DBMS > ArcadeDB vs. EsgynDB vs. Newts

System Properties Comparison ArcadeDB vs. EsgynDB vs. Newts

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Editorial information provided by DB-Engines

Name

Description

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Time Series DBMS based on Cassandra

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Relational DBMS

Time Series DBMS

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

Score	0.09
Rank	#355	Overall
	#48	Document stores
	#37	Graph DBMS
	#52	Key-value stores
	#34	Time Series DBMS

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Website

arcadedb.com

www.esgyn.cn

opennms.github.io/newts

Technical documentation

docs.arcadedb.com

github.com/OpenNMS/newts/wiki

Developer

Arcade Data

Esgyn

OpenNMS Group

Initial release

2021

2015

2014

Current release

September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++, Java

Java

Server operating systems

All OS with a Java VM

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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

SQL-like query language, no joins

yes

APIs and other access methods

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

HTTP REST
Java API

Supported programming languages

Java

All languages supporting JDBC/ODBC/ADO.Net

Java

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

relationship 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

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

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