backup and restore functionality, managed service vs. self-managed, compatibility level, and indexing options. Each database has its own strengths and weaknesses, so it's important to consider your specific requirements when choosing between them.

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Advice on MongoDB, Amazon DocumentDB

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 DocumentDB
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 DocumentDB is a non-relational database service designed from the ground-up to give you the performance, scalability, and availability you need when operating mission-critical MongoDB workloads at scale. In Amazon DocumentDB, the storage and compute are decoupled, allowing each to scale independently, and you can increase the read capacity to millions of requests per second by adding up to 15 low latency read replicas in minutes, regardless of the size of your data.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	MongoDB-compatible;Fully managed;Performance at scale
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 72
Followers 82.0K	Followers 64
Votes 4.1K	Votes 0
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 0 Scalable 0 Storage elasticity 0 Easy Setup

What are some alternatives to MongoDB, Amazon DocumentDB?

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.

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

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 DocumentDB vs MongoDB: What are the differences?

Introduction

Scalability: One key difference between Amazon DocumentDB and MongoDB is their approach to scalability. Amazon DocumentDB is designed to scale horizontally, allowing you to add more instances to handle increasing workloads. MongoDB, on the other hand, scales both horizontally and vertically, offering more flexibility in terms of scaling options.
Data Consistency: Another significant difference between the two databases is their approach to data consistency. Amazon DocumentDB offers "cluster-level" consistency, where data is synchronized across all instances in the cluster in near real-time. MongoDB, on the other hand, offers "document-level" consistency, where updates to a document may take some time to propagate to other instances.
Backup and Restore: When it comes to backup and restore functionality, Amazon DocumentDB provides automated backup and recovery features, allowing you to easily restore data to a specific point-in-time. MongoDB, on the other hand, requires you to implement your own backup and restore procedures, although it provides the necessary tools for these tasks.
Managed Service vs. Self-Managed: Amazon DocumentDB is a fully managed service, meaning that Amazon takes care of the maintenance, upgrades, and scaling of the database infrastructure. MongoDB, on the other hand, can be self-managed, giving you more control over the database deployment and configuration.
Compatibility: While both Amazon DocumentDB and MongoDB are based on the same document model, they have some differences in terms of compatibility. Amazon DocumentDB is compatible with MongoDB 3.6, which means that most MongoDB applications can run on DocumentDB with little to no modification. However, some MongoDB features may not be fully supported in DocumentDB.
Indexing: Indexing is an essential aspect of database performance. Amazon DocumentDB supports a subset of MongoDB's indexing options, including single-field and compound indexes, but it does not support some advanced index types like text indexes and geospatial indexes. MongoDB, on the other hand, offers a wider range of indexing options, including the advanced ones mentioned above.

Amazon DocumentDB vs MongoDB

Overview