DBMS > AnzoGraph DB vs. Datomic vs. GeoSpock vs. SWC-DB

System Properties Comparison AnzoGraph DB vs. Datomic vs. GeoSpock vs. SWC-DB

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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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Spatial and temporal data processing engine for extreme data scale

A high performance, scalable Wide Column DBMS

Primary database model

Graph DBMS
RDF store

Relational DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

cambridgesemantics.com/anzograph

www.datomic.com

geospock.com

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

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

docs.datomic.com

Developer

Cambridge Semantics

Cognitect

GeoSpock

Alex Kashirin

Initial release

2018

2012

2020

Current release

2.3, January 2021

1.0.7075, December 2023

2.0, September 2019

0.5, April 2021

License

Commercial or Open Source

commercial

free trial version available

commercial

limited edition free

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

Java, Javascript

C++

Server operating systems

Linux

All OS with a Java VM

hosted

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

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

temporal, categorical

SQL

Support of SQL

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

ANSI SQL for query only (using Presto)

SQL-like query language

APIs and other access methods

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

RESTful HTTP API

JDBC

Proprietary protocol
Thrift

Supported programming languages

C++
Java
Python

Clojure
Java

C++

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Transaction Functions

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

But extensive use of caching in the application peers

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

none

But extensive use of caching in the application peers

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

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

User concepts

Access control

Access rights for users and roles

Access rights for users can be defined per table

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More resources

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