DBMS > Adabas vs. JanusGraph vs. Lovefield vs. Quasardb vs. VoltDB

System Properties Comparison Adabas vs. JanusGraph vs. Lovefield vs. Quasardb vs. VoltDB

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 Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Embeddable relational database for web apps written in pure JavaScript

Distributed, high-performance timeseries database

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

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

Score	3.58
Rank	#91	Overall
	#1	Multivalue DBMS

Score	1.91
Rank	#135	Overall
	#12	Graph DBMS

Score	0.32
Rank	#290	Overall
	#132	Relational DBMS

Score	0.19
Rank	#327	Overall
	#30	Time Series DBMS

Score	1.46
Rank	#159	Overall
	#74	Relational DBMS

Website

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

janusgraph.org

google.github.io/lovefield

quasar.ai

www.voltdb.com

Technical documentation

docs.janusgraph.org

github.com/google/lovefield/blob/master/docs/spec_index.md

doc.quasar.ai/master

docs.voltdb.com

Developer

Software AG

Linux Foundation; originally developed as Titan by Aurelius

Google

quasardb

VoltDB Inc.

Initial release

1971

2017

2014

2009

2010

Current release

0.6.3, February 2023

2.1.12, February 2017

3.14.1, January 2024

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

JavaScript

C++

Java, C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux
OS X
Unix
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

BSD
Linux
OS X
Windows

Linux
OS X

for development

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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.

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL-like query language

via JavaScript builder pattern

SQL-like query language

yes

only a subset of SQL 99

APIs and other access methods

HTTP API

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

with add-on software Adabas SOA Gateway

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

HTTP API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Natural

Clojure
Java
Python

JavaScript

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

in Natural

yes

Java

Triggers

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

yes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas Vista

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

none

Sharding

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

none

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

by using LevelDB

yes

Snapshots and command logging

In-memory capabilities

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

yes

using MemoryDB

yes

Transient mode

User concepts

Access control

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

User authentification and security via Rexster Graph Server

Cryptographically strong user authentication and audit trail

Users and roles with access to stored procedures

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