DBMS > GridGain vs. JaguarDB vs. SAP Adaptive Server

System Properties Comparison GridGain vs. JaguarDB vs. SAP Adaptive Server

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Editorial information provided by DB-Engines

Name

Description

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

Performant, highly scalable DBMS for AI and IoT applications

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Primary database model

Key-value store
Relational DBMS

Key-value store
Vector DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

Website

www.gridgain.com

www.jaguardb.com

www.sap.com/products/technology-platform/sybase-ase.html

Technical documentation

www.gridgain.com/docs/index.html

www.jaguardb.com/support.html

help.sap.com/docs/SAP_ASE

Developer

GridGain Systems, Inc.

DataJaguar, Inc.

SAP, Sybase

Initial release

2007

2015

1987

Current release

GridGain 8.5.1

3.3 July 2023

16.0

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

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

C++

the server part. Clients available in other languages

C and C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux

AIX
HP-UX
Linux
Solaris
Unix
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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

APIs and other access methods

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

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

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

Java and Transact-SQL

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication

Multi-source replication
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

Eventual Consistency

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

Security Hooks for custom implementations

rights management via user accounts

fine grained access rights according to SQL-standard

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More resources

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