DBMS > HugeGraph vs. RavenDB vs. RethinkDB vs. Sadas Engine vs. SwayDB

System Properties Comparison HugeGraph vs. RavenDB vs. RethinkDB vs. Sadas Engine vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

Open Source Operational and Transactional Enterprise NoSQL Document Database

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

github.com/hugegraph
hugegraph.apache.org

Technical documentation

hugegraph.apache.org/docs

ravendb.net/docs

rethinkdb.com/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Baidu

Hibernating Rhinos

The Linux Foundation

since July 2017

SADAS s.r.l.

Simer Plaha

Initial release

2018

2010

2009

2006

2018

Current release

0.9

5.4, July 2022

2.4.1, August 2020

8.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL version 3, commercial license available

Open Source

Apache Version 2

commercial

free trial version available

Open Source

GNU Affero GPL V3.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Scala

Server operating systems

Linux
macOS
Unix

Linux
macOS
Raspberry Pi
Windows

Linux
OS X
Windows

AIX
Linux
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language (RQL)

yes

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

JDBC
ODBC
Proprietary protocol

Supported programming languages

Groovy
Java
Python

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

.Net
C
C#
C++
Groovy
Java
PHP
Python

Java
Kotlin
Scala

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Triggers

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

range based

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Multi-source replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

ACID, Cluster-wide transaction available

Atomic single-document operations

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

Users, roles and permissions

Authorization levels configured per client per database

yes

users and table-level permissions

Access rights for users, groups and roles according to SQL-standard

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More resources

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