DBMS > AnzoGraph DB vs. Apache IoTDB vs. EJDB vs. mSQL vs. OrigoDB

System Properties Comparison AnzoGraph DB vs. Apache IoTDB vs. EJDB vs. mSQL vs. OrigoDB

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

mSQL (Mini SQL) is a simple and lightweight RDBMS

A fully ACID in-memory object graph database

Primary database model

Document store
Object oriented DBMS

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

cambridgesemantics.com/anzograph

iotdb.apache.org

github.com/Softmotions/ejdb

hughestech.com.au/products/msql

origodb.com

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

github.com/Softmotions/ejdb/blob/master/README.md

origodb.com/docs

Developer

Cambridge Semantics

Apache Software Foundation

Softmotions

Hughes Technologies

Robert Friberg et al

Initial release

2018

2012

1994

2009

under the name LiveDB

Current release

2.3, January 2021

1.1.0, April 2023

4.4, October 2021

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

Open Source

GPLv2

commercial

free licenses can be provided

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux

All OS with a Java VM (>= 1.8)

server-less

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL-like query language

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

JDBC
Native API

in-process shared library

JDBC
ODBC

.NET Client API
HTTP API
LINQ

Supported programming languages

C++
Java
Python

C
C#
C++
Go
Java
Python
Scala

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency
Strong Consistency with Raft

none

Foreign keys

Referential integrity

not needed in graphs

typically not needed, however similar functionality with collection joins possible

depending on model

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

Write ahead log

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users and roles

yes

Role based authorization

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