DBMS > AnzoGraph DB vs. GridDB vs. InfinityDB vs. STSdb vs. SWC-DB

System Properties Comparison AnzoGraph DB vs. GridDB vs. InfinityDB vs. STSdb vs. SWC-DB

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

Scalable in-memory time series database optimized for IoT and Big Data

A Java embedded Key-Value Store which extends the Java Map interface

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Key-value store
Relational DBMS

Time Series 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	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

cambridgesemantics.com/anzograph

griddb.net

boilerbay.com

github.com/STSSoft/STSdb4

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

Technical documentation

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

docs.griddb.net

boilerbay.com/infinitydb/manual

Developer

Cambridge Semantics

Toshiba Corporation

Boiler Bay Inc.

STS Soft SC

Alex Kashirin

Initial release

2018

2013

2002

2011

2020

Current release

2.3, January 2021

5.1, August 2022

4.0

4.0.8, September 2015

0.5, April 2021

License

Commercial or Open Source

commercial

free trial version available

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

commercial

Open Source

GPLv2, commercial license available

Open Source

GPL V3

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

C++

Java

C++

Server operating systems

Linux

All OS with a Java VM

Windows

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

primitive types and user defined types (classes)

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

manual creation possible, using inversions based on multi-value capability

SQL

Support of SQL

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

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language

APIs and other access methods

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

.NET Client API

Proprietary protocol
Thrift

Supported programming languages

C++
Java
Python

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Java

C#
Java

C++

Server-side scripts

Stored procedures

user defined functions and aggregates

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

ACID at container level

ACID

Optimistic locking for transactions; no isolation for bulk loads

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 database

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

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