DBMS > AnzoGraph DB vs. Apache Cassandra vs. GeoSpock vs. OrientDB

System Properties Comparison AnzoGraph DB vs. Apache Cassandra vs. GeoSpock vs. OrientDB

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

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

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Spatial and temporal data processing engine for extreme data scale

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Graph DBMS
RDF store

Wide column store

Relational DBMS

Document store
Graph DBMS
Key-value store

Secondary database models

Vector DBMS

starting with release V5

Time Series DBMS

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

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	98.94
Rank	#11	Overall
	#1	Wide column stores

Score	3.02
Rank	#88	Overall
	#16	Document stores
	#6	Graph DBMS
	#12	Key-value stores

Website

cambridgesemantics.com/anzograph

cassandra.apache.org

geospock.com

orientdb.org

Technical documentation

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

cassandra.apache.org/doc/latest

www.orientdb.com/docs/last/index.html

Developer

Cambridge Semantics

Apache Software Foundation

Apache top level project, originally developped by Facebook

GeoSpock

OrientDB LTD; CallidusCloud; SAP

Initial release

2018

2008

2010

Current release

2.3, January 2021

5.0-rc1, July 2024

2.0, September 2019

3.2.29, March 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache version 2

commercial

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Java, Javascript

Java

Server operating systems

Linux

BSD
Linux
OS X
Windows

hosted

All OS with a Java JDK (>= JDK 6)

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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

restricted

only equality queries, not always the best performing solution

temporal, categorical

yes

SQL

Support of SQL

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

SQL-like SELECT, DML and DDL statements (CQL)

ANSI SQL for query only (using Presto)

SQL-like query language, no joins

APIs and other access methods

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

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

JDBC

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

C++
Java
Python

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

Java, Javascript

Triggers

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

no "single point of failure"

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

selectable replication factor

Representation of geographical distribution of servers is possible

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

relationship in graphs

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

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.

yes

User concepts

Access control

Access rights for users and roles

Access rights for users can be defined per object

Access rights for users can be defined per table

Access rights for users and roles; record level security configurable

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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