DBMS > Adabas vs. Drizzle vs. EJDB vs. JaguarDB vs. Postgres-XL

System Properties Comparison Adabas vs. Drizzle vs. EJDB vs. JaguarDB vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

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

used typically together with the Natural programming platform

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Performant, highly scalable DBMS for AI and IoT applications

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Multivalue DBMS

Relational DBMS

Document store

Key-value store
Vector DBMS

Relational DBMS

Secondary database models

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	0.27
Rank	#297	Overall
	#44	Document stores

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

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

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

github.com/Softmotions/ejdb

www.jaguardb.com

www.postgres-xl.org

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

www.jaguardb.com/support.html

www.postgres-xl.org/documentation

Developer

Software AG

Drizzle project, originally started by Brian Aker

Softmotions

DataJaguar, Inc.

Initial release

1971

2008

2012

2015

2014

since 2012, originally named StormDB

Current release

7.2.4, September 2012

3.3 July 2023

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

GNU GPL

Open Source

GPLv2

Open Source

GPL V3.0

Open Source

Mozilla public license

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++

the server part. Clients available in other languages

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

FreeBSD
Linux
OS X

server-less

Linux

Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

with proprietary extensions

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

distributed, parallel query execution

APIs and other access methods

HTTP API

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

with add-on software Adabas SOA Gateway

JDBC

in-process shared library

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

Natural

C
C++
Java
PHP

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

in Natural

user defined functions

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

Multi-source replication
Source-replica replication

none

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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.

User concepts

Access control

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

rights management via user accounts

fine grained access rights according to SQL-standard

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