DBMS > Graph Engine vs. OpenMLDB vs. RavenDB vs. Sadas Engine

System Properties Comparison Graph Engine vs. OpenMLDB vs. RavenDB vs. Sadas Engine

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

An open-source machine learning database that provides a feature platform for training and inference

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Primary database model

Graph DBMS
Key-value store

Time Series DBMS

Document store

Relational DBMS

Secondary database models

Relational DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

Technical documentation

www.graphengine.io/docs/manual

openmldb.ai/docs/zh/main

ravendb.net/docs

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

Developer

Microsoft

4 Paradigm Inc.

Hibernating Rhinos

SADAS s.r.l.

Initial release

2010

2020

2010

2006

Current release

2024-2 February 2024

5.4, July 2022

8.0

License

Commercial or Open Source

Open Source

MIT License

Open Source

AGPL version 3, commercial license available

commercial

free trial version available

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

.NET and C

C++, Java, Scala

C++

Server operating systems

.NET

Linux

Linux
macOS
Raspberry Pi
Windows

AIX
Linux
Windows

Data scheme

yes

Fixed schema

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

SQL-like query language (RQL)

yes

APIs and other access methods

RESTful HTTP API

JDBC
SQLAlchemy

.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

C#
C++
F#
Visual Basic

C++
Go
Java
Python
Scala

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

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

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate 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

Transaction concepts

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

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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'

User concepts

Access control

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Trinity
30 October 2010, Microsoft

Open source Microsoft Graph Engine takes on Neo4j
13 February 2017, InfoWorld

IBM releases Graph, a service that can outperform SQL databases
27 July 2016, GeekWire

Aerospike Is Now a Graph Database, Too
21 June 2023, Datanami

The graph analytics landscape 2019 - DataScienceCentral.com
27 February 2019, Data Science Central

provided by Google News

MLOp practice: using OpenMLDB in the real-time anti-fraud model for the bank's online transaction
23 August 2021, Towards Data Science

Compared to Native Spark 3.0, We Have Achieved Significant Optimization Effects in the AI
3 August 2021, Towards Data Science

Predictive maintenance — 5minutes demo of an end to end machine learning project
13 August 2021, Towards Data Science

provided by Google News

RavenDB Launches Version 6.0 Lightning Fast Queries, Data Integrations, Corax Indexing Engine, and Sharding
3 October 2023, PR Newswire

Install the NoSQL RavenDB Data System
14 May 2021, The New Stack

RavenDB Adds Graph Queries
15 May 2019, Datanami

Review: NoSQL database RavenDB
20 March 2019, TechGenix

How I Created a RavenDB Python Client
23 September 2016, Visual Studio Magazine

provided by Google News

Share this page