DBMS > Adabas vs. HugeGraph vs. IBM Db2 vs. Kinetica vs. MySQL

System Properties Comparison Adabas vs. HugeGraph vs. IBM Db2 vs. Kinetica vs. MySQL

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

A fast-speed and highly-scalable Graph DBMS

Common in IBM host environments, 2 different versions for host and Windows/Linux

Fully vectorized database across both GPUs and CPUs

Widely used open source RDBMS

Primary database model

Multivalue DBMS

Graph DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

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

github.com/hugegraph
hugegraph.apache.org

www.ibm.com/products/db2

www.kinetica.com

www.mysql.com

Technical documentation

hugegraph.apache.org/docs

www.ibm.com/docs/en/db2

docs.kinetica.com

dev.mysql.com/doc

Developer

Software AG

Baidu

IBM

Kinetica

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

1971

2018

1983

host version

2012

1995

Current release

0.9

12.1, October 2016

7.1, August 2021

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free version is available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

C, C++

C and C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux
macOS
Unix

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux

FreeBSD
Linux
OS X
Solaris
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

also supports composite index and range index

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

HTTP API

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

with add-on software Adabas SOA Gateway

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Natural

Groovy
Java
Python

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

C++
Java
JavaScript (Node.js)
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

in Natural

asynchronous Gremlin script jobs

yes

user defined functions

yes

proprietary syntax

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

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

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

only with Windows/Unix/Linux Version

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

with separate tools (MQ, InfoSphere)

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

GPU vRAM or System RAM

yes

User concepts

Access control

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

Users, roles and permissions

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

Users with fine-grained authorization concept

no user groups or roles

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