DBMS > Adabas vs. CouchDB vs. Google BigQuery vs. Graph Engine vs. PostGIS

System Properties Comparison Adabas vs. CouchDB vs. Google BigQuery vs. Graph Engine vs. PostGIS

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

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Spatial extension of PostgreSQL

Primary database model

Multivalue DBMS

Document store

Relational DBMS

Graph DBMS
Key-value store

Spatial DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Relational DBMS

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Website

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

couchdb.apache.org

cloud.google.com/bigquery

www.graphengine.io

postgis.net

Technical documentation

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

postgis.net/documentation

Developer

Software AG

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Microsoft

Initial release

1971

2005

2010

2005

Current release

3.3.3, December 2023

3.4.2, February 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Open Source

MIT License

Open Source

GPL v2.0

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

Erlang

.NET and C

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Android
BSD
Linux
OS X
Solaris
Windows

hosted

.NET

Data scheme

yes

schema-free

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

via views

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

APIs and other access methods

HTTP API

with add-on software Adabas SOA Gateway
SOAP-based API

with add-on software Adabas SOA Gateway

RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

Natural

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

Server-side scripts

Stored procedures

in Natural

View functions in JavaScript

user defined functions

in JavaScript

yes

user defined functions

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

improved architecture with release 2.0

none

horizontal partitioning

yes

based on PostgreSQL

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

Multi-source replication
Source-replica replication

yes

based on PostgreSQL

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

atomic operations within a single document possible

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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)

Access rights for users can be defined per database

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

based on PostgreSQL

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