DBMS > Adabas vs. Apache Druid vs. eXtremeDB vs. MySQL vs. Sphinx

System Properties Comparison Adabas vs. Apache Druid vs. eXtremeDB vs. MySQL vs. Sphinx

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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Natively in-memory DBMS with options for persistency, high-availability and clustering

Widely used open source RDBMS

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Multivalue DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Search engine

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	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

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

Technical documentation

druid.apache.org/docs/latest/design

www.mcobject.com/docs/extremedb.htm

dev.mysql.com/doc

sphinxsearch.com/docs

Developer

Software AG

Apache Software Foundation and contributors

McObject

Oracle

since 2010, originally MySQL AB, then Sun

Sphinx Technologies Inc.

Initial release

1971

2012

2001

1995

2001

Current release

29.0.1, April 2024

8.2, 2021

8.4.0, April 2024

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux
OS X
Unix

AIX
HP-UX
Linux
macOS
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-less columns are supported

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.

support of XML interfaces available

yes

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL for querying

yes

with the option: eXtremeSQL

yes

with proprietary extensions

SQL-like query language (SphinxQL)

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
RESTful HTTP/JSON API

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Proprietary protocol

Supported programming languages

Natural

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C#
C++
Java
Lua
Python
Scala

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

in Natural

yes

proprietary syntax

Triggers

yes

by defining events

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

manual/auto, time-based

horizontal partitioning / sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes, via HDFS, S3 or other storage engines

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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)

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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