FaunaDB vs RavenDB

Overview

RavenDB

Stacks79

Followers82

Votes9

GitHub Stars3.9K

Forks850

Fauna

Stacks112

Followers153

Votes27

FaunaDB vs RavenDB: What are the differences?

FaunaDB and RavenDB are both popular NoSQL databases that offer different features and functionalities. While both databases are designed for scalability and performance, there are key differences that set them apart.

Data Model: FaunaDB uses a flexible and powerful document data model, similar to other NoSQL databases, which allows for nested data structures and easy querying without sacrificing scalability. On the other hand, RavenDB uses a JSON-based schema-less document model, making it easier for developers to work with and providing dynamic schema capabilities.
Consistency Model: FaunaDB provides strong consistency guarantees, ensuring that updates to the database are immediately visible across all replicas. RavenDB, on the other hand, offers eventual consistency by default, making it more suitable for scenarios where real-time data synchronization is not critical.
Transactions: FaunaDB supports distributed ACID transactions, allowing multiple operations to be grouped together and executed atomically to maintain data integrity. RavenDB also supports transactions, but they are limited to the boundaries of a single document, which may be a limitation for certain use cases that require complex transactional operations across multiple documents.
Query Language: FaunaDB uses its own query language called the Fauna Query Language (FQL), which is expressive and powerful, allowing for complex queries and aggregations. RavenDB uses a LINQ-based query language, making it more familiar to developers who are already using .NET technologies.
Multi-Model Support: FaunaDB is primarily a document database but also supports graph and relational data models, allowing for diverse data management capabilities within the same database. RavenDB, on the other hand, focuses on the document model and does not natively support other data models.
Availability: FaunaDB provides a global distributed database that automatically handles data replication and failover across multiple regions, ensuring high availability and fault tolerance. RavenDB can be deployed in a cluster but does not provide the same level of built-in global data replication and automatic failover.

In summary, FaunaDB and RavenDB differ in their data model, consistency guarantees, transaction support, query language, multi-model support, and availability. These differences make them suitable for different use cases, depending on the specific requirements of the application.

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CLI (Node.js)

Manual

Detailed Comparison

RavenDB	Fauna
As a document database it remains true to the core principles of these type of storage mechanisms. Somehow it managed to combine the best of relational databases with that of document databases.	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-Platform; ACID Transactions	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
GitHub Stars 3.9K	GitHub Stars -
GitHub Forks 850	GitHub Forks -
Stacks 79	Stacks 112
Followers 82	Followers 153
Votes 9	Votes 27
Pros & Cons
Pros 4 Embedded Library 3 Easy of use 2 NoSql	Pros 5 100% ACID 4 Removes server provisioning or maintenance 4 Generous free tier 3 Also supports SQL, CQL 3 No more n+1 problems (+ GraphQL) Cons 1 Log stack traces to avoid improper exception handling 1 Must keep app secrets encrypted 1 Susceptible to DDoS (& others) use timeouts throttling
Integrations
Python Windows Java Ruby Linux	No integrations available

What are some alternatives to RavenDB, 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.

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

FaunaDB vs RavenDB: What are the differences?

Data Model: FaunaDB uses a flexible and powerful document data model, similar to other NoSQL databases, which allows for nested data structures and easy querying without sacrificing scalability. On the other hand, RavenDB uses a JSON-based schema-less document model, making it easier for developers to work with and providing dynamic schema capabilities.
Consistency Model: FaunaDB provides strong consistency guarantees, ensuring that updates to the database are immediately visible across all replicas. RavenDB, on the other hand, offers eventual consistency by default, making it more suitable for scenarios where real-time data synchronization is not critical.
Transactions: FaunaDB supports distributed ACID transactions, allowing multiple operations to be grouped together and executed atomically to maintain data integrity. RavenDB also supports transactions, but they are limited to the boundaries of a single document, which may be a limitation for certain use cases that require complex transactional operations across multiple documents.
Query Language: FaunaDB uses its own query language called the Fauna Query Language (FQL), which is expressive and powerful, allowing for complex queries and aggregations. RavenDB uses a LINQ-based query language, making it more familiar to developers who are already using .NET technologies.
Multi-Model Support: FaunaDB is primarily a document database but also supports graph and relational data models, allowing for diverse data management capabilities within the same database. RavenDB, on the other hand, focuses on the document model and does not natively support other data models.
Availability: FaunaDB provides a global distributed database that automatically handles data replication and failover across multiple regions, ensuring high availability and fault tolerance. RavenDB can be deployed in a cluster but does not provide the same level of built-in global data replication and automatic failover.

FaunaDB vs RavenDB

Overview