DBMS > Altibase vs. Faircom DB vs. JanusGraph vs. KeyDB vs. Tkrzw

System Properties Comparison Altibase vs. Faircom DB vs. JanusGraph vs. KeyDB vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

A Graph DBMS optimized for distributed clusters

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

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Graph DBMS

Key-value store

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

Score	0.87
Rank	#200	Overall
	#93	Relational DBMS

Score	0.18
Rank	#314	Overall
	#45	Key-value stores
	#141	Relational DBMS

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	0.63
Rank	#232	Overall
	#32	Key-value stores

Score	0.00
Rank	#385	Overall
	#61	Key-value stores

Website

altibase.com

www.faircom.com/products/faircom-db

janusgraph.org

github.com/Snapchat/KeyDB
keydb.dev

dbmx.net/tkrzw

Technical documentation

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

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

docs.janusgraph.org

docs.keydb.dev

Developer

Altibase

FairCom Corporation

Linux Foundation; originally developed as Titan by Aurelius

EQ Alpha Technology Ltd.

Mikio Hirabayashi

Initial release

1999

1979

2017

2019

2020

Current release

v7.3, 2023, August 2023

V13, July 2024

1.0.0, October 2023

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

Restricted, free version available

Open Source

Apache 2.0

Open Source

BSD-3

Open Source

Apache Version 2.0

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++

ANSI C, C++

Java

C++

Server operating systems

AIX
HP-UX
Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
OS X
Unix
Windows

Linux

Linux
macOS

Data scheme

yes

schema free, schema optional, schema required, partial schema,

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

SQL

Support of SQL

ANSI SQL-92

yes, ANSI SQL with proprietary extensions

APIs and other access methods

JDBC
ODBC
Proprietary protocol

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Proprietary protocol

RESP - REdis Serialization Protoco

Supported programming languages

C
C++
Java

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Clojure
Java
Python

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

.Net, JavaScript, C/C++

yes

Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

yes

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency
Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

ACID

Optimistic locking, atomic execution of commands blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

yes

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

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

Fine grained access rights according to SQL-standard with additional protections for files

User authentification and security via Rexster Graph Server

simple password-based access control and ACL

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