DBMS > GridGain vs. STSdb vs. TempoIQ vs. TerminusDB vs. Ultipa

System Properties Comparison GridGain vs. STSdb vs. TempoIQ vs. TerminusDB vs. Ultipa

Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Description

GridGain is an in-memory computing platform, built on Apache Ignite

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Key-value store
Relational DBMS

Key-value store

Time Series DBMS

Graph DBMS

Secondary database models

Document store
RDF store

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

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Website

www.gridgain.com

github.com/STSSoft/STSdb4

tempoiq.com (offline)

terminusdb.com

www.ultipa.com

Technical documentation

www.gridgain.com/docs/index.html

terminusdb.github.io/terminusdb/#

www.ultipa.com/document

Developer

GridGain Systems, Inc.

STS Soft SC

TempoIQ

DataChemist Ltd.

Ultipa

Initial release

2007

2011

2012

2018

2019

Current release

GridGain 8.5.1

4.0.8, September 2015

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

GPLv2, commercial license available

commercial

Open Source

GPL V3

commercial

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

Java, C++, .Net

Prolog, Rust

Server operating systems

Linux
OS X
Solaris
Windows

Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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.

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language (WOQL)

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

.NET Client API

HTTP API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

RESTful HTTP API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C#
Java

C#
Java
JavaScript

Node.js
Python
Ruby

JavaScript
Python

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes

Triggers

yes (cache interceptors and events)

yes

Realtime Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

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

in-memory journaling

In-memory capabilities

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

yes

User concepts

Access control

Security Hooks for custom implementations

simple authentication-based access control

Role-based access control

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More resources

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