ActorDB vs RavenDB

Overview

RavenDB

Stacks79

Followers82

Votes9

GitHub Stars3.9K

Forks850

ActorDB

Stacks1

Followers12

Votes0

GitHub Stars1.9K

Forks71

ActorDB vs RavenDB: What are the differences?

1. Data Model Flexibility: ActorDB utilizes the Actor Model with data distributed in partitions, allowing for high scalability and fault tolerance. On the other hand, RavenDB offers a document-oriented data model, enabling easy storage and retrieval of structured data in a flexible manner.

2. Query Language Support: ActorDB supports SQL as its query language, allowing users to leverage their familiarity with SQL for querying the database. In contrast, RavenDB offers LINQ (Language Integrated Query) support, which is a query language that integrates with C# code for querying data.

3. Consistency Model: ActorDB offers strong consistency through the use of distributed transactions, ensuring that all nodes in the system have consistent data at any given point in time. RavenDB, on the other hand, utilizes an eventual consistency model by default, prioritizing high availability and partition tolerance over strong consistency.

4. Deployment Options: ActorDB is designed to be deployed in a distributed environment, allowing for horizontal scalability across multiple nodes. In comparison, RavenDB offers both self-hosted and cloud deployment options, providing flexibility in deployment choices according to the organization's needs and preferences.

5. Replication Mechanisms: ActorDB employs multi-master replication, enabling multiple nodes to accept both read and write operations independently. In contrast, RavenDB supports both master-slave and master-master replication configurations, offering versatility in setting up replication mechanisms based on specific use cases.

6. Programming Language Support: ActorDB is designed to be language-agnostic, providing client libraries for multiple programming languages to interact with the database. On the other hand, RavenDB integrates seamlessly with .NET ecosystem, offering extensive support for C# and other .NET languages for developing applications that interact with the database.

In Summary, ActorDB and RavenDB differ in aspects such as data model flexibility, query language support, consistency model, deployment options, replication mechanisms, and programming language support.

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CLI (Node.js)

Manual

Detailed Comparison

RavenDB	ActorDB
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.	ActorDB is a distributed SQL database... with the scalability of a KV store, while keeping the query capabilities of a relational database. ActorDB is ideal as a server side database for apps.
Multi-Platform; ACID Transactions	Complete horizontal scalability. All nodes are equivalent and you can have as many nodes as you need.;Full featured ACID database.;Suitable for very large datasets over many actors and servers.;No special drivers needed. Use the mysql driver of your language of choice.;Easy to configure and administer.;No global locks. Only the actors (one or many) involved in a transaction are locked during a write. All other actors are unaffected.;Uses stable reliable SQL and storage engines: SQLite on top of LMDB.;Inherits SQLite features like JSON support and common table expressions.
Statistics
GitHub Stars 3.9K	GitHub Stars 1.9K
GitHub Forks 850	GitHub Forks 71
Stacks 79	Stacks 1
Followers 82	Followers 12
Votes 9	Votes 0
Pros & Cons
Pros 4 Embedded Library 3 Easy of use 2 NoSql	No community feedback yet
Integrations
Python Windows Java Ruby Linux	No integrations available

What are some alternatives to RavenDB, ActorDB?

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.