DBMS > JanusGraph vs. OpenMLDB vs. Sadas Engine

System Properties Comparison JanusGraph vs. OpenMLDB vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

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

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

Primary database model

Graph DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	0.11
Rank	#339	Overall
	#30	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Website

janusgraph.org

openmldb.ai

www.sadasengine.com

Technical documentation

docs.janusgraph.org

openmldb.ai/docs/zh/main

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

Developer

Linux Foundation; originally developed as Titan by Aurelius

4 Paradigm Inc.

SADAS s.r.l.

Initial release

2017

2020

2006

Current release

1.0.0, October 2023

2024-2 February 2024

8.0

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

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

Java

C++, Java, Scala

C++

Server operating systems

Linux
OS X
Unix
Windows

Linux

AIX
Linux
Windows

Data scheme

yes

Fixed schema

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

APIs and other access methods

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
SQLAlchemy

JDBC
ODBC
Proprietary protocol

Supported programming languages

Clojure
Java
Python

C++
Go
Java
Python
Scala

.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

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

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

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More resources

Recent citations in the news

Simple Deployment of a Graph Database: JanusGraph
12 October 2020, Towards Data Science

Database Deep Dives: JanusGraph
8 August 2019, ibm.com

JanusGraph Picks Up Where TitanDB Left Off
13 January 2017, Datanami

Nordstrom Builds Flexible Backend Ops with Kubernetes, Spark and JanusGraph
3 October 2019, The New Stack

Compose for JanusGraph arrives on Bluemix
15 September 2017, ibm.com

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

Predictive maintenance — 5minutes demo of an end to end machine learning project
13 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

provided by Google News

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