Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

ArangoDB

Stacks273

Followers442

Votes192

ArangoDB vs MongoDB: What are the differences?

ArangoDB vs MongoDB

ArangoDB and MongoDB are both popular NoSQL databases that are widely used in the industry. While they share some similarities, there are key differences between the two that make them suitable for different use cases.

Data Model: One of the major differences between ArangoDB and MongoDB is their data model. ArangoDB is a multi-model database that supports key-value pairs, documents, and graphs, allowing developers to work with different data models within a single database. On the other hand, MongoDB is a document database that stores data in flexible, JSON-like documents.
Query Language: Another notable difference is the query language used by ArangoDB and MongoDB. ArangoDB uses AQL (ArangoDB Query Language), which is a declarative language similar to SQL, allowing developers to write complex queries using a familiar syntax. MongoDB, on the other hand, uses a query language based on JSON-like objects, making it more suited for developers who prefer a more flexible and expressive query syntax.
Performance: When it comes to performance, ArangoDB and MongoDB have different strengths. ArangoDB is known for its ability to handle complex graph queries efficiently, making it ideal for applications that heavily rely on graph operations. MongoDB, on the other hand, excels in handling large volumes of simple document-based operations, making it well-suited for scalability and high-throughput use cases.
Transactions: The support for transactions is another difference between ArangoDB and MongoDB. ArangoDB provides full support for multi-document transactions, allowing developers to perform multiple read and write operations in an atomic and consistent manner. MongoDB, on the other hand, introduced support for multi-document transactions in recent versions, but it is still limited to replica sets and requires additional configuration.
Replication and Sharding: Replication and sharding are crucial for scalability and fault tolerance in distributed databases. ArangoDB supports both replication and sharding out of the box, making it easier to scale and ensure high availability. MongoDB also supports replication and sharding, but the configuration and setup process can be more complex compared to ArangoDB.
Community and Ecosystem: The community and ecosystem around ArangoDB and MongoDB also differ. MongoDB has a larger and more mature community with extensive documentation, third-party libraries, and tools available, making it easier to find resources and get support. ArangoDB, while growing, has a smaller community and might have limited resources compared to MongoDB.

In summary, ArangoDB and MongoDB have several key differences including their data model, query languages, performance characteristics, support for transactions, replication and sharding capabilities, and community and ecosystem. Choosing between the two depends on the specific requirements and use case of the application.

🔥 Trending in Databases on StackShare

PostgreSQL Databases

A powerful, open source object-relational database system

Try it View Docs Alternatives

Try

431

83.9k

Advice on MongoDB, ArangoDB

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	ArangoDB
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.	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.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	multi-model nosql db; acid; transactions; javascript; database; nosql; sharding; replication; query language; joins; aql; documents; graphs; key-values; graphdb
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 273
Followers 82.0K	Followers 442
Votes 4.1K	Votes 192
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 37 Grahps and documents in one DB 26 Intuitive and rich query language 25 Good documentation 25 Open source 21 Joins for collections Cons 3 Web ui has still room for improvement 2 No support for blueprints standard, using custom AQL

What are some alternatives to MongoDB, ArangoDB?

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.

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.

CouchDB

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.