DBMS > AnzoGraph DB vs. Faircom DB vs. OpenMLDB vs. Sequoiadb

System Properties Comparison AnzoGraph DB vs. Faircom DB vs. OpenMLDB vs. Sequoiadb

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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

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

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

NewSQL database with distributed OLTP and SQL

Primary database model

Graph DBMS
RDF store

Key-value store
Relational DBMS

Time Series DBMS

Document store
Relational DBMS

Secondary database models

Relational 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	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	0.02
Rank	#367	Overall
	#37	Time Series DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Website

cambridgesemantics.com/anzograph

www.faircom.com/products/faircom-db

openmldb.ai

www.sequoiadb.com

Technical documentation

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

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

openmldb.ai/docs/zh/main

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Cambridge Semantics

FairCom Corporation

4 Paradigm Inc.

Sequoiadb Ltd.

Initial release

2018

1979

2020

2013

Current release

2.3, January 2021

V12, November 2020

2024-2 February 2024

License

Commercial or Open Source

commercial

free trial version available

commercial

Restricted, free version available

Open Source

Server: AGPL; Client: Apache V2

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

ANSI C, C++

C++, Java, Scala

C++

Server operating systems

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

schema free, schema optional, schema required, partial schema,

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

oid, date, timestamp, binary, regex

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

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

yes, ANSI SQL with proprietary extensions

yes

SQL-like query language

APIs and other access methods

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

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

JDBC
SQLAlchemy

proprietary protocol using JSON

Supported programming languages

C++
Java
Python

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C++
Go
Java
Python
Scala

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

.Net, JavaScript, C/C++

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

yes

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

Fine grained access rights according to SQL-standard with additional protections for files

fine grained access rights according to SQL-standard

simple password-based access control

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More resources

Recent citations in the news

AnzoGraph review: A graph database for deep analytics
15 April 2019, InfoWorld

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