DBMS > Adabas vs. Amazon Redshift vs. Hawkular Metrics vs. Percona Server for MongoDB vs. Solr

System Properties Comparison Adabas vs. Amazon Redshift vs. Hawkular Metrics vs. Percona Server for MongoDB vs. Solr

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

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

Primary database model

Secondary database models

Spatial DBMS

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

Score	2.79
Rank	#102	Overall
	#2	Multivalue DBMS

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	0.60
Rank	#246	Overall
	#39	Document stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

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

aws.amazon.com/redshift

www.hawkular.org

www.percona.com/mongodb/software/percona-server-for-mongodb

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

www.hawkular.org/hawkular-metrics/docs/user-guide

docs.percona.com/percona-distribution-for-mongodb

solr.apache.org/resources.html

Developer

Software AG

Amazon (based on PostgreSQL)

Community supported by Red Hat

Percona

Apache Software Foundation

Initial release

1971

2012

2014

2015

2006

Current release

3.4.10-2.10, November 2017

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL Version 2

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Java

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

hosted

Linux
OS X
Windows

Linux

All OS with a Java VM

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

Data scheme

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

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

HTTP REST

proprietary protocol using JSON

Java API
RESTful HTTP/JSON API

Supported programming languages

Natural

All languages supporting JDBC/ODBC

Go
Java
Python
Ruby

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

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

Server-side scripts

Stored procedures

in Natural

user defined functions

in Python

JavaScript

Java plugins

Triggers

yes

via Hawkular Alerting

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

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

selectable replication factor

based on Cassandra

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
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

via In-Memory Engine

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

Access rights for users and roles

yes

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