DBMS > Adabas vs. Apache Druid vs. MySQL vs. Spark SQL vs. Teradata

System Properties Comparison Adabas vs. Apache Druid vs. MySQL vs. Spark SQL vs. Teradata

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

Widely used open source RDBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Multivalue DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	2.79
Rank	#102	Overall
	#2	Multivalue DBMS

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

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

Technical documentation

druid.apache.org/docs/latest/design

dev.mysql.com/doc

spark.apache.org/docs/latest/sql-programming-guide.html

docs.teradata.com

Developer

Software AG

Apache Software Foundation and contributors

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Teradata

Initial release

1971

2012

1995

2014

1984

Current release

29.0.1, April 2024

8.4.0, April 2024

3.5.0 ( 2.13), September 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

Scala

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux
OS X
Unix

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

hosted
Linux

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.

yes

Secondary indexes

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL for querying

yes

with proprietary extensions

SQL-like DML and DDL statements

yes

SQL 2016 + 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

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

Natural

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

Java
Python
R
Scala

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

in Natural

yes

proprietary syntax

yes

UDFs, stored procedures, table functions in parallel

Triggers

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 with MySQL Cluster or MySQL Fabric

yes, utilizing Spark Core

Sharding

Hashing

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

Multi-source replication
Source-replica replication

none

Multi-source replication
Source-replica replication

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

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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)

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

fine grained access rights according to SQL-standard

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