DBMS > AnzoGraph DB vs. Coveo vs. eXtremeDB vs. Ingres vs. RocksDB

System Properties Comparison AnzoGraph DB vs. Coveo vs. eXtremeDB vs. Ingres vs. RocksDB

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

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

Natively in-memory DBMS with options for persistency, high-availability and clustering

Well established RDBMS

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Graph DBMS
RDF store

Search engine

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

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	2.28
Rank	#114	Overall
	#11	Search engines

Score	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

cambridgesemantics.com/anzograph

www.coveo.com

www.mcobject.com

www.actian.com/databases/ingres

rocksdb.org

Technical documentation

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

docs.coveo.com

www.mcobject.com/docs/extremedb.htm

docs.actian.com/ingres

github.com/facebook/rocksdb/wiki

Developer

Cambridge Semantics

Coveo

McObject

Actian Corporation

Facebook, Inc.

Initial release

2018

2012

2001

1974

originally developed at University Berkely in early 1970s

2013

Current release

2.3, January 2021

8.2, 2021

11.2, May 2022

8.11.4, April 2024

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

C++

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
macOS
Solaris
Windows

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

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.

support of XML interfaces available

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

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

yes

with the option: eXtremeSQL

yes

APIs and other access methods

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

RESTful HTTP API

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

C++ API
Java API

Supported programming languages

C++
Java
Python

C#
Java
JavaScript
Python

.Net
C
C#
C++
Java
Lua
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

yes

by defining events

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

yes

horizontal partitioning / sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Ingres Replicator

yes

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

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

yes

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

MVCC

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.

yes

User concepts

Access control

Access rights for users and roles

granular access controls, API key management, content filters

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
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Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
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Licensing and pricing models

For server use cases, there is a simple per-server license irrespective of the number...
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