ArangoDB vs FaunaDB

Overview

ArangoDB

Stacks273

Followers442

Votes192

Fauna

Stacks112

Followers153

Votes27

ArangoDB vs FaunaDB: What are the differences?

Data Model Flexibility: ArangoDB supports a flexible data model that allows for key-value, document, and graph data storage within the same database, while FaunaDB primarily focuses on document-oriented data storage. This means that ArangoDB may be more suitable for use cases that require multi-model capabilities.
Scale and Performance: ArangoDB is known for its robust scaling capabilities, allowing for distributed setups and high availability configurations. FaunaDB also supports scaling but is more tailored towards global distribution and consistency, making it a preferred choice for applications requiring strong data consistency guarantees across multiple regions.
Query Language: ArangoDB uses its own query language called AQL (ArangoDB Query Language) which is similar to SQL but optimized for different data models, while FaunaDB uses FQL (Fauna Query Language) which is designed for complex data relationships and also supports ACID transactions. Developers familiar with SQL may find ArangoDB easier to adopt initially.
Consistency Models: ArangoDB uses a flexible consistency model that allows users to choose between different levels of consistency (from eventual to strong), providing a good balance between performance and data integrity. On the other hand, FaunaDB employs a strict consistency model by default, ensuring that all operations are globally consistent, which is crucial for highly regulated industries.
Deployment Options: ArangoDB can be deployed on-premises, in the cloud, or in a hybrid environment, offering flexibility in deployment options. FaunaDB, on the other hand, is a cloud-native database that is optimized for serverless architectures, making it a seamless choice for applications running on cloud platforms like AWS, Azure, or Google Cloud.
Community and Support: ArangoDB has a thriving open-source community with extensive documentation and a strong developer community, providing support through forums and GitHub. FaunaDB offers comprehensive documentation and professional support options for enterprise customers, ensuring reliable service and assistance for critical applications.

In Summary, ArangoDB and FaunaDB differ in their data model flexibility, scaling capabilities, query languages, consistency models, deployment options, and community support offerings.

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CLI (Node.js)

Manual

Detailed Comparison

ArangoDB	Fauna
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.	Escape the boundaries imposed by legacy databases with a data API that is simple to adopt, highly productive to use, and offers the capabilities that your business needs, without the operational pain typically associated with databases.
multi-model nosql db; acid; transactions; javascript; database; nosql; sharding; replication; query language; joins; aql; documents; graphs; key-values; graphdb	Native support for GraphQL and others. Easily access any data with any API. No middleware necessary.; Access all data via a data model that best suits your needs - relational, document, graph or composite.; A unique approach to indexing makes it simpler to write efficient queries that scale with your application.; Build SaaS apps more easily with native multi-tenancy and query-level QoS controls to prevent workload collisions.; Eliminate data anomalies with multi-region ACID transactions that don't limit number of keys or documents.; Data-driven RBAC that combines with SSL to offers reliable protection, and yet is simple to understand and codify.; Travel back in time with temporal querying. Run queries at a point-in-time or as change feeds. Track how your data evolved.; Dynamically replicates your data to global locations, so that your queries run fast no matter where your users are.; Easily deploy a FaunaDB cluster on your workstation accompanied by a powerful shell and tools to simplify your workflow.;
Statistics
Stacks 273	Stacks 112
Followers 442	Followers 153
Votes 192	Votes 27
Pros & Cons
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	Pros 5 100% ACID 4 Generous free tier 4 Removes server provisioning or maintenance 3 Works well with GraphQL 3 Low latency global CDN's Cons 1 Log stack traces to avoid improper exception handling 1 Must keep app secrets encrypted 1 Susceptible to DDoS (& others) use timeouts throttling

What are some alternatives to ArangoDB, Fauna?

MongoDB

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.

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.

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ArangoDB vs FaunaDB: What are the differences?

Data Model Flexibility: ArangoDB supports a flexible data model that allows for key-value, document, and graph data storage within the same database, while FaunaDB primarily focuses on document-oriented data storage. This means that ArangoDB may be more suitable for use cases that require multi-model capabilities.
Scale and Performance: ArangoDB is known for its robust scaling capabilities, allowing for distributed setups and high availability configurations. FaunaDB also supports scaling but is more tailored towards global distribution and consistency, making it a preferred choice for applications requiring strong data consistency guarantees across multiple regions.
Query Language: ArangoDB uses its own query language called AQL (ArangoDB Query Language) which is similar to SQL but optimized for different data models, while FaunaDB uses FQL (Fauna Query Language) which is designed for complex data relationships and also supports ACID transactions. Developers familiar with SQL may find ArangoDB easier to adopt initially.
Consistency Models: ArangoDB uses a flexible consistency model that allows users to choose between different levels of consistency (from eventual to strong), providing a good balance between performance and data integrity. On the other hand, FaunaDB employs a strict consistency model by default, ensuring that all operations are globally consistent, which is crucial for highly regulated industries.
Deployment Options: ArangoDB can be deployed on-premises, in the cloud, or in a hybrid environment, offering flexibility in deployment options. FaunaDB, on the other hand, is a cloud-native database that is optimized for serverless architectures, making it a seamless choice for applications running on cloud platforms like AWS, Azure, or Google Cloud.
Community and Support: ArangoDB has a thriving open-source community with extensive documentation and a strong developer community, providing support through forums and GitHub. FaunaDB offers comprehensive documentation and professional support options for enterprise customers, ensuring reliable service and assistance for critical applications.

In Summary, ArangoDB and FaunaDB differ in their data model flexibility, scaling capabilities, query languages, consistency models, deployment options, and community support offerings.