DBMS > Adabas vs. DataFS vs. EventStoreDB vs. GridDB vs. Hive

System Properties Comparison Adabas vs. DataFS vs. EventStoreDB vs. GridDB vs. Hive

Editorial information provided by DB-Engines

Name

Description

OLTP - DBMS for mainframes and Linux/Unix/Windows environments

used typically together with the Natural programming platform

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Industrial-strength, open-source database solution built from the ground up for event sourcing.

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Primary database model

Secondary database models

Graph DBMS

Key-value store
Relational DBMS

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

Score	2.79
Rank	#102	Overall
	#2	Multivalue DBMS

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Website

www.softwareag.com/en_corporate/platform/adabas-natural.html

Technical documentation

dev.mobiland.com/Overview.xsp

developers.eventstore.com

docs.griddb.net

cwiki.apache.org/confluence/display/Hive/Home

Developer

Software AG

Mobiland AG

Event Store Limited

Toshiba Corporation

Apache Software Foundation

initially developed by Facebook

Initial release

1971

2018

2012

2013

2012

Current release

1.1.263, October 2022

21.2, February 2021

5.1, August 2022

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

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

Open Source

Apache Version 2

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

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Windows

Linux
Windows

Linux

All OS with a Java VM

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

with add-on software Adabas SOA Gateway
SOAP-based API

with add-on software Adabas SOA Gateway

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC
Thrift

Supported programming languages

Natural

.Net
C
C#
C++
VB.Net

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

C++
Java
PHP
Python

Server-side scripts

Stored procedures

in Natural

yes

user defined functions and integration of map-reduce

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

yes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas Vista

Proprietary Sharding system

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID at container level

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

only with OS-specific tools (e.g. IBM RACF, CA Top Secret)

Windows-Profile

Access rights for users can be defined per database

Access rights for users, groups and roles

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

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