DBMS > Adabas vs. Axibase vs. Datomic vs. GeoSpock vs. Microsoft Azure Cosmos DB

System Properties Comparison Adabas vs. Axibase vs. Datomic vs. GeoSpock vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

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

used typically together with the Natural programming platform

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Spatial and temporal data processing engine for extreme data scale

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Multivalue DBMS

Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Time Series DBMS

Spatial DBMS

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

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

axibase.com/docs/atsd/finance

www.datomic.com

geospock.com

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.datomic.com

learn.microsoft.com/azure/cosmos-db

Developer

Software AG

Axibase Corporation

Cognitect

GeoSpock

Microsoft

Initial release

1971

2013

2012

2014

Current release

15585

1.0.6735, June 2023

2.0, September 2019

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

limited edition free

commercial

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

Java, Clojure

Java, Javascript

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux

All OS with a Java VM

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

JSON types

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.

Secondary indexes

yes

temporal, categorical

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL-like query language

ANSI SQL for query only (using Presto)

SQL-like query language

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
Proprietary protocol (Network API)
RESTful HTTP API

RESTful HTTP API

JDBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

Natural

Go
Java
PHP
Python
R
Ruby

Clojure
Java

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

in Natural

yes

Transaction Functions

JavaScript

Triggers

yes

By using transaction functions

JavaScript

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

But extensive use of caching in the application peers

Automatic sharding

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

Source-replica replication

none

But extensive use of caching in the application peers

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

User concepts

Access control

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

Access rights for users can be defined per table

Access rights can be defined down to the item level

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