DBMS > Altibase vs. JanusGraph vs. Lovefield vs. NCache vs. TimesTen

System Properties Comparison Altibase vs. JanusGraph vs. Lovefield vs. NCache vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

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

Open-Source and Enterprise in-memory Key-Value Store

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	0.87
Rank	#200	Overall
	#93	Relational DBMS

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	0.91
Rank	#196	Overall
	#29	Key-value stores

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

altibase.com

janusgraph.org

google.github.io/lovefield

www.alachisoft.com/ncache

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

docs.janusgraph.org

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

www.alachisoft.com/resources/docs

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Altibase

Linux Foundation; originally developed as Titan by Aurelius

Google

Alachisoft

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

1999

2017

2014

2005

1998

Current release

v7.3, 2023, August 2023

1.0.0, October 2023

2.1.12, February 2017

5.3.3, April 2024

Release 22.1

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Enterprise Edition available

commercial

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

C++

Java

JavaScript

C#, .NET, .NET Core, Java

Server operating systems

AIX
HP-UX
Linux

Linux
OS X
Unix
Windows

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

tested with Chrome, Firefox, IE, Safari

Linux
Windows

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

SQL

Support of SQL

ANSI SQL-92

SQL-like query language

via JavaScript builder pattern

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

yes

APIs and other access methods

JDBC
ODBC
Proprietary protocol

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

IDistributedCache
JCache
LINQ
Proprietary native API

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

C
C++
Java

Clojure
Java
Python

JavaScript

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

support for stored procedures with SQL-Server CLR

PL/SQL

Triggers

yes

Using read-only observers

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

none

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes, with selectable consistency level

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

optimistic locking and pessimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

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 means of logfiles and checkpoints

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
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Market metrics

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Licensing and pricing models

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