DBMS > JanusGraph vs. KeyDB vs. Microsoft SQL Server vs. OpenQM vs. Riak KV

System Properties Comparison JanusGraph vs. KeyDB vs. Microsoft SQL Server vs. OpenQM vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

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

Microsofts flagship relational DBMS

QpenQM is a high-performance, self-tuning, multi-value DBMS

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

janusgraph.org

github.com/Snapchat/KeyDB
keydb.dev

www.microsoft.com/en-us/sql-server

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

Technical documentation

docs.janusgraph.org

docs.keydb.dev

learn.microsoft.com/en-US/sql/sql-server

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Linux Foundation; originally developed as Titan by Aurelius

EQ Alpha Technology Ltd.

Microsoft

Rocket Software, originally Martin Phillips

OpenSource, formerly Basho Technologies

Initial release

2017

2019

1989

1993

2009

Current release

0.6.3, February 2023

SQL Server 2022, November 2022

3.4-12

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD-3

commercial

restricted free version is available

Open Source

GPLv2, extended commercial license available

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Erlang

Server operating systems

Linux
OS X
Unix
Windows

Linux

Linux
Windows

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
OS X

Data scheme

yes

schema-free

yes

with some exceptions

schema-free

Typing

predefined data types such as float or date

yes

partial

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

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

by using the Redis Search module

yes

restricted

SQL

Support of SQL

yes

APIs and other access methods

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Proprietary protocol

RESP - REdis Serialization Protoco

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP API
Native Erlang Interface

Supported programming languages

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#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net
Basic
C
Java
Objective C
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

Lua

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

yes

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

yes

selectable replication factor

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

Eventual Consistency
Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

links between data sets can be stored

Transaction concepts

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

ACID

Optimistic locking, atomic execution of commands blocks and scripts

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

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

User concepts

Access control

User authentification and security via Rexster Graph Server

simple password-based access control and ACL

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

yes, using Riak Security

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