DBMS > GridGain vs. Lovefield vs. Sadas Engine vs. Ultipa

System Properties Comparison GridGain vs. Lovefield vs. Sadas Engine vs. Ultipa

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Editorial information provided by DB-Engines

Name

Description

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

Embeddable relational database for web apps written in pure JavaScript

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Graph 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	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Score	0.11
Rank	#338	Overall
	#32	Graph DBMS

Website

www.gridgain.com

google.github.io/lovefield

www.sadasengine.com

www.ultipa.com

Technical documentation

www.gridgain.com/docs/index.html

github.com/google/lovefield/blob/master/docs/spec_index.md

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

www.ultipa.com/document

Developer

GridGain Systems, Inc.

Google

SADAS s.r.l.

Ultipa

Initial release

2007

2014

2006

2019

Current release

GridGain 8.5.1

2.1.12, February 2017

8.0

License

Commercial or Open Source

commercial, open source

Open Source

Apache 2.0

commercial

free trial version available

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

JavaScript

C++

Server operating systems

Linux
OS X
Solaris
Windows
z/OS

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

AIX
Linux
Windows

Data scheme

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

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

JDBC
ODBC
Proprietary protocol

RESTful HTTP API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

JavaScript

.Net
C
C#
C++
Groovy
Java
PHP
Python

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

Server-side scripts

Stored procedures

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

Triggers

yes (cache interceptors and events)

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

managed by 'Learn by Usage'

User concepts

Access control

Role-based access control
Security Hooks for custom implementations

Access rights for users, groups and roles according to SQL-standard

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More resources

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