DBMS > Adabas vs. Amazon Redshift vs. Apache Druid vs. Solr vs. STSdb

System Properties Comparison Adabas vs. Amazon Redshift vs. Apache Druid vs. Solr vs. STSdb

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

Large scale data warehouse service for use with business intelligence tools

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

A widely used distributed, scalable search engine based on Apache Lucene

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Multivalue DBMS

Relational DBMS

Relational DBMS
Time Series DBMS

Search engine

Key-value store

Secondary database models

Spatial DBMS

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

Score	3.58
Rank	#91	Overall
	#1	Multivalue DBMS

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Score	0.06
Rank	#365	Overall
	#55	Key-value stores

Website

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

aws.amazon.com/redshift

druid.apache.org

solr.apache.org

github.com/STSSoft/STSdb4

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

solr.apache.org/resources.html

Developer

Software AG

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Apache Software Foundation

STS Soft SC

Initial release

1971

2012

2006

2011

Current release

29.0.1, April 2024

9.5.0, February 2024

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

Apache Version 2

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

hosted

Linux
OS X
Unix

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Windows

Data scheme

yes

schema-less columns are supported

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

yes

primitive types and user defined types (classes)

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

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

does not fully support an SQL-standard

SQL for querying

Solr Parallel SQL Interface

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
ODBC

JDBC
RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

.NET Client API

Supported programming languages

Natural

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C#
Java

Server-side scripts

Stored procedures

in Natural

user defined functions

in Python

Java plugins

Triggers

yes

User configurable commands triggered on index changes

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

yes, via HDFS, S3 or other storage engines

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

optimistic locking

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)

fine grained access rights according to SQL-standard

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

yes

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