DBMS > GridGain vs. IRONdb vs. Transwarp StellarDB vs. Yaacomo

System Properties Comparison GridGain vs. IRONdb vs. Transwarp StellarDB vs. Yaacomo

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A distributed graph DBMS built for enterprise-level graph applications

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Time Series DBMS

Graph DBMS

Relational DBMS

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

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Graph DBMS

Website

www.gridgain.com

www.circonus.com/solutions/time-series-database/

www.transwarp.cn/en/product/stellardb

yaacomo.com

Technical documentation

www.gridgain.com/docs/index.html

docs.circonus.com/irondb/category/getting-started

Developer

GridGain Systems, Inc.

Circonus LLC.

Transwarp

Q2WEB GmbH

Initial release

2007

2017

2009

Current release

GridGain 8.5.1

V0.10.20, January 2018

License

Commercial or Open Source

commercial, open source

commercial

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

Java, C++, .Net, Python, REST, SQL

C and C++

Server operating systems

Linux
OS X
Solaris
Windows
z/OS

Linux

Android
Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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 (Circonus Analytics Query Language: CAQL)

SQL-like query language

yes

APIs and other access methods

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

HTTP API

OpenCypher

JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Server-side scripts

Stored procedures

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

yes, in Lua

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

configurable replication factor, datacenter aware

Source-replica replication

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

Immediate consistency per node, eventual consistency across nodes

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 capabilities

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

yes

User concepts

Access control

Role-based access control
Security Hooks for custom implementations

yes

fine grained access rights according to SQL-standard

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More resources

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